How to count the element in a column and take the result as a new column?

My favorite. factorize does not sort and has time complexity of O(n). For big data sets, factorize should be preferred over np.unique

i, u = df.id.factorize()df.assign(Count=np.bincount(i)[i])   id  Count0   1      21   1      22   3      1

`np.unique` and `np.bincount`

u, i = np.unique(df.id, return_inverse=True)df.assign(Count=np.bincount(i)[i])   id  Count0   1      21   1      22   3      1

python pandas dataframe

Assign the new count column to the dataframe by grouping on id and then transforming that column with value_counts (or size).

>>> f.assign(count=f.groupby('id')['id'].transform('value_counts'))   id  count0   1      21   1      22   3      1

python pandas dataframe

df['count'] = df['id'].map(df['id'].value_counts())#alternative#from collections import Counter#df['count'] = df['id'].map(Counter(df['id']))

Detail:

print (df['id'].value_counts())1    23    1Name: id, dtype: int64

Or GroupBy.transform for return Series with same size as original DataFrame with GroupBy.size:

df['count'] = df.groupby('id')['id'].transform('size')print (df)   id count0   1     21   1     22   3     1