How to resample OHLCV data into 5-min?

Question

I have this set of data

2016-08-09 12:39:00,536.7841,536.7849,536.6141,536.7849,0.656
2016-08-09 12:40:00,536.6749,536.6749,536.6749,536.6749,0.2642
2016-08-09 12:41:00,535.84,535.84,535.615,535.615,0.348
2016-08-09 12:42:00,535.5401,535.5401,534.1801,534.1801,0.507
2016-08-09 12:43:00,534.5891,534.8753,534.5891,534.807,0.656
2016-08-09 12:44:00,534.8014,534.878,534.8014,534.8416,0.502
2016-08-09 12:45:00,534.8131,534.8131,534.2303,534.6736,0.552
2016-08-09 12:47:00,534.756,538.5999,534.756,534.7836,0.62647241
2016-08-09 12:48:00,536.0557,536.6864,536.0557,536.6864,1.2614
2016-08-09 12:49:00,536.8966,537.7289,536.8966,537.7289,0.532
2016-08-09 12:50:00,537.9829,539.2199,537.9829,539.2199,0.67752932
2016-08-09 12:51:00,538.5,539.2199,538.5,539.2199,0.43768953

I want to resample it to 5-minutes OHCLV, so I did this code:

import pandas as pd

df= pd.read_csv("C:\Users\Araujo's PC\Desktop\python_scripts\CSV\cex_btc.csv",
                names=['timestamps','open','high','low','close','volume'])

df.set_index('timestamps',inplace=True)

ohlc_dict = {
    'open':'first',
    'high':'max',
    'low':'min',
    'close':'last',
    'volume':'sum'
    }

df.resample('5T', how=ohlc_dict)

print df

It appears me this error:

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'

Can someone help me on this?

Answer 1

You just need to convert the values in your timestamps column to pandas timestamps before setting the index with their values. They are currently just text fields I believe.

df['timestamps'] = pd.to_datetime(df['timestamps'])
df.set_index('timestamps', inplace=True)

>>> df.resample('5T', how=ohlc_dict)

                         high     close      open       low    volume
timestamps                                                           
2016-08-09 12:35:00  536.7849  536.7849  536.7841  536.6141  0.656000
2016-08-09 12:40:00  536.6749  534.8416  536.6749  534.1801  2.277200
2016-08-09 12:45:00  538.5999  537.7289  534.8131  534.2303  2.971872
2016-08-09 12:50:00  539.2199  539.2199  537.9829  537.9829  1.115219

You can also try to parse these when reading the csv:

pd.read_csv(filename, parse_dates=['timestamps'],
            names=['timestamps','open','high','low','close','volume'])