{"id":158,"date":"2023-03-15T17:19:55","date_gmt":"2023-03-15T17:19:55","guid":{"rendered":"https:\/\/jirivalasek.com\/?page_id=158"},"modified":"2023-11-17T14:45:09","modified_gmt":"2023-11-17T14:45:09","slug":"python-projekt","status":"publish","type":"page","link":"https:\/\/jirivalasek.com\/?page_id=158","title":{"rendered":"Python"},"content":{"rendered":"\t\t
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ANAL\u00ddZA STANIC <\/strong>SD\u00cdLEN\u00ddCH KOL<\/strong><\/h4>

V <\/strong><\/h4>

EDINBURGHU<\/strong><\/h4>

Pou\u017eit\u00e9 technologie: Pandas, Python, SQL, Jupyter.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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    \n
  • Identifikujte aktivn\u00ed a neaktivn\u00ed stanice,<\/li>\n
  • identifikujte nejfrekventovan\u011bj\u0161\u00ed stanice,<\/li>\n
  • identifikujte stanice, na kter\u00fdch se kola hromad\u00ed a stanice, kde potenci\u00e1ln\u011b chyb\u00ed,<\/li>\n
  • spo\u010d\u00edtejte vzd\u00e1lenosti mezi jednotliv\u00fdmi stanicemi,<\/li>\n
  • jak dlouho trv\u00e1 jedna v\u00fdp\u016fj\u010dka? Najd\u011bte odlehl\u00e9 hodnoty, zobrazte histogram.<\/li>\n
  • \n

    Prove\u0165e anal\u00fdzu popt\u00e1vky:<\/p>\n

      \n
    • zobrazte v\u00fdvoj popt\u00e1vky po p\u016fj\u010dov\u00e1n\u00ed kol v \u010dase,<\/li>\n
    • identifikujte p\u0159\u00ed\u010diny v\u00fdkyv\u016f popt\u00e1vky,<\/li>\n
    • zjist\u011bte vliv po\u010das\u00ed na popt\u00e1vku po kolech (\u00fadaje o po\u010das\u00ed v Edinburghu jsou v tabulce edinburgh_weather<\/code>),<\/li>\n
    • p\u016fj\u010duj\u00ed si lid\u00e9 kola v\u00edce o v\u00edkendu ne\u017e b\u011bhem pracovn\u00edho t\u00fddne?<\/li>\n<\/ul>\n<\/li>\n
    •  <\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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      V\u00ddSTUPY<\/strong><\/h4>

      ANAL\u00ddZY<\/strong><\/h4>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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      \u00a0<\/p>

      • Vid\u00edme jednozna\u010dn\u00fd vliv sez\u00f3ny na popt\u00e1vku,<\/li>
      • vliv event\u016f ve m\u011bst\u011b,<\/li>
      • vliv Covidov\u00fdch lockdown\u016f ve Skotsku – prvn\u00ed ohl\u00e1\u0161en 20.3.2020 a postupn\u00e9 uvol\u0148ov\u00e1n\u00ed pravidel od 11.5.2020 a\u017e do n\u00e1vratu d\u011bt\u00ed do \u0161kol 11.8.2020.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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        B\u011bhem jara a l\u00e9ta byl vliv po\u010das\u00ed na v\u00fdp\u016fj\u010dky kol v Edinburghu sp\u00ed\u0161e m\u00e9n\u011b v\u00fdznamn\u00fd. B\u011bhem sledovan\u00e9ho obdob\u00ed bylo po\u010das\u00ed bez vich\u0159ic, extr\u00e9mn\u00edch teplot, nebo mimo\u0159\u00e1dn\u011b vydatn\u00fdch sr\u00e1\u017eek. Zd\u00e1 se, \u017ee v\u011bt\u0161\u00ed vliv na v\u00fdp\u016fj\u010dky m\u011blo po\u0159\u00e1d\u00e1n\u00ed event\u016f ve m\u011bst\u011b. <\/p>

        Efekt teploty vzduchu na popt\u00e1vku je st\u0159edn\u00ed: R = 0,5.<\/p>\n

        B\u011bhem hlavn\u00ed sez\u00f3ny se vliv teploty vzduchu ztr\u00e1c\u00ed: R = 0,36.<\/p>\n

        Po odstran\u011bn\u00ed vlivu event\u016f ve m\u011bst\u011b je vliv po\u010das\u00ed na v\u00fdp\u016fj\u010dky je\u0161t\u011b men\u0161\u00ed: R = 0,31.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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        Lid\u00e9 si kola p\u016fj\u010duj\u00ed o v\u00edkendu trochu<\/p>

        v\u00edce, ne\u017e b\u011bhem pracovn\u00edho t\u00fddne.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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        Pr\u016fm\u011brn\u00fd denn\u00ed po\u010det v\u00fdp\u016fj\u010dek b\u011bhem pracovn\u00edch dn\u016f: \n\n 387\n\n Pr\u016fm\u011brn\u00fd denn\u00ed po\u010det v\u00fdp\u016fj\u010dek b\u011bhem v\u00edkendu: \n\n 443\n\n<\/strong><\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Doba v\u00fdp\u016fj\u010dky mezi 5 a 20 minutami jednozna\u010dn\u011b dominuje po\u010dtem kolem 180 tis. z\u00e1znam\u016f z celkov\u00fdch 438 259 v\u00fdp\u016fj\u010dek.<\/p>
        Asi 80% lid\u00ed vr\u00e1t\u00ed kolo do 60 minut.<\/p>
        \u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        <\/div>\n\t\t\t\t\t\t\t
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        Deset nejfrekventovan\u011bj\u0161\u00edch stanic (v\u00fdp\u016fj\u010dky + vratky)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        <\/div>\n\t\t\t\t\t\t\t
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        Jedna z mo\u017enost\u00ed tabulky vzd\u00e1lenost\u00ed mezi stanicemi.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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        POSTUP<\/h4>
        Na\u010dten\u00ed dat do Jupyter notebook.<\/p>
        V\u00fdstupem je tabulka s daty o v\u00fdp\u016fj\u010dk\u00e1ch kol. Vid\u00edme identifikace stanic v\u00fdp\u016fj\u010dek a vr\u00e1cen\u00ed v\u010detn\u011b jejich n\u00e1zv\u016f, popisu, sou\u0159adnic a doby v\u00fdp\u016fj\u010dky v sekund\u00e1ch.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        alchemy_conn = sqlalchemy.create_engine(conn_string)
        edbikes_df = pd.read_sql(‘edinburgh_bikes’,conn_string,index_col=[‘index’])
        edbikes_df.head()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        index<\/th>\u00a0started<\/th>\u00a0ended<\/th>\u00a0duration<\/th>start station id\u00a0<\/th>start station name\u00a0<\/th>start station description\u00a0<\/th>start station latitude\u00a0<\/th>start station longitude\u00a0<\/th>end station id\u00a0<\/th>end station name\u00a0<\/th>\u00a0end station description<\/th>end station latitude\u00a0<\/th>end station longitude\u00a0<\/th><\/tr><\/thead>
        0<\/th>2018-09-15 08:52:05<\/td>2018-09-15 09:11:48<\/td>1182<\/td>247<\/td>Charlotte Square<\/td>North Corner of Charlotte Square<\/td>55.952335<\/td>-3.207101<\/td>259<\/td>St Andrew Square<\/td>North East corner<\/td>55.954728<\/td>-3.192653<\/td><\/tr>
        1<\/th>2018-09-15 09:24:33<\/td>2018-09-15 09:41:09<\/td>995<\/td>259<\/td>St Andrew Square<\/td>North East corner<\/td>55.954749<\/td>-3.192774<\/td>262<\/td>Canonmills<\/td>near Tesco’s<\/td>55.962804<\/td>-3.196284<\/td><\/tr>
        2<\/th>2018-09-15 09:48:54<\/td>2018-09-15 10:46:40<\/td>3466<\/td>262<\/td>Canonmills<\/td>near Tesco’s<\/td>55.962804<\/td>-3.196284<\/td>250<\/td>Victoria Quay<\/td>Entrance to Scottish Government Office<\/td>55.977638<\/td>-3.174116<\/td><\/tr>
        3<\/th>2018-09-16 12:01:36<\/td>2018-09-16 12:25:26<\/td>1430<\/td>255<\/td>Kings Buildings 4<\/td>X-Y Cafe<\/td>55.922001<\/td>-3.176902<\/td>254<\/td>Kings Building 3<\/td>Kings Building House<\/td>55.923479<\/td>-3.175385<\/td><\/tr>
        4<\/th>2018-09-16 12:03:43<\/td>2018-09-16 12:11:16<\/td>452<\/td>255<\/td>Kings Buildings 4<\/td>X-Y Cafe<\/td>55.922001<\/td>-3.176902<\/td>253<\/td>Kings Building 2<\/td>Sanderson Building<\/td>55.923202<\/td>-3.171646<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Informace o datech.<\/p>
        Tabulk\u00e1 m\u00e1 438 259 z\u00e1znam\u016f. N\u011bkter\u00e9 sloupce maj\u00ed ni\u017e\u0161\u00ed po\u010dty z\u00e1znam\u016f, tak\u017ee v nich data chyb\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edbikes_df.info()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        <class 'pandas.core.frame.DataFrame'>\nInt64Index: 438259 entries, 0 to 12640\nData columns (total 13 columns):\n #   Column                     Non-Null Count   Dtype  \n---  ------                     --------------   -----  \n 0   started_at                 438259 non-null  object \n 1   ended_at                   438259 non-null  object \n 2   duration                   438259 non-null  int64  \n 3   start_station_id           438259 non-null  int64  \n 4   start_station_name         438259 non-null  object \n 5   start_station_description  435549 non-null  object \n 6   start_station_latitude     438259 non-null  float64\n 7   start_station_longitude    438259 non-null  float64\n 8   end_station_id             438259 non-null  int64  \n 9   end_station_name           438259 non-null  object \n 10  end_station_description    435256 non-null  object \n 11  end_station_latitude       438259 non-null  float64\n 12  end_station_longitude      438259 non-null  float64\ndtypes: float64(4), int64(3), object(6)\nmemory usage: 46.8+ MB<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Z\u00e1znamy jsou z obdob\u00ed od 15.9.2018 do 30.6.2021.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edbikes_df[[‘started_at’,’ended_at’]].sort_values(‘started_at’)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \u00a0<\/th>\u00a0<\/th><\/tr>
        index<\/th>\u00a0started_at<\/th>ended_at<\/span>\u00a0<\/th><\/tr><\/thead>
        0<\/th>2018-09-15 08:52:05<\/td>2018-09-15 09:11:48<\/td><\/tr>
        1<\/th>2018-09-15 09:24:33<\/td>2018-09-15 09:41:09<\/td><\/tr>
        2<\/th>2018-09-15 09:48:54<\/td>2018-09-15 10:46:40<\/td><\/tr>
        3<\/th>2018-09-16 12:01:36<\/td>2018-09-16 12:25:26<\/td><\/tr>
        4<\/th>2018-09-16 12:03:43<\/td>2018-09-16 12:11:16<\/td><\/tr>
        …<\/th>…<\/td>…<\/td><\/tr>
        12636<\/th>2021-06-30 23:30:31<\/td>2021-07-01 00:06:10<\/td><\/tr>
        12637<\/th>2021-06-30 23:36:16<\/td>2021-07-01 00:05:40<\/td><\/tr>
        12638<\/th>2021-06-30 23:49:03<\/td>2021-07-01 00:11:25<\/td><\/tr>
        12639<\/th>2021-06-30 23:49:03<\/td>2021-07-01 00:11:52<\/td><\/tr>
        12640<\/th>2021-06-30 23:58:33<\/td>2021-07-01 00:07:15<\/td><\/tr><\/tbody><\/table>
        438259 rows \u00d7 2 columns<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Hled\u00e1n\u00ed chyb\u011bj\u00edc\u00edch hodnot.<\/p>
        Ve sloupci start_station_description chyb\u00ed 2710 hodnot a ve sloupci end_station_description jich chyb\u00ed 3003.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edbikes_df.isna().sum()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        started_at                      0\nended_at                        0\nduration                        0\nstart_station_id                0\nstart_station_name              0\nstart_station_description    2710\nstart_station_latitude          0\nstart_station_longitude         0\nend_station_id                  0\nend_station_name                0\nend_station_description      3003\nend_station_latitude            0\nend_station_longitude           0\ndtype: int64<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Identifikace \u0159\u00e1dk\u016f s chyb\u011bj\u00edc\u00edmi hodnotami v start_station_description.<\/p>
        Hodnoty chyb\u00ed v po\u010d\u00e1te\u010dn\u00edch stanic\u00edch s n\u00e1zvem ‘Dalry Road Lidl’.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        condition = edbikes_df[[‘start_station_name’,’start_station_description’]].isna().any(axis=1)
        edbikes_df.loc[condition,’start_station_name’].unique()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        array(['Dalry Road Lidl'], dtype=object)<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Identifikace \u0159\u00e1dk\u016f s chyb\u011bj\u00edc\u00edmi hodnotami v end_station_description.<\/p>
        Hodnoty chyb\u00ed v kone\u010dn\u00fdch stanic\u00edch s n\u00e1zvem ‘Dalry Road Lidl’.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        condition = edbikes_df[[‘end_station_name’,’end_station_description’]].isna().any(axis=1)
        edbikes_df.loc[condition,’end_station_name’].unique()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        array(['Dalry Road Lidl'], dtype=object)<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Dopln\u011bn\u00ed chyb\u011bj\u00edc\u00edch hodnot do tabulky.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edbikes_df.fillna(‘Dalry Road Lidl’,inplace=True)<\/p>
        edbikes_df.info()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        <class 'pandas.core.frame.DataFrame'>\nInt64Index: 438259 entries, 0 to 12640\nData columns (total 13 columns):\n #   Column                     Non-Null Count   Dtype  \n---  ------                     --------------   -----  \n 0   started_at                 438259 non-null  object \n 1   ended_at                   438259 non-null  object \n 2   duration                   438259 non-null  int64  \n 3   start_station_id           438259 non-null  int64  \n 4   start_station_name         438259 non-null  object \n 5   start_station_description  438259 non-null  object \n 6   start_station_latitude     438259 non-null  float64\n 7   start_station_longitude    438259 non-null  float64\n 8   end_station_id             438259 non-null  int64  \n 9   end_station_name           438259 non-null  object \n 10  end_station_description    438259 non-null  object \n 11  end_station_latitude       438259 non-null  float64\n 12  end_station_longitude      438259 non-null  float64\ndtypes: float64(4), int64(3), object(6)<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        V\u00fdpis po\u010d\u00e1te\u010dn\u00edch stanic: ID, jm\u00e9no, popis, sou\u0159adnice.<\/p>
        drop_duplicates() vy\u0159ad\u00ed duplicity (srovn\u00e1v\u00e1 \u0159\u00e1dky tabulky jako celek).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        start = (edbikes_df[[‘start_station_id’,’start_station_name’,’start_station_latitude’,’start_station_longitude’, ‘start_station_description’]].drop_duplicates()
        .rename(columns={‘start_station_id’:’station_id’,’start_station_name’:’station_name’,’start_station_latitude’:’latitude’,
        ‘start_station_longitude’:’longitude’,’start_station_description’:’station_description’})
        )
        print(‘Start stations:’)
        print(‘\\n’,start)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Start stations:\n\n        station_id               station_name   latitude  longitude  \\\nindex                                                                \n0             247           Charlotte Square  55.952335  -3.207101   \n1             259           St Andrew Square  55.954749  -3.192774   \n2             262                 Canonmills  55.962804  -3.196284   \n3             255          Kings Buildings 4  55.922001  -3.176902   \n5             253           Kings Building 2  55.923202  -3.171646   \n...           ...                        ...        ...        ...   \n5853         2268               Picady Place  55.956535  -3.186248   \n5970         2268              Picardy Place  55.956535  -3.186248   \n3490         2265   Musselburgh Brunton Hall  55.943961  -3.058307   \n33            358                 Leith Walk  55.965176  -3.176180   \n9257         1739  Edinburgh Royal Infirmary  55.921354  -3.136971   \n\n                      station_description  \nindex                                      \n0        North Corner of Charlotte Square  \n1                       North East corner  \n2                            near Tesco's  \n3                                X-Y Cafe  \n5                      Sanderson Building  \n...                                   ...  \n5853                  Outside Omni Centre  \n5970                  Outside Omni Centre  \n3490     Adjacent to the Brunton Theatre   \n33     Leith Walk opposite Dalmeny Street  \n9257      Front of new Sick Kids Hospital  \n\n[241 rows x 5 columns]<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        V\u00fdpis kone\u010dn\u00fdch stanic: ID, jm\u00e9no, popis, sou\u0159adnice.<\/p>
        drop_duplicates() vy\u0159ad\u00ed duplicity (pouze pokud se hodnoty shoduj\u00ed ve v\u0161ech vypsan\u00fdch sloupc\u00edch).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        end = (edbikes_df[[‘end_station_id’,’end_station_name’,’end_station_latitude’,’end_station_longitude’,’end_station_description’]].drop_duplicates()
        .rename(columns={‘end_station_id’:’station_id’,’end_station_name’:’station_name’,’end_station_latitude’:’latitude’,
        ‘end_station_longitude’:’longitude’,’end_station_description’:’station_description’})
        )
        print(“End stations:”)
        print(‘\\n’,end)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        End stations:\n\n        station_id               station_name   latitude  longitude  \\\nindex                                                                \n0             259           St Andrew Square  55.954728  -3.192653   \n1             262                 Canonmills  55.962804  -3.196284   \n2             250              Victoria Quay  55.977638  -3.174116   \n3             254           Kings Building 3  55.923479  -3.175385   \n4             253           Kings Building 2  55.923202  -3.171646   \n...           ...                        ...        ...        ...   \n5806         2268               Picady Place  55.956535  -3.186248   \n5963         2268              Picardy Place  55.956535  -3.186248   \n3184         2265   Musselburgh Brunton Hall  55.943961  -3.058307   \n12970         358                 Leith Walk  55.965176  -3.176180   \n9272         1739  Edinburgh Royal Infirmary  55.921354  -3.136971   \n\n                          station_description  \nindex                                          \n0                           North East corner  \n1                                near Tesco's  \n2      Entrance to Scottish Government Office  \n3                        Kings Building House  \n4                          Sanderson Building  \n...                                       ...  \n5806                      Outside Omni Centre  \n5963                      Outside Omni Centre  \n3184         Adjacent to the Brunton Theatre   \n12970      Leith Walk opposite Dalmeny Street  \n9272          Front of new Sick Kids Hospital  \n\n[243 rows x 5 columns]<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Vytvo\u0159en\u00ed data frame ‘stations’ spojen\u00edm ‘start’ a ‘end’ stanic.<\/p>
        !! Pr\u016fzkum a \u00faprava dat je i o \u010dten\u00ed a hled\u00e1n\u00edch chyb, p\u0159eklep\u016f a jejich n\u00e1prav\u011b!!<\/p>
        V seznamu jsou stanice se stejn\u00fdm n\u00e1zvem, ale pod r\u016fzn\u00fdm ID. N\u011bkter\u00e9 stanice stanice se stejn\u00fdm n\u00e1zvem se jen m\u00edrn\u011b li\u0161\u00ed sou\u0159adnicemi, nebo popisem.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        stations = pd.concat([end,start]).drop_duplicates().sort_values(‘station_id’)
        print(“All stations at least once used to start or once to end:”)
        stations.sort_values(‘station_id’)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        All stations at least once used to start or once to end:\n<\/pre><\/div><\/div>
        [11]:<\/div>
        \u00a0<\/th>station_id<\/th>station_name<\/th>latitude<\/th>longitude<\/th>station_description<\/th><\/tr>
        index<\/th>\u00a0<\/th>\u00a0<\/th>\u00a0<\/th>\u00a0<\/th>\u00a0<\/th><\/tr><\/thead>
        27<\/th>171<\/td>George Square<\/td>55.943084<\/td>-3.188311<\/td>George Square, south side in front of University library<\/td><\/tr>
        48<\/th>183<\/td>Waverley Bridge<\/td>55.951344<\/td>-3.191421<\/td>near the top of the South ramp<\/td><\/tr>
        16696<\/th>183<\/td>Waverley Bridge<\/td>55.951981<\/td>-3.191890<\/td>near the top of the South ramp<\/td><\/tr>
        14<\/th>189<\/td>City Chambers<\/td>55.950109<\/td>-3.190258<\/td>City Chambers Quadrangle<\/td><\/tr>
        135<\/th>225<\/td>Waverley Court<\/td>55.951734<\/td>-3.184179<\/td>On Waverley Court forecourt<\/td><\/tr>
        1278<\/th>241<\/td>Depot<\/td>55.972373<\/td>-3.155833<\/td>Depot<\/td><\/tr>
        1126<\/th>242<\/td>Virtual Depot<\/td>55.972402<\/td>-3.155794<\/td>Virtual Depot<\/td><\/tr>
        10<\/th>246<\/td>Royal Commonwealth Pool<\/td>55.939000<\/td>-3.173924<\/td>Royal Commonwealth Pool Entrance<\/td><\/tr>
        28<\/th>247<\/td>Charlotte Square<\/td>55.952335<\/td>-3.207101<\/td>North Corner of Charlotte Square<\/td><\/tr>
        608<\/th>248<\/td>Bristo Square<\/td>55.946004<\/td>-3.188665<\/td>Bristo Square, near Potterrow<\/td><\/tr>
        6766<\/th>248<\/td>Bristo Square<\/td>55.945834<\/td>-3.189053<\/td>Bristo Square, near Potterrow<\/td><\/tr>
        12<\/th>248<\/td>Bristo Square<\/td>55.946004<\/td>-3.188665<\/td>Bristo Square, near Potter Row<\/td><\/tr>
        66<\/th>249<\/td>Fountainbridge<\/td>55.943357<\/td>-3.209248<\/td>Fountainbridge by Gardner’s Crescent<\/td><\/tr>
        2<\/th>250<\/td>Victoria Quay<\/td>55.977638<\/td>-3.174116<\/td>Entrance to Scottish Government Office<\/td><\/tr>
        1696<\/th>250<\/td>Victoria Quay<\/td>55.977617<\/td>-3.174126<\/td>Entrance to Scottish Government Office<\/td><\/tr>
        1001<\/th>251<\/td>Waverley Station<\/td>55.952641<\/td>-3.187527<\/td>Platform level near Calton Road Exit<\/td><\/tr>
        119<\/th>251<\/td>Waverley Station<\/td>55.952641<\/td>-3.187527<\/td>Near Calton Road Exit<\/td><\/tr>
        5<\/th>252<\/td>Kings Buildings 1<\/td>55.924185<\/td>-3.173831<\/td>Grant Institute<\/td><\/tr>
        4<\/th>253<\/td>Kings Building 2<\/td>55.923202<\/td>-3.171646<\/td>Sanderson Building<\/td><\/tr>
        3<\/th>254<\/td>Kings Building 3<\/td>55.923479<\/td>-3.175385<\/td>Kings Building House<\/td><\/tr>
        138<\/th>255<\/td>Kings Buildings 4<\/td>55.922001<\/td>-3.176902<\/td>X-Y Cafe<\/td><\/tr>
        146<\/th>256<\/td>St Andrews House<\/td>55.953164<\/td>-3.181682<\/td>beside Jacobs ladder<\/td><\/tr>
        16<\/th>257<\/td>Portobello<\/td>55.957004<\/td>-3.116888<\/td>Edinburgh Leisure Tumbles Centre<\/td><\/tr>
        13<\/th>258<\/td>Stockbridge<\/td>55.958566<\/td>-3.208070<\/td>Hamilton Place by Dean Bank Lane<\/td><\/tr>
        1798<\/th>259<\/td>St Andrew Square<\/td>55.954906<\/td>-3.192444<\/td>North East corner<\/td><\/tr>
        0<\/th>259<\/td>St Andrew Square<\/td>55.954728<\/td>-3.192653<\/td>North East corner<\/td><\/tr>
        24<\/th>259<\/td>St Andrew Square<\/td>55.954749<\/td>-3.192774<\/td>North East corner<\/td><\/tr>
        147<\/th>260<\/td>Lauriston Place<\/td>55.944772<\/td>-3.197266<\/td>near Chalmers Street<\/td><\/tr>
        2024<\/th>261<\/td>Brunswick Place – Virtual<\/td>55.960930<\/td>-3.181005<\/td>Junction Brunswick Street and Elm Row<\/td><\/tr>
        68<\/th>261<\/td>Brunswick Place<\/td>55.960930<\/td>-3.181005<\/td>Junction Brunswick Street and Elm Row<\/td><\/tr>
        1<\/th>262<\/td>Canonmills<\/td>55.962804<\/td>-3.196284<\/td>near Tesco’s<\/td><\/tr>
        3868<\/th>264<\/td>Pollock Halls<\/td>55.940081<\/td>-3.171747<\/td>Pollock Halls Entrance<\/td><\/tr>
        7<\/th>264<\/td>Pollock Halls<\/td>55.939963<\/td>-3.171586<\/td>Pollock Halls Entrance<\/td><\/tr>
        11<\/th>265<\/td>Meadows East<\/td>55.939809<\/td>-3.182739<\/td>Melville Terrace<\/td><\/tr>
        1638<\/th>265<\/td>Meadows East<\/td>55.939809<\/td>-3.182739<\/td>Between Melville Terrace and Sciennes<\/td><\/tr>
        178<\/th>266<\/td>Victoria Park<\/td>55.974247<\/td>-3.194482<\/td>Near Craighall Road<\/td><\/tr>
        3788<\/th>266<\/td>Victoria Park<\/td>55.975189<\/td>-3.191030<\/td>Near Craighall Road<\/td><\/tr>
        25<\/th>267<\/td>Launch Day Event<\/td>55.942551<\/td>-3.191381<\/td>The Meadows<\/td><\/tr>
        227<\/th>273<\/td>Shrubhill<\/td>55.962537<\/td>-3.179473<\/td>Sainsbury’s Leith Walk<\/td><\/tr>
        356<\/th>275<\/td>Riego Street<\/td>55.945160<\/td>-3.203678<\/td>Riego Street on junction with East Fountainbridge<\/td><\/tr>
        1206<\/th>277<\/td>Waitrose, Comely Bank<\/td>55.959504<\/td>-3.223428<\/td>Fettes Avenue<\/td><\/tr>
        4304<\/th>280<\/td>Smarter Travel Station<\/td>53.395525<\/td>-2.990138<\/td>The Street<\/td><\/tr>
        1945<\/th>284<\/td>Leith Links<\/td>55.969199<\/td>-3.166885<\/td>Corner of Vanburgh Place and Lochend Road<\/td><\/tr>
        2064<\/th>285<\/td>Ocean Terminal<\/td>55.981286<\/td>-3.176351<\/td>Debenhams \/ Puregym entrance<\/td><\/tr>
        3140<\/th>289<\/td>Castle Street<\/td>55.951501<\/td>-3.203184<\/td>Near Rose Street<\/td><\/tr>
        3213<\/th>290<\/td>Bruntsfield<\/td>55.937159<\/td>-3.206435<\/td>Corner of Bruntsfield Terrace and Bruntsfield Place<\/td><\/tr>
        12115<\/th>290<\/td>Bruntsfield links<\/td>55.937159<\/td>-3.206435<\/td>Corner of Bruntsfield Terrace and Bruntsfield Place<\/td><\/tr>
        3931<\/th>296<\/td>Castle Terrace<\/td>55.946766<\/td>-3.202038<\/td>Corner of Castle Terrace and Lady Lawson St<\/td><\/tr>
        3594<\/th>297<\/td>Royal Infirmary<\/td>55.924295<\/td>-3.133510<\/td>Cycleway off Little France<\/td><\/tr>
        9928<\/th>299<\/td>Depot Virtual<\/td>55.972335<\/td>-3.155782<\/td>Depot internal station<\/td><\/tr>
        1729<\/th>340<\/td>Meadow Place<\/td>55.940300<\/td>-3.194592<\/td>Corner of Meadow Place and Melville Drive<\/td><\/tr>
        1092<\/th>341<\/td>Warrender Park Road<\/td>55.938363<\/td>-3.198031<\/td>Corner of Warrender Park Road & Spottiswoode Street<\/td><\/tr>
        1306<\/th>342<\/td>Whitehouse Loan<\/td>55.936329<\/td>-3.202295<\/td>Corner of Whitehouse Loan & Bruntsfield Crescent<\/td><\/tr>
        1732<\/th>343<\/td>Thirlestane Road<\/td>55.935365<\/td>-3.198671<\/td>Corner of Thirlestane Road & St. Margaret’s Place<\/td><\/tr>
        1434<\/th>344<\/td>Marchmont Crescent<\/td>55.936397<\/td>-3.194252<\/td>Corner of Marchmont Crescent & Marchmont Road<\/td><\/tr>
        1954<\/th>345<\/td>Colinton Road<\/td>55.934035<\/td>-3.210803<\/td>Colinton Road outside Starbucks<\/td><\/tr>
        7112<\/th>345<\/td>Colinton Road<\/td>55.933398<\/td>-3.212429<\/td>Colinton Road outside Starbucks<\/td><\/tr>
        7756<\/th>345<\/td>Colinton Road<\/td>55.933398<\/td>-3.212429<\/td>Colinton Road next to Napier University<\/td><\/tr>
        1700<\/th>346<\/td>Morningside Road<\/td>55.927986<\/td>-3.209739<\/td>Corner of Morningside Road & Morningside Park by M & S car park<\/td><\/tr>
        1061<\/th>347<\/td>Simon Square<\/td>55.944868<\/td>-3.182671<\/td>Corner of Simon Square & Gilmour Street<\/td><\/tr>
        1823<\/th>349<\/td>Orchard Brae House<\/td>55.955083<\/td>-3.223634<\/td>Outside Orchard Brae House on Queensferry Road<\/td><\/tr>
        1252<\/th>350<\/td>Dalry Road Lidl<\/td>55.941754<\/td>-3.222524<\/td>outside Lidl<\/td><\/tr>
        1264<\/th>351<\/td>Dalry Road Co-op<\/td>55.942715<\/td>-3.221431<\/td>By co-op (outside maplins)<\/td><\/tr>
        1223<\/th>352<\/td>Dundee Terrace<\/td>55.939729<\/td>-3.220603<\/td>Dundee Terrace outside “glass supplies”<\/td><\/tr>
        1221<\/th>353<\/td>Gibson Terrace<\/td>55.940493<\/td>-3.217144<\/td>Outside student accomodation<\/td><\/tr>
        1609<\/th>354<\/td>South Trinity Road<\/td>55.971269<\/td>-3.207816<\/td>Corner of South Trinity Road & Ferry Road<\/td><\/tr>
        1614<\/th>355<\/td>Inverleith Row<\/td>55.964146<\/td>-3.202074<\/td>Corner of Inverleith Row & Inverleith Terrace<\/td><\/tr>
        1329<\/th>356<\/td>East London Street<\/td>55.959943<\/td>-3.187329<\/td>Outside St. Mary’s Primary School<\/td><\/tr>
        1309<\/th>357<\/td>Macdonald Road<\/td>55.963995<\/td>-3.185189<\/td>Macdonald Road<\/td><\/tr>
        1328<\/th>358<\/td>Leith Walk<\/td>55.965040<\/td>-3.176686<\/td>Leith Walk opposite Dalmeny Street<\/td><\/tr>
        12970<\/th>358<\/td>Leith Walk<\/td>55.965176<\/td>-3.176180<\/td>Leith Walk opposite Dalmeny Street<\/td><\/tr>
        1501<\/th>359<\/td>Causewayside<\/td>55.936430<\/td>-3.180115<\/td>Causewayside outside NLS<\/td><\/tr>
        1424<\/th>365<\/td>Novotel<\/td>55.944896<\/td>-3.199635<\/td>Novotel<\/td><\/tr>
        1963<\/th>366<\/td>Newhaven Road \/ Dudley Gardens<\/td>55.975921<\/td>-3.191346<\/td>Corner of Dudley Gardens on Newhaven Road<\/td><\/tr>
        1676<\/th>366<\/td>Dudley Gardens<\/td>55.975921<\/td>-3.191346<\/td>Corner of Dudley Gardens on Newhaven Road<\/td><\/tr>
        1714<\/th>366<\/td>Newhaven Road \/ Dudley Gardens<\/td>55.975927<\/td>-3.191318<\/td>Corner of Dudley Gardens on Newhaven Road<\/td><\/tr>
        5831<\/th>648<\/td>Western General<\/td>55.963458<\/td>-3.232810<\/td>Western General virtual station across from public multi storey<\/td><\/tr>
        1523<\/th>820<\/td>Newkirkgate<\/td>55.970704<\/td>-3.171624<\/td>13 point docking station beside queen victoria monument<\/td><\/tr>
        5608<\/th>820<\/td>Newkirkgate<\/td>55.970704<\/td>-3.171624<\/td>19 point docking station beside queen victoria monument<\/td><\/tr>
        2941<\/th>860<\/td>Pollock Halls Virtual<\/td>55.940348<\/td>-3.172108<\/td>On pavement corner by the entrance gates<\/td><\/tr>
        6077<\/th>862<\/td>Cramond Foreshore<\/td>55.980024<\/td>-3.300622<\/td>Cramond foreshore by turning circle<\/td><\/tr>
        7405<\/th>863<\/td>Gamekeeper’s Road<\/td>55.969532<\/td>-3.307305<\/td>Corner of Gamekeeper’s Road & Whitehouse Road<\/td><\/tr>
        6633<\/th>864<\/td>Whitehouse Road<\/td>55.961527<\/td>-3.306114<\/td>Whitehouse road by Sainbury’s & bus stop<\/td><\/tr>
        7207<\/th>865<\/td>Craigleith Road<\/td>55.956619<\/td>-3.237803<\/td>Craigleith road by bus stop<\/td><\/tr>
        6404<\/th>866<\/td>Comely Bank Road<\/td>55.959407<\/td>-3.215660<\/td>Between trees on Comely Bank Road<\/td><\/tr>
        6270<\/th>867<\/td>Henderson Row<\/td>55.960260<\/td>-3.203913<\/td>Next to the Edinburgh Academy<\/td><\/tr>
        5825<\/th>868<\/td>Dundas Street<\/td>55.960944<\/td>-3.201387<\/td>Corner of Dundas Street & Henderson Row<\/td><\/tr>
        6058<\/th>869<\/td>Hillside Crescent 1<\/td>55.957910<\/td>-3.180693<\/td>West corner of Hillside Crescent & London Road<\/td><\/tr>
        12540<\/th>870<\/td>Hillside Crescent 2<\/td>55.957793<\/td>-3.175799<\/td>East corner of Hillside Crescent & London Road<\/td><\/tr>
        3860<\/th>870<\/td>Hillside Crescent<\/td>55.957793<\/td>-3.175799<\/td>East corner of Hillside Crescent & London Road<\/td><\/tr>
        5899<\/th>870<\/td>Hillside Crescent 2<\/td>55.957803<\/td>-3.175479<\/td>East corner of Hillside Crescent & London Road<\/td><\/tr>
        8094<\/th>871<\/td>St. John’s Road 1<\/td>55.942746<\/td>-3.281623<\/td>Outside Specsavers<\/td><\/tr>
        6710<\/th>872<\/td>St. John’s Road 2<\/td>55.942945<\/td>-3.290794<\/td>Outside GuitarGuitar<\/td><\/tr>
        6607<\/th>873<\/td>Corstorphine Road<\/td>55.941743<\/td>-3.271484<\/td>Outside Forestry & Land Scotland<\/td><\/tr>
        5179<\/th>874<\/td>Edinburgh Zoo<\/td>55.942120<\/td>-3.269280<\/td>Outside zoo gatehouse<\/td><\/tr>
        7224<\/th>874<\/td>Edinburgh Zoo<\/td>55.942222<\/td>-3.268697<\/td>Outside zoo gatehouse<\/td><\/tr>
        741<\/th>875<\/td>Corstorphine Road – Pinkhill<\/td>55.942364<\/td>-3.265384<\/td>Corner of Corstorphine Road & Pinkhill<\/td><\/tr>
        7709<\/th>876<\/td>Murrayfield<\/td>55.944767<\/td>-3.243688<\/td>Riversdale Crescent<\/td><\/tr>
        6855<\/th>877<\/td>Murrayfield Avenue<\/td>55.946322<\/td>-3.236542<\/td>Corner of Murrayfield Avenue & Murrayfield Place<\/td><\/tr>
        6415<\/th>878<\/td>Balgreen<\/td>55.938938<\/td>-3.251173<\/td>Opposite Jenners Depository<\/td><\/tr>
        6313<\/th>879<\/td>Gladstone Terrace<\/td>55.938024<\/td>-3.184979<\/td>Corner of Gladstone Terrace & Sciennes ROad<\/td><\/tr>
        8602<\/th>880<\/td>Logie Green Road<\/td>55.964035<\/td>-3.195674<\/td>Outside Lidl<\/td><\/tr>
        6671<\/th>881<\/td>Tollcross<\/td>55.944281<\/td>-3.202964<\/td>Outside Piccolino<\/td><\/tr>
        6603<\/th>882<\/td>EICC<\/td>55.946043<\/td>-3.210485<\/td>Between planters next to EICC<\/td><\/tr>
        227<\/th>883<\/td>Queensferry Road<\/td>55.954543<\/td>-3.234728<\/td>Corner of Queensferry Road & Orchard Road<\/td><\/tr>
        99<\/th>884<\/td>IGMM<\/td>55.962487<\/td>-3.232031<\/td>IGMM at Western General Hospital<\/td><\/tr>
        6982<\/th>885<\/td>Wester Coates Terrace<\/td>55.945609<\/td>-3.231716<\/td>Corner of Wester Coates Terrace & Roseburn Terrace<\/td><\/tr>
        6604<\/th>887<\/td>Roseburn Street<\/td>55.944466<\/td>-3.234541<\/td>Corner of Roseburn Street & Russell Gardens<\/td><\/tr>
        6573<\/th>888<\/td>Crichton Street<\/td>55.944750<\/td>-3.186542<\/td>Corner of Crichton Street & Potterow<\/td><\/tr>
        7320<\/th>889<\/td>Murrayfield Tram<\/td>55.941956<\/td>-3.237802<\/td>By the side of the station entrance<\/td><\/tr>
        831<\/th>890<\/td>West Crosscauseway<\/td>55.943862<\/td>-3.184972<\/td>Corner of West Crosscauseway & Buccleugh Street<\/td><\/tr>
        840<\/th>891<\/td>West Newington Place<\/td>55.938212<\/td>-3.178972<\/td>Corner of West Newington Place & Newington Road<\/td><\/tr>
        1625<\/th>901<\/td>Dunbar\u2019s Close Garden<\/td>55.951814<\/td>-3.178726<\/td>Inside garden<\/td><\/tr>
        3569<\/th>964<\/td>Corn Exchange – walk cycle event<\/td>55.927014<\/td>-3.248557<\/td>Temporary event station<\/td><\/tr>
        3619<\/th>965<\/td>Sustrans – walk cycle event<\/td>55.945452<\/td>-3.219680<\/td>Sustrans car parking space 31 for walk cycle event<\/td><\/tr>
        12913<\/th>965<\/td>Haymarket – Murrayfield Rugby Event<\/td>55.945452<\/td>-3.219680<\/td>Sustrans car parking space 31 for Murrayfield Rugby Event<\/td><\/tr>
        7049<\/th>980<\/td>Royal Highland Show – East Gate (19th to 23rd June)<\/td>55.940907<\/td>-3.368872<\/td>Virtual station at east gate of RHC (19th to 23rd June)<\/td><\/tr>
        4109<\/th>981<\/td>RHC – Edinburgh Festival Camping (05th to 26th August)<\/td>55.940655<\/td>-3.381606<\/td>Virtual station for Edinburgh Festival Camping at west gate of RHC (05th to 26th August)<\/td><\/tr>
        6622<\/th>981<\/td>Royal Highland Show – West Gate (19th to 23rd June)<\/td>55.940655<\/td>-3.381606<\/td>Virtual station at west gate of RHC (19th to 23rd June)<\/td><\/tr>
        6611<\/th>982<\/td>Ingliston Park and Ride (19th to 23rd June)<\/td>55.939090<\/td>-3.355913<\/td>Virtual station at Ingliston Park and Ride (19th to 23rd June)<\/td><\/tr>
        2011<\/th>991<\/td>Meadows – Edinburgh Climate Festival<\/td>55.941736<\/td>-3.190361<\/td>20 bike virtual station for edinburgh climate festival<\/td><\/tr>
        9510<\/th>1017<\/td>Crichton Street<\/td>55.944756<\/td>-3.186917<\/td>13 point physical docking station<\/td><\/tr>
        193<\/th>1017<\/td>Crichton Street<\/td>55.944784<\/td>-3.186906<\/td>East end of street<\/td><\/tr>
        10546<\/th>1017<\/td>Crichton Street<\/td>55.944756<\/td>-3.186917<\/td>East end of street<\/td><\/tr>
        10222<\/th>1018<\/td>Hunter Square<\/td>55.949692<\/td>-3.187813<\/td>Next to Tron Kirk at top of Blair Street<\/td><\/tr>
        9482<\/th>1018<\/td>Hunter Square<\/td>55.949692<\/td>-3.187813<\/td>07 point angled physical docking station<\/td><\/tr>
        10308<\/th>1019<\/td>Grassmarket<\/td>55.947097<\/td>-3.197246<\/td>West end of Grassmarket<\/td><\/tr>
        11964<\/th>1024<\/td>Meadow Place 2<\/td>55.940238<\/td>-3.194640<\/td>\u00a0<\/td><\/tr>
        2037<\/th>1024<\/td>Meadow Place<\/td>55.940238<\/td>-3.194640<\/td>End of Meadow Place<\/td><\/tr>
        1938<\/th>1025<\/td>Dundee Terrace<\/td>55.939710<\/td>-3.220589<\/td>Corner of Dundee Street & Dundee Terrace<\/td><\/tr>
        12039<\/th>1025<\/td>Dundee Terrace<\/td>55.939710<\/td>-3.220589<\/td>\u00a0<\/td><\/tr>
        4605<\/th>1026<\/td>Constitution Street<\/td>55.975360<\/td>-3.166442<\/td>Corner of Assembly Street<\/td><\/tr>
        12380<\/th>1026<\/td>Constitution Street<\/td>55.975441<\/td>-3.166806<\/td>Next to Burns Statue<\/td><\/tr>
        664<\/th>1027<\/td>Drummond Street<\/td>55.948337<\/td>-3.182261<\/td>Opposite the Pleasance<\/td><\/tr>
        2140<\/th>1027<\/td>Drummond Street<\/td>55.948337<\/td>-3.182261<\/td>Opposite the Pleasance Grand<\/td><\/tr>
        8255<\/th>1028<\/td>Hunter Square<\/td>55.949798<\/td>-3.187795<\/td>In Hunter Square next to Tron Kirk<\/td><\/tr>
        3098<\/th>1028<\/td>The Tron<\/td>55.950037<\/td>-3.187822<\/td>Next to Tron Kirk Royal Mile<\/td><\/tr>
        4936<\/th>1030<\/td>Fountain Court – Apartments (RESIDENTS ONLY)<\/td>55.943806<\/td>-3.211238<\/td>Fountain Court – Apartments (RESIDENTS ONLY)<\/td><\/tr>
        7063<\/th>1031<\/td>Eden Locke – Aparthotel (RESIDENTS ONLY)<\/td>55.952641<\/td>-3.205666<\/td>Eden Locke – Aparthotel (RESIDENTS ONLY)<\/td><\/tr>
        8337<\/th>1031<\/td>Eden Locke – Aparthotel (RESIDENTS ONLY)<\/td>55.952619<\/td>-3.205678<\/td>Eden Locke – Aparthotel (RESIDENTS ONLY)<\/td><\/tr>
        5613<\/th>1032<\/td>Holyrood Park – Woman’s Tour Of Scotland (Event 11\/08\/19)<\/td>55.951354<\/td>-3.168591<\/td>Holyrood Park – Woman’s Tour Of Scotland (Event 11\/08\/19)<\/td><\/tr>
        6481<\/th>1033<\/td>Queen Margaret University<\/td>55.931935<\/td>-3.073046<\/td>Opposite Maggies student union<\/td><\/tr>
        16730<\/th>1038<\/td>South Trinity Road<\/td>55.971325<\/td>-3.207964<\/td>Corner of South Trinity Road and Ferry Road<\/td><\/tr>
        16578<\/th>1039<\/td>Lothian Road<\/td>55.947409<\/td>-3.205765<\/td>Outside the Usher Hall<\/td><\/tr>
        1995<\/th>1040<\/td>Sighthill – Edinburgh College<\/td>55.926665<\/td>-3.289468<\/td>Sighthill Campus<\/td><\/tr>
        1016<\/th>1041<\/td>Milton Road – Edinburgh College<\/td>55.944051<\/td>-3.098567<\/td>Milton Road Campus<\/td><\/tr>
        1632<\/th>1042<\/td>Kings Buildings – Murchison House<\/td>55.924464<\/td>-3.178732<\/td>West end of campus<\/td><\/tr>
        1538<\/th>1050<\/td>EICC<\/td>55.946071<\/td>-3.210396<\/td>Outside Edinburgh International Conference Centre<\/td><\/tr>
        1546<\/th>1051<\/td>Warrender Park Road<\/td>55.938369<\/td>-3.198033<\/td>On corner with Spottiswoode Street<\/td><\/tr>
        4146<\/th>1052<\/td>Surgeons Hall<\/td>55.946643<\/td>-3.185475<\/td>East side of Nicolson Street<\/td><\/tr>
        1962<\/th>1055<\/td>Roseburn Street<\/td>55.944426<\/td>-3.234498<\/td>On corner with Russell Gardens<\/td><\/tr>
        3879<\/th>1056<\/td>Fort Kinnaird<\/td>55.934372<\/td>-3.105038<\/td>Next to Playpark\/Cafe Nero<\/td><\/tr>
        4188<\/th>1057<\/td>Pleasance – Edinburgh University Sports Fair<\/td>55.948210<\/td>-3.181597<\/td>Pleasance Sports Complex<\/td><\/tr>
        4726<\/th>1090<\/td>Hillside Crescent<\/td>55.957872<\/td>-3.175888<\/td>East end of Hillside Crescent<\/td><\/tr>
        4626<\/th>1091<\/td>Holyrood Road<\/td>55.949560<\/td>-3.180413<\/td>Opposite St Leonards Land<\/td><\/tr>
        2990<\/th>1092<\/td>Dalry Road Lidl<\/td>55.941791<\/td>-3.222415<\/td>Dalry Road Lidl<\/td><\/tr>
        4848<\/th>1092<\/td>Dalry Road Lidl<\/td>55.941791<\/td>-3.222415<\/td>\u00a0<\/td><\/tr>
        7991<\/th>1093<\/td>Belford Road<\/td>55.951974<\/td>-3.226125<\/td>Outside Scottish National Gallery of Modern Art<\/td><\/tr>
        4757<\/th>1093<\/td>Belford Road<\/td>55.951974<\/td>-3.226125<\/td>Outside Scottish National Gallery<\/td><\/tr>
        10199<\/th>1094<\/td>HSBC UK Lets Ride – Meadows Event<\/td>55.939978<\/td>-3.189862<\/td>20 point virtual docking station for UK lets Ride Event<\/td><\/tr>
        12099<\/th>1095<\/td>Dudley Gardens<\/td>55.975940<\/td>-3.191321<\/td>Opposite Victoria Park on Newhaven Road<\/td><\/tr>
        12115<\/th>1096<\/td>West Crosscauseway<\/td>55.943836<\/td>-3.184951<\/td>On island next to Buccleuch Street<\/td><\/tr>
        12165<\/th>1097<\/td>Gladstone Terrace<\/td>55.937963<\/td>-3.185021<\/td>Corner of Gladstone Terrace and Sciennes Road<\/td><\/tr>
        12228<\/th>1098<\/td>Marchmont Crescent<\/td>55.936432<\/td>-3.194150<\/td>Corner of Marchmont Road<\/td><\/tr>
        1683<\/th>1102<\/td>Haymarket Station<\/td>55.945569<\/td>-3.218185<\/td>Haymarket Station<\/td><\/tr>
        969<\/th>1102<\/td>Haymarket Station<\/td>55.945575<\/td>-3.218195<\/td>Haymarket Station<\/td><\/tr>
        154<\/th>1102<\/td>Haymarket Station<\/td>55.945582<\/td>-3.218192<\/td>Haymarket Station<\/td><\/tr>
        1305<\/th>1720<\/td>Dundas Street<\/td>55.960762<\/td>-3.201278<\/td>On corner of Henderson Row<\/td><\/tr>
        1194<\/th>1721<\/td>Tollcross<\/td>55.944248<\/td>-3.203105<\/td>Outside Piccolinos off Lauriston Place<\/td><\/tr>
        2070<\/th>1722<\/td>Cramond Foreshore<\/td>55.980031<\/td>-3.300642<\/td>Near Cramond Beach<\/td><\/tr>
        1873<\/th>1723<\/td>Heriot Watt – Edinburgh Business School<\/td>55.908786<\/td>-3.320170<\/td>Heriot Watt – Edinburgh Business School<\/td><\/tr>
        1555<\/th>1723<\/td>Heriot Watt – Edinburgh Business School<\/td>55.908810<\/td>-3.320142<\/td>Heriot Watt – Edinburgh Business School<\/td><\/tr>
        1491<\/th>1724<\/td>Heriot Watt – Student Accomodation (Anna MacLeod Halls)<\/td>55.908404<\/td>-3.328825<\/td>Heriot Watt – Student Accomodation Heriot Watt – Student Accomodation (Anna MacLeod Halls))<\/td><\/tr>
        2987<\/th>1725<\/td>Edinburgh Zoo<\/td>55.942115<\/td>-3.269287<\/td>Corstorphine Road, Edinburgh<\/td><\/tr>
        4091<\/th>1726<\/td>Simon Square<\/td>55.944859<\/td>-3.182590<\/td>Between Pleasance and Nicholson Street<\/td><\/tr>
        4083<\/th>1727<\/td>Causewayside<\/td>55.936506<\/td>-3.180166<\/td>Outside National Library of Scotland<\/td><\/tr>
        9581<\/th>1728<\/td>Portobello – Kings Road<\/td>55.957915<\/td>-3.118332<\/td>Foot of Kings Road next to the promenade<\/td><\/tr>
        9912<\/th>1729<\/td>McDonald Road<\/td>55.964031<\/td>-3.185175<\/td>Next to Fire Station<\/td><\/tr>
        9689<\/th>1730<\/td>East London Street<\/td>55.959954<\/td>-3.187198<\/td>Outside St Mary’s Primary School<\/td><\/tr>
        10018<\/th>1731<\/td>Pleasance Courtyard<\/td>55.947567<\/td>-3.181592<\/td>Edinburgh University, The Pleasance<\/td><\/tr>
        5106<\/th>1731<\/td>Pleasance Courtyard<\/td>55.947491<\/td>-3.181112<\/td>Edinburgh University, The Pleasance<\/td><\/tr>
        12637<\/th>1737<\/td>Inverleith Row<\/td>55.964118<\/td>-3.202095<\/td>Corner with Inverleith Terrace<\/td><\/tr>
        12959<\/th>1738<\/td>Wester Coates Terrace<\/td>55.945648<\/td>-3.231847<\/td>Junction with Roseburn Terrace<\/td><\/tr>
        6642<\/th>1739<\/td>Edinburgh Royal Infirmary<\/td>55.920632<\/td>-3.140541<\/td>Little France, Old Dalkeith Road, Edinburgh<\/td><\/tr>
        9272<\/th>1739<\/td>Edinburgh Royal Infirmary<\/td>55.921354<\/td>-3.136971<\/td>Front of new Sick Kids Hospital<\/td><\/tr>
        2302<\/th>1739<\/td>Edinburgh Royal Infirmary<\/td>55.921220<\/td>-3.139076<\/td>Front of new Sick Kids Hospital<\/td><\/tr>
        873<\/th>1740<\/td>Cycling Scotland Conference<\/td>55.940886<\/td>-3.240778<\/td>Murrayfield Stadium<\/td><\/tr>
        2523<\/th>1743<\/td>Logie Green Road<\/td>55.964058<\/td>-3.195700<\/td>Outside Lidl<\/td><\/tr>
        1975<\/th>1744<\/td>Morningside Road<\/td>55.927985<\/td>-3.209750<\/td>Corner of Morningside Park, beside M&S<\/td><\/tr>
        2207<\/th>1745<\/td>Scotland Street<\/td>55.960380<\/td>-3.195470<\/td>Corner with Royal Crescent<\/td><\/tr>
        1984<\/th>1746<\/td>Crescent House<\/td>55.963911<\/td>-3.191862<\/td>Crescent House<\/td><\/tr>
        4563<\/th>1747<\/td>Corstorphine Road<\/td>55.941670<\/td>-3.271524<\/td>Outside Silvan House, Forestry and Land Scotland<\/td><\/tr>
        4602<\/th>1748<\/td>Colinton Road<\/td>55.933416<\/td>-3.212397<\/td>Near Napier University Merchiston Campus<\/td><\/tr>
        5210<\/th>1749<\/td>Dean Street<\/td>55.957278<\/td>-3.214285<\/td>Corner of Dean Park Mews<\/td><\/tr>
        6911<\/th>1752<\/td>IGMM – Western General<\/td>55.962642<\/td>-3.231916<\/td>The Institute of Genetics and Molecular Medicine<\/td><\/tr>
        7129<\/th>1753<\/td>Waitrose Comely Bank<\/td>55.959536<\/td>-3.223434<\/td>On Fettes Avenue next to Waitrose<\/td><\/tr>
        7876<\/th>1754<\/td>Murrayfield Tram<\/td>55.941911<\/td>-3.237800<\/td>Next to Murrayfield Tram Station<\/td><\/tr>
        60<\/th>1756<\/td>Western General Hospital<\/td>55.963454<\/td>-3.232909<\/td>Porterfield Road<\/td><\/tr>
        11547<\/th>1756<\/td>Western General Hospital<\/td>55.962840<\/td>-3.234136<\/td>Porterfield Road<\/td><\/tr>
        9904<\/th>1757<\/td>Meggetland<\/td>55.927587<\/td>-3.233671<\/td>Meggetland Sports Complex<\/td><\/tr>
        9253<\/th>1758<\/td>Queen Margaret University<\/td>55.931980<\/td>-3.073105<\/td>University Courtyard<\/td><\/tr>
        1140<\/th>1763<\/td>Comely Bank Road<\/td>55.959410<\/td>-3.215661<\/td>Outside Raeburn Place Sports Ground<\/td><\/tr>
        1305<\/th>1764<\/td>Craigleith Road<\/td>55.956576<\/td>-3.237940<\/td>Next to Craigleith Hill Bus Stop<\/td><\/tr>
        1553<\/th>1765<\/td>Haymarket Terrace<\/td>55.946064<\/td>-3.223024<\/td>Corner of Magdala Crescent<\/td><\/tr>
        3484<\/th>1766<\/td>Balgreen Road<\/td>55.938942<\/td>-3.251111<\/td>Opposite Jenners Depository<\/td><\/tr>
        2993<\/th>1767<\/td>Bruntsfield Links<\/td>55.937123<\/td>-3.206432<\/td>Corner of Bruntsfield Links next to Public Toilets<\/td><\/tr>
        3080<\/th>1768<\/td>Thirlestane Road<\/td>55.935324<\/td>-3.198763<\/td>On corner with St Margaret’s Place<\/td><\/tr>
        4906<\/th>1769<\/td>Brunswick Place<\/td>55.960852<\/td>-3.180986<\/td>Corner of Elm Row\/Brunswick Street<\/td><\/tr>
        5049<\/th>1770<\/td>Ellersly Road<\/td>55.945046<\/td>-3.250881<\/td>Corner with A8 Corstorphine Road at Western Terrace<\/td><\/tr>
        2022<\/th>1798<\/td>Chambers Street<\/td>55.947600<\/td>-3.188920<\/td>Outside National Museum<\/td><\/tr>
        2004<\/th>1799<\/td>Murrayfield<\/td>55.944791<\/td>-3.243673<\/td>Near Stadium and Ice Rink<\/td><\/tr>
        1936<\/th>1800<\/td>Joppa<\/td>55.948949<\/td>-3.094727<\/td>East end of Promenade<\/td><\/tr>
        4346<\/th>1807<\/td>Gamekeeper’s Road<\/td>55.969443<\/td>-3.307259<\/td>Junction with Whitehouse Road<\/td><\/tr>
        5068<\/th>1808<\/td>Gorgie Road<\/td>55.938741<\/td>-3.229909<\/td>Corner with McLeod Street<\/td><\/tr>
        6158<\/th>1809<\/td>Royal Edinburgh Hospital<\/td>55.927818<\/td>-3.213308<\/td>Next to Kennedy Tower<\/td><\/tr>
        6682<\/th>1813<\/td>Milton Road – Edinburgh College<\/td>55.944066<\/td>-3.098561<\/td>Milton Road Campus<\/td><\/tr>
        6534<\/th>1814<\/td>Abbeyhill<\/td>55.955248<\/td>-3.172216<\/td>Near Abbey Mount<\/td><\/tr>
        7036<\/th>1815<\/td>Sighthill – Edinburgh College<\/td>55.926684<\/td>-3.289481<\/td>Sighthill Campus<\/td><\/tr>
        9335<\/th>1818<\/td>Dynamic Earth<\/td>55.951089<\/td>-3.175725<\/td>Outside Dynamic Earth, Holyrood Road<\/td><\/tr>
        215<\/th>1819<\/td>Heriot Watt – Edinburgh Business School<\/td>55.908823<\/td>-3.320113<\/td>Outside Edinburgh Business School<\/td><\/tr>
        1125<\/th>1820<\/td>Heriot Watt – Student Accommodation<\/td>55.908413<\/td>-3.328784<\/td>Outside Anna Macleod Halls<\/td><\/tr>
        2223<\/th>1821<\/td>Drumsheugh Place<\/td>55.951594<\/td>-3.212354<\/td>Corner with Drumsheugh Gardens<\/td><\/tr>
        2472<\/th>1822<\/td>Edinburgh Park Station<\/td>55.927383<\/td>-3.307442<\/td>Next to Rail and Tram stations<\/td><\/tr>
        2633<\/th>1823<\/td>Boroughmuir<\/td>55.940071<\/td>-3.215336<\/td>Off Gibson Terrace<\/td><\/tr>
        2142<\/th>1824<\/td>Duke Street<\/td>55.969012<\/td>-3.167395<\/td>Junction with Easter Road<\/td><\/tr>
        270<\/th>1857<\/td>City Chambers Launch Station<\/td>55.950222<\/td>-3.190270<\/td>Temporary station at City Chambers<\/td><\/tr>
        12197<\/th>1859<\/td>Edinburgh Park Central<\/td>55.931169<\/td>-3.314414<\/td>Between Lochside Court and Lochside Avenue<\/td><\/tr>
        9598<\/th>1860<\/td>Ingliston Park & Ride<\/td>55.938792<\/td>-3.355556<\/td>Next to Customer Service building<\/td><\/tr>
        6469<\/th>1864<\/td>Borrowman Square<\/td>55.982606<\/td>-3.381455<\/td>Near to Scotstoun Bus Terminus<\/td><\/tr>
        9252<\/th>1865<\/td>Dalmeny Station<\/td>55.986761<\/td>-3.382427<\/td>Next to bike shelter<\/td><\/tr>
        6130<\/th>1866<\/td>The Loan<\/td>55.989900<\/td>-3.397773<\/td>Next to East Coast Tyres<\/td><\/tr>
        8848<\/th>1868<\/td>Forth Bridge Visitors Centre<\/td>55.987743<\/td>-3.403752<\/td>Off Ferrymuir Gait<\/td><\/tr>
        8530<\/th>1869<\/td>Hopetoun Road<\/td>55.990182<\/td>-3.404604<\/td>Junction with Farquhar Terrace\/Boness Road<\/td><\/tr>
        9287<\/th>1870<\/td>Hawes Pier<\/td>55.990530<\/td>-3.385597<\/td>Off Newhalls Road<\/td><\/tr>
        11105<\/th>1871<\/td>Scotstoun House<\/td>55.981107<\/td>-3.394211<\/td>ARUP Edinburgh<\/td><\/tr>
        10051<\/th>1874<\/td>Tesco Ferrymuir<\/td>55.983766<\/td>-3.401352<\/td>Outside supermarket<\/td><\/tr>
        10024<\/th>1877<\/td>Port Edgar Marina<\/td>55.992957<\/td>-3.407156<\/td>Next to Marina Shop and Restaurant<\/td><\/tr>
        855<\/th>2259<\/td>Leith Walk North<\/td>55.967918<\/td>-3.173586<\/td>Next to Allander House<\/td><\/tr>
        14980<\/th>2263<\/td>Musselburgh Lidl<\/td>55.943880<\/td>-3.066754<\/td>Musselborough North High Street opposite Harbour Road<\/td><\/tr>
        24262<\/th>2265<\/td>Musselburgh Brunton Hall<\/td>55.943961<\/td>-3.058307<\/td>Adjacent to the Brunton Theatre<\/td><\/tr>
        21035<\/th>2265<\/td>Musselburgh Brunton Hall<\/td>55.944009<\/td>-3.058493<\/td>Adjacent to the Brunton Theatre<\/td><\/tr>
        3184<\/th>2265<\/td>Musselburgh Brunton Hall<\/td>55.943961<\/td>-3.058307<\/td>Adjacent to the Brunton Theatre<\/td><\/tr>
        5806<\/th>2268<\/td>Picady Place<\/td>55.956535<\/td>-3.186248<\/td>Outside Omni Centre<\/td><\/tr>
        5963<\/th>2268<\/td>Picardy Place<\/td>55.956535<\/td>-3.186248<\/td>Outside Omni Centre<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        P\u0159ed pokra\u010dov\u00e1n\u00edm s anal\u00fdzou jsem se rozhodl v cel\u00e9m datasetu ujednotit nesrovnalosti s ID \u010d\u00edsly a n\u00e1zvy stanic tak, aby nebyly v\u00fdsledn\u00e9 v\u00fdpo\u010dty zkresleny chybn\u011b zadan\u00fdmi daty.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Brunswick Place’,[‘start_station_id’]] = 1769
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Brunswick Place’,[‘end_station_id’]] = 1769
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Causewayside’,[‘start_station_id’]] = 1727
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Causewayside’,[‘end_station_id’]] = 1727
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Comely Bank Road’,[‘start_station_id’]] = 1763
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Comely Bank Road’,[‘end_station_id’]] = 1763
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Corstorphine Road’,[‘start_station_id’]] = 1747
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Corstorphine Road’,[‘end_station_id’]] = 1747
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Craigleith Road’,[‘start_station_id’]] = 1764
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Craigleith Road’,[‘end_station_id’]] = 1764
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Cramond Foreshore’,[‘start_station_id’]] = 1722
        edbikes_df.loc[edbikes_df[‘end_station_name’]==’Cramond Foreshore’,[‘end_station_id’]] = 1722
        edbikes_df.loc[edbikes_df[‘start_station_name’]==’Crichton Street’,[‘start_station_id’]] = 1017<\/p>
        .<\/p>
        .<\/p>
        .<\/p>
        a podobn\u011b d\u00e1le<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Vypsan\u00e9 stanice je mo\u017eno zobrazit na map\u011b pomoc\u00ed sou\u0159adnic a vizualiza\u010dn\u00edho n\u00e1stroje folium.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        import folium
        from folium import plugins<\/p>
        m_stations = folium.Map([55.948612, -3.200833],zoom_start=12) #\u00a0Vytvo\u0159\u00ed mapu se st\u0159edem v centru m\u011bsta.<\/em>
        for place, row in stations.iterrows():#\u00a0 Proch\u00e1z\u00ed \u0159\u00e1dky tabulky stations.\u00a0<\/em><\/p>
        folium.Marker(row[[‘latitude’, ‘longitude’]].values.tolist(),
        popup=folium.Popup(f”””Station name: {row[‘station_name’]}”””),
        icon=folium.Icon(icon=”home”, prefix=’fa’)
        ).add_to(m_stations)
        m_stations<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t
        Identifikace aktivn\u00edch a neaktivn\u00edch stanic.<\/p>
        Aktivn\u00ed budou stanice, kter\u00e9 byly vyu\u017eity alespo\u0148 jednou za posledn\u00ed rok z\u00e1znam\u016f.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
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        \n\t\t\t\t
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        IN:\u00a0<\/p>
        # Vyp\u00ed\u0161e IDs po\u010d\u00e1te\u010dn\u00edch stanic vyu\u017eit\u00fdch v posledn\u00edm roce:<\/p>
        start_active = (edbikes_df.query(“started_at >= ‘2020-06-30’ or ended_at >= ‘2020-06-30′”)[[‘start_station_id’]] 
        .drop_duplicates(subset=’start_station_id’).rename(columns={‘start_station_id’:’station_id’})
        )<\/p>
        # Vyp\u00ed\u0161e IDs kone\u010dn\u00fdch stanic vyu\u017eit\u00fdch v posledn\u00edm roce:<\/p>
        end_active = (edbikes_df.query(“started_at >= ‘2020-06-30’ or ended_at >= ‘2020-06-30′”)[[‘end_station_id’]]
        .drop_duplicates(subset=’end_station_id’).rename(columns={‘end_station_id’:’station_id’})
        )<\/p>
        # Vytvo\u0159\u00ed soubor s IDs stanic vyu\u017eit\u00fdch v posledn\u00edm roce – spojen\u00ed start_active a end-active:<\/p>
        active_stations = pd.concat([end_active,start_active]).drop_duplicates().sort_values(‘station_id’)<\/p>
        # Pomocn\u00e9 data frame, ze kter\u00fdch spojen\u00edm vznikne soubor s ID stanic, jejich n\u00e1zvy a sou\u0159adnicemi:<\/p>
        df_s = stations.reset_index()[[‘station_id’,’station_name’,’latitude’,’longitude’]].set_index(‘station_id’)
        df_a = active_stations.set_index(‘station_id’)<\/p>
        # Vznikne dataframe s aktivn\u00edmi stanicemi:<\/p>
        active_complete = df_a.join(df_s).dropna()
        active_complete = active_complete.reset_index().drop_duplicates(subset = ‘station_id’)<\/p>
        # V\u00fdpis prvn\u00edch 6-ti \u0159\u00e1dk\u016f:
        active_complete.head(6)<\/p>
        \u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        \u00a0<\/p>
        \u00a0<\/th>station_id<\/td>station_name<\/td>latitude<\/td>longitude<\/td><\/tr>
        1<\/th>183<\/td>Waverley Bridge<\/td>55.951344<\/td>-3.191421<\/td><\/tr>
        3<\/th>189<\/td>City Chambers<\/td>55.950109<\/td>-3.190258<\/td><\/tr>
        4<\/th>225<\/td>Waverley Court<\/td>55.951734<\/td>-3.184179<\/td><\/tr>
        5<\/th>246<\/td>Royal Commonwealth Pool<\/td>55.939000<\/td>-3.173924<\/td><\/tr>
        6<\/th>247<\/td>Charlotte Square<\/td>55.952335<\/td>-3.207101<\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        Aktivn\u00ed stanice na map\u011b<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        m_active_stations = folium.Map([55.948612, -3.200833],zoom_start=12)
        for place, row in active_complete.iterrows():
        folium.Marker(row[[‘latitude’, ‘longitude’]].values.tolist(),
        popup=folium.Popup(f”””Station name: {row[‘station_name’]}”””),
        icon=folium.Icon(icon=”home”, prefix=’fa’)
        ).add_to(m_active_stations)
        m_active_stations<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t
        \u00daprava z\u00e1ladn\u00edho data setu, aby obsahoval z\u00e1znamy pouze aktivn\u00edch stanic.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        edbikes_df = edbikes_df[edbikes_df[‘start_station_id’and’end_station_id’].isin(active_stations.values[:,0])]<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
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        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Vytvo\u0159en\u00ed tabulek se sou\u010dty v\u00fdp\u016fj\u010dek a vratek.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        df = edbikes_df.assign(cnt=0).groupby([‘start_station_id’,’started_at’]).count()[[‘cnt’]]#\u00a0Vytvo\u0159\u00ed tabulku s v\u00fdp\u016fj\u010dkami.<\/em>
        df1 = df.groupby(‘start_station_id’).sum().rename(columns={‘cnt’:’sum_borrowings’}).rename_axis(‘station_id’)# Tabulka se sou\u010dty v\u00fdp\u016fj\u010dek dle stanice.<\/em>\u00a0
        df2 = edbikes_df.assign(cnt=0).groupby([‘end_station_id’,’ended_at’]).count()[[‘cnt’]]#\u00a0Vytvo\u0159\u00ed tabulku s vratkami.<\/em>
        df3 = df2.groupby(‘end_station_id’).sum().rename(columns={‘cnt’:’sum_returns’}).rename_axis(‘station_id’)#\u00a0Tabulka se sou\u010dty vratek dle stanice.\u00a0<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        NZ v\u00fd\u0161e vytvo\u0159en\u00e9 tabulky zobraz\u00edme popisnou statistiku v\u00fdp\u016fj\u010dek.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        (df1[[‘sum_borrowings’]]\/1019).describe()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        sum_borrowings<\/p>
        count 161.000000<\/p>
        mean 2.508299 # Pr\u016fm\u011brn\u011b 2,5 v\u00fdp\u016fj\u010dky za den<\/p>
        std 2.941329<\/p>
        min0.000981 # Minim\u00e1ln\u011b 1 v\u00fdp\u016fj\u010dka za 1019 dn\u016f.<\/p>
        25 % 0.219823<\/p>
        50 % 1.424926 # Median 1,4 za den.<\/em><\/p>
        75 % 3.866536<\/p>
        max 16.265947 #\u00a0Maxim\u00e1ln\u011b 16 za den.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        Popisn\u00e1 statistika vratek.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
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        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        (df3[[‘sum_returns’]]\/1019).describe()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        sum_returns<\/p>
        count 112.000000<\/p>
        mean 3.605680 # Pr\u016fm\u011brn\u011b 3,6 vratek za den.<\/p>
        std 3.316777<\/p>
        min 0.005888 # Minim\u00e1ln\u011b jedna vratka za 170 dn\u016f.<\/p>
        25% 1.361138<\/p>
        50% 2.635427 # Medi\u00e1n 2,64 vratek za den.<\/p>
        75% 4.948724<\/p>
        max 16.345437 # Maximum 16 vratek za den.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        V\u00fdpis nejfrekventovan\u011bj\u0161\u00edch stanic.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
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        \n\t\t\t\t
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        IN:<\/p>
        activity_df = df3.join(df1)# Vytvo\u0159en\u00ed tabulky se sou\u010dty v\u00fdp\u016fj\u010dek i vratek.
        activity_df.fillna(value=0,axis=1,inplace=True) # Vypln\u011bn\u00ed chyb\u011bj\u00edc\u00edch hodnot nulou.
        activity_df[‘frequency’]=activity_df.sum_returns+activity_df.sum_borrowings# P\u0159id\u00e1 sloupec s frekvenc\u00ed dle stanice.<\/p>
        activity_df[‘day_frequency’]= (activity_df.frequency\/1019).astype(int)# P\u0159id\u00e1 denn\u00ed frekvenci.<\/p>
        (<\/p>
        \u00a0stations.join(activity_df, on=’station_id’)[[‘station_id’,’station_name’,’frequency’,’day_frequency’]].drop_duplicates(subset=’station_id’).dropna()# Dopln\u00ed jm\u00e9na stanic.<\/p>
        \u00a0.sort_values(‘frequency’,ascending=False).head(10) # Se\u0159ad\u00ed dle frekvence sestupn\u011b a zobraz\u00ed prvn\u00edch 10.<\/p>
        )<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Ur\u010den\u00ed kde kola sp\u00ed\u0161e chyb\u00ed a kde p\u0159eb\u00fdvaj\u00ed.<\/p>
        Nejprve do tabulky frekvenc\u00ed p\u0159id\u00e1me procentu\u00e1ln\u00ed pod\u00edl vratek.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
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        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        activity_df = activity_df.assign(returns_percent=(activity_df.sum_returns\/activity_df.frequency*100).round(2))
        activity_df.head(10)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
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        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Funkce, kterou aplikujeme tak, aby \u0159ekla, kde kola chyb\u00ed, nebo se hromad\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
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        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        def usage (x):
        if x > 50:
        return “rather accumulates”
        elif x < 50:
        return “rather missing”<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        V\u00fdpis prvn\u00edch deseti stanic, kde se kola hromad\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        activity_df.assign(use=activity_df[‘returns_percent’].apply(usage)).sort_values(‘returns_percent’,ascending=False).head(10)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        V\u00fdpis prvn\u00edch deseti stanic, kde kola chyb\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        activity_df.assign(use=activity_df[‘returns_percent’].apply(usage)).sort_values(‘returns_percent’).head(10)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        ZOBRAZEN\u00cd VZD\u00c1LENOST\u00cd MEZI STANICEMI.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        !pip install geopy
        from geopy.distance import geodesic
        from itertools import combinations<\/p>
        combs = list(combinations(stations.index,2)) # Vytvo\u0159\u00ed list se v\u0161emi mo\u017en\u00fdmi kombinacemi dvou stanic.<\/p>
        coords = np.array(list(combinations(stations[[‘latitude’, ‘longitude’]].values, 2))) # Vytvo\u0159\u00ed list se v\u0161emi mo\u017en\u00fdmi kombinacemi sou\u0159adnic.<\/p>
        coords = coords.reshape(coords.shape[0],4) # Vytvo\u0159\u00ed tabulku o 4 sloupc\u00edch a po\u010dtem \u0159\u00e1dk\u016f jako v\u00fd\u0161e list.<\/p>
        coords = pd.DataFrame(coords) # Z coords array na dataframe.<\/p>
        # Funkce na v\u00fdpo\u010det vzd\u00e1lenost\u00ed:<\/p>
        def geodesic_vec(a, b, c, d):
        rs = geodesic( (a, b), (c, d) ).kilometers
        return rs<\/p>
        distances = list()
        for data,row in coords.iterrows():
        dists = np.round(geodesic_vec(row[0],row[1],row[2],row[3]),2)
        distances.append(dists) # List se vzd\u00e1lenostmi.<\/p>
        distances_df = pd.DataFrame(distances,pd.Index(combs,names=[‘City1′,’City2’]),columns=[‘distance’]) # Tabulka vzd\u00e1lenost\u00ed.<\/p>
        distances_df<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        DOBA TRV\u00c1N\u00cd V\u00ddP\u016eJ\u010cEK<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        duration_df = edbikes_df[[‘duration’]].assign(duration_min=round(edbikes_df.duration\/60))<\/p>
        # Vytvo\u0159\u00ed dataset s dobou trv\u00e1n\u00ed v\u00fdp\u016fj\u010dek v minut\u00e1ch.<\/em><\/p>
        duration_df[[‘duration_min’]].describe() # Deskriptivn\u00ed statistika trv\u00e1n\u00ed v\u00fdp\u016fj\u010dek.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        Ur\u010den\u00ed odlehl\u00fdch hodnot p\u0159ed zobrazen\u00edm histogramu doby v\u00fdp\u016fj\u010dek:<\/p>
        1) Ur\u010den\u00ed interquartile range(IQR): (42 – 10) * 1,5 = 48
        2) P\u0159i\u010dten\u00ed IQR k 75 percentilu: 48 + 42 = 90
        3) V\u0161echny hodnoty nad 90 jsou odlehl\u00e9.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        import matplotlib.pyplot as plt
        plt.hist(duration_df[duration_df[‘duration_min’].isin(range(5,91))][[‘duration_min’]],bins = 6)
        plt.xlabel(‘minutes’)
        plt.ylabel(‘count’)
        plt.title(‘DURATION IN MINUTES’)
        plt.grid(True)
        plt.show()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        PO\u010cET V\u00ddP\u016eJ\u010cEK ZA DEN<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        df4 = edbikes_df[[‘started_at’]]#\u00a0Vybere sloupec s za\u010d\u00e1tky v\u00fdp\u016fj\u010dek.<\/em>
        df4 = df4.assign (started_at_day = pd.to_datetime(df4[‘started_at’]).dt.date) #\u00a0P\u0159id\u00e1 sloupec ve form\u00e1tu data.<\/em>
        df4 = df4[[‘started_at_day’]].assign(borrowings_count=0).groupby(‘started_at_day’).count()#\u00a0P\u0159id\u00e1po\u010dtyv\u00fdp\u016fj\u010dekzaden.<\/em>
        df4.index = pd.to_datetime(df4.index) #\u00a0Zm\u011bn\u00ed form\u00e1t indexu.<\/em>
        df4<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        V\u00ddVOJ POPT\u00c1VKY V \u010cASE<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        hour_df = edbikes_df.assign(started_at_day = pd.to_datetime(edbikes_df[‘started_at’]).dt.date, hours = pd.to_datetime(edbikes_df[‘started_at’]).dt.hour) # P\u0159id\u00e1 sloupce se dny a hodinami.<\/em>
        day_df = hour_df[hour_df[‘hours’].isin(range(6,19))]#\u00a0Vybere jen denn\u00ed dobu.<\/em>
        day_df = day_df.groupby(‘started_at_day’).count()[[‘hours’]]#\u00a0Vypo\u010d\u00edt\u00e1 sumu v\u00fdp\u016fj\u010dek b\u011bhem denn\u00ed doby.<\/em><\/p>
        night_df = hour_df[~hour_df[‘hours’].isin(range(6,19))] #\u00a0Vybere no\u010dn\u00ed dobu.<\/em>
        night_df = night_df.groupby(‘started_at_day’).count()[[‘hours’]]#\u00a0Vypo\u010d\u00edt\u00e1 v\u00fdp\u016fj\u010dky b\u011bhem noci.<\/em><\/p>
        fig,ax = plt.subplots(figsize=(19,4)) #\u00a0Vytvo\u0159\u00ed pr\u00e1zdn\u00fd graf.<\/em>
        ax.plot(day_df) #\u00a0P\u0159id\u00e1 po\u010dty denn\u00edch v\u00fdp\u016fj\u010dek.<\/em>
        ax.plot(night_df) #\u00a0P\u0159id\u00e1 po\u010dty no\u010dn\u00edch v\u00fdp\u016fj\u010dek.<\/em>
        ax.legend(labels = [‘day’,’night’])\u00a0
        ax.grid()
        plt.show()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        P\u016eJ\u010cUJ\u00cd SI LID\u00c9 KOLA V\u00cdCE O V\u00cdKENDU NEBO P\u0158ES T\u00ddDEN?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        weekday_df = (edbikes_df.assign(started_at_day = pd.to_datetime(edbikes_df[‘started_at’])
        .dt.date, day_of_week = pd.to_datetime(edbikes_df[‘started_at’]).dt.dayofweek)#\u00a0P\u0159id\u00e1 \u010d\u00edslo dne v t\u00fddnu.<\/em>
        )
        week_df = weekday_df[weekday_df[‘day_of_week’].isin(range(0,5))] #\u00a0Vybere jen pracovn\u00ed dny.<\/em>
        week_df = week_df.groupby(‘started_at_day’).count()[[‘started_at’]] #\u00a0Vypo\u010d\u00edt\u00e1 v\u00fdp\u016fj\u010dky b\u011bhem pracovn\u00edch dn\u016f.<\/em>
        weekend_df = weekday_df[~weekday_df[‘day_of_week’].isin(range(0,5))]#\u00a0Vybere jen v\u00edkendy.<\/em>
        weekend_df = weekend_df.groupby(‘started_at_day’).count()[[‘started_at’]] #\u00a0Vypo\u010d\u00edt\u00e1 v\u00fdp\u016fj\u010dky b\u011bhem v\u00edkendu.<\/em>
        # Vytiskne v\u00fdsledky<\/em>:
        print(‘Rental daily average working days: ‘)
        print(‘\\n’,week_df.describe().iloc[1,0])
        print(‘\\n’,’Rental daily average weekends: ‘)
        print(‘\\n’,weekend_df.describe().iloc[1,0])
        print(‘\\n’,’Daily borrowings during weekends are slightly higher than during working days.’)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        Rental daily average working days: \n\n 387<\/strong>\n\n Rental daily average weekends: \n\n 443<\/strong>\n\n Daily borrowings during weekends are slightly higher than during working days.<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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        NA\u010cTEN\u00cd DAT O PO\u010cAS\u00cd V EDINBURGHU<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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        IN:<\/p>
        edweather_df = pd.read_sql(‘edinburgh_weather’,conn_string)
        edweather_df[‘date’] = pd.to_datetime(edweather_df[‘date’])#\u00a0Zm\u011bn\u00ed form\u00e1t data.<\/em>
        edweather_df = edweather_df.set_index(‘date’)#\u00a0Datum do indexu.<\/em>
        edweather_df<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
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        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        V tabulce dat o po\u010das\u00ed nechyb\u00ed \u017e\u00e1dn\u00e9 hodnoty.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        edweather_df.isna().sum()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        P\u0159ed dal\u0161\u00edm zpracov\u00e1n\u00edm dat o po\u010das\u00ed zm\u011bn\u00edme v\u0161echny \u00fadaje na numerick\u00e9 hodnoty.\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        IN:<\/p>
        #\u00a0Funkce na p\u0159i\u0159azen\u00ed stupn\u011b viditelnosti:<\/em>
        def vis (x):
        if x == ‘Excellent’:
        return 4
        elif x == ‘Good’:
        return 3
        elif x == ‘Average’:
        return 2
        else:
        return 1<\/p>
        #\u00a0Zm\u011bna na numerick\u00e9 hodnoty:<\/em>
        edweather_df = (edweather_df.assign(temp_celsius = edweather_df.temp.str.extract(“([-+]?\\d+)”).astype(int),
        feels_celsius = edweather_df.feels.str.extract(“([-+]?\\d+)”).astype(int),
        wind_kmh = edweather_df.wind.str.extract(“([-+]?\\d+)”).astype(int),
        gust_kmh = edweather_df.gust.str.extract(“([-+]?\\d+)”).astype(int),
        rain_mm = edweather_df.rain.str.extract(“([-+]?\\d*\\.\\d+|[-+]?\\d+)”).astype(float),
        humidity_percent = edweather_df.humidity.str.extract(“([-+]?\\d+)”).astype(int),
        cloud_percent = edweather_df.cloud.str.extract(“([-+]?\\d+)”).astype(int),
        pressure_mb = edweather_df.pressure.str.extract(“([-+]?\\d+)”).astype(int),
        vis_point = edweather_df.vis.apply(vis))
        )[[‘time’,’temp_celsius’, ‘feels_celsius’, ‘wind_kmh’, ‘gust_kmh’, ‘rain_mm’, ‘humidity_percent’, ‘cloud_percent’, ‘pressure_mb’, ‘vis_point’]]<\/p>
        edweather_df<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t
        \n\t\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
        Deskriptivn\u00ed statistika jednotliv\u00fdch m\u011b\u0159en\u00ed po\u010das\u00ed. V\u00fdsledky:<\/p>
        • rozmez\u00ed teplot od -4 do +27\u00b0C,<\/li>
        • maxim\u00e1ln\u00ed rychlost v\u011btru 54 km\/h,<\/li>
        • maxim\u00e1ln\u00ed sr\u00e1\u017eky 23,4 mm.
          Data potvrzuj\u00ed, \u017ee za obdob\u00ed m\u011b\u0159en\u00ed bylo v Edinburghu m\u00edrn\u00e9 po\u010das\u00ed bez extr\u00e9mn\u00edch sr\u00e1\u017eek a v\u011btru.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          edweather_df.describe()<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          \u00daprava na denn\u00ed pr\u016fm\u011brn\u00e9 hodnoty m\u011b\u0159en\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          edweather_df = edweather_df.groupby(‘date’).mean()<\/p>
          edweather_df<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          Abychom otestovali korelace mezi po\u010dtem v\u00fdp\u016fj\u010dek a po\u010das\u00edm, p\u0159id\u00e1me po\u010dty v\u00fdp\u016fj\u010dek ke ka\u017ed\u00e9mu dni.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          edweather_df = df4.join(edweather_df,how=’inner’)
          edweather_df<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          Korela\u010dn\u00ed koeficienty. Ovliv\u0148uje po\u010das\u00ed po\u010det v\u00fdp\u016fj\u010dek?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          print(“Correlation between temperature and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘feels_celsius’]).round(2))<\/p>
          \u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>
          Correlation between temperature and rentals: \n\n 0.43<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          Efekt teploty b\u011bhem hlavn\u00ed sezony je men\u0161\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          start_date = ‘2020-06-01’
          end_date = ‘2020-08-30’
          selection = (edweather_df.index>=start_date)&(edweather_df.index<=end_date)
          season_df = edweather_df.loc[selection]
          print(“Correlation between temperature and rentals from June to end August: “)
          print(‘\\n’,season_df[‘borrowings_count’].corr(season_df[‘feels_celsius’]).round(2))<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>
          Correlation between temperature and rentals from June to end August: \n\n 0.36<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          Ostatn\u00ed vlivy po\u010das\u00ed.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t\t
          \n\t\t\t
          \n\t\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          IN:<\/p>
          print(“Correlation between wind speed and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘wind_kmh’]).round(2))<\/p>
          print(“Correlation between gusty wind and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘gust_kmh’]).round(2))<\/p>
          print(“Correlation between rain fall and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘rain_mm’]).round(2))<\/p>
          print(“Correlation between humidity and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘humidity_percent’]).round(2))<\/p>
          print(“Correlation between clouds nad rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘cloud_percent’]).round(2))<\/p>
          print(“Correlation between atmospheric pressure and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘pressure_mb’]).round(2))<\/p>
          print(“Correlation between visibility factor and rentals: “)
          print(‘\\n’,edweather_df[‘borrowings_count’].corr(edweather_df[‘vis_point’]).round(2))<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t
          OUT:<\/p>
          Correlation between wind speed and rentals: \n\n -0.2

          <\/pre>
          Correlation between gusty wind and rentals: \n\n -0.24

          <\/pre>
          Correlation between rain fall and rentals: \n\n -0.06

          <\/pre>
          Correlation between humidity and rentals: \n\n -0.21

          <\/pre>
          Correlation between clouds nad rentals: \n\n -0.06

          <\/pre>
          Correlation between atmospheric pressure and rentals: \n\n 0.11

          <\/pre>
          Correlation between visibility factor and rentals: \n\n 0.06<\/pre>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"
          ANAL\u00ddZA STANIC SD\u00cdLEN\u00ddCH KOL V EDINBURGHU Pou\u017eit\u00e9 technologie: Pandas, Python, SQL, Jupyter. Identifikujte aktivn\u00ed a neaktivn\u00ed stanice, identifikujte nejfrekventovan\u011bj\u0161\u00ed stanice, identifikujte stanice, na kter\u00fdch se kola hromad\u00ed a stanice, kde potenci\u00e1ln\u011b chyb\u00ed, spo\u010d\u00edtejte vzd\u00e1lenosti mezi jednotliv\u00fdmi stanicemi, jak dlouho trv\u00e1 jedna v\u00fdp\u016fj\u010dka? Najd\u011bte odlehl\u00e9 hodnoty, zobrazte histogram. Prove\u0165e anal\u00fdzu popt\u00e1vky: zobrazte v\u00fdvoj popt\u00e1vky po p\u016fj\u010dov\u00e1n\u00ed […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-158","page","type-page","status-publish","hentry"],"yoast_head":"\nPython - DATASYNTHIQ - Ji\u0159\u00ed Val\u00e1\u0161ek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jirivalasek.com\/?page_id=158\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python - DATASYNTHIQ - Ji\u0159\u00ed Val\u00e1\u0161ek\" \/>\n<meta property=\"og:description\" content=\"ANAL\u00ddZA STANIC SD\u00cdLEN\u00ddCH KOL V EDINBURGHU Pou\u017eit\u00e9 technologie: Pandas, Python, SQL, Jupyter. Identifikujte aktivn\u00ed a neaktivn\u00ed stanice, identifikujte nejfrekventovan\u011bj\u0161\u00ed stanice, identifikujte stanice, na kter\u00fdch se kola hromad\u00ed a stanice, kde potenci\u00e1ln\u011b chyb\u00ed, spo\u010d\u00edtejte vzd\u00e1lenosti mezi jednotliv\u00fdmi stanicemi, jak dlouho trv\u00e1 jedna v\u00fdp\u016fj\u010dka? Najd\u011bte odlehl\u00e9 hodnoty, zobrazte histogram. 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