CODEWITHSUNDEEP
pythonpandasdataframe
d.doo
d.doo

Reputation: 53

pandas sort column values

ie)

        count
2015-01  2
2015-02  1
2015-03  4

for the group i tried pd.groupby(b,by=[b.index.month,b.index.year])

but there was object has no attribute 'month' error

Upvotes: 1

Views: 92

Answers (1)

piRSquared
piRSquared

Reputation: 294258

Apply sorted with the key parameter set to pd.to_datetime

df.assign(date=df.date.apply(sorted, key=pd.to_datetime))

  id                                  date
0  a              [2015-02-01, 2015-03-01]
1  b                          [2015-03-01]
2  s              [2015-01-01, 2015-03-01]
3  f  [2015-01-01, 2015-01-01, 2015-03-01]

Then use pd.value_counts

pd.value_counts(pd.to_datetime(df.date.sum()).strftime('%Y-%m'))

2015-03    4
2015-01    3
2015-02    1
dtype: int64

debugging

You should be able to copy and paste this code... please verify that it runs as expected.

import pandas as pd

df = pd.DataFrame(dict(
        id=list('absf'),
        date=[
            ['2015-03-01', '2015-02-01'],
            ['2015-03-01'],
            ['2015-01-01', '2015-03-01'],
            ['2015-01-01', '2015-01-01', '2015-03-01']
        ]
    ))[['id', 'date']]

print(df.assign(date=df.date.apply(sorted, key=pd.to_datetime)))
print()
print(pd.value_counts(pd.to_datetime(df.date.sum()).strftime('%Y-%m')))

You should expect to see

  id                                  date
0  a              [2015-02-01, 2015-03-01]
1  b                          [2015-03-01]
2  s              [2015-01-01, 2015-03-01]
3  f  [2015-01-01, 2015-01-01, 2015-03-01]

2015-03    4
2015-01    3
2015-02    1
dtype: int64

Upvotes: 1

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