Python Pandas将多个列替换为零到Nan

Question

列出了加载到pandas数据帧'df2'中的人员的属性.对于清理,我想用np.nan替换零值(0或'0').

df2.dtypes

ID                   object
Name                 object
Weight              float64
Height              float64
BootSize             object
SuitSize             object
Type                 object
dtype: object

将值0设置为np.nan的工作代码:

df2.loc[df2['Weight'] == 0,'Weight'] = np.nan
df2.loc[df2['Height'] == 0,'Height'] = np.nan
df2.loc[df2['BootSize'] == '0','BootSize'] = np.nan
df2.loc[df2['SuitSize'] == '0','SuitSize'] = np.nan

相信这可以用类似/更短的方式完成:

df2[["Weight","Height","BootSize","SuitSize"]].astype(str).replace('0',np.nan)

但是上述方法不起作用.零保持在df2.如何解决这个问题？

Answer 1

我想你需要replace通过dict:

cols = ["Weight","Height","BootSize","SuitSize","Type"]
df2[cols] = df2[cols].replace({'0':np.nan, 0:np.nan})

Answer 2

您可以使用 'replace' 方法并将要在列表中替换的值作为第一个参数传递，并将所需的值作为第二个参数传递：

cols = ["Weight","Height","BootSize","SuitSize","Type"]
df2[cols] = df2[cols].replace(['0', 0], np.nan)

Answer 3

尝试：

df2.replace(to_replace={
             'Weight':{0:np.nan}, 
             'Height':{0:np.nan},
             'BootSize':{'0':np.nan},
             'SuitSize':{'0':np.nan},
                 })