group by week in pandas
First, convert column date
to_datetime
and subtract one week as we want the sum for the week ahead of the date and not the week before that date.
Then use groupby
with Grouper
by W-MON and aggregate sum
:
df['Date'] = pd.to_datetime(df['Date']) - pd.to_timedelta(7, unit='d')
df = df.groupby(['Name', pd.Grouper(key='Date', freq='W-MON')])['Quantity']
.sum()
.reset_index()
.sort_values('Date')
print (df)
Name Date Quantity
0 Apple 2017-07-10 90
3 orange 2017-07-10 20
1 Apple 2017-07-17 30
2 Orange 2017-07-24 40
Pandas Group by date weekly
Use DataFrame.resample
by W
with sum
:
#convert date column to datetimes
df['date'] = pd.to_datetime(df['date'])
df1 = df.resample('W', on='date')['count1','count2'].sum()
Or use Grouper
:
df1 = df.groupby(pd.Grouper(freq='W', key='date'))['count1','count2'].sum()
print (df1)
count1 count2
date
2019-12-15 3 75
2019-12-22 4 43
Group data by week in Pandas
import pandas as pd
Name = ["Apple", "Orange", "Apple", "Orange", "Apple", "Banana", "Apple","Orange"]
Date = ["2022-03-15","2022-03-16","2022-03-17","2022-03-18","2022-03-19","2022-03-20","2019-12-19","2004-01-07"]
author = ["sahil_1","sahil_2","sahil_3","sahil_1","sahil_2","sahil_3","sahil_3","sahil_1"]
df = pd.DataFrame(zip(Name,Date,author), columns=["Name", "Date", "Author"])
df['Date'] = pd.to_datetime(df['Date']) - pd.to_timedelta(7, unit='d')
x = df.groupby(['Name', pd.Grouper(key='Date', freq='W-MON')])['Name'].count()
print(x)
Pandas - Group data by week and add column for count of rows in group
If your dataset is a dataframe, you can use:
df.assign(Count=1).groupby('Date')['Count'].count()
If it's a series:
series.to_frame().assign(Count=1).groupby('Date')['Count'].count()
For example:
df = pd.DataFrame({'Date':['2015-09-05',
'2015-09-05',
'2015-07-08',
'2017-09-05',
'2018-09-05',
'2018-09-05']})
df.assign(Count=1).groupby('Date')['Count'].count().reset_index()
Returns:
Date Count
0 2015-07-08 1
1 2015-09-05 2
2 2017-09-05 1
3 2018-09-05 2
How to group by week (start is Thursday) using pandas?
First convert columns to numbers and datetimes by to_datetime
:
df_users['Users_gain'] = df_users['Users_gain'].astype(int)
df_users['Date'] = pd.to_datetime(df_users['Date'], format='%d.%m.%Y')
Then aggregate by DataFrame.resample
or with Grouper
by day Wednesday
:
df_users = df_users.resample('W-Wed',on='Date')['Users_gain'].sum().reset_index()
#alternative
#df_users = df_users.groupby(pd.Grouper(key='Date', freq='W-Wed')).sum().reset_index()
Last change format of datetimes with subtract 6 days and Series.dt.strftime
:
s = (df_users['Date'] - pd.offsets.DateOffset(days=6)).dt.strftime('%d.%m.%Y-')
df_users['Date'] = s + df_users['Date'].dt.strftime('%d.%m.%Y')
print (df_users)
Date Users_gain
0 13.02.2020-19.02.2020 6
1 20.02.2020-26.02.2020 10
groupby week - pandas dataframe
Try extract the iso calendar (year-week-day), then groupby:
s = dt.index.isocalendar()
dt.groupby([s.year, s.week]).sum()
You would get something like this:
a b c d e
year week
2019 1 18 33 31 26 25
2 36 31 25 28 31
3 33 22 44 22 29
4 36 36 35 33 31
5 27 30 26 31 36
Pandas grouping by week
May be with the caveat of the definition of the first day of a week, you could take something in the following code.
df = pd.DataFrame(data=d)
df['Date']=pd.to_datetime(df['Date'])
I. Discontinuous index
Monday is chosen as the first day of week
#(1) Build a series of first_day_of_week, monday is chosen as the first day of week
weeks_index = df['Date'] - df['Date'].dt.weekday * np.timedelta64(1, 'D')
#(2) Groupby and some tidying
df2 = ( df.groupby([df['Name'], weeks_index])
.count()
.rename(columns={'Date':'Count'})
.swaplevel() # weeks to first level
.sort_index()
.unstack(1).fillna(0.0)
.astype(int)
.rename_axis('first_day_of_week')
)
>>> print(df2)
Name A B C D K M R
first_day_of_week
2021-08-30 1 0 0 0 0 0 0
2021-09-06 0 0 3 1 0 0 0
2021-09-13 0 0 0 0 1 0 0
2021-09-20 0 0 0 1 0 0 1
2021-09-27 0 0 0 0 1 1 0
2021-10-18 0 1 0 0 0 0 0
II. Continuous index
This part does not differ much of the previous one.
We build a continuous version of the index to be use to reindex
Monday is chosen as the first day of week (obviouly for the two indices)
#(1a) Build a series of first_day_of_week, monday is chosen as the
weeks_index = df['Date'] - df['Date'].dt.weekday * np.timedelta64(1, 'D')
#(1b) Build a continuous series of first_day_of_week
continuous_weeks_index = pd.date_range(start=weeks_index.min(),
end=weeks_index.max(),
freq='W-MON') # monday
#(2) Groupby, unstack, reindex, and some tidying
df2 = ( df
# groupby and count
.groupby([df['Name'], weeks_index])
.count()
.rename(columns={'Date':'Count'})
# unstack on weeks
.swaplevel() # weeks to first level
.sort_index()
.unstack(1)
# reindex to insert weeks with no data
.reindex(continuous_weeks_index) # new index
# clean up
.fillna(0.0)
.astype(int)
.rename_axis('first_day_of_week')
)
>>>print(df2)
Name A B C D K M R
first_day_of_week
2021-08-30 1 0 0 0 0 0 0
2021-09-06 0 0 3 1 0 0 0
2021-09-13 0 0 0 0 1 0 0
2021-09-20 0 0 0 1 0 0 1
2021-09-27 0 0 0 0 1 1 0
2021-10-04 0 0 0 0 0 0 0
2021-10-11 0 0 0 0 0 0 0
2021-10-18 0 1 0 0 0 0 0
Last step if needed
df2.stack()
Aggregating weekly data by group into monthly sums in pandas
'Week' is not in the year_month format you need in your expected output, so you need to first convert them into year_month
by:
date = df['Week'].str.split(' ', expand=True)[0]
year_month = pd.to_datetime(date, errors='coerce').dt.strftime('%Y-%b').fillna(date)
before you use groupby
:
df.groupby([year_month, 'Clinic']).sum()
pandas group by week
Probably you have date
column as a string.
In order to use it in a Grouper
with a frequency, start from converting this column to DateTime
:
df['date'] = pd.to_datetime(df['date'])
Then, as date
column is an "ordinary" data column (not the index), use key='date'
parameter and a frequency.
To sum up, below you have a working example:
import pandas as pd
d = [['2018-08-19 19:08:19', 'pga', 'yes'],
['2018-08-19 19:09:27', 'pga', 'no'],
['2018-08-19 19:10:45', 'lry', 'no'],
['2018-09-07 19:12:31', 'lry', 'yes'],
['2018-09-19 19:13:07', 'pga', 'yes'],
['2018-10-22 19:13:20', 'lry', 'no']]
df = pd.DataFrame(data=d, columns=['date', 'user', 'answer'])
df['date'] = pd.to_datetime(df['date'])
gr = df.groupby(pd.Grouper(key='date',freq='W'))
for name, group in gr:
print(' ', name)
if len(group) > 0:
print(group)
Note that the group key (name
) is the ending date of a week, so dates from group members are earlier or equal to the date printed above.
You can change it passing label='left'
parameter to Grouper
.
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