在熊猫中生成堆叠条形图以及检查按钮

Question

我有一个近20k行的数据集。该数据集的属性如下： 1）出生年份; 2）性别;和
3）区域名称; 现在我想创建一个堆叠条形图象下面这样：

这是数据帧的一瞥：

我怎么能做到这一点使用熊猫吗？还是考虑数据集大小还有其他更好的方法吗？

数据框：

"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

有你需要获取数据以正确的格式，然后才能绘制它的几个步骤。首先，数据需要根据AreaCode和Sex（如果需要，可能按年份）汇总。我们可以在数据帧上调用.groupby，然后调用.sum来聚合数据。

但是，它仍然不会以我们想要的形式存在。要制作堆叠条形图，每个堆栈都是一个列。所以我们需要一个male和female列。换句话说，我们需要将数据转换为拆分sex列。

示例代码：

# 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)