パンダのパンダにStacked Barchartをチェックボタンと一緒に作成

Question

私は約20k行のデータセットを持っています。そのデータセットの属性は以下の通りです： 1）誕生年; 2）性別;および
3）エリア名。

そして、これは、データフレームを垣間見るです： は今、私は以下のような積み重ね棒グラフを作成したい

どのように私はこの使用してパンダを達成することができますか？データセットのサイズを考えれば他にも良い方法がありますか？

DATAFRAME：

"Year","SexCd","Sex","StatZoneSort","StatZoneLang","AreaCode","AreaName","Number Of Births" 
2015,2,"W",1101,"Zähringerstrasse",11,"Rathaus",2 
2015,1,"M",1101,"Zähringerstrasse",11,"Rathaus",2 
2015,2,"W",1102,"Zentralbibliothek",11,"Rathaus",1 
2015,1,"M",1102,"Zentralbibliothek",11,"Rathaus",3 
2015,1,"M",1103,"Grossmünster",11,"Rathaus",6 
2015,2,"W",1103,"Grossmünster",11,"Rathaus",4 
2015,1,"M",1104,"Oberdorf",11,"Rathaus",2 
2015,2,"W",1104,"Oberdorf",11,"Rathaus",1 
2015,1,"M",1201,"Central",12,"Hochschulen",3 
2015,2,"W",1201,"Central",12,"Hochschulen",1 
2015,1,"M",1301,"Schipfe",13,"Lindenhof",4 
2015,2,"W",1301,"Schipfe",13,"Lindenhof",1 
2015,1,"M",1403,"Selnaustrasse",14,"City",4 
2015,2,"W",1403,"Selnaustrasse",14,"City",1 
2015,1,"M",2101,"Bahnhof Wollishofen",21,"Wollishofen",3 
2015,2,"W",2101,"Bahnhof Wollishofen",21,"Wollishofen",6 
2015,1,"M",2102,"Bellariastrasse",21,"Wollishofen",31 
2015,2,"W",2102,"Bellariastrasse",21,"Wollishofen",19 
2015,1,"M",2103,"Jugendherberge",21,"Wollishofen",7 
2015,2,"W",2103,"Jugendherberge",21,"Wollishofen",6 
2015,1,"M",2104,"Morgental",21,"Wollishofen",13 
2015,2,"W",2104,"Morgental",21,"Wollishofen",12 
2015,1,"M",2106,"Waschanstalt",21,"Wollishofen",3 
2015,2,"W",2107,"Auf der Egg",21,"Wollishofen",10 
2015,1,"M",2107,"Auf der Egg",21,"Wollishofen",8 
2015,1,"M",2108,"Neubühl",21,"Wollishofen",14 
2015,2,"W",2108,"Neubühl",21,"Wollishofen",22 
2015,2,"W",2109,"Entlisberg",21,"Wollishofen",12 
2015,1,"M",2109,"Entlisberg",21,"Wollishofen",17 
2015,1,"M",2110,"Verenastrasse",21,"Wollishofen",9 
2015,2,"W",2110,"Verenastrasse",21,"Wollishofen",8 
2015,1,"M",2111,"Seeblickstrasse",21,"Wollishofen",4 
2015,2,"W",2111,"Seeblickstrasse",21,"Wollishofen",4 
2015,1,"M",2301,"Höckler",23,"Leimbach",15 
2015,2,"W",2301,"Höckler",23,"Leimbach",10 
2015,2,"W",2302,"Mahrbachweg",23,"Leimbach",16 
2015,1,"M",2302,"Mahrbachweg",23,"Leimbach",10 
2015,1,"M",2303,"Sihlweidstrasse",23,"Leimbach",18 
2015,2,"W",2303,"Sihlweidstrasse",23,"Leimbach",21 
2015,1,"M",2401,"Parkring",24,"Enge",10 
2015,2,"W",2401,"Parkring",24,"Enge",7 
2015,1,"M",2402,"Kongresshaus",24,"Enge",8 
2015,2,"W",2402,"Kongresshaus",24,"Enge",4 
2015,2,"W",2403,"Belvoir-Park",24,"Enge",10 
2015,1,"M",2403,"Belvoir-Park",24,"Enge",15 
2015,2,"W",2404,"Museum Rietberg",24,"Enge",29 
2015,1,"M",2404,"Museum Rietberg",24,"Enge",22 
2015,2,"W",2405,"Rieterplatz",24,"Enge",20 
2015,1,"M",2405,"Rieterplatz",24,"Enge",26 
2015,2,"W",2406,"Gartenstrasse",24,"Enge",2 
2015,2,"W",3101,"Höfliweg",31,"Alt-Wiedikon",31 
2015,1,"M",3101,"Höfliweg",31,"Alt-Wiedikon",33 
2015,1,"M",3102,"Goldbrunnenplatz",31,"Alt-Wiedikon",20 
2015,2,"W",3102,"Goldbrunnenplatz",31,"Alt-Wiedikon",17 
2015,1,"M",3103,"Gotthelfstrasse",31,"Alt-Wiedikon",14 
2015,2,"W",3103,"Gotthelfstrasse",31,"Alt-Wiedikon",12 
2015,1,"M",3104,"Manesseplatz",31,"Alt-Wiedikon",22 
2015,2,"W",3104,"Manesseplatz",31,"Alt-Wiedikon",27 
2015,2,"W",3105,"Binz",31,"Alt-Wiedikon",17 
2015,1,"M",3105,"Binz",31,"Alt-Wiedikon",21 
2015,2,"W",3106,"Saalsporthalle",31,"Alt-Wiedikon",25 
2015,1,"M",3106,"Saalsporthalle",31,"Alt-Wiedikon",31 
2015,1,"M",3301,"Heuried",33,"Friesenberg",6 
2015,2,"W",3301,"Heuried",33,"Friesenberg",13 
2015,1,"M",3302,"Gehrenholz",33,"Friesenberg",8 
2015,2,"W",3302,"Gehrenholz",33,"Friesenberg",5 
2015,1,"M",3303,"Uetliberg",33,"Friesenberg",10 
2015,2,"W",3303,"Uetliberg",33,"Friesenberg",6 
2015,2,"W",3304,"Strassenverkehrsamt",33,"Friesenberg",8 
2015,1,"M",3304,"Strassenverkehrsamt",33,"Friesenberg",9 
2015,2,"W",3305,"Albisgüetli",33,"Friesenberg",12 
2015,1,"M",3305,"Albisgüetli",33,"Friesenberg",8 
2015,1,"M",3306,"Triemli",33,"Friesenberg",5 
2015,2,"W",3306,"Triemli",33,"Friesenberg",9 
2015,2,"W",3401,"Schaufelbergerstrasse",34,"Sihlfeld",22 
2015,1,"M",3401,"Schaufelbergerstrasse",34,"Sihlfeld",23 
2015,1,"M",3402,"Friedhof Sihlfeld",34,"Sihlfeld",8 
2015,2,"W",3402,"Friedhof Sihlfeld",34,"Sihlfeld",8 
2015,1,"M",3403,"Brahmsstrasse",34,"Sihlfeld",18 
2015,2,"W",3403,"Brahmsstrasse",34,"Sihlfeld",12 
2015,2,"W",3404,"Fritschistrasse",34,"Sihlfeld",19 
2015,1,"M",3404,"Fritschistrasse",34,"Sihlfeld",8 
2015,1,"M",3405,"Idaplatz",34,"Sihlfeld",23 
2015,2,"W",3405,"Idaplatz",34,"Sihlfeld",25 
2015,1,"M",3406,"Zwinglihaus",34,"Sihlfeld",23 
2015,2,"W",3406,"Zwinglihaus",34,"Sihlfeld",24 
2015,2,"W",3407,"Bahnhof Wiedikon",34,"Sihlfeld",23 
2015,1,"M",3407,"Bahnhof Wiedikon",34,"Sihlfeld",24 
2015,1,"M",3408,"Sihlhölzli",34,"Sihlfeld",21 
2015,2,"W",3408,"Sihlhölzli",34,"Sihlfeld",21 
2015,1,"M",4101,"Kalkbreite",41,"Werd",11 
2015,2,"W",4101,"Kalkbreite",41,"Werd",19 

Answer 1

あなたがそれをプロットすることができます前に、適切な形式でデータを取得するために必要ないくつかのステップがあります。まず、AreaCodeとSexに従ってデータを集計する必要があります（場合によっては年によって）。 .groupbyに続いてデータフレームに.sumと呼んで、データを集約することができます。

しかし、それでも私たちが望む形にはなりません。積み上げ棒グラフを作成するには、各スタックは列です。したがって、maleとfemaleの列が必要です。つまり、データをピボットしてsex列を分割する必要があります。

例コード：

# read in the data 
df = pd.read_csv('text.txt') 

# aggregate by the columns of interest 
agg_df = df.groupby(['AreaCode','Sex']).sum() 

# move "AreaCode" and "Sex" out of the index and back to columns 
agg_df.reset_index(inplace=True) 

# pivot the data, setting the AreaCode as the row indices, splitting 
# 'Sex' into 'M' and 'W' columns, and using num.births as the values 
piv_df = agg_df.pivot(index='AreaCode', columns='Sex', values='Number Of Births') 

# plot as a stacked bar 
piv_df.plot.bar(stacked=True)