找到包含0值列的行,并将其设置为“无熊猫”

如果使用数值 None 转换为 NaN 和整数到 float 按设计:

df_o.loc[(df_o['a'] == 0) & (df_o['b'] == 0), ['a','b']] = None

print (df_o)
     a    b
0  0.0  1.0
1  1.0  3.0
2  1.0  3.0
3  6.0  3.0
4  NaN  NaN
5  1.0  5.0
6  NaN  NaN

另一个解决方案 DataFrame.all True 每行s axis=1 :

df_o.loc[(df_o[['a', 'b']] == 0).all(axis=1), ['a','b']] = None
print (df_o)
     a    b
0  0.0  1.0
1  1.0  3.0
2  1.0  3.0
3  6.0  3.0
4  NaN  NaN
5  1.0  5.0
6  NaN  NaN

:

print ((df_o[['a', 'b']] == 0))
       a      b
0   True  False
1  False  False
2  False  False
3  False  False
4   True   True
5  False  False
6   True   True

print ((df_o[['a', 'b']] == 0).all(axis=1))
0    False
1    False
2    False
3    False
4     True
5    False
6     True
dtype: bool

我能想到的一个办法就是这样。创建数据帧的额外副本,并在主数据帧上将该值设置为“无”时分别检查两者。不是最干净的解决方案,但是:

import pandas as pd
import numpy as np

df = pd.DataFrame()

df['f'] = [5,5,5,5,5,5,5]
df['a'] = [0,1,1,6,0,1,0]
df['b'] = [1,3,3,3,0,5,0]

df1 = df.copy()
df['a'] = np.where((df.a == 0) & (df.b == 0), None, df.a)
df['b'] = np.where((df1.a == 0) & (df1.b == 0), None, df.b)
print(df)

   f     a     b
0  5     0     1
1  5     1     3
2  5     1     3
3  5     6     3
4  5  None  None
5  5     1     5
6  5  None  None

df.replace(0, np.nan) --获取NaNs(可能更有用) df.replace(0, 'None') --你真正想要的

这当然不是最优雅的方法,但也许这有帮助。

import pandas as pd
data = {'a': [0,1,1,6,0,1,0],
        'b':[1,3,3,3,0,5,0]}

df_o = pd.DataFrame.from_dict(data)
df_None = df_o[(df_o['a'] == 0) & (df_o['b'] == 0)]
df_o.loc[df_None.index,:] = None
print(df_o)

输出:

    a    b
0  0.0  1.0
1  1.0  3.0
2  1.0  3.0
3  6.0  3.0
4  NaN  NaN
5  1.0  5.0
6  NaN  NaN

我就是这样做的:

import pandas as pd


a = pd.Series([0, 1, 1, 6, 0, 1, 0])
b = pd.Series([1, 3, 3, 3, 0, 5 ,0])

data = pd.DataFrame({'a': a, 'b': b})


v = [[data[i][j] for i in data] == [0, 0] for j in range(len(data['a']))]   # spot null rows

a = [None if v[i] else a[i] for i in range(len(a))]
b = [None if v[i] else b[i] for i in range(len(b))]

data = pd.DataFrame({'a': a, 'b': b})


print(data)

     a    b
0  0.0  1.0
1  1.0  3.0
2  1.0  3.0
3  6.0  3.0
4  NaN  NaN
5  1.0  5.0
6  NaN  NaN