Python pandas make new column from data in existing column and from another dataframe -


i have dataframe called 'mydata', , if do

len(mydata.loc['2015-9-2'])

it counts number of rows in mydata have date, , returns number like

1067

i have dataframe called 'yourdata' looks like

     timestamp 51   2015-06-22 52   2015-06-23 53   2015-06-24 54   2015-06-25 43   2015-07-13

now want use each date in yourdata instead of typing in each date

len(mydata.loc['2015-9-2'])

i can iterate through 'yourdata' using them like

len(mydata.loc[yourdata['timestamp']])

and produce new dataframe results or add new column yourdata result each date, i'm lost how this?

the following not work

yourdata['result'] = len(mydata.loc[yourdata['timestamp']])

neither this

yourdata['result'] = len(mydata.loc[yourdata.iloc[:,-3]])

this work

yourdata['result'] = len(mydata.loc['2015-9-2'])

buts no want use date in each row not fixed date.

edit: first few rows of mydata

    timestamp            bpm  0  2015-08-30 16:48:00   65  1  2015-08-30 16:48:10   65  2  2015-08-30 16:48:15   66  3  2015-08-30 16:48:20   67  4  2015-08-30 16:48:30   70 

import numpy np import pandas pd  mydata = pd.dataframe({'timestamp': ['2015-06-22 16:48:00']*3 +                                     ['2015-06-23 16:48:00']*2 +                                     ['2015-06-24 16:48:00'] +                                     ['2015-06-25 16:48:00']*4 +                                     ['2015-07-13 16:48:00',                                      '2015-08-13 16:48:00'],                        'bpm': [65]*8 + [70]*4}) mydata['timestamp'] = pd.to_datetime(mydata['timestamp']) print(mydata)  #     bpm           timestamp # 0    65 2015-06-22 16:48:00 # 1    65 2015-06-22 16:48:00 # 2    65 2015-06-22 16:48:00 # 3    65 2015-06-23 16:48:00 # 4    65 2015-06-23 16:48:00 # 5    65 2015-06-24 16:48:00 # 6    65 2015-06-25 16:48:00 # 7    65 2015-06-25 16:48:00 # 8    70 2015-06-25 16:48:00 # 9    70 2015-06-25 16:48:00 # 10   70 2015-07-13 16:48:00 # 11   70 2015-08-13 16:48:00  yourdata = pd.series(['2015-06-22', '2015-06-23', '2015-06-24',                       '2015-06-25', '2015-07-13'], name='timestamp') yourdata = pd.to_datetime(yourdata).to_frame() print(yourdata)  # 0   2015-06-22 # 1   2015-06-23 # 2   2015-06-24 # 3   2015-06-25 # 4   2015-07-13  result = (mydata.set_index('timestamp').resample('d')                 .size().loc[yourdata['timestamp']]                 .reset_index()) result.columns = ['timestamp', 'result'] print(result)  #    timestamp  result # 0 2015-06-22       3 # 1 2015-06-23       2 # 2 2015-06-24       1 # 3 2015-06-25       4 # 4 2015-07-13       1

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