Budeme pracovat se dvěma datovými soubory:

Nejprve naimportujeme knihovnu pandas a zkusíme si s ní trochu hrát.

In [1]:
import pandas

Načtení souboru ve formátu CSV uděláme pomocí funkce read_csv. První argument udává jméno souboru. Další argumenty jsou:

  • index_col: Název sloupce, pomocí kterého chceme data indexovat. Pokud nic nezadáme, Pandas vytvoří nový sloupec s čísly.
  • parse_dates: Tímto dané sloupce načteme jako data místo řetězců. Pozorný čtenář se všimne, že ve výpisu je sloupců s daty víc, ale my budeme potřebovat pouze tento jeden a na ostatních nám až tak moc nezáleží.
  • dayfirst: Pandas zkouší uhodnout, v jakém formátu jsou data zadaná. Den a měsíc je ale problematické rozlišit, takže tímto řekneme, že nejdříve je v datu den, až potom měsíc.
In [4]:
data = pandas.read_csv("2016-zavady_vo-1.csv", index_col="Číslo závady",
                       parse_dates=["Datum nahlášení závady"],
                       dayfirst=True)
data
Out[4]:
EC_SVE EČ objektu Katastr Ulice Sektor závady Rok závady Datum nahlášení závady Typ závady Plánovaný termín opravy Stav opravy Způsob opravy Datum opravy
Číslo závady
1 S-1148-011 Z-500 Řečkovice TEREZY NOVÁKOVÉ Brno - západ 2 016 2016-01-01 Z vypadený jistící prvek 8.1.2016 Definitivní vypadený jistící prvek 4.1.2016
2 S-1148-012 NaN Řečkovice TEREZY NOVÁKOVÉ Brno - západ 2 016 2016-01-01 S svítidlo-nesvítí 8.1.2016 Definitivní neopraveno 4.1.2016
3 S-1456-012 NaN Zábrdovice ŠPITÁLKA Brno - východ 2 016 2016-01-02 S svítidlo-bliká 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
4 S-1256-217 NaN Komín VESLAŘSKÁ Brno - západ 2 016 2016-01-02 S svítidlo-nesvítí 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
5 S-0478-003 Z-612 Staré Brno KOPEČNÁ Brno - východ 2 016 2016-01-02 Z vypadený jistící prvek 9.1.2016 Definitivní vypadený jistící prvek 4.1.2016
6 S-0703-001 R-0703-002 Starý Lískovec MÁCHALOVA Brno - západ 2 016 2016-01-02 S svítidlo-nesvítí 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
7 S-1257-019 Z-547 Veveří VEVEŘÍ Brno - východ 2 016 2016-01-02 SO nekomunikuje čip 9.1.2016 Definitivní při kontrole vše v pořádku 3.1.2016
8 S-0687-129 NaN Město Brno MORAVSKÉ NÁMĚSTÍ Brno - východ 2 016 2016-01-02 S svítidlo-nesvítí 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
9 S-1202-005 Z-518 Bosonohy U SMYČKY Brno - západ 2 016 2016-01-02 Z vypadený jistící prvek 9.1.2016 Definitivní vypadený jistící prvek 2.1.2016
10 S-0305-013 NaN Bosonohy HOŠTICKÁ Brno - západ 2 016 2016-01-02 S stožár-poškozen 9.1.2016 Definitivní není v majetku TsB 2.1.2016
11 S-0942-016 Z-425 Ivanovice PŘÍJEZDOVÁ Brno - západ 2 016 2016-01-02 Z vybitá PLC baterie 9.1.2016 Definitivní Z výměna baterie 4.1.2016
12 S-1485-038 Z-531 Stránice ÚVOZ Brno - východ 2 016 2016-01-03 SO nekomunikuje čip 10.1.2016 Definitivní jiná závada 4.1.2016
13 S-1179-133 Z-627 Slatina TUŘANKA Brno - východ 2 016 2016-01-03 Z vybitá PLC baterie 10.1.2016 Definitivní Z výměna baterie 4.1.2016
14 S-1269-049 NaN Slatina VLNITÁ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
15 S-0375-191 Z-186 Nový Lískovec JIHLAVSKÁ Brno - západ 2 016 2016-01-03 Z vypnuta regulace, viz.poznámka 10.1.2016 Definitivní jiná závada 4.1.2016
16 S-1360-009 NaN Maloměřice ZIMNÍ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
17 S-1360-008 NaN Maloměřice ZIMNÍ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
18 S-1204-034 NaN Kohoutovice U VELKÉ CENY Brno - západ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
19 S-0652-077 NaN Staré Brno MENDLOVO NÁMĚSTÍ Brno - východ 2 016 2016-01-03 S svítidlo-nesvítí 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
20 S-0455-038 Z-619 Královo Pole KOCIÁNKA Brno - východ 2 016 2016-01-04 Z vypadený jistící prvek 11.1.2016 Definitivní vypadený jistící prvek 4.1.2016
21 S-0527-003 Z-236 Žabovřesky KRÁLOVA Brno - západ 2 016 2016-01-04 Z vybitá PLC baterie 11.1.2016 Definitivní Z výměna baterie 4.1.2016
22 S-0808-035 Z-357 Židenice OTAKARA ŠEVČÍKA Brno - východ 2 016 2016-01-04 viz. poznámka 11.1.2016 Definitivní jiná závada 4.1.2016
23 S-0526-036 Z-035 Starý Lískovec KRYMSKÁ Brno - západ 2 016 2016-01-04 viz. poznámka 11.1.2016 Definitivní při kontrole vše v pořádku 5.1.2016
24 S-1354-003 NaN Žabovřesky ZEMKOVA Brno - západ 2 016 2016-01-04 S svítidlo-nesvítí 11.1.2016 Definitivní vypadený jistící prvek 5.1.2016
25 S-1259-003 NaN Židenice VINAŘICKÉHO Brno - východ 2 016 2016-01-04 S svítidlo-bliká 11.1.2016 Definitivní S výměna světelného zdroje 5.1.2016
26 S-0831-019 NaN Staré Brno PELLICOVA Brno - východ 2 016 2016-01-04 S svítidlo-nesvítí 11.1.2016 Definitivní S výměna světelného zdroje 5.1.2016
27 S-0421-007 R-0421-002 Stránice KAMPELÍKOVA Brno - západ 2 016 2016-01-04 S svítidlo-nesvítí 11.1.2016 Definitivní S výměna světelného zdroje 5.1.2016
28 S-0808-034 Z-357 Židenice OTAKARA ŠEVČÍKA Brno - východ 2 016 2016-01-04 Z vypnuta regulace, viz.poznámka 11.1.2016 Definitivní Z regulace seřízena 5.1.2016
29 S-0579-005 Z-261 Stránice LERCHOVA Brno - západ 2 016 2016-01-04 Z vybitá PLC baterie 11.1.2016 Definitivní Z výměna baterie 6.1.2016
30 S-1187-017 R-1187-002 Veveří TŮMOVA Brno - západ 2 016 2016-01-04 S svítidlo-nesvítí 11.1.2016 Definitivní S výměna světelného zdroje 5.1.2016
... ... ... ... ... ... ... ... ... ... ... ... ...
2 641 S-1070-036 R-1070-007 Lesná SOBĚŠICKÁ Brno - východ 2 016 2016-12-27 R poškozen 3.1.2017 Provizorní vandalizmus 27.12.2016
2 642 S-0560-054 NaN Trnitá KŘENOVÁ Brno - východ 2 016 2016-12-27 S svítidlo-bliká 3.1.2017 Definitivní S výměna světelného zdroje 3.1.2017
2 643 S-0271-001 R-0271-001 Černá Pole HELFERTOVA Brno - východ 2 016 2016-12-27 R poškozen 3.1.2017 Provizorní R oprava RS 27.12.2016
2 644 S-0770-067 R-0770-008 Černá Pole NÁM. SNP Brno - východ 2 016 2016-12-27 R poškozen 3.1.2017 Provizorní vandalizmus 28.12.2016
2 645 S-0066-001 Z-034 Řečkovice BOSKOVICKÁ Brno - západ 2 016 2016-12-27 Z vypadený jistící prvek 3.1.2017 Definitivní vypadený jistící prvek 27.12.2016
2 646 S-0956-008 Z-428 Bystrc RAKOVECKÁ Brno - západ 2 016 2016-12-27 Z vypadený jistící prvek 3.1.2017 Definitivní oprava volného vedení 28.12.2016
2 647 S-1438-009 Z-480 Bosonohy ŠEVČENKOVA Brno - západ 2 016 2016-12-27 Z vypadený jistící prvek 3.1.2017 Definitivní oprava volného vedení 28.12.2016
2 648 S-1202-006 Z-518 Bosonohy U SMYČKY Brno - západ 2 016 2016-12-27 Z vypadený jistící prvek 3.1.2017 Definitivní oprava volného vedení 28.12.2016
2 649 S-1343-035 NaN Dvorska ZAPLETALOVA Brno - východ 2 016 2016-12-28 viz. poznámka 4.1.2017 Definitivní jiná závada 28.12.2016
2 650 S-0175-001 NaN Husovice DUKELSKÁ TŘÍDA Brno - východ 2 016 2016-12-28 viz. poznámka 4.1.2017 Definitivní havárie 28.12.2016
2 651 S-0066-001 Z-034 Řečkovice BOSKOVICKÁ Brno - západ 2 016 2016-12-28 Z vypadený jistící prvek 4.1.2017 Definitivní S výměna světelného zdroje 29.12.2016
2 652 S-1496-022 Z-075 Bohunice DLOUHÁ Brno - západ 2 016 2016-12-28 Z vypnuta regulace, viz.poznámka 4.1.2017 Definitivní Z regulace zapnuta 29.12.2016
2 653 S-0802-007 Z-347 Město Brno ORLÍ Brno - východ 2 016 2016-12-28 Z vybitá PLC baterie 4.1.2017 Definitivní Z výměna baterie 29.12.2016
2 654 S-1000-019 Z-513 Medlánky RYSOVA Brno - západ 2 016 2016-12-29 Z vybitá PLC baterie 5.1.2017 Definitivní Z výměna baterie 29.12.2016
2 655 S-0169-060 Z-082 Černá Pole DROBNÉHO Brno - východ 2 016 2016-12-29 Z vybitá PLC baterie 5.1.2017 Definitivní Z výměna baterie 30.12.2016
2 656 S-1225-009 Z-533 Bohunice UZBECKÁ Brno - západ 2 016 2016-12-29 Z vybitá záložní baterie 5.1.2017 Definitivní Z výměna baterie 2.1.2017
2 657 S-1098-022 NaN Židenice STARÁ OSADA Brno - východ 2 016 2016-12-29 S svítidlo-nesvítí 5.1.2017 Definitivní S výměna světelného zdroje 5.1.2017
2 658 S-1269-013 NaN Slatina VLNITÁ Brno - východ 2 016 2016-12-29 S svítidlo-nesvítí 5.1.2017 Definitivní S výměna světelného zdroje 2.1.2017
2 659 S-0375-135 Z-187 Bohunice JIHLAVSKÁ Brno - západ 2 016 2016-12-29 Z vypnuta regulace, viz.poznámka 5.1.2017 Definitivní při kontrole vše v pořádku 6.1.2017
2 660 S-0345-005 NaN Město Brno JAKUBSKÉ NÁMĚSTÍ Brno - východ 2 016 2016-12-29 S svítidlo-nesvítí 5.1.2017 Definitivní S výměna světelného zdroje 14.1.2017
2 661 S-1352-022 NaN Město Brno ZELNÝ TRH Brno - východ 2 016 2016-12-29 S svítidlo-nesvítí 5.1.2017 Definitivní S výměna světelného zdroje 4.1.2017
2 662 S-0265-006 Z-628 Černovice HAVRANÍ Brno - východ 2 016 2016-12-30 Z vybitá PLC baterie 6.1.2017 Definitivní Z výměna baterie 2.1.2017
2 663 S-1092-002 Z-459 Královo Pole SRBSKÁ Brno - západ 2 016 2016-12-30 Z vybitá PLC baterie 6.1.2017 Definitivní Z výměna baterie 2.1.2017
2 664 S-0786-017 NaN Bohunice OKROUHLÁ Brno - západ 2 016 2016-12-30 S patice-poškozena, chybí 6.1.2017 Definitivní S montáž patice 3.1.2017
2 665 S-0476-005 R-0476-002 Líšeň KONRADOVA Brno - východ 2 016 2016-12-30 R poškozen 6.1.2017 Provizorní provizorní zajištění živých částí 5.1.2017
2 666 S-1549-083 NaN Bystrc OBVODOVÁ Brno - západ 2 016 2016-12-30 S svítidlo-nesvítí 6.1.2017 Definitivní S výměna světelného zdroje 2.1.2017
2 667 S-1339-004 R-1339-001 Líšeň ZAHRADNÍ Brno - východ 2 016 2016-12-30 S svítidlo-bliká 6.1.2017 Definitivní S výměna světelného zdroje 3.1.2017
2 668 S-0530-010 NaN Židenice KRÁSNÉHO Brno - východ 2 016 2016-12-30 S svítidlo-bliká 6.1.2017 Definitivní S výměna světelného zdroje 4.1.2017
2 669 S-0880-020 NaN Líšeň POHANKOVA Brno - východ 2 016 2016-12-31 S svítidlo-nesvítí 7.1.2017 Definitivní S výměna světelného zdroje 3.1.2017
2 670 S-1180-001 NaN Tuřany TUŘANSKÉ NÁMĚSTÍ Brno - východ 2 016 2016-12-31 S svítidlo-nesvítí 7.1.2017 Definitivní S výměna světelného zdroje 4.1.2017

2669 rows × 12 columns

Můžeme si nechat data popsat:

In [12]:
data.describe()
Out[12]:
EC_SVE EČ objektu Katastr Ulice Sektor závady Rok závady Datum nahlášení závady Typ závady Plánovaný termín opravy Stav opravy Způsob opravy Datum opravy
count 2666 1458 2669 2669 2669 2669 2669 2669 2669 2669 2668 2669
unique 1899 512 48 782 2 1 363 31 363 2 53 341
top S-1714-161 Z-531 Město Brno MERHAUTOVA Brno - východ 2 016 2016-04-02 00:00:00 S svítidlo-nesvítí 9.4.2016 Definitivní S výměna světelného zdroje 15.9.2016
freq 13 20 263 38 1502 2669 24 601 24 2589 665 28
first NaN NaN NaN NaN NaN NaN 2016-01-01 00:00:00 NaN NaN NaN NaN NaN
last NaN NaN NaN NaN NaN NaN 2016-12-31 00:00:00 NaN NaN NaN NaN NaN

Nebo si taky můžeme nechat vykreslit graf. Osa x je daná indexem, na ose y je vybraný sloupec. Datum je v našich datech jediný sloupec, kde dává trochu smysl vykreslovat graf.

In [14]:
data["Datum nahlášení závady"].plot()
Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f30dca2a470>

Můžeme si taky vybrat jenom určitou podmnožinu dat. Třeba prvních pět řádků.

In [15]:
data[:5]
Out[15]:
EC_SVE EČ objektu Katastr Ulice Sektor závady Rok závady Datum nahlášení závady Typ závady Plánovaný termín opravy Stav opravy Způsob opravy Datum opravy
Číslo závady
1 S-1148-011 Z-500 Řečkovice TEREZY NOVÁKOVÉ Brno - západ 2 016 2016-01-01 Z vypadený jistící prvek 8.1.2016 Definitivní vypadený jistící prvek 4.1.2016
2 S-1148-012 NaN Řečkovice TEREZY NOVÁKOVÉ Brno - západ 2 016 2016-01-01 S svítidlo-nesvítí 8.1.2016 Definitivní neopraveno 4.1.2016
3 S-1456-012 NaN Zábrdovice ŠPITÁLKA Brno - východ 2 016 2016-01-02 S svítidlo-bliká 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
4 S-1256-217 NaN Komín VESLAŘSKÁ Brno - západ 2 016 2016-01-02 S svítidlo-nesvítí 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
5 S-0478-003 Z-612 Staré Brno KOPEČNÁ Brno - východ 2 016 2016-01-02 Z vypadený jistící prvek 9.1.2016 Definitivní vypadený jistící prvek 4.1.2016

Nebo jenom jeden sloupec z prvních pěti řádků:

In [16]:
data["Katastr"][:5]
Out[16]:
Číslo závady
1     Řečkovice
2     Řečkovice
3    Zábrdovice
4         Komín
5    Staré Brno
Name: Katastr, dtype: object

Můžeme vybrat několik sloupců:

In [17]:
data[["Ulice", "Katastr"]]
Out[17]:
Ulice Katastr
Číslo závady
1 TEREZY NOVÁKOVÉ Řečkovice
2 TEREZY NOVÁKOVÉ Řečkovice
3 ŠPITÁLKA Zábrdovice
4 VESLAŘSKÁ Komín
5 KOPEČNÁ Staré Brno
6 MÁCHALOVA Starý Lískovec
7 VEVEŘÍ Veveří
8 MORAVSKÉ NÁMĚSTÍ Město Brno
9 U SMYČKY Bosonohy
10 HOŠTICKÁ Bosonohy
11 PŘÍJEZDOVÁ Ivanovice
12 ÚVOZ Stránice
13 TUŘANKA Slatina
14 VLNITÁ Slatina
15 JIHLAVSKÁ Nový Lískovec
16 ZIMNÍ Maloměřice
17 ZIMNÍ Maloměřice
18 U VELKÉ CENY Kohoutovice
19 MENDLOVO NÁMĚSTÍ Staré Brno
20 KOCIÁNKA Královo Pole
21 KRÁLOVA Žabovřesky
22 OTAKARA ŠEVČÍKA Židenice
23 KRYMSKÁ Starý Lískovec
24 ZEMKOVA Žabovřesky
25 VINAŘICKÉHO Židenice
26 PELLICOVA Staré Brno
27 KAMPELÍKOVA Stránice
28 OTAKARA ŠEVČÍKA Židenice
29 LERCHOVA Stránice
30 TŮMOVA Veveří
... ... ...
2 641 SOBĚŠICKÁ Lesná
2 642 KŘENOVÁ Trnitá
2 643 HELFERTOVA Černá Pole
2 644 NÁM. SNP Černá Pole
2 645 BOSKOVICKÁ Řečkovice
2 646 RAKOVECKÁ Bystrc
2 647 ŠEVČENKOVA Bosonohy
2 648 U SMYČKY Bosonohy
2 649 ZAPLETALOVA Dvorska
2 650 DUKELSKÁ TŘÍDA Husovice
2 651 BOSKOVICKÁ Řečkovice
2 652 DLOUHÁ Bohunice
2 653 ORLÍ Město Brno
2 654 RYSOVA Medlánky
2 655 DROBNÉHO Černá Pole
2 656 UZBECKÁ Bohunice
2 657 STARÁ OSADA Židenice
2 658 VLNITÁ Slatina
2 659 JIHLAVSKÁ Bohunice
2 660 JAKUBSKÉ NÁMĚSTÍ Město Brno
2 661 ZELNÝ TRH Město Brno
2 662 HAVRANÍ Černovice
2 663 SRBSKÁ Královo Pole
2 664 OKROUHLÁ Bohunice
2 665 KONRADOVA Líšeň
2 666 OBVODOVÁ Bystrc
2 667 ZAHRADNÍ Líšeň
2 668 KRÁSNÉHO Židenice
2 669 POHANKOVA Líšeň
2 670 TUŘANSKÉ NÁMĚSTÍ Tuřany

2669 rows × 2 columns

Základní analýza pro sloupce s řetězci může mýt spočítání četností: kolikrát se která hodnota ve sloupci objevuje. Zjistíme 10 ulic s nejvíce poruchami:

In [19]:
data["Ulice"].value_counts()[:10]
Out[19]:
MERHAUTOVA          38
LIBUŠINA TŘÍDA      32
VÍDEŇSKÁ            31
MORAVSKÉ NÁMĚSTÍ    29
OSTRAVSKÁ           28
JIHLAVSKÁ           28
ÚVOZ                24
ČESKÁ               22
HRADECKÁ            22
VESLAŘSKÁ           21
Name: Ulice, dtype: int64

Tato data můžeme zase vykreslit do grafu. Výchozí čárový graf tady není vhodný, proto radši použijeme sloupcový graf (v horizontální variantě).

In [24]:
data["Ulice"].value_counts()[:10].plot(kind="barh", figsize=(15,8))
Out[24]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f30dc7eae48>

Podmnožinu dat můžeme vybrat stejným způsobem jako v numpy pomocí pravdivostního výrazu. Nejpreve najdeme všechny řádky, které mají daný typ poruchy, a potom si vybereme pouze tyto řádky.

In [35]:
blikajici = data[ data["Typ závady"] == "S svítidlo-bliká" ]
blikajici
Out[35]:
EC_SVE EČ objektu Katastr Ulice Sektor závady Rok závady Datum nahlášení závady Typ závady Plánovaný termín opravy Stav opravy Způsob opravy Datum opravy
Číslo závady
3 S-1456-012 NaN Zábrdovice ŠPITÁLKA Brno - východ 2 016 2016-01-02 S svítidlo-bliká 9.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
14 S-1269-049 NaN Slatina VLNITÁ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
16 S-1360-009 NaN Maloměřice ZIMNÍ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
17 S-1360-008 NaN Maloměřice ZIMNÍ Brno - východ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
18 S-1204-034 NaN Kohoutovice U VELKÉ CENY Brno - západ 2 016 2016-01-03 S svítidlo-bliká 10.1.2016 Definitivní S výměna světelného zdroje 4.1.2016
25 S-1259-003 NaN Židenice VINAŘICKÉHO Brno - východ 2 016 2016-01-04 S svítidlo-bliká 11.1.2016 Definitivní S výměna světelného zdroje 5.1.2016
35 S-0625-011 NaN Řečkovice MARIE HÜBNEROVÉ Brno - západ 2 016 2016-01-05 S svítidlo-bliká 12.1.2016 Definitivní S výměna svítidla 18.1.2016
50 S-1261-091 R-1261-008 Židenice VINIČNÍ Brno - východ 2 016 2016-01-07 S svítidlo-bliká 14.1.2016 Definitivní S výměna světelného zdroje 21.3.2016
52 S-0404-006 NaN Chrlice K LÁZINKÁM Brno - východ 2 016 2016-01-08 S svítidlo-bliká 15.1.2016 Definitivní S výměna světelného zdroje 11.1.2016
83 S-0775-001 NaN Líšeň OBECKÁ Brno - východ 2 016 2016-01-11 S svítidlo-bliká 18.1.2016 Definitivní S výměna světelného zdroje 13.1.2016
90 S-0446-008 NaN Komárov KLÁŠTERSKÉHO Brno - východ 2 016 2016-01-12 S svítidlo-bliká 19.1.2016 Definitivní S výměna světelného zdroje 15.1.2016
95 S-0159-006 NaN Žabovřesky DOLEŽALOVA Brno - západ 2 016 2016-01-12 S svítidlo-bliká 19.1.2016 Definitivní S výměna světelného zdroje 13.1.2016
98 S-0507-003 NaN Řečkovice KOŘENSKÉHO Brno - západ 2 016 2016-01-13 S svítidlo-bliká 20.1.2016 Definitivní S výměna světelného zdroje 13.1.2016
99 S-0507-002 NaN Řečkovice KOŘENSKÉHO Brno - západ 2 016 2016-01-13 S svítidlo-bliká 20.1.2016 Definitivní S výměna světelného zdroje 13.1.2016
109 S-1058-003 NaN Komárov SLUNNÁ Brno - východ 2 016 2016-01-13 S svítidlo-bliká 20.1.2016 Definitivní S výměna světelného zdroje 15.1.2016
119 S-0731-004 NaN Lesná NARCISOVÁ Brno - východ 2 016 2016-01-14 S svítidlo-bliká 21.1.2016 Definitivní S výměna světelného zdroje 15.1.2016
126 S-1307-005 NaN Židenice VÁPENKA Brno - východ 2 016 2016-01-15 S svítidlo-bliká 22.1.2016 Definitivní S výměna světelného zdroje 18.1.2016
130 S-0134-002 NaN Líšeň CHMELNICE Brno - východ 2 016 2016-01-17 S svítidlo-bliká 24.1.2016 Definitivní S výměna světelného zdroje 19.1.2016
137 S-0883-009 NaN Štýřice POLNÍ Brno - západ 2 016 2016-01-18 S svítidlo-bliká 25.1.2016 Definitivní S výměna světelného zdroje 18.1.2016
142 S-1575-015 NaN Líšeň SEDLÁČKOVA Brno - východ 2 016 2016-01-18 S svítidlo-bliká 25.1.2016 Definitivní S výměna světelného zdroje 19.1.2016
163 S-1120-005 NaN Líšeň STŘELNICE Brno - východ 2 016 2016-01-20 S svítidlo-bliká 27.1.2016 Definitivní S výměna světelného zdroje 21.1.2016
170 S-0739-006 NaN Husovice NETUŠILOVA Brno - východ 2 016 2016-01-21 S svítidlo-bliká 28.1.2016 Definitivní S výměna světelného zdroje 22.1.2016
177 S-1020-005 R-1020-006 Stránice SEDLÁKOVA Brno - západ 2 016 2016-01-22 S svítidlo-bliká 29.1.2016 Definitivní S výměna světelného zdroje 25.1.2016
190 S-0504-017 NaN Královo Pole KOŠINOVA Brno - západ 2 016 2016-01-25 S svítidlo-bliká 1.2.2016 Definitivní S výměna světelného zdroje 26.1.2016
209 S-0113-010 NaN Černá Pole BŘENKOVA Brno - východ 2 016 2016-01-27 S svítidlo-bliká 3.2.2016 Definitivní S výměna světelného zdroje 27.1.2016
212 S-1100-011 NaN Ponava STAŇKOVA Brno - západ 2 016 2016-01-27 S svítidlo-bliká 3.2.2016 Definitivní S výměna světelného zdroje 28.1.2016
216 S-0022-006 NaN Židenice BALBÍNOVA Brno - východ 2 016 2016-01-27 S svítidlo-bliká 3.2.2016 Definitivní S výměna světelného zdroje 28.1.2016
218 S-0763-015 NaN Město Brno NÁM. SVOBODY Brno - východ 2 016 2016-01-28 S svítidlo-bliká 4.2.2016 Definitivní S výměna světelného zdroje 28.1.2016
226 S-0060-034 NaN Stránice BOHUSLAVA MARTINŮ Brno - západ 2 016 2016-01-28 S svítidlo-bliká 4.2.2016 Definitivní S výměna světelného zdroje 29.1.2016
233 S-0866-014 NaN Komín PODLESÍ Brno - západ 2 016 2016-01-29 S svítidlo-bliká 5.2.2016 Definitivní S výměna světelného zdroje 1.2.2016
... ... ... ... ... ... ... ... ... ... ... ... ...
2 419 S-1049-022 NaN Slatina SLAVKOVSKÁ Brno - východ 2 016 2016-11-30 S svítidlo-bliká 7.12.2016 Definitivní S výměna světelného zdroje 20.12.2016
2 464 S-1043-004 NaN Komárov SLADKÉHO Brno - východ 2 016 2016-12-05 S svítidlo-bliká 12.12.2016 Definitivní S výměna světelného zdroje 21.12.2016
2 471 S-1141-007 NaN Zábrdovice SÝPKA Brno - východ 2 016 2016-12-05 S svítidlo-bliká 12.12.2016 Definitivní S výměna světelného zdroje 12.12.2016
2 476 S-1731-042 NaN Útěchov u Brna VE VILKÁCH Brno - východ 2 016 2016-12-06 S svítidlo-bliká 13.12.2016 Definitivní S výměna světelného zdroje 12.12.2016
2 479 S-1253-010 NaN Husovice VENHUDOVA Brno - východ 2 016 2016-12-06 S svítidlo-bliká 13.12.2016 Definitivní S výměna zapalovače 12.12.2016
2 485 S-0577-006 NaN Jehnice LELEKOVICKÁ Brno - západ 2 016 2016-12-07 S svítidlo-bliká 14.12.2016 Definitivní S výměna světelného zdroje 8.12.2016
2 488 S-1160-011 R-1160-002 Žabovřesky TOPOLKY Brno - západ 2 016 2016-12-07 S svítidlo-bliká 14.12.2016 Definitivní S výměna světelného zdroje 8.12.2016
2 490 S-0627-020 NaN Lesná MARIE MAJEROVÉ Brno - východ 2 016 2016-12-07 S svítidlo-bliká 14.12.2016 Definitivní S výměna světelného zdroje 12.12.2016
2 522 S-0304-035 NaN Líšeň HOUBALOVA Brno - východ 2 016 2016-12-11 S svítidlo-bliká 18.12.2016 Definitivní S výměna světelného zdroje 16.12.2016
2 525 S-0589-006 NaN Lesná LILIOVÁ Brno - východ 2 016 2016-12-12 S svítidlo-bliká 19.12.2016 Definitivní S výměna světelného zdroje 6.3.2017
2 526 S-0079-035 NaN Řečkovice BRATŘÍ KŘIČKŮ Brno - západ 2 016 2016-12-12 S svítidlo-bliká 19.12.2016 Definitivní S výměna světelného zdroje 13.12.2016
2 534 S-1538-048 NaN Nový Lískovec KONIKLECOVÁ Brno - západ 2 016 2016-12-13 S svítidlo-bliká 20.12.2016 Definitivní S výměna světelného zdroje 14.12.2016
2 536 S-0899-042 NaN Žabovřesky POZNAŇSKÁ Brno - západ 2 016 2016-12-13 S svítidlo-bliká 20.12.2016 Definitivní S výměna světelného zdroje 14.12.2016
2 539 S-0079-013 R-0079-006 Řečkovice BRATŘÍ KŘIČKŮ Brno - západ 2 016 2016-12-13 S svítidlo-bliká 20.12.2016 Definitivní S výměna světelného zdroje 14.12.2016
2 541 S-0056-005 NaN Líšeň BODLÁKOVA Brno - východ 2 016 2016-12-13 S svítidlo-bliká 20.12.2016 Definitivní S výměna světelného zdroje 20.12.2016
2 546 S-1851-011 NaN Soběšice KLARISKY Brno - východ 2 016 2016-12-14 S svítidlo-bliká 21.12.2016 Definitivní S výměna světelného zdroje 19.1.2017
2 551 S-1032-015 NaN Židenice SKORKOVSKÉHO Brno - východ 2 016 2016-12-15 S svítidlo-bliká 22.12.2016 Definitivní S výměna světelného zdroje 20.12.2016
2 554 S-0222-004 NaN Stránice FRANTIŠKY STRÁNECKÉ Brno - západ 2 016 2016-12-15 S svítidlo-bliká 22.12.2016 Definitivní S výměna světelného zdroje 16.12.2016
2 555 S-0891-001 NaN Dolní Heršpice POPLUŽNÍ Brno - východ 2 016 2016-12-15 S svítidlo-bliká 22.12.2016 Definitivní S výměna světelného zdroje 21.12.2016
2 561 S-0775-006 NaN Líšeň OBECKÁ Brno - východ 2 016 2016-12-15 S svítidlo-bliká 22.12.2016 Definitivní S výměna světelného zdroje 21.12.2016
2 573 S-0398-005 R-0398-003 Město Brno JÁNSKÁ Brno - východ 2 016 2016-12-17 S svítidlo-bliká 24.12.2016 Definitivní S výměna světelného zdroje 21.12.2016
2 577 S-1484-023 NaN Soběšice ÚTĚCHOVSKÁ Brno - východ 2 016 2016-12-17 S svítidlo-bliká 24.12.2016 Definitivní S výměna světelného zdroje 19.12.2016
2 582 S-0789-051 NaN Černovice OLOMOUCKÁ Brno - východ 2 016 2016-12-17 S svítidlo-bliká 24.12.2016 Definitivní při kontrole vše v pořádku 22.12.2016
2 591 S-1076-008 NaN Jundrov SOSNOVÁ Brno - západ 2 016 2016-12-19 S svítidlo-bliká 26.12.2016 Definitivní S výměna světelného zdroje 20.12.2016
2 593 S-0594-044 NaN Lesná LOOSOVA Brno - východ 2 016 2016-12-19 S svítidlo-bliká 26.12.2016 Definitivní S výměna světelného zdroje 23.12.2016
2 601 S-0386-059 NaN Líšeň JOSEFY FAIMONOVÉ Brno - východ 2 016 2016-12-20 S svítidlo-bliká 27.12.2016 Definitivní S výměna světelného zdroje 22.12.2016
2 606 S-0051-003 R-0051-001 Černovice BLATOUCHOVÁ Brno - východ 2 016 2016-12-21 S svítidlo-bliká 28.12.2016 Definitivní S výměna světelného zdroje 22.12.2016
2 642 S-0560-054 NaN Trnitá KŘENOVÁ Brno - východ 2 016 2016-12-27 S svítidlo-bliká 3.1.2017 Definitivní S výměna světelného zdroje 3.1.2017
2 667 S-1339-004 R-1339-001 Líšeň ZAHRADNÍ Brno - východ 2 016 2016-12-30 S svítidlo-bliká 6.1.2017 Definitivní S výměna světelného zdroje 3.1.2017
2 668 S-0530-010 NaN Židenice KRÁSNÉHO Brno - východ 2 016 2016-12-30 S svítidlo-bliká 6.1.2017 Definitivní S výměna světelného zdroje 4.1.2017

278 rows × 12 columns

Na této podmnožině můžeme zase spočítat četnosti:

In [36]:
pocty_blikajicich = blikajici["Katastr"].value_counts()
pocty_blikajicich
Out[36]:
Židenice              28
Líšeň                 26
Černá Pole            18
Řečkovice             17
Lesná                 15
Město Brno            14
Královo Pole          13
Žabovřesky            12
Veveří                12
Husovice              12
Zábrdovice             8
Komín                  8
Bystrc                 8
Slatina                7
Štýřice                6
Staré Brno             6
Černovice              5
Stránice               5
Kohoutovice            5
Chrlice                5
Komárov                5
Bosonohy               4
Jundrov                3
Starý Lískovec         3
Bohunice               3
Ponava                 3
Útěchov u Brna         3
Medlánky               2
Kníničky               2
Soběšice               2
Obřany                 2
Žebětín                2
Ořešín                 2
Brněnské Ivanovice     2
Maloměřice             2
Trnitá                 2
Sadová                 1
Horní Heršpice         1
Jehnice                1
Nový Lískovec          1
Dolní Heršpice         1
Pisárky                1
Name: Katastr, dtype: int64

Co třeba zkusit zjistit, ve kterém katastru je nejvíce hlášených blikajících světel v poměru k ostatním poruchám?

In [37]:
celkove_pocty = data["Katastr"].value_counts()
pomery = pocty_blikajicich / celkove_pocty
pomery = pomery.dropna().sort_values(ascending=False)
pomery
Out[37]:
Útěchov u Brna        0.428571
Ořešín                0.400000
Sadová                0.333333
Chrlice               0.333333
Kníničky              0.333333
Husovice              0.250000
Řečkovice             0.223684
Líšeň                 0.214876
Bosonohy              0.210526
Zábrdovice            0.177778
Veveří                0.166667
Brněnské Ivanovice    0.166667
Židenice              0.160920
Soběšice              0.142857
Lesná                 0.128205
Černá Pole            0.127660
Ponava                0.120000
Žabovřesky            0.118812
Žebětín               0.100000
Medlánky              0.100000
Jundrov               0.096774
Obřany                0.095238
Královo Pole          0.091549
Dolní Heršpice        0.090909
Bystrc                0.089888
Komárov               0.089286
Stránice              0.086207
Komín                 0.082474
Štýřice               0.081081
Staré Brno            0.077922
Jehnice               0.071429
Černovice             0.070423
Trnitá                0.066667
Kohoutovice           0.057471
Město Brno            0.053232
Maloměřice            0.052632
Starý Lískovec        0.052632
Slatina               0.052632
Horní Heršpice        0.037037
Bohunice              0.029412
Nový Lískovec         0.017241
Pisárky               0.016393
Name: Katastr, dtype: float64

Tato data opět můžeme dostat do grafu:

In [38]:
pomery.plot(kind="bar", figsize=(15,5))
Out[38]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe5e69e19b0>

Můžeme zkusit zjistit, jestli je nějaký den, kdy je hlášeno více poruch než v jiné dny. Pro jednoduchost se omezíme na jeden katastr.

Začneme výběrem podmnožiny dat.

In [39]:
slatina = data[data["Katastr"] == "Slatina"]
slatina = slatina[["Katastr", "Ulice", "Datum nahlášení závady"]]
slatina
Out[39]:
Katastr Ulice Datum nahlášení závady
Číslo závady
13 Slatina TUŘANKA 2016-01-03
14 Slatina VLNITÁ 2016-01-03
39 Slatina POMEZNÍ 2016-01-06
55 Slatina HVIEZDOSLAVOVA 2016-01-08
56 Slatina V NOVÉ ČTVRTI 2016-01-08
57 Slatina POMEZNÍ 2016-01-08
89 Slatina ŘÍPSKÁ 2016-01-12
104 Slatina V NOVÉ ČTVRTI 2016-01-13
105 Slatina POMEZNÍ 2016-01-13
106 Slatina V NOVÉ ČTVRTI 2016-01-13
107 Slatina HVIEZDOSLAVOVA 2016-01-13
120 Slatina HVIEZDOSLAVOVA 2016-01-14
150 Slatina HVIEZDOSLAVOVA 2016-01-19
166 Slatina VLNITÁ 2016-01-20
175 Slatina OSTRAVSKÁ 2016-01-22
179 Slatina HVIEZDOSLAVOVA 2016-01-22
180 Slatina POMEZNÍ 2016-01-22
247 Slatina TILHONOVA 2016-02-01
318 Slatina TUŘANKA 2016-02-14
376 Slatina OSTRAVSKÁ 2016-02-19
381 Slatina OSTRAVSKÁ 2016-02-19
412 Slatina ROUSÍNOVSKÁ 2016-02-24
432 Slatina MOUTNICKÁ 2016-02-26
436 Slatina TILHONOVA 2016-02-27
468 Slatina PŘEMYSLOVO NÁMĚSTÍ 2016-03-03
486 Slatina BLAŽOVICKÁ 2016-03-05
487 Slatina BLAŽOVICKÁ 2016-03-05
507 Slatina OSTRAVSKÁ 2016-03-09
562 Slatina HVIEZDOSLAVOVA 2016-03-15
564 Slatina ŘÍPSKÁ 2016-03-15
... ... ... ...
1 693 Slatina SLATINKA 2016-09-04
1 699 Slatina ROUSÍNOVSKÁ 2016-09-05
1 705 Slatina MIKULČICKÁ 2016-09-05
1 707 Slatina OSTRAVSKÁ 2016-09-06
1 718 Slatina SLATINKA 2016-09-07
1 758 Slatina PŘEMYSLOVO NÁMĚSTÍ 2016-09-12
1 776 Slatina LANGROVA 2016-09-14
1 800 Slatina OSTRAVSKÁ 2016-09-17
1 809 Slatina ŠMILOVSKÉHO 2016-09-18
1 812 Slatina LANGROVA 2016-09-19
1 841 Slatina POMEZNÍ 2016-09-22
1 846 Slatina POMEZNÍ 2016-09-23
1 847 Slatina HVIEZDOSLAVOVA 2016-09-24
1 852 Slatina HVIEZDOSLAVOVA 2016-09-25
1 859 Slatina POMEZNÍ 2016-09-27
1 867 Slatina HVIEZDOSLAVOVA 2016-09-28
1 869 Slatina HVIEZDOSLAVOVA 2016-09-28
1 886 Slatina HVIEZDOSLAVOVA 2016-09-30
2 076 Slatina LANGROVA 2016-10-21
2 092 Slatina ČERNOVIČKY 2016-10-24
2 100 Slatina PŘEMYSLOVO NÁMĚSTÍ 2016-10-24
2 162 Slatina DĚDICKÁ 2016-11-02
2 192 Slatina MIKULČICKÁ 2016-11-04
2 211 Slatina TILHONOVA 2016-11-07
2 342 Slatina LANGROVA 2016-11-21
2 419 Slatina SLAVKOVSKÁ 2016-11-30
2 556 Slatina MIKULČICKÁ 2016-12-15
2 565 Slatina ŘÍPSKÁ 2016-12-16
2 621 Slatina ŘÍPSKÁ 2016-12-24
2 658 Slatina VLNITÁ 2016-12-29

133 rows × 3 columns

Na začátku jsme si nastavili index na číslo poruchy. Pro tuto analýzu ale bude praktičtější indexovat pomocí data nahlášení poruchy. Index můžeme změnít, takže to můžeme napravit bez nového načítání dat. Nejprve se ale podívejme, jak vypadá index teď.

In [40]:
slatina.index
Out[40]:
Index(['13', '14', '39', '55', '56', '57', '89', '104', '105', '106',
       ...
       '2 100', '2 162', '2 192', '2 211', '2 342', '2 419', '2 556', '2 565',
       '2 621', '2 658'],
      dtype='object', name='Číslo závady', length=133)

Nastavíme nový index a podíváme se na data:

In [11]:
slatina_podle_data = slatina.set_index("Datum nahlášení závady")
slatina_podle_data[:5]
Out[11]:
Katastr Ulice
Datum nahlášení závady
2016-01-03 Slatina TUŘANKA
2016-01-03 Slatina VLNITÁ
2016-01-06 Slatina POMEZNÍ
2016-01-08 Slatina HVIEZDOSLAVOVA
2016-01-08 Slatina V NOVÉ ČTVRTI

I na index samotný.

In [12]:
slatina_podle_data.index
Out[12]:
DatetimeIndex(['2016-01-03', '2016-01-03', '2016-01-06', '2016-01-08',
               '2016-01-08', '2016-01-08', '2016-01-12', '2016-01-13',
               '2016-01-13', '2016-01-13',
               ...
               '2016-10-24', '2016-11-02', '2016-11-04', '2016-11-07',
               '2016-11-21', '2016-11-30', '2016-12-15', '2016-12-16',
               '2016-12-24', '2016-12-29'],
              dtype='datetime64[ns]', name='Datum nahlášení závady', length=133, freq=None)

Pokud je indexem datum, můžeme s ním pracovat a podívat se třeba jenom na den v měsíci:

In [13]:
slatina_podle_data.index.day
Out[13]:
Int64Index([ 3,  3,  6,  8,  8,  8, 12, 13, 13, 13,
            ...
            24,  2,  4,  7, 21, 30, 15, 16, 24, 29],
           dtype='int64', name='Datum nahlášení závady', length=133)

Nebo na den v týdnu. Překvapivě 0 znamená pondělí:

In [54]:
slatina_podle_data.index.weekday
Out[54]:
Int64Index([6, 6, 2, 4, 4, 4, 1, 2, 2, 2,
            ...
            0, 2, 4, 0, 0, 2, 3, 4, 5, 3],
           dtype='int64', name='Datum nahlášení závady', length=133)

Takto získaný den v týdnu si můžeme přidat do datového rámce:

In [14]:
slatina_podle_data.loc[:, "den v týdnu"] = slatina_podle_data.index.weekday
slatina_podle_data[:5]
Out[14]:
Katastr Ulice den v týdnu
Datum nahlášení závady
2016-01-03 Slatina TUŘANKA 6
2016-01-03 Slatina VLNITÁ 6
2016-01-06 Slatina POMEZNÍ 2
2016-01-08 Slatina HVIEZDOSLAVOVA 4
2016-01-08 Slatina V NOVÉ ČTVRTI 4

Pomocí metody groupby můžeme data shlukovat podle hodnot určitého sloupce. Tato metoda vrací objekt reprezentující několik shluků. Nás primárně zajímá, kolik je v každé skupině položek (tedy kolik poruch bylo hlášeno v ten který den).

Další možnosti agregování skupin jsou třeba sum nebo mean. Většina ale dává smysl hlavně pro číselné sloupce.

In [16]:
pocty_podle_dne = slatina_podle_data.groupby("den v týdnu").aggregate("size")
pocty_podle_dne
Out[16]:
den v týdnu
0    17
1    23
2    20
3    14
4    24
5    21
6    14
dtype: int64

Pro snažší čtení by bylo lepší zobrazovat jména dní místo čísel. Můžeme si nastavit nový index čistě pomocí seznamu hodnot:

In [17]:
pocty_podle_dne.index = ["Po", "Út", "St", "Čt", "Pá", "So", "Ne"]
pocty_podle_dne
Out[17]:
Po    17
Út    23
St    20
Čt    14
Pá    24
So    21
Ne    14
dtype: int64

A nakonec vykreslíme graf:

In [63]:
pocty_podle_dne.plot(kind="bar")
Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f30dc47bbe0>

Mapa

Pro vykreslování map existuje mnoho různých knihoven. Tady si ukážeme geopandas, která má poměrně hezké rozhraní. Její velkou nevýhodou je komplikovaná instalace na Windows.

Nejprve si knohovnu naimportujeme, potom načteme soubor s daty a vybereme pouze Brno.

Dostaneme datový rámec jako v Pandas. Několik posledních sloupců ale obsahuje zajímavé hodnoty. Kromě souřadnic tam najdeme geometry: definici tvaru nějaké oblasti v mapě.

In [22]:
import geopandas
mapa = geopandas.read_file("Městské_obvody_a_městské_části__polygony.shp", encoding="utf-8")
brno = mapa[mapa["NAZ_OBEC"] == "Brno"].copy()
brno.head()
Out[22]:
OBJECTID KOD_MOaMC NAZ_ZKR_MO NAZ_MOaMC KOD_OBEC NAZ_OBEC KOD_ZUJ NAZ_ZUJ KOD_OKRES KOD_LAU1 NAZ_LAU1 KOD_KRAJ KOD_CZNUTS NAZ_CZNUTS SX SY SHAPE_Leng SHAPE_Area geometry
68 69 550973 Brno-střed Brno-střed 582786 Brno 550973 Brno-střed 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -599107.126826 -1.161208e+06 21011.712879 1.464882e+07 POLYGON ((-598972.9499999993 -1159048.82009999...
69 70 550990 Brno-Žabovřesky Brno-Žabovřesky 582786 Brno 550990 Brno-Žabovřesky 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -600420.609804 -1.158450e+06 10865.113284 4.348780e+06 POLYGON ((-600602.1099999994 -1157005.50010000...
70 71 551007 Brno-Královo Pole Brno-Královo Pole 582786 Brno 551007 Brno-Královo Pole 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -598393.224288 -1.157041e+06 18814.138777 1.007402e+07 POLYGON ((-597252.6999999993 -1155051.28999999...
71 72 551031 Brno-sever Brno-sever 582786 Brno 551031 Brno-sever 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -596387.912140 -1.156034e+06 28461.268033 1.224207e+07 POLYGON ((-596552.0500000007 -1152248.35000000...
72 73 551058 Brno-Židenice Brno-Židenice 582786 Brno 551058 Brno-Židenice 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -595306.099433 -1.160699e+06 13108.262329 5.045712e+06 POLYGON ((-594706.5100000016 -1159323.91, -594...

Zkusíme si nakreslit mapu všech brněnských částí:

In [25]:
from matplotlib import pyplot
brno.plot(figsize=(10,10))
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe5e486e978>

To by asi mohlo být Brno. Kdybychom ale měli popisky pro jednotlivé oblasti, možná by to bylo přehlednější. Můžeme si je přidat. Nejprve ale potřebujeme vědět, kam popisek nakreslit. Můžeme si pro každou oblast spočítat vhodný bod, a přidat ho do dat.

In [26]:
brno["coords"] = brno["geometry"].apply(lambda x: x.representative_point().coords[:])
brno["coords"] = [coords[0] for coords in brno["coords"]]

Teď můžeme vykreslit mapu jako předtím, a potom pro každou oblast přidat anotaci:

In [29]:
brno.plot(figsize=(10,10))
for idx, radek in brno.iterrows():
    pyplot.annotate(s=radek["NAZ_MOaMC"], xy=radek["coords"], horizontalalignment="center")

Jako poslední příklad si do mapy zkusíme znázornit, kolik v dané oblasti je hlášeno poruch veřejného osvětlení. Na to budeme muset spočítat, kolik ji vlastně je, a přidat data do rámce s mapovými informace.

Spojování datových rámců je možné jako po řádcích, tak po sloupcích. Pokud chceme spojovat po sloupcích, potřebujeme index, podle kterého se dají poznat stejné řádky. Nastavíme si proto index na název městské části.

In [30]:
brno = brno.set_index("NAZ_MOaMC", drop=False)
brno.head()
Out[30]:
OBJECTID KOD_MOaMC NAZ_ZKR_MO NAZ_MOaMC KOD_OBEC NAZ_OBEC KOD_ZUJ NAZ_ZUJ KOD_OKRES KOD_LAU1 NAZ_LAU1 KOD_KRAJ KOD_CZNUTS NAZ_CZNUTS SX SY SHAPE_Leng SHAPE_Area geometry coords
NAZ_MOaMC
Brno-střed 69 550973 Brno-střed Brno-střed 582786 Brno 550973 Brno-střed 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -599107.126826 -1.161208e+06 21011.712879 1.464882e+07 POLYGON ((-598972.9499999993 -1159048.82009999... (-599089.0881825134, -1161418.7800999992)
Brno-Žabovřesky 70 550990 Brno-Žabovřesky Brno-Žabovřesky 582786 Brno 550990 Brno-Žabovřesky 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -600420.609804 -1.158450e+06 10865.113284 4.348780e+06 POLYGON ((-600602.1099999994 -1157005.50010000... (-600242.0081863957, -1158264.8850999996)
Brno-Královo Pole 71 551007 Brno-Královo Pole Brno-Královo Pole 582786 Brno 551007 Brno-Královo Pole 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -598393.224288 -1.157041e+06 18814.138777 1.007402e+07 POLYGON ((-597252.6999999993 -1155051.28999999... (-598973.9478040718, -1157189.1750499997)
Brno-sever 72 551031 Brno-sever Brno-sever 582786 Brno 551031 Brno-sever 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -596387.912140 -1.156034e+06 28461.268033 1.224207e+07 POLYGON ((-596552.0500000007 -1152248.35000000... (-596332.4486210528, -1156330.1843500007)
Brno-Židenice 73 551058 Brno-Židenice Brno-Židenice 582786 Brno 551058 Brno-Židenice 40711 CZ0642 Brno-město 3115 CZ064 Jihomoravský kraj -595306.099433 -1.160699e+06 13108.262329 5.045712e+06 POLYGON ((-594706.5100000016 -1159323.91, -594... (-595741.415136773, -1160651.2446999997)

Počet poruch v různých katastrech už máme spočítaný. Potřebujeme ale data trochu zpracovat, aby si odpovídaly názvy částí. Bohužel ne každý katastr patří do jediné městské části, takže tady dojde k určitým nepřesnostem.

In [41]:
poruchy = dict(celkove_pocty)
poruchy["Řečkovice a Mokrá Hora"] = poruchy.pop("Řečkovice") + poruchy.pop("Mokrá Hora")
poruchy["Maloměřice a Obřany"] = poruchy.pop("Maloměřice") + poruchy.pop("Obřany")
poruchy["Ivanovice"] = poruchy.pop("Brněnské Ivanovice")
poruchy["střed"] = poruchy.pop("Město Brno") + poruchy.pop("Staré Brno") + poruchy.pop("Štýřice") + poruchy.pop("Veveří") + poruchy.pop("Pisárky") + poruchy.pop("Stránice")
poruchy["Útěchov"] = poruchy.pop("Útěchov u Brna")
poruchy["sever"] = poruchy.pop("Soběšice") + poruchy.pop("Lesná") + poruchy.pop("Husovice") + poruchy.pop("Černá Pole") + poruchy.pop("Zábrdovice")
poruchy["jih"] = poruchy.pop("Dolní Heršpice") + poruchy.pop("Horní Heršpice") + poruchy.pop("Komárov") + poruchy.pop("Přízřenice") + poruchy.pop("Trnitá")
poruchy["Královo Pole"] += poruchy.pop("Ponava") + poruchy.pop("Sadová")
poruchy["Tuřany"] += poruchy.pop("Dvorska") + poruchy.pop("Holásky")
poruchy = [(f"Brno-{k}", v) for k, v in poruchy.items()]
poruchy_df = pandas.DataFrame.from_records(poruchy, columns=["NAZ", "pocet"], index="NAZ")
poruchy_df
Out[41]:
pocet
NAZ
Brno-Židenice 174
Brno-Královo Pole 170
Brno-Slatina 133
Brno-Líšeň 121
Brno-Bohunice 102
Brno-Žabovřesky 101
Brno-Komín 97
Brno-Bystrc 89
Brno-Kohoutovice 87
Brno-Černovice 71
Brno-Nový Lískovec 58
Brno-Starý Lískovec 57
Brno-Jundrov 31
Brno-Medlánky 20
Brno-Žebětín 20
Brno-Bosonohy 19
Brno-Chrlice 15
Brno-Jehnice 14
Brno-Tuřany 14
Brno-Kníničky 6
Brno-Ivanovice 12
Brno-Ořešín 5
Brno-Řečkovice a Mokrá Hora 78
Brno-Maloměřice a Obřany 59
Brno-střed 605
Brno-Útěchov 7
Brno-sever 365
Brno-jih 134

Tento nový rámec teď můžeme spojit s mapovými podklady. Výchozí spojování je po řádcích, proto musíme určit osu.

In [43]:
brno_s_poruchami = pandas.concat([brno, poruchy_df], sort=False, axis=1)
brno_s_poruchami
Out[43]:
OBJECTID KOD_MOaMC NAZ_ZKR_MO NAZ_MOaMC KOD_OBEC NAZ_OBEC KOD_ZUJ NAZ_ZUJ KOD_OKRES KOD_LAU1 ... KOD_KRAJ KOD_CZNUTS NAZ_CZNUTS SX SY SHAPE_Leng SHAPE_Area geometry coords pocet
Brno-střed 69 550973 Brno-střed Brno-střed 582786 Brno 550973 Brno-střed 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -599107.126826 -1.161208e+06 21011.712879 1.464882e+07 POLYGON ((-598972.9499999993 -1159048.82009999... (-599089.0881825134, -1161418.7800999992) 605.0
Brno-Žabovřesky 70 550990 Brno-Žabovřesky Brno-Žabovřesky 582786 Brno 550990 Brno-Žabovřesky 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -600420.609804 -1.158450e+06 10865.113284 4.348780e+06 POLYGON ((-600602.1099999994 -1157005.50010000... (-600242.0081863957, -1158264.8850999996) 101.0
Brno-Královo Pole 71 551007 Brno-Královo Pole Brno-Královo Pole 582786 Brno 551007 Brno-Královo Pole 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -598393.224288 -1.157041e+06 18814.138777 1.007402e+07 POLYGON ((-597252.6999999993 -1155051.28999999... (-598973.9478040718, -1157189.1750499997) 170.0
Brno-sever 72 551031 Brno-sever Brno-sever 582786 Brno 551031 Brno-sever 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -596387.912140 -1.156034e+06 28461.268033 1.224207e+07 POLYGON ((-596552.0500000007 -1152248.35000000... (-596332.4486210528, -1156330.1843500007) 365.0
Brno-Židenice 73 551058 Brno-Židenice Brno-Židenice 582786 Brno 551058 Brno-Židenice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -595306.099433 -1.160699e+06 13108.262329 5.045712e+06 POLYGON ((-594706.5100000016 -1159323.91, -594... (-595741.415136773, -1160651.2446999997) 174.0
Brno-Černovice 74 551066 Brno-Černovice Brno-Černovice 582786 Brno 551066 Brno-Černovice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -595373.018355 -1.162892e+06 10449.323314 6.291806e+06 POLYGON ((-596356.1099999994 -1161487.45010000... (-595322.4330897855, -1162854.1150499992) 71.0
Brno-jih 75 551074 Brno-jih Brno-jih 582786 Brno 551074 Brno-jih 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -598118.332968 -1.164941e+06 19718.090816 1.279240e+07 POLYGON ((-596731.120000001 -1161671.18, -5967... (-598325.5423521271, -1164508.7350499984) 134.0
Brno-Bohunice 76 551082 Brno-Bohunice Brno-Bohunice 582786 Brno 551082 Brno-Bohunice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -600545.202641 -1.163337e+06 10195.831411 3.017709e+06 POLYGON ((-601002.7100000009 -1161967.82009999... (-600625.2344267721, -1163263.2112499997) 102.0
Brno-Starý Lískovec 77 551091 Brno-Starý Lískovec Brno-Starý Lískovec 582786 Brno 551091 Brno-Starý Lískovec 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -601824.274064 -1.163777e+06 9638.476181 3.283573e+06 POLYGON ((-601051.9600000009 -1162542.89999999... (-601873.3829225947, -1163770.700100001) 57.0
Brno-Nový Lískovec 78 551112 Brno-Nový Lískovec Brno-Nový Lískovec 582786 Brno 551112 Brno-Nový Lískovec 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -602113.027342 -1.162014e+06 5849.668623 1.654432e+06 POLYGON ((-601902.0199999996 -1161269.00010000... (-602141.5874489998, -1161991.3051000014) 58.0
Brno-Kohoutovice 79 551147 Brno-Kohoutovice Brno-Kohoutovice 582786 Brno 551147 Brno-Kohoutovice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -602724.318354 -1.160502e+06 9945.563722 4.085491e+06 POLYGON ((-602977.370000001 -1159558.5601, -60... (-602845.8283676622, -1160474.31745) 87.0
Brno-Jundrov 80 551171 Brno-Jundrov Brno-Jundrov 582786 Brno 551171 Brno-Jundrov 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -602469.191502 -1.158802e+06 11761.802804 4.221080e+06 POLYGON ((-603188.6799999997 -1157361.87000000... (-602340.8641554874, -1158993.5900500007) 31.0
Brno-Bystrc 81 551198 Brno-Bystrc Brno-Bystrc 582786 Brno 551198 Brno-Bystrc 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -607059.680294 -1.155566e+06 39812.756010 2.724224e+07 POLYGON ((-608145.9400000013 -1150485.60000000... (-607374.9921216916, -1154503.6900500003) 89.0
Brno-Kníničky 82 551210 Brno-Kníničky Brno-Kníničky 582786 Brno 551210 Brno-Kníničky 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -606000.584398 -1.152704e+06 22000.104018 1.092800e+07 POLYGON ((-607550.8299999982 -1149918.64999999... (-605819.1174333331, -1153034.6750000007) 6.0
Brno-Komín 83 551228 Brno-Komín Brno-Komín 582786 Brno 551228 Brno-Komín 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -602024.339840 -1.156080e+06 16272.660264 7.598840e+06 POLYGON ((-602512.9499999993 -1153508.30999999... (-601832.7081711115, -1156108.9000000004) 97.0
Brno-Medlánky 84 551236 Brno-Medlánky Brno-Medlánky 582786 Brno 551236 Brno-Medlánky 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -600645.914466 -1.155057e+06 10601.740155 3.510207e+06 POLYGON ((-601463.7300000004 -1153941.05999999... (-600770.6437999412, -1154899.8100000005) 20.0
Brno-Řečkovice a Mokrá Hora 85 551244 Brno-Řečkov. a M.Hora Brno-Řečkovice a Mokrá Hora 582786 Brno 551244 Brno-Řečkovice a Mokrá Hora 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -598775.848808 -1.154307e+06 16472.916389 7.571217e+06 POLYGON ((-598721.129999999 -1153198.4201, -59... (-598767.8359843933, -1154394.9800000004) 78.0
Brno-Maloměřice a Obřany 86 551252 Brno-Maloměřice a Obř. Brno-Maloměřice a Obřany 582786 Brno 551252 Brno-Maloměřice a Obřany 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -594712.425429 -1.157349e+06 17036.942625 9.291087e+06 POLYGON ((-595813.620000001 -1154939.23, -5958... (-594459.9119527972, -1157225.0899999999) 59.0
Brno-Vinohrady 87 551279 Brno-Vinohrady Brno-Vinohrady 582786 Brno 551279 Brno-Vinohrady 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -594000.500322 -1.159879e+06 9640.930008 1.955207e+06 POLYGON ((-592737.5500000007 -1158832.30009999... (-593998.9414789472, -1159853.3806499988) NaN
Brno-Líšeň 88 551287 Brno-Líšeň Brno-Líšeň 582786 Brno 551287 Brno-Líšeň 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -591448.485685 -1.159769e+06 23753.010689 1.570760e+07 POLYGON ((-589128.0199999996 -1156195.84, -589... (-591500.1399165738, -1159344.9351000004) 121.0
Brno-Slatina 89 551295 Brno-Slatina Brno-Slatina 582786 Brno 551295 Brno-Slatina 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -592953.302418 -1.163336e+06 12221.935650 5.829742e+06 POLYGON ((-593091.3500000015 -1161596.96999999... (-592977.0980891805, -1163369.8249999993) 133.0
Brno-Tuřany 90 551309 Brno-Tuřany Brno-Tuřany 582786 Brno 551309 Brno-Tuřany 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -594104.817780 -1.166296e+06 21357.605905 1.784122e+07 POLYGON ((-595983.120000001 -1163916.359999999... (-594598.9249158911, -1166357.4499999993) 14.0
Brno-Chrlice 91 551317 Brno-Chrlice Brno-Chrlice 582786 Brno 551317 Brno-Chrlice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -595592.270326 -1.168729e+06 16026.030228 9.492907e+06 POLYGON ((-596347.5100000016 -1166738.84, -596... (-595516.1788480217, -1168558.8000000007) 15.0
Brno-Bosonohy 92 551325 Brno-Bosonohy Brno-Bosonohy 582786 Brno 551325 Brno-Bosonohy 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -604417.861598 -1.161660e+06 13888.641269 7.147886e+06 POLYGON ((-605152.1799999997 -1160070.39009999... (-604119.7012236556, -1161715.2551000006) 19.0
Brno-Žebětín 93 551368 Brno-Žebětín Brno-Žebětín 582786 Brno 551368 Brno-Žebětín 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -606460.760666 -1.158772e+06 23206.733123 1.359924e+07 POLYGON ((-606417.8599999994 -1156672.88010000... (-606467.2769532457, -1158575.460000001) 20.0
Brno-Ivanovice 94 551376 Brno-Ivanovice Brno-Ivanovice 582786 Brno 551376 Brno-Ivanovice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -600046.704618 -1.152829e+06 10054.771687 2.446103e+06 POLYGON ((-599444.4100000001 -1151725.62000000... (-600026.629237962, -1152798.205050001) 12.0
Brno-Jehnice 95 551406 Brno-Jehnice Brno-Jehnice 582786 Brno 551406 Brno-Jehnice 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -598347.215759 -1.151818e+06 12194.556985 4.072894e+06 POLYGON ((-597563.6400000006 -1150055.85999999... (-598684.7447784491, -1151578.1449999996) 14.0
Brno-Ořešín 96 551422 Brno-Ořešín Brno-Ořešín 582786 Brno 551422 Brno-Ořešín 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -596850.057372 -1.151235e+06 8671.946618 3.064507e+06 POLYGON ((-596615.2800000012 -1150145.77, -596... (-596828.6547211574, -1151328.875) 5.0
Brno-Útěchov 97 551431 Brno-Útěchov Brno-Útěchov 582786 Brno 551431 Brno-Útěchov 40711 CZ0642 ... 3115 CZ064 Jihomoravský kraj -595171.280371 -1.150839e+06 6203.556569 1.178099e+06 POLYGON ((-594745.6900000013 -1150536.32, -594... (-595180.3209008181, -1150669.1050000004) 7.0

29 rows × 21 columns

Nakonec můžeme vykreslit graf, ve kterém budeme definovat barvy pomocí hodnot ve sloupci pocet. Taky si přidáme legendu.

In [77]:
from matplotlib import pyplot
brno_s_poruchami.plot(figsize=(10,10), column="pocet", legend=True)
for idx, radek in brno_s_poruchami.iterrows():
    pyplot.annotate(s=radek["NAZ_MOaMC"], xy=radek["coords"], horizontalalignment="center")

{
  "data": {
    "sessionMaterial": {
      "id": "session-material:2019/brno-jaro-knihovny:pandas:1",
      "title": "Pandas a poruchy veřejného osvětlení v Brně",
      "html": "\n          \n    \n\n    <div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Budeme pracovat se dv&#x11B;ma datov&#xFD;mi soubory:</p>\n<ul>\n<li>evidence poruch ve&#x159;ejn&#xE9;ho osv&#x11B;tlen&#xED; v Brn&#x11B; z <a href=\"https://data.brno.cz/en/dataset/?id=zavady-vo\">https://data.brno.cz/en/dataset/?id=zavady-vo</a>. Data jsou ve form&#xE1;tu XLSX, kter&#xFD; si p&#x159;evedeme do CSV pomoc&#xED; liboln&#xE9;ho tabulkov&#xE9;ho procesoru.</li>\n<li>Seznam m&#x11B;stsk&#xFD;ch &#x10D;&#xE1;st&#xED; a obvod&#x16F; z <a href=\"http://arccr-arcdata.opendata.arcgis.com/datasets/34ee5c20c3b54e6b82fd111d01905843_7\">http://arccr-arcdata.opendata.arcgis.com/datasets/34ee5c20c3b54e6b82fd111d01905843_7</a> ve form&#xE1;tu Shapefile.</li>\n</ul>\n<p>Nejprve naimportujeme knihovnu <code>pandas</code> a zkus&#xED;me si s n&#xED; trochu hr&#xE1;t.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[1]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">pandas</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Na&#x10D;ten&#xED; souboru ve form&#xE1;tu CSV ud&#x11B;l&#xE1;me pomoc&#xED; funkce <code>read_csv</code>. Prvn&#xED; argument ud&#xE1;v&#xE1; jm&#xE9;no souboru. Dal&#x161;&#xED; argumenty jsou:</p>\n<ul>\n<li><code>index_col</code>: N&#xE1;zev sloupce, pomoc&#xED; kter&#xE9;ho chceme data indexovat. Pokud nic nezad&#xE1;me, Pandas vytvo&#x159;&#xED; nov&#xFD; sloupec s &#x10D;&#xED;sly.</li>\n<li><code>parse_dates</code>: T&#xED;mto dan&#xE9; sloupce na&#x10D;teme jako data m&#xED;sto &#x159;et&#x11B;zc&#x16F;. Pozorn&#xFD; &#x10D;ten&#xE1;&#x159; se v&#x161;imne, &#x17E;e ve v&#xFD;pisu je sloupc&#x16F; s daty v&#xED;c, ale my budeme pot&#x159;ebovat pouze tento jeden a na ostatn&#xED;ch n&#xE1;m a&#x17E; tak moc nez&#xE1;le&#x17E;&#xED;.</li>\n<li><code>dayfirst</code>: Pandas zkou&#x161;&#xED; uhodnout, v jak&#xE9;m form&#xE1;tu jsou data zadan&#xE1;. Den a m&#x11B;s&#xED;c je ale problematick&#xE9; rozli&#x161;it, tak&#x17E;e t&#xED;mto &#x159;ekneme, &#x17E;e nejd&#x159;&#xED;ve je v datu den, a&#x17E; potom m&#x11B;s&#xED;c.</li>\n</ul>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[4]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span> <span class=\"o\">=</span> <span class=\"n\">pandas</span><span class=\"o\">.</span><span class=\"n\">read_csv</span><span class=\"p\">(</span><span class=\"s2\">&quot;2016-zavady_vo-1.csv&quot;</span><span class=\"p\">,</span> <span class=\"n\">index_col</span><span class=\"o\">=</span><span class=\"s2\">&quot;&#x10C;&#xED;slo z&#xE1;vady&quot;</span><span class=\"p\">,</span>\n                       <span class=\"n\">parse_dates</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&quot;</span><span class=\"p\">],</span>\n                       <span class=\"n\">dayfirst</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n<span class=\"n\">data</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[4]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EC_SVE</th>\n      <th>E&#x10C; objektu</th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>Sektor z&#xE1;vady</th>\n      <th>Rok z&#xE1;vady</th>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th>Typ z&#xE1;vady</th>\n      <th>Pl&#xE1;novan&#xFD; term&#xED;n opravy</th>\n      <th>Stav opravy</th>\n      <th>Zp&#x16F;sob opravy</th>\n      <th>Datum opravy</th>\n    </tr>\n    <tr>\n      <th>&#x10C;&#xED;slo z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>S-1148-011</td>\n      <td>Z-500</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-01</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>8.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S-1148-012</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-01</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>8.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>neopraveno</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S-1456-012</td>\n      <td>NaN</td>\n      <td>Z&#xE1;brdovice</td>\n      <td>&#x160;PIT&#xC1;LKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S-1256-217</td>\n      <td>NaN</td>\n      <td>Kom&#xED;n</td>\n      <td>VESLA&#x158;SK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S-0478-003</td>\n      <td>Z-612</td>\n      <td>Star&#xE9; Brno</td>\n      <td>KOPE&#x10C;N&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>S-0703-001</td>\n      <td>R-0703-002</td>\n      <td>Star&#xFD; L&#xED;skovec</td>\n      <td>M&#xC1;CHALOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>S-1257-019</td>\n      <td>Z-547</td>\n      <td>Veve&#x159;&#xED;</td>\n      <td>VEVE&#x158;&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>SO nekomunikuje &#x10D;ip</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>p&#x159;i kontrole v&#x161;e v po&#x159;&#xE1;dku</td>\n      <td>3.1.2016</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>S-0687-129</td>\n      <td>NaN</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>MORAVSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>S-1202-005</td>\n      <td>Z-518</td>\n      <td>Bosonohy</td>\n      <td>U SMY&#x10C;KY</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>2.1.2016</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>S-0305-013</td>\n      <td>NaN</td>\n      <td>Bosonohy</td>\n      <td>HO&#x160;TICK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sto&#x17E;&#xE1;r-po&#x161;kozen</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>nen&#xED; v majetku TsB</td>\n      <td>2.1.2016</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>S-0942-016</td>\n      <td>Z-425</td>\n      <td>Ivanovice</td>\n      <td>P&#x158;&#xCD;JEZDOV&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>S-1485-038</td>\n      <td>Z-531</td>\n      <td>Str&#xE1;nice</td>\n      <td>&#xDA;VOZ</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>SO nekomunikuje &#x10D;ip</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>jin&#xE1; z&#xE1;vada</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>S-1179-133</td>\n      <td>Z-627</td>\n      <td>Slatina</td>\n      <td>TU&#x158;ANKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>S-1269-049</td>\n      <td>NaN</td>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>S-0375-191</td>\n      <td>Z-186</td>\n      <td>Nov&#xFD; L&#xED;skovec</td>\n      <td>JIHLAVSK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>Z vypnuta regulace, viz.pozn&#xE1;mka</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>jin&#xE1; z&#xE1;vada</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S-1360-009</td>\n      <td>NaN</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n      <td>ZIMN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>S-1360-008</td>\n      <td>NaN</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n      <td>ZIMN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S-1204-034</td>\n      <td>NaN</td>\n      <td>Kohoutovice</td>\n      <td>U VELK&#xC9; CENY</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>S-0652-077</td>\n      <td>NaN</td>\n      <td>Star&#xE9; Brno</td>\n      <td>MENDLOVO N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>S-0455-038</td>\n      <td>Z-619</td>\n      <td>Kr&#xE1;lovo Pole</td>\n      <td>KOCI&#xC1;NKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>S-0527-003</td>\n      <td>Z-236</td>\n      <td>&#x17D;abov&#x159;esky</td>\n      <td>KR&#xC1;LOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>S-0808-035</td>\n      <td>Z-357</td>\n      <td>&#x17D;idenice</td>\n      <td>OTAKARA &#x160;EV&#x10C;&#xCD;KA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>viz. pozn&#xE1;mka</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>jin&#xE1; z&#xE1;vada</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>S-0526-036</td>\n      <td>Z-035</td>\n      <td>Star&#xFD; L&#xED;skovec</td>\n      <td>KRYMSK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>viz. pozn&#xE1;mka</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>p&#x159;i kontrole v&#x161;e v po&#x159;&#xE1;dku</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>S-1354-003</td>\n      <td>NaN</td>\n      <td>&#x17D;abov&#x159;esky</td>\n      <td>ZEMKOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>S-1259-003</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>VINA&#x158;ICK&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>S-0831-019</td>\n      <td>NaN</td>\n      <td>Star&#xE9; Brno</td>\n      <td>PELLICOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>S-0421-007</td>\n      <td>R-0421-002</td>\n      <td>Str&#xE1;nice</td>\n      <td>KAMPEL&#xCD;KOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>S-0808-034</td>\n      <td>Z-357</td>\n      <td>&#x17D;idenice</td>\n      <td>OTAKARA &#x160;EV&#x10C;&#xCD;KA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>Z vypnuta regulace, viz.pozn&#xE1;mka</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z regulace se&#x159;&#xED;zena</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>S-0579-005</td>\n      <td>Z-261</td>\n      <td>Str&#xE1;nice</td>\n      <td>LERCHOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>6.1.2016</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>S-1187-017</td>\n      <td>R-1187-002</td>\n      <td>Veve&#x159;&#xED;</td>\n      <td>T&#x16E;MOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;641</th>\n      <td>S-1070-036</td>\n      <td>R-1070-007</td>\n      <td>Lesn&#xE1;</td>\n      <td>SOB&#x11A;&#x160;ICK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>R po&#x161;kozen</td>\n      <td>3.1.2017</td>\n      <td>Provizorn&#xED;</td>\n      <td>vandalizmus</td>\n      <td>27.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;642</th>\n      <td>S-0560-054</td>\n      <td>NaN</td>\n      <td>Trnit&#xE1;</td>\n      <td>K&#x158;ENOV&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;643</th>\n      <td>S-0271-001</td>\n      <td>R-0271-001</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n      <td>HELFERTOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>R po&#x161;kozen</td>\n      <td>3.1.2017</td>\n      <td>Provizorn&#xED;</td>\n      <td>R oprava RS</td>\n      <td>27.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;644</th>\n      <td>S-0770-067</td>\n      <td>R-0770-008</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n      <td>N&#xC1;M. SNP</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>R po&#x161;kozen</td>\n      <td>3.1.2017</td>\n      <td>Provizorn&#xED;</td>\n      <td>vandalizmus</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;645</th>\n      <td>S-0066-001</td>\n      <td>Z-034</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>BOSKOVICK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>27.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;646</th>\n      <td>S-0956-008</td>\n      <td>Z-428</td>\n      <td>Bystrc</td>\n      <td>RAKOVECK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>oprava voln&#xE9;ho veden&#xED;</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;647</th>\n      <td>S-1438-009</td>\n      <td>Z-480</td>\n      <td>Bosonohy</td>\n      <td>&#x160;EV&#x10C;ENKOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>oprava voln&#xE9;ho veden&#xED;</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;648</th>\n      <td>S-1202-006</td>\n      <td>Z-518</td>\n      <td>Bosonohy</td>\n      <td>U SMY&#x10C;KY</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>oprava voln&#xE9;ho veden&#xED;</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;649</th>\n      <td>S-1343-035</td>\n      <td>NaN</td>\n      <td>Dvorska</td>\n      <td>ZAPLETALOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-28</td>\n      <td>viz. pozn&#xE1;mka</td>\n      <td>4.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>jin&#xE1; z&#xE1;vada</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;650</th>\n      <td>S-0175-001</td>\n      <td>NaN</td>\n      <td>Husovice</td>\n      <td>DUKELSK&#xC1; T&#x158;&#xCD;DA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-28</td>\n      <td>viz. pozn&#xE1;mka</td>\n      <td>4.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>hav&#xE1;rie</td>\n      <td>28.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;651</th>\n      <td>S-0066-001</td>\n      <td>Z-034</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>BOSKOVICK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-28</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>29.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;652</th>\n      <td>S-1496-022</td>\n      <td>Z-075</td>\n      <td>Bohunice</td>\n      <td>DLOUH&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-28</td>\n      <td>Z vypnuta regulace, viz.pozn&#xE1;mka</td>\n      <td>4.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z regulace zapnuta</td>\n      <td>29.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;653</th>\n      <td>S-0802-007</td>\n      <td>Z-347</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>ORL&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-28</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>4.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>29.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;654</th>\n      <td>S-1000-019</td>\n      <td>Z-513</td>\n      <td>Medl&#xE1;nky</td>\n      <td>RYSOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>29.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;655</th>\n      <td>S-0169-060</td>\n      <td>Z-082</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n      <td>DROBN&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>30.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;656</th>\n      <td>S-1225-009</td>\n      <td>Z-533</td>\n      <td>Bohunice</td>\n      <td>UZBECK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>Z vybit&#xE1; z&#xE1;lo&#x17E;n&#xED; baterie</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>2.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;657</th>\n      <td>S-1098-022</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>STAR&#xC1; OSADA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;658</th>\n      <td>S-1269-013</td>\n      <td>NaN</td>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>2.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;659</th>\n      <td>S-0375-135</td>\n      <td>Z-187</td>\n      <td>Bohunice</td>\n      <td>JIHLAVSK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>Z vypnuta regulace, viz.pozn&#xE1;mka</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>p&#x159;i kontrole v&#x161;e v po&#x159;&#xE1;dku</td>\n      <td>6.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;660</th>\n      <td>S-0345-005</td>\n      <td>NaN</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>JAKUBSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>14.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;661</th>\n      <td>S-1352-022</td>\n      <td>NaN</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>ZELN&#xDD; TRH</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-29</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>5.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;662</th>\n      <td>S-0265-006</td>\n      <td>Z-628</td>\n      <td>&#x10C;ernovice</td>\n      <td>HAVRAN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>2.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;663</th>\n      <td>S-1092-002</td>\n      <td>Z-459</td>\n      <td>Kr&#xE1;lovo Pole</td>\n      <td>SRBSK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>Z vybit&#xE1; PLC baterie</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>Z v&#xFD;m&#x11B;na baterie</td>\n      <td>2.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;664</th>\n      <td>S-0786-017</td>\n      <td>NaN</td>\n      <td>Bohunice</td>\n      <td>OKROUHL&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S patice-po&#x161;kozena, chyb&#xED;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S mont&#xE1;&#x17E; patice</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;665</th>\n      <td>S-0476-005</td>\n      <td>R-0476-002</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>KONRADOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>R po&#x161;kozen</td>\n      <td>6.1.2017</td>\n      <td>Provizorn&#xED;</td>\n      <td>provizorn&#xED; zaji&#x161;t&#x11B;n&#xED; &#x17E;iv&#xFD;ch &#x10D;&#xE1;st&#xED;</td>\n      <td>5.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;666</th>\n      <td>S-1549-083</td>\n      <td>NaN</td>\n      <td>Bystrc</td>\n      <td>OBVODOV&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>2.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;667</th>\n      <td>S-1339-004</td>\n      <td>R-1339-001</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>ZAHRADN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;668</th>\n      <td>S-0530-010</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>KR&#xC1;SN&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;669</th>\n      <td>S-0880-020</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>POHANKOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-31</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>7.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;670</th>\n      <td>S-1180-001</td>\n      <td>NaN</td>\n      <td>Tu&#x159;any</td>\n      <td>TU&#x158;ANSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-31</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>7.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2017</td>\n    </tr>\n  </tbody>\n</table>\n<p>2669 rows &#xD7; 12 columns</p>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>M&#x16F;&#x17E;eme si nechat data popsat:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[12]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"o\">.</span><span class=\"n\">describe</span><span class=\"p\">()</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[12]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EC_SVE</th>\n      <th>E&#x10C; objektu</th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>Sektor z&#xE1;vady</th>\n      <th>Rok z&#xE1;vady</th>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th>Typ z&#xE1;vady</th>\n      <th>Pl&#xE1;novan&#xFD; term&#xED;n opravy</th>\n      <th>Stav opravy</th>\n      <th>Zp&#x16F;sob opravy</th>\n      <th>Datum opravy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2666</td>\n      <td>1458</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2669</td>\n      <td>2668</td>\n      <td>2669</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>1899</td>\n      <td>512</td>\n      <td>48</td>\n      <td>782</td>\n      <td>2</td>\n      <td>1</td>\n      <td>363</td>\n      <td>31</td>\n      <td>363</td>\n      <td>2</td>\n      <td>53</td>\n      <td>341</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>S-1714-161</td>\n      <td>Z-531</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>MERHAUTOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-04-02 00:00:00</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>9.4.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>15.9.2016</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>13</td>\n      <td>20</td>\n      <td>263</td>\n      <td>38</td>\n      <td>1502</td>\n      <td>2669</td>\n      <td>24</td>\n      <td>601</td>\n      <td>24</td>\n      <td>2589</td>\n      <td>665</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>first</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016-01-01 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>last</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2016-12-31 00:00:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nebo si taky m&#x16F;&#x17E;eme nechat vykreslit graf. Osa x je dan&#xE1; indexem, na ose y je vybran&#xFD; sloupec. Datum je v na&#x161;ich datech jedin&#xFD; sloupec, kde d&#xE1;v&#xE1; trochu smysl vykreslovat graf.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[14]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">()</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[14]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f30dca2a470&gt;</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>M&#x16F;&#x17E;eme si taky vybrat jenom ur&#x10D;itou podmno&#x17E;inu dat. T&#x159;eba prvn&#xED;ch p&#x11B;t &#x159;&#xE1;dk&#x16F;.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[15]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[:</span><span class=\"mi\">5</span><span class=\"p\">]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[15]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EC_SVE</th>\n      <th>E&#x10C; objektu</th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>Sektor z&#xE1;vady</th>\n      <th>Rok z&#xE1;vady</th>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th>Typ z&#xE1;vady</th>\n      <th>Pl&#xE1;novan&#xFD; term&#xED;n opravy</th>\n      <th>Stav opravy</th>\n      <th>Zp&#x16F;sob opravy</th>\n      <th>Datum opravy</th>\n    </tr>\n    <tr>\n      <th>&#x10C;&#xED;slo z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>S-1148-011</td>\n      <td>Z-500</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-01</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>8.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>S-1148-012</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-01</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>8.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>neopraveno</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>S-1456-012</td>\n      <td>NaN</td>\n      <td>Z&#xE1;brdovice</td>\n      <td>&#x160;PIT&#xC1;LKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>S-1256-217</td>\n      <td>NaN</td>\n      <td>Kom&#xED;n</td>\n      <td>VESLA&#x158;SK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-nesv&#xED;t&#xED;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>S-0478-003</td>\n      <td>Z-612</td>\n      <td>Star&#xE9; Brno</td>\n      <td>KOPE&#x10C;N&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>Z vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>vypaden&#xFD; jist&#xED;c&#xED; prvek</td>\n      <td>4.1.2016</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nebo jenom jeden sloupec z prvn&#xED;ch p&#x11B;ti &#x159;&#xE1;dk&#x16F;:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[16]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">][:</span><span class=\"mi\">5</span><span class=\"p\">]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[16]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&#x10C;&#xED;slo z&#xE1;vady\n1     &#x158;e&#x10D;kovice\n2     &#x158;e&#x10D;kovice\n3    Z&#xE1;brdovice\n4         Kom&#xED;n\n5    Star&#xE9; Brno\nName: Katastr, dtype: object</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>M&#x16F;&#x17E;eme vybrat n&#x11B;kolik sloupc&#x16F;:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[17]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[[</span><span class=\"s2\">&quot;Ulice&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">]]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[17]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Ulice</th>\n      <th>Katastr</th>\n    </tr>\n    <tr>\n      <th>&#x10C;&#xED;slo z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>&#x158;e&#x10D;kovice</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TEREZY NOV&#xC1;KOV&#xC9;</td>\n      <td>&#x158;e&#x10D;kovice</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>&#x160;PIT&#xC1;LKA</td>\n      <td>Z&#xE1;brdovice</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>VESLA&#x158;SK&#xC1;</td>\n      <td>Kom&#xED;n</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>KOPE&#x10C;N&#xC1;</td>\n      <td>Star&#xE9; Brno</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M&#xC1;CHALOVA</td>\n      <td>Star&#xFD; L&#xED;skovec</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>VEVE&#x158;&#xCD;</td>\n      <td>Veve&#x159;&#xED;</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>MORAVSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>M&#x11B;sto Brno</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>U SMY&#x10C;KY</td>\n      <td>Bosonohy</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>HO&#x160;TICK&#xC1;</td>\n      <td>Bosonohy</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>P&#x158;&#xCD;JEZDOV&#xC1;</td>\n      <td>Ivanovice</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>&#xDA;VOZ</td>\n      <td>Str&#xE1;nice</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>TU&#x158;ANKA</td>\n      <td>Slatina</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>VLNIT&#xC1;</td>\n      <td>Slatina</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>JIHLAVSK&#xC1;</td>\n      <td>Nov&#xFD; L&#xED;skovec</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>ZIMN&#xCD;</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>ZIMN&#xCD;</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>U VELK&#xC9; CENY</td>\n      <td>Kohoutovice</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>MENDLOVO N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Star&#xE9; Brno</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>KOCI&#xC1;NKA</td>\n      <td>Kr&#xE1;lovo Pole</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>KR&#xC1;LOVA</td>\n      <td>&#x17D;abov&#x159;esky</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>OTAKARA &#x160;EV&#x10C;&#xCD;KA</td>\n      <td>&#x17D;idenice</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>KRYMSK&#xC1;</td>\n      <td>Star&#xFD; L&#xED;skovec</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>ZEMKOVA</td>\n      <td>&#x17D;abov&#x159;esky</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>VINA&#x158;ICK&#xC9;HO</td>\n      <td>&#x17D;idenice</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>PELLICOVA</td>\n      <td>Star&#xE9; Brno</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>KAMPEL&#xCD;KOVA</td>\n      <td>Str&#xE1;nice</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>OTAKARA &#x160;EV&#x10C;&#xCD;KA</td>\n      <td>&#x17D;idenice</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>LERCHOVA</td>\n      <td>Str&#xE1;nice</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>T&#x16E;MOVA</td>\n      <td>Veve&#x159;&#xED;</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;641</th>\n      <td>SOB&#x11A;&#x160;ICK&#xC1;</td>\n      <td>Lesn&#xE1;</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;642</th>\n      <td>K&#x158;ENOV&#xC1;</td>\n      <td>Trnit&#xE1;</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;643</th>\n      <td>HELFERTOVA</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;644</th>\n      <td>N&#xC1;M. SNP</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;645</th>\n      <td>BOSKOVICK&#xC1;</td>\n      <td>&#x158;e&#x10D;kovice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;646</th>\n      <td>RAKOVECK&#xC1;</td>\n      <td>Bystrc</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;647</th>\n      <td>&#x160;EV&#x10C;ENKOVA</td>\n      <td>Bosonohy</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;648</th>\n      <td>U SMY&#x10C;KY</td>\n      <td>Bosonohy</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;649</th>\n      <td>ZAPLETALOVA</td>\n      <td>Dvorska</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;650</th>\n      <td>DUKELSK&#xC1; T&#x158;&#xCD;DA</td>\n      <td>Husovice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;651</th>\n      <td>BOSKOVICK&#xC1;</td>\n      <td>&#x158;e&#x10D;kovice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;652</th>\n      <td>DLOUH&#xC1;</td>\n      <td>Bohunice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;653</th>\n      <td>ORL&#xCD;</td>\n      <td>M&#x11B;sto Brno</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;654</th>\n      <td>RYSOVA</td>\n      <td>Medl&#xE1;nky</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;655</th>\n      <td>DROBN&#xC9;HO</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;656</th>\n      <td>UZBECK&#xC1;</td>\n      <td>Bohunice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;657</th>\n      <td>STAR&#xC1; OSADA</td>\n      <td>&#x17D;idenice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;658</th>\n      <td>VLNIT&#xC1;</td>\n      <td>Slatina</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;659</th>\n      <td>JIHLAVSK&#xC1;</td>\n      <td>Bohunice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;660</th>\n      <td>JAKUBSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>M&#x11B;sto Brno</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;661</th>\n      <td>ZELN&#xDD; TRH</td>\n      <td>M&#x11B;sto Brno</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;662</th>\n      <td>HAVRAN&#xCD;</td>\n      <td>&#x10C;ernovice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;663</th>\n      <td>SRBSK&#xC1;</td>\n      <td>Kr&#xE1;lovo Pole</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;664</th>\n      <td>OKROUHL&#xC1;</td>\n      <td>Bohunice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;665</th>\n      <td>KONRADOVA</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;666</th>\n      <td>OBVODOV&#xC1;</td>\n      <td>Bystrc</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;667</th>\n      <td>ZAHRADN&#xCD;</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;668</th>\n      <td>KR&#xC1;SN&#xC9;HO</td>\n      <td>&#x17D;idenice</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;669</th>\n      <td>POHANKOVA</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;670</th>\n      <td>TU&#x158;ANSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>Tu&#x159;any</td>\n    </tr>\n  </tbody>\n</table>\n<p>2669 rows &#xD7; 2 columns</p>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Z&#xE1;kladn&#xED; anal&#xFD;za pro sloupce s &#x159;et&#x11B;zci m&#x16F;&#x17E;e m&#xFD;t spo&#x10D;&#xED;t&#xE1;n&#xED; &#x10D;etnost&#xED;: kolikr&#xE1;t se kter&#xE1; hodnota ve sloupci objevuje. Zjist&#xED;me 10 ulic s nejv&#xED;ce poruchami:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[19]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Ulice&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()[:</span><span class=\"mi\">10</span><span class=\"p\">]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[19]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>MERHAUTOVA          38\nLIBU&#x160;INA T&#x158;&#xCD;DA      32\nV&#xCD;DE&#x147;SK&#xC1;            31\nMORAVSK&#xC9; N&#xC1;M&#x11A;ST&#xCD;    29\nOSTRAVSK&#xC1;           28\nJIHLAVSK&#xC1;           28\n&#xDA;VOZ                24\n&#x10C;ESK&#xC1;               22\nHRADECK&#xC1;            22\nVESLA&#x158;SK&#xC1;           21\nName: Ulice, dtype: int64</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Tato data m&#x16F;&#x17E;eme zase vykreslit do grafu. V&#xFD;choz&#xED; &#x10D;&#xE1;rov&#xFD; graf tady nen&#xED; vhodn&#xFD;, proto rad&#x161;i pou&#x17E;ijeme sloupcov&#xFD; graf (v horizont&#xE1;ln&#xED; variant&#x11B;).</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[24]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Ulice&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()[:</span><span class=\"mi\">10</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">kind</span><span class=\"o\">=</span><span class=\"s2\">&quot;barh&quot;</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span><span class=\"mi\">8</span><span class=\"p\">))</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[24]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f30dc7eae48&gt;</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img src=\"data:image/png;base64,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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podmno&#x17E;inu dat m&#x16F;&#x17E;eme vybrat stejn&#xFD;m zp&#x16F;sobem jako v numpy pomoc&#xED; pravdivostn&#xED;ho v&#xFD;razu. Nejpreve najdeme v&#x161;echny &#x159;&#xE1;dky, kter&#xE9; maj&#xED; dan&#xFD; typ poruchy, a potom si vybereme pouze tyto &#x159;&#xE1;dky.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[35]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">blikajici</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Typ z&#xE1;vady&quot;</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"s2\">&quot;S sv&#xED;tidlo-blik&#xE1;&quot;</span> <span class=\"p\">]</span>\n<span class=\"n\">blikajici</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[35]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>EC_SVE</th>\n      <th>E&#x10C; objektu</th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>Sektor z&#xE1;vady</th>\n      <th>Rok z&#xE1;vady</th>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th>Typ z&#xE1;vady</th>\n      <th>Pl&#xE1;novan&#xFD; term&#xED;n opravy</th>\n      <th>Stav opravy</th>\n      <th>Zp&#x16F;sob opravy</th>\n      <th>Datum opravy</th>\n    </tr>\n    <tr>\n      <th>&#x10C;&#xED;slo z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3</th>\n      <td>S-1456-012</td>\n      <td>NaN</td>\n      <td>Z&#xE1;brdovice</td>\n      <td>&#x160;PIT&#xC1;LKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-02</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>9.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>S-1269-049</td>\n      <td>NaN</td>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>S-1360-009</td>\n      <td>NaN</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n      <td>ZIMN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>S-1360-008</td>\n      <td>NaN</td>\n      <td>Malom&#x11B;&#x159;ice</td>\n      <td>ZIMN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>S-1204-034</td>\n      <td>NaN</td>\n      <td>Kohoutovice</td>\n      <td>U VELK&#xC9; CENY</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-03</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>10.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2016</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>S-1259-003</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>VINA&#x158;ICK&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-04</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>11.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>5.1.2016</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>S-0625-011</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>MARIE H&#xDC;BNEROV&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-05</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>12.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#xED;tidla</td>\n      <td>18.1.2016</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>S-1261-091</td>\n      <td>R-1261-008</td>\n      <td>&#x17D;idenice</td>\n      <td>VINI&#x10C;N&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-07</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>14.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.3.2016</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>S-0404-006</td>\n      <td>NaN</td>\n      <td>Chrlice</td>\n      <td>K L&#xC1;ZINK&#xC1;M</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-08</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>15.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>11.1.2016</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>S-0775-001</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>OBECK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-11</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>18.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>13.1.2016</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>S-0446-008</td>\n      <td>NaN</td>\n      <td>Kom&#xE1;rov</td>\n      <td>KL&#xC1;&#x160;TERSK&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-12</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>19.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>15.1.2016</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>S-0159-006</td>\n      <td>NaN</td>\n      <td>&#x17D;abov&#x159;esky</td>\n      <td>DOLE&#x17D;ALOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-12</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>19.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>13.1.2016</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>S-0507-003</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>KO&#x158;ENSK&#xC9;HO</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>13.1.2016</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>S-0507-002</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>KO&#x158;ENSK&#xC9;HO</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>13.1.2016</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>S-1058-003</td>\n      <td>NaN</td>\n      <td>Kom&#xE1;rov</td>\n      <td>SLUNN&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>15.1.2016</td>\n    </tr>\n    <tr>\n      <th>119</th>\n      <td>S-0731-004</td>\n      <td>NaN</td>\n      <td>Lesn&#xE1;</td>\n      <td>NARCISOV&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-14</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>21.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>15.1.2016</td>\n    </tr>\n    <tr>\n      <th>126</th>\n      <td>S-1307-005</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>V&#xC1;PENKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-15</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>22.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>18.1.2016</td>\n    </tr>\n    <tr>\n      <th>130</th>\n      <td>S-0134-002</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>CHMELNICE</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-17</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>24.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>19.1.2016</td>\n    </tr>\n    <tr>\n      <th>137</th>\n      <td>S-0883-009</td>\n      <td>NaN</td>\n      <td>&#x160;t&#xFD;&#x159;ice</td>\n      <td>POLN&#xCD;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-18</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>25.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>18.1.2016</td>\n    </tr>\n    <tr>\n      <th>142</th>\n      <td>S-1575-015</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>SEDL&#xC1;&#x10C;KOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-18</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>25.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>19.1.2016</td>\n    </tr>\n    <tr>\n      <th>163</th>\n      <td>S-1120-005</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>ST&#x158;ELNICE</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-20</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>27.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.1.2016</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>S-0739-006</td>\n      <td>NaN</td>\n      <td>Husovice</td>\n      <td>NETU&#x160;ILOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-21</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>28.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>22.1.2016</td>\n    </tr>\n    <tr>\n      <th>177</th>\n      <td>S-1020-005</td>\n      <td>R-1020-006</td>\n      <td>Str&#xE1;nice</td>\n      <td>SEDL&#xC1;KOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-22</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>29.1.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>25.1.2016</td>\n    </tr>\n    <tr>\n      <th>190</th>\n      <td>S-0504-017</td>\n      <td>NaN</td>\n      <td>Kr&#xE1;lovo Pole</td>\n      <td>KO&#x160;INOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-25</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>1.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>26.1.2016</td>\n    </tr>\n    <tr>\n      <th>209</th>\n      <td>S-0113-010</td>\n      <td>NaN</td>\n      <td>&#x10C;ern&#xE1; Pole</td>\n      <td>B&#x158;ENKOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-27</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>3.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>27.1.2016</td>\n    </tr>\n    <tr>\n      <th>212</th>\n      <td>S-1100-011</td>\n      <td>NaN</td>\n      <td>Ponava</td>\n      <td>STA&#x147;KOVA</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-27</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>3.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>28.1.2016</td>\n    </tr>\n    <tr>\n      <th>216</th>\n      <td>S-0022-006</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>BALB&#xCD;NOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-27</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>3.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>28.1.2016</td>\n    </tr>\n    <tr>\n      <th>218</th>\n      <td>S-0763-015</td>\n      <td>NaN</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>N&#xC1;M. SVOBODY</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-28</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>4.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>28.1.2016</td>\n    </tr>\n    <tr>\n      <th>226</th>\n      <td>S-0060-034</td>\n      <td>NaN</td>\n      <td>Str&#xE1;nice</td>\n      <td>BOHUSLAVA MARTIN&#x16E;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-28</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>4.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>29.1.2016</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>S-0866-014</td>\n      <td>NaN</td>\n      <td>Kom&#xED;n</td>\n      <td>PODLES&#xCD;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-01-29</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>5.2.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>1.2.2016</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;419</th>\n      <td>S-1049-022</td>\n      <td>NaN</td>\n      <td>Slatina</td>\n      <td>SLAVKOVSK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-11-30</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>7.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>20.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;464</th>\n      <td>S-1043-004</td>\n      <td>NaN</td>\n      <td>Kom&#xE1;rov</td>\n      <td>SLADK&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-05</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>12.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;471</th>\n      <td>S-1141-007</td>\n      <td>NaN</td>\n      <td>Z&#xE1;brdovice</td>\n      <td>S&#xDD;PKA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-05</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>12.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>12.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;476</th>\n      <td>S-1731-042</td>\n      <td>NaN</td>\n      <td>&#xDA;t&#x11B;chov u Brna</td>\n      <td>VE VILK&#xC1;CH</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-06</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>13.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>12.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;479</th>\n      <td>S-1253-010</td>\n      <td>NaN</td>\n      <td>Husovice</td>\n      <td>VENHUDOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-06</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>13.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na zapalova&#x10D;e</td>\n      <td>12.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;485</th>\n      <td>S-0577-006</td>\n      <td>NaN</td>\n      <td>Jehnice</td>\n      <td>LELEKOVICK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-07</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>14.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>8.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;488</th>\n      <td>S-1160-011</td>\n      <td>R-1160-002</td>\n      <td>&#x17D;abov&#x159;esky</td>\n      <td>TOPOLKY</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-07</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>14.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>8.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;490</th>\n      <td>S-0627-020</td>\n      <td>NaN</td>\n      <td>Lesn&#xE1;</td>\n      <td>MARIE MAJEROV&#xC9;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-07</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>14.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>12.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;522</th>\n      <td>S-0304-035</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>HOUBALOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-11</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>18.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>16.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;525</th>\n      <td>S-0589-006</td>\n      <td>NaN</td>\n      <td>Lesn&#xE1;</td>\n      <td>LILIOV&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-12</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>19.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>6.3.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;526</th>\n      <td>S-0079-035</td>\n      <td>NaN</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>BRAT&#x158;&#xCD; K&#x158;I&#x10C;K&#x16E;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-12</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>19.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>13.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;534</th>\n      <td>S-1538-048</td>\n      <td>NaN</td>\n      <td>Nov&#xFD; L&#xED;skovec</td>\n      <td>KONIKLECOV&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>14.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;536</th>\n      <td>S-0899-042</td>\n      <td>NaN</td>\n      <td>&#x17D;abov&#x159;esky</td>\n      <td>POZNA&#x147;SK&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>14.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;539</th>\n      <td>S-0079-013</td>\n      <td>R-0079-006</td>\n      <td>&#x158;e&#x10D;kovice</td>\n      <td>BRAT&#x158;&#xCD; K&#x158;I&#x10C;K&#x16E;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>14.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;541</th>\n      <td>S-0056-005</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>BODL&#xC1;KOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-13</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>20.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>20.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;546</th>\n      <td>S-1851-011</td>\n      <td>NaN</td>\n      <td>Sob&#x11B;&#x161;ice</td>\n      <td>KLARISKY</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-14</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>21.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>19.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;551</th>\n      <td>S-1032-015</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>SKORKOVSK&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-15</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>22.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>20.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;554</th>\n      <td>S-0222-004</td>\n      <td>NaN</td>\n      <td>Str&#xE1;nice</td>\n      <td>FRANTI&#x160;KY STR&#xC1;NECK&#xC9;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-15</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>22.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>16.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;555</th>\n      <td>S-0891-001</td>\n      <td>NaN</td>\n      <td>Doln&#xED; Her&#x161;pice</td>\n      <td>POPLU&#x17D;N&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-15</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>22.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;561</th>\n      <td>S-0775-006</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>OBECK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-15</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>22.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;573</th>\n      <td>S-0398-005</td>\n      <td>R-0398-003</td>\n      <td>M&#x11B;sto Brno</td>\n      <td>J&#xC1;NSK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-17</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>24.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>21.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;577</th>\n      <td>S-1484-023</td>\n      <td>NaN</td>\n      <td>Sob&#x11B;&#x161;ice</td>\n      <td>&#xDA;T&#x11A;CHOVSK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-17</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>24.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>19.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;582</th>\n      <td>S-0789-051</td>\n      <td>NaN</td>\n      <td>&#x10C;ernovice</td>\n      <td>OLOMOUCK&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-17</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>24.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>p&#x159;i kontrole v&#x161;e v po&#x159;&#xE1;dku</td>\n      <td>22.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;591</th>\n      <td>S-1076-008</td>\n      <td>NaN</td>\n      <td>Jundrov</td>\n      <td>SOSNOV&#xC1;</td>\n      <td>Brno - z&#xE1;pad</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-19</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>26.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>20.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;593</th>\n      <td>S-0594-044</td>\n      <td>NaN</td>\n      <td>Lesn&#xE1;</td>\n      <td>LOOSOVA</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-19</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>26.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>23.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;601</th>\n      <td>S-0386-059</td>\n      <td>NaN</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>JOSEFY FAIMONOV&#xC9;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-20</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>27.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>22.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;606</th>\n      <td>S-0051-003</td>\n      <td>R-0051-001</td>\n      <td>&#x10C;ernovice</td>\n      <td>BLATOUCHOV&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-21</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>28.12.2016</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>22.12.2016</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;642</th>\n      <td>S-0560-054</td>\n      <td>NaN</td>\n      <td>Trnit&#xE1;</td>\n      <td>K&#x158;ENOV&#xC1;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-27</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>3.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;667</th>\n      <td>S-1339-004</td>\n      <td>R-1339-001</td>\n      <td>L&#xED;&#x161;e&#x148;</td>\n      <td>ZAHRADN&#xCD;</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>3.1.2017</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;668</th>\n      <td>S-0530-010</td>\n      <td>NaN</td>\n      <td>&#x17D;idenice</td>\n      <td>KR&#xC1;SN&#xC9;HO</td>\n      <td>Brno - v&#xFD;chod</td>\n      <td>2&#xA0;016</td>\n      <td>2016-12-30</td>\n      <td>S sv&#xED;tidlo-blik&#xE1;</td>\n      <td>6.1.2017</td>\n      <td>Definitivn&#xED;</td>\n      <td>S v&#xFD;m&#x11B;na sv&#x11B;teln&#xE9;ho zdroje</td>\n      <td>4.1.2017</td>\n    </tr>\n  </tbody>\n</table>\n<p>278 rows &#xD7; 12 columns</p>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Na t&#xE9;to podmno&#x17E;in&#x11B; m&#x16F;&#x17E;eme zase spo&#x10D;&#xED;tat &#x10D;etnosti:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[36]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pocty_blikajicich</span> <span class=\"o\">=</span> <span class=\"n\">blikajici</span><span class=\"p\">[</span><span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()</span>\n<span class=\"n\">pocty_blikajicich</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[36]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&#x17D;idenice              28\nL&#xED;&#x161;e&#x148;                 26\n&#x10C;ern&#xE1; Pole            18\n&#x158;e&#x10D;kovice             17\nLesn&#xE1;                 15\nM&#x11B;sto Brno            14\nKr&#xE1;lovo Pole          13\n&#x17D;abov&#x159;esky            12\nVeve&#x159;&#xED;                12\nHusovice              12\nZ&#xE1;brdovice             8\nKom&#xED;n                  8\nBystrc                 8\nSlatina                7\n&#x160;t&#xFD;&#x159;ice                6\nStar&#xE9; Brno             6\n&#x10C;ernovice              5\nStr&#xE1;nice               5\nKohoutovice            5\nChrlice                5\nKom&#xE1;rov                5\nBosonohy               4\nJundrov                3\nStar&#xFD; L&#xED;skovec         3\nBohunice               3\nPonava                 3\n&#xDA;t&#x11B;chov u Brna         3\nMedl&#xE1;nky               2\nKn&#xED;ni&#x10D;ky               2\nSob&#x11B;&#x161;ice               2\nOb&#x159;any                 2\n&#x17D;eb&#x11B;t&#xED;n                2\nO&#x159;e&#x161;&#xED;n                 2\nBrn&#x11B;nsk&#xE9; Ivanovice     2\nMalom&#x11B;&#x159;ice             2\nTrnit&#xE1;                 2\nSadov&#xE1;                 1\nHorn&#xED; Her&#x161;pice         1\nJehnice                1\nNov&#xFD; L&#xED;skovec          1\nDoln&#xED; Her&#x161;pice         1\nPis&#xE1;rky                1\nName: Katastr, dtype: int64</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Co t&#x159;eba zkusit zjistit, ve kter&#xE9;m katastru je nejv&#xED;ce hl&#xE1;&#x161;en&#xFD;ch blikaj&#xED;c&#xED;ch sv&#x11B;tel v pom&#x11B;ru k ostatn&#xED;m poruch&#xE1;m?</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[37]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">celkove_pocty</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">value_counts</span><span class=\"p\">()</span>\n<span class=\"n\">pomery</span> <span class=\"o\">=</span> <span class=\"n\">pocty_blikajicich</span> <span class=\"o\">/</span> <span class=\"n\">celkove_pocty</span>\n<span class=\"n\">pomery</span> <span class=\"o\">=</span> <span class=\"n\">pomery</span><span class=\"o\">.</span><span class=\"n\">dropna</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">sort_values</span><span class=\"p\">(</span><span class=\"n\">ascending</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n<span class=\"n\">pomery</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[37]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&#xDA;t&#x11B;chov u Brna        0.428571\nO&#x159;e&#x161;&#xED;n                0.400000\nSadov&#xE1;                0.333333\nChrlice               0.333333\nKn&#xED;ni&#x10D;ky              0.333333\nHusovice              0.250000\n&#x158;e&#x10D;kovice             0.223684\nL&#xED;&#x161;e&#x148;                 0.214876\nBosonohy              0.210526\nZ&#xE1;brdovice            0.177778\nVeve&#x159;&#xED;                0.166667\nBrn&#x11B;nsk&#xE9; Ivanovice    0.166667\n&#x17D;idenice              0.160920\nSob&#x11B;&#x161;ice              0.142857\nLesn&#xE1;                 0.128205\n&#x10C;ern&#xE1; Pole            0.127660\nPonava                0.120000\n&#x17D;abov&#x159;esky            0.118812\n&#x17D;eb&#x11B;t&#xED;n               0.100000\nMedl&#xE1;nky              0.100000\nJundrov               0.096774\nOb&#x159;any                0.095238\nKr&#xE1;lovo Pole          0.091549\nDoln&#xED; Her&#x161;pice        0.090909\nBystrc                0.089888\nKom&#xE1;rov               0.089286\nStr&#xE1;nice              0.086207\nKom&#xED;n                 0.082474\n&#x160;t&#xFD;&#x159;ice               0.081081\nStar&#xE9; Brno            0.077922\nJehnice               0.071429\n&#x10C;ernovice             0.070423\nTrnit&#xE1;                0.066667\nKohoutovice           0.057471\nM&#x11B;sto Brno            0.053232\nMalom&#x11B;&#x159;ice            0.052632\nStar&#xFD; L&#xED;skovec        0.052632\nSlatina               0.052632\nHorn&#xED; Her&#x161;pice        0.037037\nBohunice              0.029412\nNov&#xFD; L&#xED;skovec         0.017241\nPis&#xE1;rky               0.016393\nName: Katastr, dtype: float64</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Tato data op&#x11B;t m&#x16F;&#x17E;eme dostat do grafu:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[38]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pomery</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">kind</span><span class=\"o\">=</span><span class=\"s2\">&quot;bar&quot;</span><span class=\"p\">,</span> <span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">15</span><span class=\"p\">,</span><span class=\"mi\">5</span><span class=\"p\">))</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[38]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fe5e69e19b0&gt;</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img src=\"data:image/png;base64,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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>M&#x16F;&#x17E;eme zkusit zjistit, jestli je n&#x11B;jak&#xFD; den, kdy je hl&#xE1;&#x161;eno v&#xED;ce poruch ne&#x17E; v jin&#xE9; dny. Pro jednoduchost se omez&#xED;me na jeden katastr.</p>\n<p>Za&#x10D;neme v&#xFD;b&#x11B;rem podmno&#x17E;iny dat.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[39]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina</span> <span class=\"o\">=</span> <span class=\"n\">data</span><span class=\"p\">[</span><span class=\"n\">data</span><span class=\"p\">[</span><span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"s2\">&quot;Slatina&quot;</span><span class=\"p\">]</span>\n<span class=\"n\">slatina</span> <span class=\"o\">=</span> <span class=\"n\">slatina</span><span class=\"p\">[[</span><span class=\"s2\">&quot;Katastr&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;Ulice&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&quot;</span><span class=\"p\">]]</span>\n<span class=\"n\">slatina</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[39]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n    </tr>\n    <tr>\n      <th>&#x10C;&#xED;slo z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13</th>\n      <td>Slatina</td>\n      <td>TU&#x158;ANKA</td>\n      <td>2016-01-03</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>2016-01-03</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-01-06</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-01-08</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>Slatina</td>\n      <td>V NOV&#xC9; &#x10C;TVRTI</td>\n      <td>2016-01-08</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-01-08</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Slatina</td>\n      <td>&#x158;&#xCD;PSK&#xC1;</td>\n      <td>2016-01-12</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>Slatina</td>\n      <td>V NOV&#xC9; &#x10C;TVRTI</td>\n      <td>2016-01-13</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-01-13</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Slatina</td>\n      <td>V NOV&#xC9; &#x10C;TVRTI</td>\n      <td>2016-01-13</td>\n    </tr>\n    <tr>\n      <th>107</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-01-13</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-01-14</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-01-19</td>\n    </tr>\n    <tr>\n      <th>166</th>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>2016-01-20</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-01-22</td>\n    </tr>\n    <tr>\n      <th>179</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-01-22</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-01-22</td>\n    </tr>\n    <tr>\n      <th>247</th>\n      <td>Slatina</td>\n      <td>TILHONOVA</td>\n      <td>2016-02-01</td>\n    </tr>\n    <tr>\n      <th>318</th>\n      <td>Slatina</td>\n      <td>TU&#x158;ANKA</td>\n      <td>2016-02-14</td>\n    </tr>\n    <tr>\n      <th>376</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-02-19</td>\n    </tr>\n    <tr>\n      <th>381</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-02-19</td>\n    </tr>\n    <tr>\n      <th>412</th>\n      <td>Slatina</td>\n      <td>ROUS&#xCD;NOVSK&#xC1;</td>\n      <td>2016-02-24</td>\n    </tr>\n    <tr>\n      <th>432</th>\n      <td>Slatina</td>\n      <td>MOUTNICK&#xC1;</td>\n      <td>2016-02-26</td>\n    </tr>\n    <tr>\n      <th>436</th>\n      <td>Slatina</td>\n      <td>TILHONOVA</td>\n      <td>2016-02-27</td>\n    </tr>\n    <tr>\n      <th>468</th>\n      <td>Slatina</td>\n      <td>P&#x158;EMYSLOVO N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>2016-03-03</td>\n    </tr>\n    <tr>\n      <th>486</th>\n      <td>Slatina</td>\n      <td>BLA&#x17D;OVICK&#xC1;</td>\n      <td>2016-03-05</td>\n    </tr>\n    <tr>\n      <th>487</th>\n      <td>Slatina</td>\n      <td>BLA&#x17D;OVICK&#xC1;</td>\n      <td>2016-03-05</td>\n    </tr>\n    <tr>\n      <th>507</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-03-09</td>\n    </tr>\n    <tr>\n      <th>562</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-03-15</td>\n    </tr>\n    <tr>\n      <th>564</th>\n      <td>Slatina</td>\n      <td>&#x158;&#xCD;PSK&#xC1;</td>\n      <td>2016-03-15</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;693</th>\n      <td>Slatina</td>\n      <td>SLATINKA</td>\n      <td>2016-09-04</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;699</th>\n      <td>Slatina</td>\n      <td>ROUS&#xCD;NOVSK&#xC1;</td>\n      <td>2016-09-05</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;705</th>\n      <td>Slatina</td>\n      <td>MIKUL&#x10C;ICK&#xC1;</td>\n      <td>2016-09-05</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;707</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-09-06</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;718</th>\n      <td>Slatina</td>\n      <td>SLATINKA</td>\n      <td>2016-09-07</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;758</th>\n      <td>Slatina</td>\n      <td>P&#x158;EMYSLOVO N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>2016-09-12</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;776</th>\n      <td>Slatina</td>\n      <td>LANGROVA</td>\n      <td>2016-09-14</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;800</th>\n      <td>Slatina</td>\n      <td>OSTRAVSK&#xC1;</td>\n      <td>2016-09-17</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;809</th>\n      <td>Slatina</td>\n      <td>&#x160;MILOVSK&#xC9;HO</td>\n      <td>2016-09-18</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;812</th>\n      <td>Slatina</td>\n      <td>LANGROVA</td>\n      <td>2016-09-19</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;841</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-09-22</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;846</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-09-23</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;847</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-09-24</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;852</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-09-25</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;859</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2016-09-27</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;867</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-09-28</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;869</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-09-28</td>\n    </tr>\n    <tr>\n      <th>1&#xA0;886</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>2016-09-30</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;076</th>\n      <td>Slatina</td>\n      <td>LANGROVA</td>\n      <td>2016-10-21</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;092</th>\n      <td>Slatina</td>\n      <td>&#x10C;ERNOVI&#x10C;KY</td>\n      <td>2016-10-24</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;100</th>\n      <td>Slatina</td>\n      <td>P&#x158;EMYSLOVO N&#xC1;M&#x11A;ST&#xCD;</td>\n      <td>2016-10-24</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;162</th>\n      <td>Slatina</td>\n      <td>D&#x11A;DICK&#xC1;</td>\n      <td>2016-11-02</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;192</th>\n      <td>Slatina</td>\n      <td>MIKUL&#x10C;ICK&#xC1;</td>\n      <td>2016-11-04</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;211</th>\n      <td>Slatina</td>\n      <td>TILHONOVA</td>\n      <td>2016-11-07</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;342</th>\n      <td>Slatina</td>\n      <td>LANGROVA</td>\n      <td>2016-11-21</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;419</th>\n      <td>Slatina</td>\n      <td>SLAVKOVSK&#xC1;</td>\n      <td>2016-11-30</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;556</th>\n      <td>Slatina</td>\n      <td>MIKUL&#x10C;ICK&#xC1;</td>\n      <td>2016-12-15</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;565</th>\n      <td>Slatina</td>\n      <td>&#x158;&#xCD;PSK&#xC1;</td>\n      <td>2016-12-16</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;621</th>\n      <td>Slatina</td>\n      <td>&#x158;&#xCD;PSK&#xC1;</td>\n      <td>2016-12-24</td>\n    </tr>\n    <tr>\n      <th>2&#xA0;658</th>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>2016-12-29</td>\n    </tr>\n  </tbody>\n</table>\n<p>133 rows &#xD7; 3 columns</p>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Na za&#x10D;&#xE1;tku jsme si nastavili index na &#x10D;&#xED;slo poruchy. Pro tuto anal&#xFD;zu ale bude prakti&#x10D;t&#x11B;j&#x161;&#xED; indexovat pomoc&#xED; data nahl&#xE1;&#x161;en&#xED; poruchy. Index m&#x16F;&#x17E;eme zm&#x11B;n&#xED;t, tak&#x17E;e to m&#x16F;&#x17E;eme napravit bez nov&#xE9;ho na&#x10D;&#xED;t&#xE1;n&#xED; dat. Nejprve se ale pod&#xED;vejme, jak vypad&#xE1; index te&#x10F;.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[40]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina</span><span class=\"o\">.</span><span class=\"n\">index</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[40]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>Index([&apos;13&apos;, &apos;14&apos;, &apos;39&apos;, &apos;55&apos;, &apos;56&apos;, &apos;57&apos;, &apos;89&apos;, &apos;104&apos;, &apos;105&apos;, &apos;106&apos;,\n       ...\n       &apos;2&#xA0;100&apos;, &apos;2&#xA0;162&apos;, &apos;2&#xA0;192&apos;, &apos;2&#xA0;211&apos;, &apos;2&#xA0;342&apos;, &apos;2&#xA0;419&apos;, &apos;2&#xA0;556&apos;, &apos;2&#xA0;565&apos;,\n       &apos;2&#xA0;621&apos;, &apos;2&#xA0;658&apos;],\n      dtype=&apos;object&apos;, name=&apos;&#x10C;&#xED;slo z&#xE1;vady&apos;, length=133)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nastav&#xED;me nov&#xFD; index a pod&#xED;v&#xE1;me se na data:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[11]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina_podle_data</span> <span class=\"o\">=</span> <span class=\"n\">slatina</span><span class=\"o\">.</span><span class=\"n\">set_index</span><span class=\"p\">(</span><span class=\"s2\">&quot;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">slatina_podle_data</span><span class=\"p\">[:</span><span class=\"mi\">5</span><span class=\"p\">]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[11]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n    </tr>\n    <tr>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-03</th>\n      <td>Slatina</td>\n      <td>TU&#x158;ANKA</td>\n    </tr>\n    <tr>\n      <th>2016-01-03</th>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>Slatina</td>\n      <td>V NOV&#xC9; &#x10C;TVRTI</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>I na index samotn&#xFD;.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[12]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">index</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[12]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>DatetimeIndex([&apos;2016-01-03&apos;, &apos;2016-01-03&apos;, &apos;2016-01-06&apos;, &apos;2016-01-08&apos;,\n               &apos;2016-01-08&apos;, &apos;2016-01-08&apos;, &apos;2016-01-12&apos;, &apos;2016-01-13&apos;,\n               &apos;2016-01-13&apos;, &apos;2016-01-13&apos;,\n               ...\n               &apos;2016-10-24&apos;, &apos;2016-11-02&apos;, &apos;2016-11-04&apos;, &apos;2016-11-07&apos;,\n               &apos;2016-11-21&apos;, &apos;2016-11-30&apos;, &apos;2016-12-15&apos;, &apos;2016-12-16&apos;,\n               &apos;2016-12-24&apos;, &apos;2016-12-29&apos;],\n              dtype=&apos;datetime64[ns]&apos;, name=&apos;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&apos;, length=133, freq=None)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pokud je indexem datum, m&#x16F;&#x17E;eme s n&#xED;m pracovat a pod&#xED;vat se t&#x159;eba jenom na den v m&#x11B;s&#xED;ci:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[13]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">day</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[13]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>Int64Index([ 3,  3,  6,  8,  8,  8, 12, 13, 13, 13,\n            ...\n            24,  2,  4,  7, 21, 30, 15, 16, 24, 29],\n           dtype=&apos;int64&apos;, name=&apos;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&apos;, length=133)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nebo na den v t&#xFD;dnu. P&#x159;ekvapiv&#x11B; 0 znamen&#xE1; pond&#x11B;l&#xED;:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[54]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">weekday</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[54]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>Int64Index([6, 6, 2, 4, 4, 4, 1, 2, 2, 2,\n            ...\n            0, 2, 4, 0, 0, 2, 3, 4, 5, 3],\n           dtype=&apos;int64&apos;, name=&apos;Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady&apos;, length=133)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Takto z&#xED;skan&#xFD; den v t&#xFD;dnu si m&#x16F;&#x17E;eme p&#x159;idat do datov&#xE9;ho r&#xE1;mce:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[14]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">loc</span><span class=\"p\">[:,</span> <span class=\"s2\">&quot;den v t&#xFD;dnu&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">index</span><span class=\"o\">.</span><span class=\"n\">weekday</span>\n<span class=\"n\">slatina_podle_data</span><span class=\"p\">[:</span><span class=\"mi\">5</span><span class=\"p\">]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[14]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Katastr</th>\n      <th>Ulice</th>\n      <th>den v t&#xFD;dnu</th>\n    </tr>\n    <tr>\n      <th>Datum nahl&#xE1;&#x161;en&#xED; z&#xE1;vady</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2016-01-03</th>\n      <td>Slatina</td>\n      <td>TU&#x158;ANKA</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2016-01-03</th>\n      <td>Slatina</td>\n      <td>VLNIT&#xC1;</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2016-01-06</th>\n      <td>Slatina</td>\n      <td>POMEZN&#xCD;</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>Slatina</td>\n      <td>HVIEZDOSLAVOVA</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2016-01-08</th>\n      <td>Slatina</td>\n      <td>V NOV&#xC9; &#x10C;TVRTI</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pomoc&#xED; metody <code>groupby</code> m&#x16F;&#x17E;eme data shlukovat podle hodnot ur&#x10D;it&#xE9;ho sloupce. Tato metoda vrac&#xED; objekt reprezentuj&#xED;c&#xED; n&#x11B;kolik shluk&#x16F;. N&#xE1;s prim&#xE1;rn&#x11B; zaj&#xED;m&#xE1;, kolik je v ka&#x17E;d&#xE9; skupin&#x11B; polo&#x17E;ek (tedy kolik poruch bylo hl&#xE1;&#x161;eno v ten kter&#xFD; den).</p>\n<p>Dal&#x161;&#xED; mo&#x17E;nosti agregov&#xE1;n&#xED; skupin jsou t&#x159;eba <code>sum</code> nebo <code>mean</code>. V&#x11B;t&#x161;ina ale d&#xE1;v&#xE1; smysl hlavn&#x11B; pro &#x10D;&#xED;seln&#xE9; sloupce.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[16]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pocty_podle_dne</span> <span class=\"o\">=</span> <span class=\"n\">slatina_podle_data</span><span class=\"o\">.</span><span class=\"n\">groupby</span><span class=\"p\">(</span><span class=\"s2\">&quot;den v t&#xFD;dnu&quot;</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">aggregate</span><span class=\"p\">(</span><span class=\"s2\">&quot;size&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">pocty_podle_dne</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[16]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>den v t&#xFD;dnu\n0    17\n1    23\n2    20\n3    14\n4    24\n5    21\n6    14\ndtype: int64</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pro sna&#x17E;&#x161;&#xED; &#x10D;ten&#xED; by bylo lep&#x161;&#xED; zobrazovat jm&#xE9;na dn&#xED; m&#xED;sto &#x10D;&#xED;sel. M&#x16F;&#x17E;eme si nastavit nov&#xFD; index &#x10D;ist&#x11B; pomoc&#xED; seznamu hodnot:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[17]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pocty_podle_dne</span><span class=\"o\">.</span><span class=\"n\">index</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"s2\">&quot;Po&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;&#xDA;t&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;St&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;&#x10C;t&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;P&#xE1;&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;So&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;Ne&quot;</span><span class=\"p\">]</span>\n<span class=\"n\">pocty_podle_dne</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[17]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>Po    17\n&#xDA;t    23\nSt    20\n&#x10C;t    14\nP&#xE1;    24\nSo    21\nNe    14\ndtype: int64</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>A nakonec vykresl&#xED;me graf:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[63]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pocty_podle_dne</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">kind</span><span class=\"o\">=</span><span class=\"s2\">&quot;bar&quot;</span><span class=\"p\">)</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[63]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7f30dc47bbe0&gt;</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img src=\"data:image/png;base64,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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h1>Mapa</h1>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pro vykreslov&#xE1;n&#xED; map existuje mnoho r&#x16F;zn&#xFD;ch knihoven. Tady si uk&#xE1;&#x17E;eme <code>geopandas</code>, kter&#xE1; m&#xE1; pom&#x11B;rn&#x11B; hezk&#xE9; rozhran&#xED;. Jej&#xED; velkou nev&#xFD;hodou je komplikovan&#xE1; instalace na Windows.</p>\n<p>Nejprve si knohovnu naimportujeme, potom na&#x10D;teme soubor s daty a vybereme pouze Brno.</p>\n<p>Dostaneme datov&#xFD; r&#xE1;mec jako v Pandas. N&#x11B;kolik posledn&#xED;ch sloupc&#x16F; ale obsahuje zaj&#xED;mav&#xE9; hodnoty. Krom&#x11B; sou&#x159;adnic tam najdeme <code>geometry</code>: definici tvaru n&#x11B;jak&#xE9; oblasti v map&#x11B;.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[22]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">geopandas</span>\n<span class=\"n\">mapa</span> <span class=\"o\">=</span> <span class=\"n\">geopandas</span><span class=\"o\">.</span><span class=\"n\">read_file</span><span class=\"p\">(</span><span class=\"s2\">&quot;M&#x11B;stsk&#xE9;_obvody_a_m&#x11B;stsk&#xE9;_&#x10D;&#xE1;sti__polygony.shp&quot;</span><span class=\"p\">,</span> <span class=\"n\">encoding</span><span class=\"o\">=</span><span class=\"s2\">&quot;utf-8&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">brno</span> <span class=\"o\">=</span> <span class=\"n\">mapa</span><span class=\"p\">[</span><span class=\"n\">mapa</span><span class=\"p\">[</span><span class=\"s2\">&quot;NAZ_OBEC&quot;</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"s2\">&quot;Brno&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">copy</span><span class=\"p\">()</span>\n<span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">()</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[22]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>KOD_MOaMC</th>\n      <th>NAZ_ZKR_MO</th>\n      <th>NAZ_MOaMC</th>\n      <th>KOD_OBEC</th>\n      <th>NAZ_OBEC</th>\n      <th>KOD_ZUJ</th>\n      <th>NAZ_ZUJ</th>\n      <th>KOD_OKRES</th>\n      <th>KOD_LAU1</th>\n      <th>NAZ_LAU1</th>\n      <th>KOD_KRAJ</th>\n      <th>KOD_CZNUTS</th>\n      <th>NAZ_CZNUTS</th>\n      <th>SX</th>\n      <th>SY</th>\n      <th>SHAPE_Leng</th>\n      <th>SHAPE_Area</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>68</th>\n      <td>69</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-599107.126826</td>\n      <td>-1.161208e+06</td>\n      <td>21011.712879</td>\n      <td>1.464882e+07</td>\n      <td>POLYGON ((-598972.9499999993 -1159048.82009999...</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>70</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600420.609804</td>\n      <td>-1.158450e+06</td>\n      <td>10865.113284</td>\n      <td>4.348780e+06</td>\n      <td>POLYGON ((-600602.1099999994 -1157005.50010000...</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>71</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598393.224288</td>\n      <td>-1.157041e+06</td>\n      <td>18814.138777</td>\n      <td>1.007402e+07</td>\n      <td>POLYGON ((-597252.6999999993 -1155051.28999999...</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>72</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>Brno-sever</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-596387.912140</td>\n      <td>-1.156034e+06</td>\n      <td>28461.268033</td>\n      <td>1.224207e+07</td>\n      <td>POLYGON ((-596552.0500000007 -1152248.35000000...</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>73</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595306.099433</td>\n      <td>-1.160699e+06</td>\n      <td>13108.262329</td>\n      <td>5.045712e+06</td>\n      <td>POLYGON ((-594706.5100000016 -1159323.91, -594...</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Zkus&#xED;me si nakreslit mapu v&#x161;ech brn&#x11B;nsk&#xFD;ch &#x10D;&#xE1;st&#xED;:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[25]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">matplotlib</span> <span class=\"k\">import</span> <span class=\"n\">pyplot</span>\n<span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,</span><span class=\"mi\">10</span><span class=\"p\">))</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[25]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0x7fe5e486e978&gt;</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>To by asi mohlo b&#xFD;t Brno. Kdybychom ale m&#x11B;li popisky pro jednotliv&#xE9; oblasti, mo&#x17E;n&#xE1; by to bylo p&#x159;ehledn&#x11B;j&#x161;&#xED;. M&#x16F;&#x17E;eme si je p&#x159;idat. Nejprve ale pot&#x159;ebujeme v&#x11B;d&#x11B;t, kam popisek nakreslit. M&#x16F;&#x17E;eme si pro ka&#x17E;dou oblast spo&#x10D;&#xED;tat vhodn&#xFD; bod, a p&#x159;idat ho do dat.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[26]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">brno</span><span class=\"p\">[</span><span class=\"s2\">&quot;coords&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">brno</span><span class=\"p\">[</span><span class=\"s2\">&quot;geometry&quot;</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">apply</span><span class=\"p\">(</span><span class=\"k\">lambda</span> <span class=\"n\">x</span><span class=\"p\">:</span> <span class=\"n\">x</span><span class=\"o\">.</span><span class=\"n\">representative_point</span><span class=\"p\">()</span><span class=\"o\">.</span><span class=\"n\">coords</span><span class=\"p\">[:])</span>\n<span class=\"n\">brno</span><span class=\"p\">[</span><span class=\"s2\">&quot;coords&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"n\">coords</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"k\">for</span> <span class=\"n\">coords</span> <span class=\"ow\">in</span> <span class=\"n\">brno</span><span class=\"p\">[</span><span class=\"s2\">&quot;coords&quot;</span><span class=\"p\">]]</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Te&#x10F; m&#x16F;&#x17E;eme vykreslit mapu jako p&#x159;edt&#xED;m, a potom pro ka&#x17E;dou oblast p&#x159;idat anotaci:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[29]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,</span><span class=\"mi\">10</span><span class=\"p\">))</span>\n<span class=\"k\">for</span> <span class=\"n\">idx</span><span class=\"p\">,</span> <span class=\"n\">radek</span> <span class=\"ow\">in</span> <span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">iterrows</span><span class=\"p\">():</span>\n    <span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">annotate</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"o\">=</span><span class=\"n\">radek</span><span class=\"p\">[</span><span class=\"s2\">&quot;NAZ_MOaMC&quot;</span><span class=\"p\">],</span> <span class=\"n\">xy</span><span class=\"o\">=</span><span class=\"n\">radek</span><span class=\"p\">[</span><span class=\"s2\">&quot;coords&quot;</span><span class=\"p\">],</span> <span class=\"n\">horizontalalignment</span><span class=\"o\">=</span><span class=\"s2\">&quot;center&quot;</span><span class=\"p\">)</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jako posledn&#xED; p&#x159;&#xED;klad si do mapy zkus&#xED;me zn&#xE1;zornit, kolik v dan&#xE9; oblasti je hl&#xE1;&#x161;eno poruch ve&#x159;ejn&#xE9;ho osv&#x11B;tlen&#xED;. Na to budeme muset spo&#x10D;&#xED;tat, kolik ji vlastn&#x11B; je, a p&#x159;idat data do r&#xE1;mce s mapov&#xFD;mi informace.</p>\n<p>Spojov&#xE1;n&#xED; datov&#xFD;ch r&#xE1;mc&#x16F; je mo&#x17E;n&#xE9; jako po &#x159;&#xE1;dc&#xED;ch, tak po sloupc&#xED;ch. Pokud chceme spojovat po sloupc&#xED;ch, pot&#x159;ebujeme index, podle kter&#xE9;ho se daj&#xED; poznat stejn&#xE9; &#x159;&#xE1;dky. Nastav&#xED;me si proto index na n&#xE1;zev m&#x11B;stsk&#xE9; &#x10D;&#xE1;sti.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[30]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">brno</span> <span class=\"o\">=</span> <span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">set_index</span><span class=\"p\">(</span><span class=\"s2\">&quot;NAZ_MOaMC&quot;</span><span class=\"p\">,</span> <span class=\"n\">drop</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">)</span>\n<span class=\"n\">brno</span><span class=\"o\">.</span><span class=\"n\">head</span><span class=\"p\">()</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[30]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>KOD_MOaMC</th>\n      <th>NAZ_ZKR_MO</th>\n      <th>NAZ_MOaMC</th>\n      <th>KOD_OBEC</th>\n      <th>NAZ_OBEC</th>\n      <th>KOD_ZUJ</th>\n      <th>NAZ_ZUJ</th>\n      <th>KOD_OKRES</th>\n      <th>KOD_LAU1</th>\n      <th>NAZ_LAU1</th>\n      <th>KOD_KRAJ</th>\n      <th>KOD_CZNUTS</th>\n      <th>NAZ_CZNUTS</th>\n      <th>SX</th>\n      <th>SY</th>\n      <th>SHAPE_Leng</th>\n      <th>SHAPE_Area</th>\n      <th>geometry</th>\n      <th>coords</th>\n    </tr>\n    <tr>\n      <th>NAZ_MOaMC</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brno-st&#x159;ed</th>\n      <td>69</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-599107.126826</td>\n      <td>-1.161208e+06</td>\n      <td>21011.712879</td>\n      <td>1.464882e+07</td>\n      <td>POLYGON ((-598972.9499999993 -1159048.82009999...</td>\n      <td>(-599089.0881825134, -1161418.7800999992)</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;abov&#x159;esky</th>\n      <td>70</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600420.609804</td>\n      <td>-1.158450e+06</td>\n      <td>10865.113284</td>\n      <td>4.348780e+06</td>\n      <td>POLYGON ((-600602.1099999994 -1157005.50010000...</td>\n      <td>(-600242.0081863957, -1158264.8850999996)</td>\n    </tr>\n    <tr>\n      <th>Brno-Kr&#xE1;lovo Pole</th>\n      <td>71</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598393.224288</td>\n      <td>-1.157041e+06</td>\n      <td>18814.138777</td>\n      <td>1.007402e+07</td>\n      <td>POLYGON ((-597252.6999999993 -1155051.28999999...</td>\n      <td>(-598973.9478040718, -1157189.1750499997)</td>\n    </tr>\n    <tr>\n      <th>Brno-sever</th>\n      <td>72</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>Brno-sever</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-596387.912140</td>\n      <td>-1.156034e+06</td>\n      <td>28461.268033</td>\n      <td>1.224207e+07</td>\n      <td>POLYGON ((-596552.0500000007 -1152248.35000000...</td>\n      <td>(-596332.4486210528, -1156330.1843500007)</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;idenice</th>\n      <td>73</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>Brno-m&#x11B;sto</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595306.099433</td>\n      <td>-1.160699e+06</td>\n      <td>13108.262329</td>\n      <td>5.045712e+06</td>\n      <td>POLYGON ((-594706.5100000016 -1159323.91, -594...</td>\n      <td>(-595741.415136773, -1160651.2446999997)</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Po&#x10D;et poruch v r&#x16F;zn&#xFD;ch katastrech u&#x17E; m&#xE1;me spo&#x10D;&#xED;tan&#xFD;. Pot&#x159;ebujeme ale data trochu zpracovat, aby si odpov&#xED;daly n&#xE1;zvy &#x10D;&#xE1;st&#xED;. Bohu&#x17E;el ne ka&#x17E;d&#xFD; katastr pat&#x159;&#xED; do jedin&#xE9; m&#x11B;stsk&#xE9; &#x10D;&#xE1;sti, tak&#x17E;e tady dojde k ur&#x10D;it&#xFD;m nep&#x159;esnostem.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[41]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">poruchy</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">celkove_pocty</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;&#x158;e&#x10D;kovice a Mokr&#xE1; Hora&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;&#x158;e&#x10D;kovice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Mokr&#xE1; Hora&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;Malom&#x11B;&#x159;ice a Ob&#x159;any&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Malom&#x11B;&#x159;ice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Ob&#x159;any&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;Ivanovice&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Brn&#x11B;nsk&#xE9; Ivanovice&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;st&#x159;ed&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;M&#x11B;sto Brno&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Star&#xE9; Brno&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;&#x160;t&#xFD;&#x159;ice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Veve&#x159;&#xED;&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Pis&#xE1;rky&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Str&#xE1;nice&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;&#xDA;t&#x11B;chov&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;&#xDA;t&#x11B;chov u Brna&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;sever&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Sob&#x11B;&#x161;ice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Lesn&#xE1;&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Husovice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;&#x10C;ern&#xE1; Pole&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Z&#xE1;brdovice&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;jih&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Doln&#xED; Her&#x161;pice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Horn&#xED; Her&#x161;pice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Kom&#xE1;rov&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;P&#x159;&#xED;z&#x159;enice&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Trnit&#xE1;&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;Kr&#xE1;lovo Pole&quot;</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Ponava&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Sadov&#xE1;&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span><span class=\"p\">[</span><span class=\"s2\">&quot;Tu&#x159;any&quot;</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Dvorska&quot;</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">pop</span><span class=\"p\">(</span><span class=\"s2\">&quot;Hol&#xE1;sky&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy</span> <span class=\"o\">=</span> <span class=\"p\">[(</span><span class=\"n\">f</span><span class=\"s2\">&quot;Brno-</span><span class=\"si\">{k}</span><span class=\"s2\">&quot;</span><span class=\"p\">,</span> <span class=\"n\">v</span><span class=\"p\">)</span> <span class=\"k\">for</span> <span class=\"n\">k</span><span class=\"p\">,</span> <span class=\"n\">v</span> <span class=\"ow\">in</span> <span class=\"n\">poruchy</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">()]</span>\n<span class=\"n\">poruchy_df</span> <span class=\"o\">=</span> <span class=\"n\">pandas</span><span class=\"o\">.</span><span class=\"n\">DataFrame</span><span class=\"o\">.</span><span class=\"n\">from_records</span><span class=\"p\">(</span><span class=\"n\">poruchy</span><span class=\"p\">,</span> <span class=\"n\">columns</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;NAZ&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;pocet&quot;</span><span class=\"p\">],</span> <span class=\"n\">index</span><span class=\"o\">=</span><span class=\"s2\">&quot;NAZ&quot;</span><span class=\"p\">)</span>\n<span class=\"n\">poruchy_df</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[41]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pocet</th>\n    </tr>\n    <tr>\n      <th>NAZ</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brno-&#x17D;idenice</th>\n      <td>174</td>\n    </tr>\n    <tr>\n      <th>Brno-Kr&#xE1;lovo Pole</th>\n      <td>170</td>\n    </tr>\n    <tr>\n      <th>Brno-Slatina</th>\n      <td>133</td>\n    </tr>\n    <tr>\n      <th>Brno-L&#xED;&#x161;e&#x148;</th>\n      <td>121</td>\n    </tr>\n    <tr>\n      <th>Brno-Bohunice</th>\n      <td>102</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;abov&#x159;esky</th>\n      <td>101</td>\n    </tr>\n    <tr>\n      <th>Brno-Kom&#xED;n</th>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>Brno-Bystrc</th>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>Brno-Kohoutovice</th>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x10C;ernovice</th>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>Brno-Nov&#xFD; L&#xED;skovec</th>\n      <td>58</td>\n    </tr>\n    <tr>\n      <th>Brno-Star&#xFD; L&#xED;skovec</th>\n      <td>57</td>\n    </tr>\n    <tr>\n      <th>Brno-Jundrov</th>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>Brno-Medl&#xE1;nky</th>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;eb&#x11B;t&#xED;n</th>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>Brno-Bosonohy</th>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>Brno-Chrlice</th>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>Brno-Jehnice</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Brno-Tu&#x159;any</th>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>Brno-Kn&#xED;ni&#x10D;ky</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>Brno-Ivanovice</th>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>Brno-O&#x159;e&#x161;&#xED;n</th>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x158;e&#x10D;kovice a Mokr&#xE1; Hora</th>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>Brno-Malom&#x11B;&#x159;ice a Ob&#x159;any</th>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>Brno-st&#x159;ed</th>\n      <td>605</td>\n    </tr>\n    <tr>\n      <th>Brno-&#xDA;t&#x11B;chov</th>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>Brno-sever</th>\n      <td>365</td>\n    </tr>\n    <tr>\n      <th>Brno-jih</th>\n      <td>134</td>\n    </tr>\n  </tbody>\n</table>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Tento nov&#xFD; r&#xE1;mec te&#x10F; m&#x16F;&#x17E;eme spojit s mapov&#xFD;mi podklady. V&#xFD;choz&#xED; spojov&#xE1;n&#xED; je po &#x159;&#xE1;dc&#xED;ch, proto mus&#xED;me ur&#x10D;it osu.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[43]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">brno_s_poruchami</span> <span class=\"o\">=</span> <span class=\"n\">pandas</span><span class=\"o\">.</span><span class=\"n\">concat</span><span class=\"p\">([</span><span class=\"n\">brno</span><span class=\"p\">,</span> <span class=\"n\">poruchy_df</span><span class=\"p\">],</span> <span class=\"n\">sort</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">)</span>\n<span class=\"n\">brno_s_poruchami</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt output_prompt\">Out[43]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n<div>\n<style>.lesson-content .dataframe tbody tr th:only-of-type {\n    vertical-align: middle\n    }\n.lesson-content .dataframe tbody tr th {\n    vertical-align: top\n    }\n.lesson-content .dataframe thead th {\n    text-align: right\n    }</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>OBJECTID</th>\n      <th>KOD_MOaMC</th>\n      <th>NAZ_ZKR_MO</th>\n      <th>NAZ_MOaMC</th>\n      <th>KOD_OBEC</th>\n      <th>NAZ_OBEC</th>\n      <th>KOD_ZUJ</th>\n      <th>NAZ_ZUJ</th>\n      <th>KOD_OKRES</th>\n      <th>KOD_LAU1</th>\n      <th>...</th>\n      <th>KOD_KRAJ</th>\n      <th>KOD_CZNUTS</th>\n      <th>NAZ_CZNUTS</th>\n      <th>SX</th>\n      <th>SY</th>\n      <th>SHAPE_Leng</th>\n      <th>SHAPE_Area</th>\n      <th>geometry</th>\n      <th>coords</th>\n      <th>pocet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Brno-st&#x159;ed</th>\n      <td>69</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550973</td>\n      <td>Brno-st&#x159;ed</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-599107.126826</td>\n      <td>-1.161208e+06</td>\n      <td>21011.712879</td>\n      <td>1.464882e+07</td>\n      <td>POLYGON ((-598972.9499999993 -1159048.82009999...</td>\n      <td>(-599089.0881825134, -1161418.7800999992)</td>\n      <td>605.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;abov&#x159;esky</th>\n      <td>70</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>550990</td>\n      <td>Brno-&#x17D;abov&#x159;esky</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600420.609804</td>\n      <td>-1.158450e+06</td>\n      <td>10865.113284</td>\n      <td>4.348780e+06</td>\n      <td>POLYGON ((-600602.1099999994 -1157005.50010000...</td>\n      <td>(-600242.0081863957, -1158264.8850999996)</td>\n      <td>101.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Kr&#xE1;lovo Pole</th>\n      <td>71</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551007</td>\n      <td>Brno-Kr&#xE1;lovo Pole</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598393.224288</td>\n      <td>-1.157041e+06</td>\n      <td>18814.138777</td>\n      <td>1.007402e+07</td>\n      <td>POLYGON ((-597252.6999999993 -1155051.28999999...</td>\n      <td>(-598973.9478040718, -1157189.1750499997)</td>\n      <td>170.0</td>\n    </tr>\n    <tr>\n      <th>Brno-sever</th>\n      <td>72</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>Brno-sever</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551031</td>\n      <td>Brno-sever</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-596387.912140</td>\n      <td>-1.156034e+06</td>\n      <td>28461.268033</td>\n      <td>1.224207e+07</td>\n      <td>POLYGON ((-596552.0500000007 -1152248.35000000...</td>\n      <td>(-596332.4486210528, -1156330.1843500007)</td>\n      <td>365.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;idenice</th>\n      <td>73</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551058</td>\n      <td>Brno-&#x17D;idenice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595306.099433</td>\n      <td>-1.160699e+06</td>\n      <td>13108.262329</td>\n      <td>5.045712e+06</td>\n      <td>POLYGON ((-594706.5100000016 -1159323.91, -594...</td>\n      <td>(-595741.415136773, -1160651.2446999997)</td>\n      <td>174.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x10C;ernovice</th>\n      <td>74</td>\n      <td>551066</td>\n      <td>Brno-&#x10C;ernovice</td>\n      <td>Brno-&#x10C;ernovice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551066</td>\n      <td>Brno-&#x10C;ernovice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595373.018355</td>\n      <td>-1.162892e+06</td>\n      <td>10449.323314</td>\n      <td>6.291806e+06</td>\n      <td>POLYGON ((-596356.1099999994 -1161487.45010000...</td>\n      <td>(-595322.4330897855, -1162854.1150499992)</td>\n      <td>71.0</td>\n    </tr>\n    <tr>\n      <th>Brno-jih</th>\n      <td>75</td>\n      <td>551074</td>\n      <td>Brno-jih</td>\n      <td>Brno-jih</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551074</td>\n      <td>Brno-jih</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598118.332968</td>\n      <td>-1.164941e+06</td>\n      <td>19718.090816</td>\n      <td>1.279240e+07</td>\n      <td>POLYGON ((-596731.120000001 -1161671.18, -5967...</td>\n      <td>(-598325.5423521271, -1164508.7350499984)</td>\n      <td>134.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Bohunice</th>\n      <td>76</td>\n      <td>551082</td>\n      <td>Brno-Bohunice</td>\n      <td>Brno-Bohunice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551082</td>\n      <td>Brno-Bohunice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600545.202641</td>\n      <td>-1.163337e+06</td>\n      <td>10195.831411</td>\n      <td>3.017709e+06</td>\n      <td>POLYGON ((-601002.7100000009 -1161967.82009999...</td>\n      <td>(-600625.2344267721, -1163263.2112499997)</td>\n      <td>102.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Star&#xFD; L&#xED;skovec</th>\n      <td>77</td>\n      <td>551091</td>\n      <td>Brno-Star&#xFD; L&#xED;skovec</td>\n      <td>Brno-Star&#xFD; L&#xED;skovec</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551091</td>\n      <td>Brno-Star&#xFD; L&#xED;skovec</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-601824.274064</td>\n      <td>-1.163777e+06</td>\n      <td>9638.476181</td>\n      <td>3.283573e+06</td>\n      <td>POLYGON ((-601051.9600000009 -1162542.89999999...</td>\n      <td>(-601873.3829225947, -1163770.700100001)</td>\n      <td>57.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Nov&#xFD; L&#xED;skovec</th>\n      <td>78</td>\n      <td>551112</td>\n      <td>Brno-Nov&#xFD; L&#xED;skovec</td>\n      <td>Brno-Nov&#xFD; L&#xED;skovec</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551112</td>\n      <td>Brno-Nov&#xFD; L&#xED;skovec</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-602113.027342</td>\n      <td>-1.162014e+06</td>\n      <td>5849.668623</td>\n      <td>1.654432e+06</td>\n      <td>POLYGON ((-601902.0199999996 -1161269.00010000...</td>\n      <td>(-602141.5874489998, -1161991.3051000014)</td>\n      <td>58.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Kohoutovice</th>\n      <td>79</td>\n      <td>551147</td>\n      <td>Brno-Kohoutovice</td>\n      <td>Brno-Kohoutovice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551147</td>\n      <td>Brno-Kohoutovice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-602724.318354</td>\n      <td>-1.160502e+06</td>\n      <td>9945.563722</td>\n      <td>4.085491e+06</td>\n      <td>POLYGON ((-602977.370000001 -1159558.5601, -60...</td>\n      <td>(-602845.8283676622, -1160474.31745)</td>\n      <td>87.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Jundrov</th>\n      <td>80</td>\n      <td>551171</td>\n      <td>Brno-Jundrov</td>\n      <td>Brno-Jundrov</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551171</td>\n      <td>Brno-Jundrov</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-602469.191502</td>\n      <td>-1.158802e+06</td>\n      <td>11761.802804</td>\n      <td>4.221080e+06</td>\n      <td>POLYGON ((-603188.6799999997 -1157361.87000000...</td>\n      <td>(-602340.8641554874, -1158993.5900500007)</td>\n      <td>31.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Bystrc</th>\n      <td>81</td>\n      <td>551198</td>\n      <td>Brno-Bystrc</td>\n      <td>Brno-Bystrc</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551198</td>\n      <td>Brno-Bystrc</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-607059.680294</td>\n      <td>-1.155566e+06</td>\n      <td>39812.756010</td>\n      <td>2.724224e+07</td>\n      <td>POLYGON ((-608145.9400000013 -1150485.60000000...</td>\n      <td>(-607374.9921216916, -1154503.6900500003)</td>\n      <td>89.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Kn&#xED;ni&#x10D;ky</th>\n      <td>82</td>\n      <td>551210</td>\n      <td>Brno-Kn&#xED;ni&#x10D;ky</td>\n      <td>Brno-Kn&#xED;ni&#x10D;ky</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551210</td>\n      <td>Brno-Kn&#xED;ni&#x10D;ky</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-606000.584398</td>\n      <td>-1.152704e+06</td>\n      <td>22000.104018</td>\n      <td>1.092800e+07</td>\n      <td>POLYGON ((-607550.8299999982 -1149918.64999999...</td>\n      <td>(-605819.1174333331, -1153034.6750000007)</td>\n      <td>6.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Kom&#xED;n</th>\n      <td>83</td>\n      <td>551228</td>\n      <td>Brno-Kom&#xED;n</td>\n      <td>Brno-Kom&#xED;n</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551228</td>\n      <td>Brno-Kom&#xED;n</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-602024.339840</td>\n      <td>-1.156080e+06</td>\n      <td>16272.660264</td>\n      <td>7.598840e+06</td>\n      <td>POLYGON ((-602512.9499999993 -1153508.30999999...</td>\n      <td>(-601832.7081711115, -1156108.9000000004)</td>\n      <td>97.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Medl&#xE1;nky</th>\n      <td>84</td>\n      <td>551236</td>\n      <td>Brno-Medl&#xE1;nky</td>\n      <td>Brno-Medl&#xE1;nky</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551236</td>\n      <td>Brno-Medl&#xE1;nky</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600645.914466</td>\n      <td>-1.155057e+06</td>\n      <td>10601.740155</td>\n      <td>3.510207e+06</td>\n      <td>POLYGON ((-601463.7300000004 -1153941.05999999...</td>\n      <td>(-600770.6437999412, -1154899.8100000005)</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x158;e&#x10D;kovice a Mokr&#xE1; Hora</th>\n      <td>85</td>\n      <td>551244</td>\n      <td>Brno-&#x158;e&#x10D;kov. a M.Hora</td>\n      <td>Brno-&#x158;e&#x10D;kovice a Mokr&#xE1; Hora</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551244</td>\n      <td>Brno-&#x158;e&#x10D;kovice a Mokr&#xE1; Hora</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598775.848808</td>\n      <td>-1.154307e+06</td>\n      <td>16472.916389</td>\n      <td>7.571217e+06</td>\n      <td>POLYGON ((-598721.129999999 -1153198.4201, -59...</td>\n      <td>(-598767.8359843933, -1154394.9800000004)</td>\n      <td>78.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Malom&#x11B;&#x159;ice a Ob&#x159;any</th>\n      <td>86</td>\n      <td>551252</td>\n      <td>Brno-Malom&#x11B;&#x159;ice a Ob&#x159;.</td>\n      <td>Brno-Malom&#x11B;&#x159;ice a Ob&#x159;any</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551252</td>\n      <td>Brno-Malom&#x11B;&#x159;ice a Ob&#x159;any</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-594712.425429</td>\n      <td>-1.157349e+06</td>\n      <td>17036.942625</td>\n      <td>9.291087e+06</td>\n      <td>POLYGON ((-595813.620000001 -1154939.23, -5958...</td>\n      <td>(-594459.9119527972, -1157225.0899999999)</td>\n      <td>59.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Vinohrady</th>\n      <td>87</td>\n      <td>551279</td>\n      <td>Brno-Vinohrady</td>\n      <td>Brno-Vinohrady</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551279</td>\n      <td>Brno-Vinohrady</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-594000.500322</td>\n      <td>-1.159879e+06</td>\n      <td>9640.930008</td>\n      <td>1.955207e+06</td>\n      <td>POLYGON ((-592737.5500000007 -1158832.30009999...</td>\n      <td>(-593998.9414789472, -1159853.3806499988)</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Brno-L&#xED;&#x161;e&#x148;</th>\n      <td>88</td>\n      <td>551287</td>\n      <td>Brno-L&#xED;&#x161;e&#x148;</td>\n      <td>Brno-L&#xED;&#x161;e&#x148;</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551287</td>\n      <td>Brno-L&#xED;&#x161;e&#x148;</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-591448.485685</td>\n      <td>-1.159769e+06</td>\n      <td>23753.010689</td>\n      <td>1.570760e+07</td>\n      <td>POLYGON ((-589128.0199999996 -1156195.84, -589...</td>\n      <td>(-591500.1399165738, -1159344.9351000004)</td>\n      <td>121.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Slatina</th>\n      <td>89</td>\n      <td>551295</td>\n      <td>Brno-Slatina</td>\n      <td>Brno-Slatina</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551295</td>\n      <td>Brno-Slatina</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-592953.302418</td>\n      <td>-1.163336e+06</td>\n      <td>12221.935650</td>\n      <td>5.829742e+06</td>\n      <td>POLYGON ((-593091.3500000015 -1161596.96999999...</td>\n      <td>(-592977.0980891805, -1163369.8249999993)</td>\n      <td>133.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Tu&#x159;any</th>\n      <td>90</td>\n      <td>551309</td>\n      <td>Brno-Tu&#x159;any</td>\n      <td>Brno-Tu&#x159;any</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551309</td>\n      <td>Brno-Tu&#x159;any</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-594104.817780</td>\n      <td>-1.166296e+06</td>\n      <td>21357.605905</td>\n      <td>1.784122e+07</td>\n      <td>POLYGON ((-595983.120000001 -1163916.359999999...</td>\n      <td>(-594598.9249158911, -1166357.4499999993)</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Chrlice</th>\n      <td>91</td>\n      <td>551317</td>\n      <td>Brno-Chrlice</td>\n      <td>Brno-Chrlice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551317</td>\n      <td>Brno-Chrlice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595592.270326</td>\n      <td>-1.168729e+06</td>\n      <td>16026.030228</td>\n      <td>9.492907e+06</td>\n      <td>POLYGON ((-596347.5100000016 -1166738.84, -596...</td>\n      <td>(-595516.1788480217, -1168558.8000000007)</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Bosonohy</th>\n      <td>92</td>\n      <td>551325</td>\n      <td>Brno-Bosonohy</td>\n      <td>Brno-Bosonohy</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551325</td>\n      <td>Brno-Bosonohy</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-604417.861598</td>\n      <td>-1.161660e+06</td>\n      <td>13888.641269</td>\n      <td>7.147886e+06</td>\n      <td>POLYGON ((-605152.1799999997 -1160070.39009999...</td>\n      <td>(-604119.7012236556, -1161715.2551000006)</td>\n      <td>19.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#x17D;eb&#x11B;t&#xED;n</th>\n      <td>93</td>\n      <td>551368</td>\n      <td>Brno-&#x17D;eb&#x11B;t&#xED;n</td>\n      <td>Brno-&#x17D;eb&#x11B;t&#xED;n</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551368</td>\n      <td>Brno-&#x17D;eb&#x11B;t&#xED;n</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-606460.760666</td>\n      <td>-1.158772e+06</td>\n      <td>23206.733123</td>\n      <td>1.359924e+07</td>\n      <td>POLYGON ((-606417.8599999994 -1156672.88010000...</td>\n      <td>(-606467.2769532457, -1158575.460000001)</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Ivanovice</th>\n      <td>94</td>\n      <td>551376</td>\n      <td>Brno-Ivanovice</td>\n      <td>Brno-Ivanovice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551376</td>\n      <td>Brno-Ivanovice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-600046.704618</td>\n      <td>-1.152829e+06</td>\n      <td>10054.771687</td>\n      <td>2.446103e+06</td>\n      <td>POLYGON ((-599444.4100000001 -1151725.62000000...</td>\n      <td>(-600026.629237962, -1152798.205050001)</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>Brno-Jehnice</th>\n      <td>95</td>\n      <td>551406</td>\n      <td>Brno-Jehnice</td>\n      <td>Brno-Jehnice</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551406</td>\n      <td>Brno-Jehnice</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-598347.215759</td>\n      <td>-1.151818e+06</td>\n      <td>12194.556985</td>\n      <td>4.072894e+06</td>\n      <td>POLYGON ((-597563.6400000006 -1150055.85999999...</td>\n      <td>(-598684.7447784491, -1151578.1449999996)</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>Brno-O&#x159;e&#x161;&#xED;n</th>\n      <td>96</td>\n      <td>551422</td>\n      <td>Brno-O&#x159;e&#x161;&#xED;n</td>\n      <td>Brno-O&#x159;e&#x161;&#xED;n</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551422</td>\n      <td>Brno-O&#x159;e&#x161;&#xED;n</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-596850.057372</td>\n      <td>-1.151235e+06</td>\n      <td>8671.946618</td>\n      <td>3.064507e+06</td>\n      <td>POLYGON ((-596615.2800000012 -1150145.77, -596...</td>\n      <td>(-596828.6547211574, -1151328.875)</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>Brno-&#xDA;t&#x11B;chov</th>\n      <td>97</td>\n      <td>551431</td>\n      <td>Brno-&#xDA;t&#x11B;chov</td>\n      <td>Brno-&#xDA;t&#x11B;chov</td>\n      <td>582786</td>\n      <td>Brno</td>\n      <td>551431</td>\n      <td>Brno-&#xDA;t&#x11B;chov</td>\n      <td>40711</td>\n      <td>CZ0642</td>\n      <td>...</td>\n      <td>3115</td>\n      <td>CZ064</td>\n      <td>Jihomoravsk&#xFD; kraj</td>\n      <td>-595171.280371</td>\n      <td>-1.150839e+06</td>\n      <td>6203.556569</td>\n      <td>1.178099e+06</td>\n      <td>POLYGON ((-594745.6900000013 -1150536.32, -594...</td>\n      <td>(-595180.3209008181, -1150669.1050000004)</td>\n      <td>7.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>29 rows &#xD7; 21 columns</p>\n</div>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nakonec m&#x16F;&#x17E;eme vykreslit graf, ve kter&#xE9;m budeme definovat barvy pomoc&#xED; hodnot ve sloupci <code>pocet</code>. Taky si p&#x159;id&#xE1;me legendu.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In&#xA0;[77]:</div>\n<div class=\"inner_cell\">\n    <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">matplotlib</span> <span class=\"k\">import</span> <span class=\"n\">pyplot</span>\n<span class=\"n\">brno_s_poruchami</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">figsize</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"mi\">10</span><span class=\"p\">,</span><span class=\"mi\">10</span><span class=\"p\">),</span> <span class=\"n\">column</span><span class=\"o\">=</span><span class=\"s2\">&quot;pocet&quot;</span><span class=\"p\">,</span> <span class=\"n\">legend</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n<span class=\"k\">for</span> <span class=\"n\">idx</span><span class=\"p\">,</span> <span class=\"n\">radek</span> <span class=\"ow\">in</span> <span class=\"n\">brno_s_poruchami</span><span class=\"o\">.</span><span class=\"n\">iterrows</span><span class=\"p\">():</span>\n    <span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">annotate</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"o\">=</span><span class=\"n\">radek</span><span class=\"p\">[</span><span class=\"s2\">&quot;NAZ_MOaMC&quot;</span><span class=\"p\">],</span> <span class=\"n\">xy</span><span class=\"o\">=</span><span class=\"n\">radek</span><span class=\"p\">[</span><span class=\"s2\">&quot;coords&quot;</span><span class=\"p\">],</span> <span class=\"n\">horizontalalignment</span><span class=\"o\">=</span><span class=\"s2\">&quot;center&quot;</span><span class=\"p\">)</span>\n</pre></div>\n\n    </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n    <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n \n\n\n\n\n        "
    }
  }
}