Assign equal weights in new column pandas based on count of values

Question

I have the following df

dictionary1 = {'trade_date': {1350: 20151201,
  6175: 20151201,
  3100: 20151201,
  5650: 20151201,
  3575: 20151201,
     1: 20170301,
     2: 20170301},
 'comId': {1350: '257762',
  6175: '1038328',
  3100: '315476',
  5650: '658776',
  3575: '329376',
     1: '123456',
     2: '987654'},
 'return': {1350: -0.0018,
  6175: 0.0023,
  3100: -0.0413,
  5650: 0.1266,
  3575: 0.0221,
  1: 0.9,
  2: 0.01}}
df1 = pd.DataFrame(dictionary1)

I want to create a new column called weights and assign equal weights based on the number of entries for a particular trade_date .

In other words, the output should be like this:

dictionary2 = {'trade_date': {1350: 20151201,
  6175: 20151201,
  3100: 20151201,
  5650: 20151201,
  3575: 20151201,
     1: 20170301,
     2: 20170301},
 'comId': {1350: '257762',
  6175: '1038328',
  3100: '315476',
  5650: '658776',
  3575: '329376',
     1: '123456',
     2: '987654'},
 'return': {1350: -0.0018,
  6175: 0.0023,
  3100: -0.0413,
  5650: 0.1266,
  3575: 0.0221,
  1: 0.9,
  2: 0.01},
'weights':{1350: 0.2,
  6175: 0.2,
  3100: 0.2,
  5650: 0.2,
  3575: 0.2,
  1: 0.5,
  2: 0.5}}

df2 = pd.DataFrame(dictionary2)

This there are five values associated with trade_date 20151201, hence they all get a value 0.2 (1/5) in the new column weights and 2 values associated with trade_date 20170301, hence they all get the value 0.5 (1/2) in the new column weights .

Any suggestions how to do this?

Answer 1

df['weights'] = df.groupby('trade_date')['trade_date'].transform(lambda x: 1/len(x))