Pandas - How To Organised Dataframe Based On Date And Assign New Values To Column

I have a dataframe of a month excluding Saturday and Sunday, which was logged every 1 minute. v1 v2 2017-04-03 09:15:00 35.7 35.4

Solution 1:

You can groupby on index and use groupby.agg with a custom function.

df1 = res.groupby(res.index.date).agg({'v1': lambda x: x[min(x.index)], 'v2':lambda x: x[max(x.index)]})

print (df1)

             v1      v2
2017-04-03  35.782.62017-04-04  24.370.62017-04-2831.733.7

An alternative to resample dataframe to get 1st and last value from each day.

res=df.reset_index().groupby(df.index.date).agg(['first','last']).stack().set_index('index')Out[123]:v1v2index2017-04-03 09:15:00  35.735.42017-04-03 16:30:00  82.782.62017-04-04 09:15:00  24.324.22017-04-04 16:30:00  70.270.62017-04-28 09:15:00  31.731.42017-04-28 16:30:00  33.033.7

Solution 2:

Try this:

df_result = pd.DataFrame()
df_result['v1'] = res.groupby(res.index)['v1'].min()
df_result['v2'] = res.groupby(res.index)['v2'].max()

Solution 3:

There is a very interesting fonction in pandas to work with the datetime index. It is the resampling fonction. In your Case try this :

deffirst_last(entry):
   returnentry['v1'][0],entry['v2'][1]yourdataframe.resample('D').apply(first_last)

the 'D' stands for Daily resampling.

result :

Dates2017-04-03  35.782.62017-04-04  24.370.6

Solution 4:

You can reset_index and then GroupBy + apply with a custom function:

deffirst_second(x):
    return pd.Series({'v1': x['v1'].iat[0], 'v2': x['v2'].iat[-1]})

res2 = res.reset_index()
res2 = res2.groupby(res2['index'].dt.date).apply(first_second)

print(res2)

              v1    v2
index                 
2017-04-03  35.782.62017-04-04  24.370.62017-04-2831.733.7