How to calculate quarterly wise churn and retention rate with date column using python. with date column i want to group that quarterly using python.

This is used to calculate the churn count groupby quarterly

quarterly_churn_yes = out.loc[out['Churn'] == 'Yes'].groupby(out["Date"].dt.quarter).count()
print(quarterly_churn_yes["Churn"])Date
1    1154
2     114
3      68
4      69
Name: Churn, dtype: int64

This is used to calculate the churn rate groupby quarterly

total_churn = out['Churn'].count()
print(total_churn) quarterly_churn_rate = out.groupby(out["Date"].dt.quarter).apply(lambda x: quarterly_churn_yes["Churn"] / total_churn).sum()
print(quarterly_churn_rate)Date
1    0.862159
2    0.085170
3    0.050803
4    0.051550
dtype: float64

The above code i have tried to the find churn rate grouped on date column querterly wise. I am getting 1,2,3,4 but i want year wise quarterly churn rate.

For example , if i have four years in the dataframe like 2018,2014,2017 in that

2008

1    1154
2     114
3      68
4      69

2014

1    1154
2     114
3      68
4      69

I think need:

out = pd.DataFrame({ 'Date': pd.to_datetime(['2015-01-01','2015-05-01','2015-07-01','2015-10-01','2015-04-01','2015-12-01','2016-01-01','2016-02-01','2015-05-01', '2015-10-01']), 'Churn': ['Yes'] * 8 + ['No'] * 2 })
print (out)Churn       Date
0   Yes 2015-01-01
1   Yes 2015-05-01
2   Yes 2015-07-01
3   Yes 2015-10-01
4   Yes 2015-04-01
5   Yes 2015-12-01
6   Yes 2016-01-01
7   Yes 2016-02-01
8    No 2015-05-01
9    No 2015-10-01

df = (out.loc[out['Churn'] == 'Yes'].groupby([out["Date"].dt.year,out["Date"].dt.quarter])["Churn"].count().rename_axis(('year','quarter')).reset_index(name='count'))print(df)year  quarter  count
0  2015        1      1
1  2015        2      2
2  2015        3      1
3  2015        4      2
4  2016        1      2

For separate DataFrames by years is possible create dictionary of DataFrames:

dfs = dict(tuple(out.groupby(out['Date'].dt.year)))
print (dfs)
{2016:   Churn       Date
6   Yes 2016-01-01
7   Yes 2016-02-01, 2015:   Churn       Date
0   Yes 2015-01-01
1   Yes 2015-05-01
2   Yes 2015-07-01
3   Yes 2015-10-01
4   Yes 2015-04-01
5   Yes 2015-12-01
8    No 2015-05-01
9    No 2015-10-01}print (dfs.keys())
dict_keys([2016, 2015])print (dfs[2015])Churn       Date
0   Yes 2015-01-01
1   Yes 2015-05-01
2   Yes 2015-07-01
3   Yes 2015-10-01
4   Yes 2015-04-01
5   Yes 2015-12-01
8    No 2015-05-01
9    No 2015-10-01Tenure column looks like thisout["tenure"].unique() 
Out[14]: 
array([ 8, 15, 32,  9, 48, 58, 10, 29,  1, 66, 24, 68,  4, 53,  6, 20, 52,49, 71,  2, 65, 67, 27, 18, 47, 45, 43, 59, 13, 17, 72, 61, 34, 11,35, 69, 63, 30, 19, 39,  3, 46, 54, 36, 12, 41, 50, 40, 28, 44, 51,33, 21, 70, 23, 16, 56, 14, 62,  7, 25, 31, 60,  5, 42, 22, 37, 64,57, 38, 26, 55])

It contains no of months, it seems like 1 to 72.

I need to split tenure column into "range".

For example, this column contains 1 to 72 numbers, need to range up to 4 range.

like 1 to 18 --> 1 range19 to 36 --> 2nd range37 to 54 --> 3rd range like that

here i found quarterlywise churn count and with that churn count later i found churn rate with churn count and total count.

quarterly_churn_yes = out.loc[out['Churn'] == 'Yes'].groupby([out["Date"].dt.year,out["Date"].dt.quarter]).count().rename_axis(('year','quarter'))
quarterly_churn_yes["Churn"]quarterly_churn_rate = out.groupby(out["Date"].dt.quarter).apply(lambda x: quarterly_churn_yes["Churn"] / total_churn).sum()
print(quarterly_churn_rate)

Like this I need to find tenure wise 4 range to find churn count.