CODEWITHSUNDEEP
pythonpandas
user19783276
user19783276

Reputation:

Pandas : Calculate the Mean of the value_counts() from row 0 to row n

I am struggling to create a function that could first calculate the number of occurrences for each string in a specific column (from row 0 to row n) and then reduce this to one single value by calculating the mean of the value_counts from the first row to the row n.

More precisely, what I would like to do is to create a new column ['Mean'] where the value of each row n equals to the mean of the value_counts() from the first row to the nth row of the column ['Name'].


import pandas as pd
import datetime as dt

data = [["2022-11-1", 'Tom'], ["2022-11-2", 'Mike'], ["2022-11-3", 'Paul'], ["2022-11-4", 'Pauline'], ["2022-11-5", 'Pauline'], ["2022-11-6", 'Mike'], ["2022-11-7", 'Tom'], ["2022-11-8", 'Louise'], ["2022-11-9", 'Tom'], ["2022-11-10", 'Mike'], ["2022-11-11", 'Paul'], ["2022-11-12", 'Pauline'], ["2022-11-13", 'Pauline'], ["2022-11-14", 'Mike'], ["2022-11-15", 'Tom'], ["2022-11-16", 'Louise']]
df = pd.DataFrame(data, columns=['Date', 'Name'])

So for example, the 6th row of ['Mean'] should have a value of 1.25 as Pauline appeared twice, so the calcul should be (1 + 1 + 1 + 2 + 1)/5 = 1.25 .

Thank you,

Upvotes: 1

Views: 212

Answers (1)

mozway
mozway

Reputation: 260455

The logic is unclear, but assuming you want the expanding average count of values, use:

df['mean'] = pd.Series(pd.factorize(df['Name'])[0], index=df.index)
                         .expanding()
                         .apply(lambda s: s.value_counts().mean())
                      )

Output:

          Date     Name  mean
0    2022-11-1      Tom  1.00
1    2022-11-2     Mike  1.00
2    2022-11-3     Paul  1.00
3    2022-11-4  Pauline  1.00
4    2022-11-5  Pauline  1.25
5    2022-11-6     Mike  1.50
6    2022-11-7      Tom  1.75
7    2022-11-8   Louise  1.60
8    2022-11-9      Tom  1.80
9   2022-11-10     Mike  2.00
10  2022-11-11     Paul  2.20
11  2022-11-12  Pauline  2.40
12  2022-11-13  Pauline  2.60
13  2022-11-14     Mike  2.80
14  2022-11-15      Tom  3.00
15  2022-11-16   Louise  3.20

Upvotes: 1

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