Wie Pandas verwenden Datenrahmen anzupassen

Question

ich den Datenrahmen unter

mit Python Pandas auf den nächsten Datenrahmen unter

Pardon konvertieren wollen, aber ich bin sehr sehr neu für Pandas. Die Tabelle wurde bereits in einen Datenrahmen namens Filterframe importiert.

Answer 1

Verwendung 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 

Eine andere Lösung mit stack - standardmäßig 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