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Grouping by date range with pandas


Grouping by date range with pandas

By : Ye Myat Thein
Date : November 21 2020, 03:00 PM
wish helps you I am looking to group by two columns: user_id and date; however, if the dates are close enough, I want to be able to consider the two entries part of the same group and group accordingly. Date is m-d-y , I'd convert this to a datetime column and then use pd.TimeGrouper:
code :
dates =  pd.to_datetime(df.date, format='%m-%d-%y')
print(dates)
0   2017-01-01
1   2017-01-01
2   2017-01-01
3   2017-01-01
4   2017-01-02
5   2017-01-02
6   2017-01-10
7   2017-02-01
Name: date, dtype: datetime64[ns]

df = (df.assign(date=dates).set_index('date')
        .groupby(['user_id', pd.TimeGrouper('3D')])
        .sum()
        .reset_index())    
print(df)
   user_id       date  val
0        1 2017-01-01    3
1        2 2017-01-01    2
2        2 2017-01-10    1
3        3 2017-01-01    1
4        3 2017-01-31    1
df = (df.assign(date=dates)
        .groupby(['user_id', pd.Grouper(key='date', freq='3D')])
        .sum()
        .reset_index())
print(df)
   user_id       date  val
0        1 2017-01-01    3
1        2 2017-01-01    2
2        2 2017-01-10    1
3        3 2017-01-01    1
4        3 2017-01-31    1


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Use result of pandas groupby to query date from column's pandas cut date range

Use result of pandas groupby to query date from column's pandas cut date range


By : stayhigh
Date : March 29 2020, 07:55 AM
I wish did fix the issue. So I've got the result of a pandas.groupby() call, and I'm wanting to query the result in a mysql select style query. Here is a MWE of the code I'm trying to work from: , You can use .loc
code :
s=df1.groupby(pd.cut(df1['date'], df2['bin_dates'])).agg({'value':np.nanmean})
s.loc['2012-10-11 3:00:00']
Out[94]: 
value    5.53283
Name: (2012-10-10 14:00:00, 2012-10-14 14:00:00], dtype: float64
Grouping by date in pandas keeping the date column

Grouping by date in pandas keeping the date column


By : user3569888
Date : March 29 2020, 07:55 AM
To fix this issue You have to remove the 'Date' from the [ ], you are already grouping by it. And don't drop the index, Date is your new index in your returning dataframe and you want to keep it
code :
target_df_count_total = data_frame.groupby('Date')['Price above Mean'].count().reset_index(level=0)
grouping date in Highcharts if the date range is too big

grouping date in Highcharts if the date range is too big


By : Tariq
Date : March 29 2020, 07:55 AM
Any of those help Highcharts can automatically manage time values in the x-Axis, provided that your chart is configured correctly. The problem in your case is that you've told Highcharts to use your categories, and it shows all of the categories.
To set up your chart to avoid this, you'll need to do two things:
Grouping rows by time-range in Pandas dataframe

Grouping rows by time-range in Pandas dataframe


By : Geenie
Date : March 29 2020, 07:55 AM
may help you . OK, I think the following is what you want, this constructs a TimeDelta from your index by subtracting all values by the first value. We then access the microseconds component and divide by 1000 and then cast the Series dtype to int:
Pandas, Grouping by date range

Pandas, Grouping by date range


By : Lukáš Král
Date : March 29 2020, 07:55 AM
Does that help Consider the dataframe df as our example. You'll want to make sure your complete column is datetime by doing df.complete = pd.to_datetime(df.complete).
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