问题:

I have to following df:

Col1    Col2
test    Something
test2   Something
test3   Something
test    Something
test2   Something
test5   Something

I want to get

Col1    Col2          Occur
test    Something     2
test2   Something     2
test3   Something     1
test    Something     2
test2   Something     2
test5   Something     1

I've tried to use:

df["Occur"] = df["Col1"].value_counts()

But it didn't help. I've got Occur column full of 'NaN'

回答1:

groupby on 'col1' and then apply transform on Col2 to return a Series with its index aligned to the original df so you can add it as a column:

In [3]:
df['Occur'] = df.groupby('Col1')['Col2'].transform(pd.Series.value_counts)
df

Out[3]:
    Col1       Col2 Occur
0   test  Something     2
1  test2  Something     2
2  test3  Something     1
3   test  Something     2
4  test2  Something     2
5  test5  Something     1

回答2:

You can also use GroupBy + transform with size:

df['Occur'] = df.groupby('Col1')['Col1'].transform('size')

print(df)

    Col1       Col2  Occur
0   test  Something      2
1  test2  Something      2
2  test3  Something      1
3   test  Something      2
4  test2  Something      2
5  test5  Something      1

回答3:

You can also do:

df['Occur']=df['Col1'].apply(df['Col1'].tolist().count)

So then:

print(df)

Is:

    Col1       Col2  Occur
0   test  Something      2
1  test2  Something      2
2  test3  Something      1
3   test  Something      2
4  test2  Something      2
5  test5  Something      1