Create column of value_counts in Pandas dataframe

df['Counts'] = df.groupby(['Color'])['Value'].transform('count')

For example,

In [102]: df = pd.DataFrame({'Color': 'Red Red Blue'.split(), 'Value': [100, 150, 50]})

In [103]: df
Out[103]: 
  Color  Value
0   Red    100
1   Red    150
2  Blue     50

In [104]: df['Counts'] = df.groupby(['Color'])['Value'].transform('count')

In [105]: df
Out[105]: 
  Color  Value  Counts
0   Red    100       2
1   Red    150       2
2  Blue     50       1

Note that transform('count') ignores NaNs. If you want to count NaNs, use transform(len).

To the anonymous editor: If you are getting an error while using transform('count') it may be due to your version of Pandas being too old. The above works with pandas version 0.15 or newer.

One other option:

z = df['Color'].value_counts 

z1 = z.to_dict() #converts to dictionary

df['Count_Column'] = df['Color'].map(z1) 

This option will give you a column with repeated values of the counts, corresponding to the frequency of each value in the 'Color' column.

This answer uses Series.map with Series.value_counts. It was tested with Pandas 1.1.

df['counts'] = df['attribute'].map(df['attribute'].value_counts())

Credit: comment by sacuL

df['Counts'] = df.Color.groupby(df.Color).transform('count')

You can do this with any series: group it by itself and call transform('count'):

>>> series = pd.Series(['Red', 'Red', 'Blue'])
>>> series.groupby(series).transform('count')
0    2
1    2
2    1
dtype: int64