多列的 Pandas Fillna 与每列的模式
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Pandas Fillna of Multiple Columns with Mode of Each Column
提问by Nick
Working with census data, I want to replace NaNs in two columns ("workclass" and "native-country") with the respective modes of those two columns. I can get the modes easily:
使用人口普查数据，我想用这两列的各自模式替换两列（“workclass”和“native-country”）中的 NaN。我可以轻松获得模式：
mode = df.filter(["workclass", "native-country"]).mode()
which returns a dataframe:
它返回一个数据帧：
workclass native-country
0 Private United-States
However,
然而，
df.filter(["workclass", "native-country"]).fillna(mode)
does notreplace the NaNs in each column with anything, let alone the mode corresponding to that column. Is there a smooth way to do this?
并不能取代与任何每一列的NaN的，更何况是对应于列模式。有没有一种顺利的方法来做到这一点？
回答by jezrael
If you want to impute missing values with the modein some columns a dataframe df, you can just fillnaby Seriescreated by select by position by iloc:
如果要归咎于与遗漏值mode在一些列的数据框df，你可以fillna通过Series按选择创建的位置由iloc：
cols = ["workclass", "native-country"]
df[cols]=df[cols].fillna(df.mode().iloc[0])
Or:
或者：
df[cols]=df[cols].fillna(mode.iloc[0])
Your solution:
您的解决方案：
df[cols]=df.filter(cols).fillna(mode.iloc[0])
Sample:
样本：
df = pd.DataFrame({'workclass':['Private','Private',np.nan, 'another', np.nan],
'native-country':['United-States',np.nan,'Canada',np.nan,'United-States'],
'col':[2,3,7,8,9]})
print (df)
col native-country workclass
0 2 United-States Private
1 3 NaN Private
2 7 Canada NaN
3 8 NaN another
4 9 United-States NaN
mode = df.filter(["workclass", "native-country"]).mode()
print (mode)
workclass native-country
0 Private United-States
cols = ["workclass", "native-country"]
df[cols]=df[cols].fillna(df.mode().iloc[0])
print (df)
col native-country workclass
0 2 United-States Private
1 3 United-States Private
2 7 Canada Private
3 8 United-States another
4 9 United-States Private
回答by Miriam Farber
You can do it like that:
你可以这样做：
df[["workclass", "native-country"]]=df[["workclass", "native-country"]].fillna(value=mode.iloc[0])
For example,
例如，
import pandas as pd
d={
'key3': [1,4,4,4,5],
'key2': [6,6,4],
'key1': [6,4,4],
}
df=pd.DataFrame.from_dict(d,orient='index').transpose()
Then dfis
然后df是
key3 key2 key1
0 1 6 6
1 4 6 4
2 4 4 4
3 4 NaN NaN
4 5 NaN NaN
Then by doing:
然后通过做：
l=df.filter(["key1", "key2"]).mode()
df[["key1", "key2"]]=df[["key1", "key2"]].fillna(value=l.iloc[0])
we get that dfis
我们得到的df是
key3 key2 key1
0 1 6 6
1 4 6 4
2 4 4 4
3 4 6 4
4 5 6 4
回答by Krishna
I think it's cleanest to use a dict as the fillna parameter 'value'
我认为使用 dict 作为填充参数“值”是最干净的
ref: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html
参考：https: //pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html
create a toy df from @miriam-farber's response
根据@miriam-farber 的回复创建一个玩具 df
import pandas as pd
d={
'key3': [1,4,4,4,5],
'key2': [6,6,4],
'key1': [6,4,4],
}
d_df=pd.DataFrame.from_dict(d,orient='index').transpose()
create a dict
创建一个字典
mode_dict = d_df.loc[:,['key2','key1']].mode().to_dict('records')[0]
use this dict in fillna method
在 fillna 方法中使用此 dict
d_df.fillna(mode_dict, inplace=True)
回答by bhavesh singh
This code impute mean to the int columns and mode to the object columns making a list of both types of columns and imputing the missing value according to the conditions.
此代码将平均值归入 int 列，将模式归入对象列，生成两种类型的列的列表，并根据条件输入缺失值。
cateogry_columns=df.select_dtypes(include=['object']).columns.tolist()
integer_columns=df.select_dtypes(include=['int64','float64']).columns.tolist()
for column in df:
if df[column].isnull().any():
if(column in cateogry_columns):
df[column]=df[column].fillna(df[column].mode()[0])
else:
df[column]=df[column].fillna(df[column].mean)`
