如何在Python中使用pd.melt

如何在Python中使用pd.melt,python,pandas,dataframe,Python,Pandas,Dataframe,此数据帧来自csv: id name A B C gpa 0 1111 Phineas NaN B NaN 3.0 1 1113 Tilly NaN NaN C 2.5 2 1110 Andres A NaN NaN 3.8 3 1112 Jax NaN B NaN 3.2 4 1114 Ray NaN B NaN 3.1 5 1115 Koda NaN NaN C 2.4 6

此数据帧来自csv:

id  name    A   B   C   gpa
0   1111    Phineas NaN B   NaN 3.0
1   1113    Tilly   NaN NaN C   2.5
2   1110    Andres  A   NaN NaN 3.8
3   1112    Jax NaN B   NaN 3.2
4   1114    Ray NaN B   NaN 3.1
5   1115    Koda    NaN NaN C   2.4
6   1120    Bruno   A   NaN NaN 3.7
7   1134    Davis   NaN NaN C   2.6
8   1102    Cassie  A   NaN NaN 4.0
我想要输出:

id  name    grade   gpa
0   1111    Phineas B   3.0
1   1113    Tilly   C   2.5
2   1110    Andres  A   3.8
3   1112    Jax     C   3.2
4   1114    Ray     B   3.1
5   1115    Koda    C   2.4
6   1120    Bruno   A   3.7
7   1134    Davis   C   2.6
8   1102    Cassie  A   4.0

代码是什么?

如果你不想使用melt,这个解决方案可能对你有用:因为每个学生都有A、B或C专用,你可以先将这些列中的所有
NaN
值转换为空字符串,然后使用
+
操作符将A、B和C列连接在一起

导入语句和启动数据帧:

import pandas as pd
import numpy as np

df = pd.DataFrame({'id':[1111,1113],
'name':['Phineas','Tilly'],
'A':[np.NaN,np.NaN],
'B':['B',np.NaN],
'C':[np.NaN,'C'],
'gpa':[3.0,2.5]
})
#     id      name    A   B   C   gpa
# 0   1111    Phineas NaN B   NaN 3.0
# 1   1113    Tilly   NaN NaN C   2.5
按列串接和输出:

df.fillna('',inplace=True) #replaces all NaN's with ""-empty strings
df['letter_grades'] = df['A'] + df['B'] + df['C'] #concatenate
df = df[['id','name','letter_grades','gpa']] #reassign dataframe identifier
print(df)

#     id     name letter_grades  gpa
#0  1111  Phineas             B  3.0
#1  1113    Tilly             C  2.5
与一起使用,在这种情况下,您不需要
melt

df['grade'] = df['A'].combine_first(df['B']).combine_first(df['C'])
df.drop(['A','B','C'], axis=1, inplace=True)
或:



@射线使用'df[[A','B','C']]=df[[A','B','C']]]。替换为('NaN',np.NaN)'。其中np为“作为np输入numpy”。
df['grade'] = df[['A','B','C']].values[df[['A','B','C']].notnull()]
df.drop(['A','B','C'], axis=1, inplace=True)
print(df)
     id     name  gpa grade
0  1111  Phineas  3.0     B
1  1113    Tilly  2.5     C
2  1110   Andres  3.8     A
3  1112      Jax  3.2     B
4  1114      Ray  3.1     B
5  1115     Koda  2.4     C
6  1120    Bruno  3.7     A
7  1134    Davis  2.6     C
8  1102   Cassie  4.0     A