Question

我想转换下面的数据框

将python pandas用于下面的下一个数据框

请原谅我，但我对熊猫非常新。该表已导入到名为filterframe的数据框中。

Answer 1

使用melt：

df1 = pd.melt(df, id_vars=['date','County'], var_name='situtation', value_name='score')
print (df1)

          date      County     situtation  score
0   22/01/2000  Buckingham    Outstanding   20.0
1   22/01/2000      London    Outstanding   46.0
2   24/06/2000  Manchester    Outstanding   13.0
3   02/07/2000    Ipswitch    Outstanding   66.0
4    04/0/2000     Crawley    Outstanding   34.0
5   22/01/2000  Buckingham           Okay   34.0
6   22/01/2000      London           Okay   35.0
7   24/06/2000  Manchester           Okay    6.0
8   02/07/2000    Ipswitch           Okay    5.0
9    04/0/2000     Crawley           Okay   88.0
10  22/01/2000  Buckingham           Good    9.0
11  22/01/2000      London           Good    3.0
12  24/06/2000  Manchester           Good    2.0
13  02/07/2000    Ipswitch           Good   66.0
14   04/0/2000     Crawley           Good   56.0
15  22/01/2000  Buckingham       Inferior    6.0
16  22/01/2000      London       Inferior    NaN
17  24/06/2000  Manchester       Inferior    NaN
18  02/07/2000    Ipswitch       Inferior    NaN
19   04/0/2000     Crawley       Inferior    NaN
20  22/01/2000  Buckingham  Very Inferior   65.0
21  22/01/2000      London  Very Inferior    7.0
22  24/06/2000  Manchester  Very Inferior    7.0
23  02/07/2000    Ipswitch  Very Inferior   56.0
24   04/0/2000     Crawley  Very Inferior   57.0

stack的另一种解决方案 - 默认为dropna=True：

df1 = df.set_index(['date','County']).stack().reset_index()
df1.columns = ['date','County','situtation','score']
print (df1)
          date      County     situtation  score
0   22/01/2000  Buckingham    Outstanding   20.0
1   22/01/2000  Buckingham           Okay   34.0
2   22/01/2000  Buckingham           Good    9.0
3   22/01/2000  Buckingham       Inferior    6.0
4   22/01/2000  Buckingham  Very Inferior   65.0
5   22/01/2000      London    Outstanding   46.0
6   22/01/2000      London           Okay   35.0
7   22/01/2000      London           Good    3.0
8   22/01/2000      London  Very Inferior    7.0
9   24/06/2000  Manchester    Outstanding   13.0
10  24/06/2000  Manchester           Okay    6.0
11  24/06/2000  Manchester           Good    2.0
12  24/06/2000  Manchester  Very Inferior    7.0
13  02/07/2000    Ipswitch    Outstanding   66.0
14  02/07/2000    Ipswitch           Okay    5.0
15  02/07/2000    Ipswitch           Good   66.0
16  02/07/2000    Ipswitch  Very Inferior   56.0
17   04/0/2000     Crawley    Outstanding   34.0
18   04/0/2000     Crawley           Okay   88.0
19   04/0/2000     Crawley           Good   56.0
20   04/0/2000     Crawley  Very Inferior   57.0

df1 = df.set_index(['date','County']).stack(dropna=False).reset_index()
df1.columns = ['date','County','situtation','score']
print (df1)
          date      County     situtation  score
0   22/01/2000  Buckingham    Outstanding   20.0
1   22/01/2000  Buckingham           Okay   34.0
2   22/01/2000  Buckingham           Good    9.0
3   22/01/2000  Buckingham       Inferior    6.0
4   22/01/2000  Buckingham  Very Inferior   65.0
5   22/01/2000      London    Outstanding   46.0
6   22/01/2000      London           Okay   35.0
7   22/01/2000      London           Good    3.0
8   22/01/2000      London       Inferior    NaN
9   22/01/2000      London  Very Inferior    7.0
10  24/06/2000  Manchester    Outstanding   13.0
11  24/06/2000  Manchester           Okay    6.0
12  24/06/2000  Manchester           Good    2.0
13  24/06/2000  Manchester       Inferior    NaN
14  24/06/2000  Manchester  Very Inferior    7.0
15  02/07/2000    Ipswitch    Outstanding   66.0
16  02/07/2000    Ipswitch           Okay    5.0
17  02/07/2000    Ipswitch           Good   66.0
18  02/07/2000    Ipswitch       Inferior    NaN
19  02/07/2000    Ipswitch  Very Inferior   56.0
20   04/0/2000     Crawley    Outstanding   34.0
21   04/0/2000     Crawley           Okay   88.0
22   04/0/2000     Crawley           Good   56.0
23   04/0/2000     Crawley       Inferior    NaN
24   04/0/2000     Crawley  Very Inferior   57.0

如何使用Pandas调整数据框

1 个答案: