Pandas groupby month and year
Question:
I have the following dataframe:
Date abc xyz
01-Jun-13 100 200
03-Jun-13 -20 50
15-Aug-13 40 -5
20-Jan-14 25 15
21-Feb-14 60 80
I need to group the data by year and month. I.e., Group by Jan 2013, Feb 2013, Mar 2013, etc…
I will be using the newly grouped data to create a plot showing abc vs xyz per year/month.
I’ve tried various combinations of groupby and sum, but I just can’t seem to get anything to work. How can I do it?
Answers:
There are different ways to do that.
-
I created the data frame to showcase the different techniques to filter your data.
df = pd.DataFrame({'Date': ['01-Jun-13', '03-Jun-13', '15-Aug-13', '20-Jan-14', '21-Feb-14'],
'abc': [100, -20, 40, 25, 60], 'xyz': [200, 50,-5, 15, 80] })
-
I separated months/year/day and separated month-year as you explained.
def getMonth(s):
return s.split("-")[1]
def getDay(s):
return s.split("-")[0]
def getYear(s):
return s.split("-")[2]
def getYearMonth(s):
return s.split("-")[1] + "-" + s.split("-")[2]
-
I created new columns: year, month, day and ‘yearMonth‘. In your case, you need one of both. You can group using two columns 'year','month' or using one column yearMonth
df['year'] = df['Date'].apply(lambda x: getYear(x))
df['month'] = df['Date'].apply(lambda x: getMonth(x))
df['day'] = df['Date'].apply(lambda x: getDay(x))
df['YearMonth'] = df['Date'].apply(lambda x: getYearMonth(x))
Output:
Date abc xyz year month day YearMonth
0 01-Jun-13 100 200 13 Jun 01 Jun-13
1 03-Jun-13 -20 50 13 Jun 03 Jun-13
2 15-Aug-13 40 -5 13 Aug 15 Aug-13
3 20-Jan-14 25 15 14 Jan 20 Jan-14
4 21-Feb-14 60 80 14 Feb 21 Feb-14
-
You can go through the different groups in groupby(..) items.
In this case, we are grouping by two columns:
for key, g in df.groupby(['year', 'month']):
print key, g
Output:
('13', 'Jun') Date abc xyz year month day YearMonth
0 01-Jun-13 100 200 13 Jun 01 Jun-13
1 03-Jun-13 -20 50 13 Jun 03 Jun-13
('13', 'Aug') Date abc xyz year month day YearMonth
2 15-Aug-13 40 -5 13 Aug 15 Aug-13
('14', 'Jan') Date abc xyz year month day YearMonth
3 20-Jan-14 25 15 14 Jan 20 Jan-14
('14', 'Feb') Date abc xyz year month day YearMonth
In this case, we are grouping by one column:
for key, g in df.groupby(['YearMonth']):
print key, g
Output:
Jun-13 Date abc xyz year month day YearMonth
0 01-Jun-13 100 200 13 Jun 01 Jun-13
1 03-Jun-13 -20 50 13 Jun 03 Jun-13
Aug-13 Date abc xyz year month day YearMonth
2 15-Aug-13 40 -5 13 Aug 15 Aug-13
Jan-14 Date abc xyz year month day YearMonth
3 20-Jan-14 25 15 14 Jan 20 Jan-14
Feb-14 Date abc xyz year month day YearMonth
4 21-Feb-14 60 80 14 Feb 21 Feb-14
-
In case you want to access a specific item, you can use get_group
print df.groupby(['YearMonth']).get_group('Jun-13')
Output:
Date abc xyz year month day YearMonth
0 01-Jun-13 100 200 13 Jun 01 Jun-13
1 03-Jun-13 -20 50 13 Jun 03 Jun-13
-
Similar to get_group. This hack would help to filter values and get the grouped values.
This also would give the same result.
print df[df['YearMonth']=='Jun-13']
Output:
Date abc xyz year month day YearMonth
0 01-Jun-13 100 200 13 Jun 01 Jun-13
1 03-Jun-13 -20 50 13 Jun 03 Jun-13
You can select list of abc or xyz values during Jun-13
print df[df['YearMonth']=='Jun-13'].abc.values
print df[df['YearMonth']=='Jun-13'].xyz.values
Output:
[100 -20] #abc values
[200 50] #xyz values
You can use this to go through the dates that you have classified as "year-month" and apply criteria on it to get related data.
for x in set(df.YearMonth):
print df[df['YearMonth']==x].abc.values
print df[df['YearMonth']==x].xyz.values
I recommend also to check this answer as well.
You can use either resample or Grouper (which resamples under the hood).
First make sure that the datetime column is actually of datetimes (hit it with pd.to_datetime). It’s easier if it’s a DatetimeIndex:
In [11]: df1
Out[11]:
abc xyz
Date
2013-06-01 100 200
2013-06-03 -20 50
2013-08-15 40 -5
2014-01-20 25 15
2014-02-21 60 80
In [12]: g = df1.groupby(pd.Grouper(freq="M")) # DataFrameGroupBy (grouped by Month)
In [13]: g.sum()
Out[13]:
abc xyz
Date
2013-06-30 80 250
2013-07-31 NaN NaN
2013-08-31 40 -5
2013-09-30 NaN NaN
2013-10-31 NaN NaN
2013-11-30 NaN NaN
2013-12-31 NaN NaN
2014-01-31 25 15
2014-02-28 60 80
In [14]: df1.resample("M", how='sum') # the same
Out[14]:
abc xyz
Date
2013-06-30 40 125
2013-07-31 NaN NaN
2013-08-31 40 -5
2013-09-30 NaN NaN
2013-10-31 NaN NaN
2013-11-30 NaN NaN
2013-12-31 NaN NaN
2014-01-31 25 15
2014-02-28 60 80
Note: Previously pd.Grouper(freq="M") was written as pd.TimeGrouper("M"). The latter is now deprecated since 0.21.
I had thought the following would work, but it doesn’t (due to as_index not being respected? I’m not sure.). I’m including this for interest’s sake.
If it’s a column (it has to be a datetime64 column! as I say, hit it with to_datetime), you can use the PeriodIndex:
In [21]: df
Out[21]:
Date abc xyz
0 2013-06-01 100 200
1 2013-06-03 -20 50
2 2013-08-15 40 -5
3 2014-01-20 25 15
4 2014-02-21 60 80
In [22]: pd.DatetimeIndex(df.Date).to_period("M") # old way
Out[22]:
<class 'pandas.tseries.period.PeriodIndex'>
[2013-06, ..., 2014-02]
Length: 5, Freq: M
In [23]: per = df.Date.dt.to_period("M") # new way to get the same
In [24]: g = df.groupby(per)
In [25]: g.sum() # dang not quite what we want (doesn't fill in the gaps)
Out[25]:
abc xyz
2013-06 80 250
2013-08 40 -5
2014-01 25 15
2014-02 60 80
To get the desired result we have to reindex…
Keep it simple:
GB = DF.groupby([(DF.index.year), (DF.index.month)]).sum()
giving you,
print(GB)
abc xyz
2013 6 80 250
8 40 -5
2014 1 25 15
2 60 80
and then you can plot like asked using,
GB.plot('abc', 'xyz', kind='scatter')
You can also do it by creating a string column with the year and month as follows:
df['date'] = df.index
df['year-month'] = df['date'].apply(lambda x: str(x.year) + ' ' + str(x.month))
grouped = df.groupby('year-month')
However this doesn’t preserve the order when you loop over the groups, e.g.
for name, group in grouped:
print(name)
Will give:
2007 11
2007 12
2008 1
2008 10
2008 11
2008 12
2008 2
2008 3
2008 4
2008 5
2008 6
2008 7
2008 8
2008 9
2009 1
2009 10
So then, if you want to preserve the order, you must do as suggested by @Q-man above:
grouped = df.groupby([df.index.year, df.index.month])
This will preserve the order in the above loop:
(2007, 11)
(2007, 12)
(2008, 1)
(2008, 2)
(2008, 3)
(2008, 4)
(2008, 5)
(2008, 6)
(2008, 7)
(2008, 8)
(2008, 9)
(2008, 10)
Some of the answers are using Date as an index instead of a column (and there’s nothing wrong with doing that).
However, for anyone who has the dates stored as a column (instead of an index), remember to access the column’s dt attribute. That is:
# First make sure `Date` is a datetime column
df['Date'] = pd.to_datetime(
arg=df['Date'],
format='%d-%b-%y' # Assuming dd-Mon-yy format
)
# Group by year and month
df.groupby(
[
df['Date'].dt.year,
df['Date'].dt.month
]
).sum()
I have the following dataframe:
Date abc xyz
01-Jun-13 100 200
03-Jun-13 -20 50
15-Aug-13 40 -5
20-Jan-14 25 15
21-Feb-14 60 80
I need to group the data by year and month. I.e., Group by Jan 2013, Feb 2013, Mar 2013, etc…
I will be using the newly grouped data to create a plot showing abc vs xyz per year/month.
I’ve tried various combinations of groupby and sum, but I just can’t seem to get anything to work. How can I do it?
There are different ways to do that.
-
I created the data frame to showcase the different techniques to filter your data.
df = pd.DataFrame({'Date': ['01-Jun-13', '03-Jun-13', '15-Aug-13', '20-Jan-14', '21-Feb-14'], 'abc': [100, -20, 40, 25, 60], 'xyz': [200, 50,-5, 15, 80] }) -
I separated months/year/day and separated month-year as you explained.
def getMonth(s): return s.split("-")[1] def getDay(s): return s.split("-")[0] def getYear(s): return s.split("-")[2] def getYearMonth(s): return s.split("-")[1] + "-" + s.split("-")[2] -
I created new columns:
year,month,dayand ‘yearMonth‘. In your case, you need one of both. You can group using two columns'year','month'or using one columnyearMonthdf['year'] = df['Date'].apply(lambda x: getYear(x)) df['month'] = df['Date'].apply(lambda x: getMonth(x)) df['day'] = df['Date'].apply(lambda x: getDay(x)) df['YearMonth'] = df['Date'].apply(lambda x: getYearMonth(x))Output:
Date abc xyz year month day YearMonth 0 01-Jun-13 100 200 13 Jun 01 Jun-13 1 03-Jun-13 -20 50 13 Jun 03 Jun-13 2 15-Aug-13 40 -5 13 Aug 15 Aug-13 3 20-Jan-14 25 15 14 Jan 20 Jan-14 4 21-Feb-14 60 80 14 Feb 21 Feb-14 -
You can go through the different groups in groupby(..) items.
In this case, we are grouping by two columns:
for key, g in df.groupby(['year', 'month']): print key, gOutput:
('13', 'Jun') Date abc xyz year month day YearMonth 0 01-Jun-13 100 200 13 Jun 01 Jun-13 1 03-Jun-13 -20 50 13 Jun 03 Jun-13 ('13', 'Aug') Date abc xyz year month day YearMonth 2 15-Aug-13 40 -5 13 Aug 15 Aug-13 ('14', 'Jan') Date abc xyz year month day YearMonth 3 20-Jan-14 25 15 14 Jan 20 Jan-14 ('14', 'Feb') Date abc xyz year month day YearMonthIn this case, we are grouping by one column:
for key, g in df.groupby(['YearMonth']): print key, gOutput:
Jun-13 Date abc xyz year month day YearMonth 0 01-Jun-13 100 200 13 Jun 01 Jun-13 1 03-Jun-13 -20 50 13 Jun 03 Jun-13 Aug-13 Date abc xyz year month day YearMonth 2 15-Aug-13 40 -5 13 Aug 15 Aug-13 Jan-14 Date abc xyz year month day YearMonth 3 20-Jan-14 25 15 14 Jan 20 Jan-14 Feb-14 Date abc xyz year month day YearMonth 4 21-Feb-14 60 80 14 Feb 21 Feb-14 -
In case you want to access a specific item, you can use
get_groupprint df.groupby(['YearMonth']).get_group('Jun-13')Output:
Date abc xyz year month day YearMonth 0 01-Jun-13 100 200 13 Jun 01 Jun-13 1 03-Jun-13 -20 50 13 Jun 03 Jun-13 -
Similar to
get_group. This hack would help to filter values and get the grouped values.This also would give the same result.
print df[df['YearMonth']=='Jun-13']Output:
Date abc xyz year month day YearMonth 0 01-Jun-13 100 200 13 Jun 01 Jun-13 1 03-Jun-13 -20 50 13 Jun 03 Jun-13You can select list of
abcorxyzvalues duringJun-13print df[df['YearMonth']=='Jun-13'].abc.values print df[df['YearMonth']=='Jun-13'].xyz.valuesOutput:
[100 -20] #abc values [200 50] #xyz valuesYou can use this to go through the dates that you have classified as "year-month" and apply criteria on it to get related data.
for x in set(df.YearMonth): print df[df['YearMonth']==x].abc.values print df[df['YearMonth']==x].xyz.values
I recommend also to check this answer as well.
You can use either resample or Grouper (which resamples under the hood).
First make sure that the datetime column is actually of datetimes (hit it with pd.to_datetime). It’s easier if it’s a DatetimeIndex:
In [11]: df1
Out[11]:
abc xyz
Date
2013-06-01 100 200
2013-06-03 -20 50
2013-08-15 40 -5
2014-01-20 25 15
2014-02-21 60 80
In [12]: g = df1.groupby(pd.Grouper(freq="M")) # DataFrameGroupBy (grouped by Month)
In [13]: g.sum()
Out[13]:
abc xyz
Date
2013-06-30 80 250
2013-07-31 NaN NaN
2013-08-31 40 -5
2013-09-30 NaN NaN
2013-10-31 NaN NaN
2013-11-30 NaN NaN
2013-12-31 NaN NaN
2014-01-31 25 15
2014-02-28 60 80
In [14]: df1.resample("M", how='sum') # the same
Out[14]:
abc xyz
Date
2013-06-30 40 125
2013-07-31 NaN NaN
2013-08-31 40 -5
2013-09-30 NaN NaN
2013-10-31 NaN NaN
2013-11-30 NaN NaN
2013-12-31 NaN NaN
2014-01-31 25 15
2014-02-28 60 80
Note: Previously pd.Grouper(freq="M") was written as pd.TimeGrouper("M"). The latter is now deprecated since 0.21.
I had thought the following would work, but it doesn’t (due to as_index not being respected? I’m not sure.). I’m including this for interest’s sake.
If it’s a column (it has to be a datetime64 column! as I say, hit it with to_datetime), you can use the PeriodIndex:
In [21]: df
Out[21]:
Date abc xyz
0 2013-06-01 100 200
1 2013-06-03 -20 50
2 2013-08-15 40 -5
3 2014-01-20 25 15
4 2014-02-21 60 80
In [22]: pd.DatetimeIndex(df.Date).to_period("M") # old way
Out[22]:
<class 'pandas.tseries.period.PeriodIndex'>
[2013-06, ..., 2014-02]
Length: 5, Freq: M
In [23]: per = df.Date.dt.to_period("M") # new way to get the same
In [24]: g = df.groupby(per)
In [25]: g.sum() # dang not quite what we want (doesn't fill in the gaps)
Out[25]:
abc xyz
2013-06 80 250
2013-08 40 -5
2014-01 25 15
2014-02 60 80
To get the desired result we have to reindex…
Keep it simple:
GB = DF.groupby([(DF.index.year), (DF.index.month)]).sum()
giving you,
print(GB)
abc xyz
2013 6 80 250
8 40 -5
2014 1 25 15
2 60 80
and then you can plot like asked using,
GB.plot('abc', 'xyz', kind='scatter')
You can also do it by creating a string column with the year and month as follows:
df['date'] = df.index
df['year-month'] = df['date'].apply(lambda x: str(x.year) + ' ' + str(x.month))
grouped = df.groupby('year-month')
However this doesn’t preserve the order when you loop over the groups, e.g.
for name, group in grouped:
print(name)
Will give:
2007 11
2007 12
2008 1
2008 10
2008 11
2008 12
2008 2
2008 3
2008 4
2008 5
2008 6
2008 7
2008 8
2008 9
2009 1
2009 10
So then, if you want to preserve the order, you must do as suggested by @Q-man above:
grouped = df.groupby([df.index.year, df.index.month])
This will preserve the order in the above loop:
(2007, 11)
(2007, 12)
(2008, 1)
(2008, 2)
(2008, 3)
(2008, 4)
(2008, 5)
(2008, 6)
(2008, 7)
(2008, 8)
(2008, 9)
(2008, 10)
Some of the answers are using Date as an index instead of a column (and there’s nothing wrong with doing that).
However, for anyone who has the dates stored as a column (instead of an index), remember to access the column’s dt attribute. That is:
# First make sure `Date` is a datetime column
df['Date'] = pd.to_datetime(
arg=df['Date'],
format='%d-%b-%y' # Assuming dd-Mon-yy format
)
# Group by year and month
df.groupby(
[
df['Date'].dt.year,
df['Date'].dt.month
]
).sum()