Python Pandas 将多列零替换为 Nan
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Python Pandas replace multiple columns zero to Nan
提问by Wouter Dunnes
List with attributes of persons loaded into pandas dataframe df2. For cleanup I want to replace value zero (0or '0') by np.nan.
列出加载到 pandas 数据框的人员的属性df2。对于清理，我想将值零（0或'0'）替换为np.nan.
df2.dtypes
ID object
Name object
Weight float64
Height float64
BootSize object
SuitSize object
Type object
dtype: object
Working code to set value zero to np.nan:
将零值设置为的工作代码np.nan：
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
Believe this can be done in a similar/shorter way:
相信这可以通过类似/更短的方式完成：
df2[["Weight","Height","BootSize","SuitSize"]].astype(str).replace('0',np.nan)
However the above does not work. The zero's remain in df2. How to tackle this?
但是，以上不起作用。零留在 df2 中。如何解决这个问题？
回答by jezrael
回答by christk
You could use the 'replace' method and pass the values that you want to replace in a list as the first parameter along with the desired one as the second parameter:
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)
回答by Ayyasamy
data['amount']=data['amount'].replace(0, np.nan)
data['duration']=data['duration'].replace(0, np.nan)
