Pandas dataframe resample without aggregation

Question

I have a dataframe defined as follows:

import datetime
import pandas as pd
import random
import numpy as np

todays_date = datetime.datetime.today().date()
index = pd.date_range(todays_date - datetime.timedelta(10), periods=10, freq='D')
index = index.append(index)
idname = ['A']*10 + ['B']*10
values = random.sample(xrange(100), 20)
data = np.vstack((idname, values)).T

tmp_df = pd.DataFrame(data, columns=['id', 'value'])
tmp_index = pd.DataFrame(index, columns=['date'])
tmp_df = pd.concat([tmp_index, tmp_df], axis=1)
tmp_df = tmp_df.set_index('date')

Note that there are 2 values for each date. I would like to resample the dataframe tmp_df on a weekly basis but keep the two separate values. I tried tmp_df.resample('W-FRI') but it doesn't seem to work.

Answer 1

The solution you're looking for is groupby , which lets you perform operations on dataframe slices (here 'A' and 'B') independently:

df.groupby('id').resample('W-FRI')

Note: your code produces an error ( No numeric types to aggregate ) because the 'value' column is not converted to int . You need to convert it first:

df['value'] = pd.to_numeric(df['value'])