CODEWITHSUNDEEP
pythonpandas
nsbm
nsbm

Reputation: 6152

How to group and count rows by month and year using Pandas?

I have a dataset with personal data such as name, height, weight and date of birth. I would build a graph with the number of people born in a particular month and year. I'm using python pandas to accomplish this and my strategy was to try to group by year and month and add using count. But the closest I got is to get the count of people by year or by month but not by both.

df['birthdate'].groupby(df.birthdate.dt.year).agg('count')

Other questions in stackoverflow point to a Grouper called TimeGrouper but searching in pandas documentation found nothing. Any idea?

Upvotes: 43

Views: 110082

Answers (5)

user1775015
user1775015

Reputation: 189

Replace date and count fields with your respective column names. This piece of code will group, sum and sort based on the given parameters. You can also change the frequency to 1M or 2M and so on...

df[['date', 'count']].groupby(pd.Grouper(key='date', freq='1M')).sum().sort_values(by='date', ascending=True)['count']

Upvotes: 0

saran3h
saran3h

Reputation: 14022

As of April 2019: This will work. Pandas version - 0.24.x

df.groupby([df.dates.dt.year.rename('year'), df.dates.dt.month.rename('month')]).size()

Upvotes: 3

EdChum
EdChum

Reputation: 394003

To group on multiple criteria, pass a list of the columns or criteria:

df['birthdate'].groupby([df.birthdate.dt.year, df.birthdate.dt.month]).agg('count')

Example:

In [165]:
df = pd.DataFrame({'birthdate':pd.date_range(start=dt.datetime(2015,12,20),end=dt.datetime(2016,3,1))})
df.groupby([df['birthdate'].dt.year, df['birthdate'].dt.month]).agg({'count'})

Out[165]:
                    birthdate
                        count
birthdate birthdate          
2015      12               12
2016      1                31
          2                29
          3                 1

UPDATE

As of version 0.23.0 the above code no longer works due to the restriction that multi-index level names must be unique, you now need to rename the levels in order for this to work:

In[107]:
df.groupby([df['birthdate'].dt.year.rename('year'), df['birthdate'].dt.month.rename('month')]).agg({'count'})

Out[107]: 
           birthdate
               count
year month          
2015 12           12
2016 1            31
     2            29
     3             1

Upvotes: 75

Andy Hayden
Andy Hayden

Reputation: 375445

You can also use the "monthly" period with to_period with the dt accessor:

In [11]: df = pd.DataFrame({'birthdate': pd.date_range(start='20-12-2015', end='3-1-2016')})

In [12]: df['birthdate'].groupby(df.birthdate.dt.to_period("M")).agg('count')
Out[12]:
birthdate
2015-12    12
2016-01    31
2016-02    29
2016-03     1
Freq: M, Name: birthdate, dtype: int64

It's worth noting if the datetime is the index (rather than a column) you can use resample:

df.resample("M").count()

Upvotes: 35

Alicia Garcia-Raboso
Alicia Garcia-Raboso

Reputation: 13913

Another solution is to set birthdate as the index and resample:

import pandas as pd

df = pd.DataFrame({'birthdate': pd.date_range(start='20-12-2015', end='3-1-2016')})
df.set_index('birthdate').resample('MS').size()

Output:

birthdate
2015-12-01    12
2016-01-01    31
2016-02-01    29
2016-03-01     1
Freq: MS, dtype: int64

Upvotes: 14

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