pandas resample function

df.resample("W").agg(['min','max','mean','std']) # resample("3T") ==> 3 minutes # resample("30S") ==> 30 seconds # resample("1H") ==> 1 hour # resample("D") ==> day # resample("W") ==> week # resample("M") ==> month # resample("Y") ==> year # resample("Q") ==> quarter # Ex. 2018-01-01 ==> 2018-03-01 , 2018-06-01 , 2018-09-01 , 2018-12-01 ##################################### # .mean() # .max() # .min() # .sum() ...... # .agg(['min','max',...]) specified functions are applied for every column

In [101]: df.resample('1H').agg({'openbid': 'first', 'highbid': 'max', 'lowbid': 'min', 'closebid': 'last'}) Out[101]: lowbid highbid closebid openbid ctime 2015-09-30 23:00:00 1.11687 1.11712 1.11708 1.117

#We can also use custom functions and apply them when resampling using the .apply(method_name) method #This is an example used in a downsampling example def custom_resampler(arraylike): return np.sum(arraylike) + 5 data.resample('Q').apply(custom_resampler)

how i resamplae a datetime column in python

>>> d = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19],
...           'volume': [50, 60, 40, 100, 50, 100, 40, 50]})
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
...                                     periods=8,
...                                     freq='W')
>>> df
   price  volume week_starting
0     10      50    2018-01-07
1     11      60    2018-01-14
2      9      40    2018-01-21
3     13     100    2018-01-28
4     14      50    2018-02-04
5     18     100    2018-02-11
6     17      40    2018-02-18
7     19      50    2018-02-25
# try below code when you want resample on datetime column to other all columns in dataframe
>>> df.resample('M', on='week_starting').mean() 
               price  volume
week_starting
2018-01-31     10.75    62.5
2018-02-28     17.00    60.0

Posted by: Guest on October-25-2020

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