根据 pandas 中另一个数据帧的掩码值在数据帧中生成 NaN
Make NaN in a dataframe based on mask value of another dataframe in pandas
我有一个数据框
df1 = pd.DataFrame([["A",1,98,56,51], ["B",1,99,74,36], ["C",1,97,82,83],["B",1,96,31,90], ["C",1,45,92,12], ["A",1,67,33,55]], columns=["id","date","c1","c2","c3"])
我有另一个具有相同列名的数据框
df2 = pd.DataFrame([["A",1,False,False,True], ["B",1,False,False,True], ["C",1,False,False,False],["B",1,False,True,False], ["C",1,True,False,True], ["A",1,False,True,False]], columns=["id","date","c1","c2","c3"])
我想要一个dataframe df_out，在df2中值为True，在df1中替换为blank/nan，如果为False则保持df1中的值。
预期输出：
df_out = pd.DataFrame([["A",1,98,56, ""], ["B",1,99,74,""], ["C",1,97,82,83],["B",1,96,"",90], ["C",1,"",92,""], ["A",1,67,"",55]], columns=["id","date","c1","c2","c3"])
怎么做？
试试 where
l = ['c1','c2','c3']
df1[l] = df1[l].where(df2[l]==False,'')
df1
Out[199]:
id date c1 c2 c3
0 A 1 98 56
1 B 1 99 74
2 C 1 97 82 83
3 B 1 96 90
4 C 1 92
5 A 1 67 55
我有一个数据框
df1 = pd.DataFrame([["A",1,98,56,51], ["B",1,99,74,36], ["C",1,97,82,83],["B",1,96,31,90], ["C",1,45,92,12], ["A",1,67,33,55]], columns=["id","date","c1","c2","c3"])
我有另一个具有相同列名的数据框
df2 = pd.DataFrame([["A",1,False,False,True], ["B",1,False,False,True], ["C",1,False,False,False],["B",1,False,True,False], ["C",1,True,False,True], ["A",1,False,True,False]], columns=["id","date","c1","c2","c3"])
我想要一个dataframe df_out，在df2中值为True，在df1中替换为blank/nan，如果为False则保持df1中的值。
预期输出：
df_out = pd.DataFrame([["A",1,98,56, ""], ["B",1,99,74,""], ["C",1,97,82,83],["B",1,96,"",90], ["C",1,"",92,""], ["A",1,67,"",55]], columns=["id","date","c1","c2","c3"])
怎么做？
试试 where
l = ['c1','c2','c3']
df1[l] = df1[l].where(df2[l]==False,'')
df1
Out[199]:
id date c1 c2 c3
0 A 1 98 56
1 B 1 99 74
2 C 1 97 82 83
3 B 1 96 90
4 C 1 92
5 A 1 67 55