converting daily stock data to weekly-based via pandas in Python

Question

I've got a DataFrame storing daily-based data which is as below:

Date              Open        High         Low       Close   Volume
2010-01-04   38.660000   39.299999   38.509998   39.279999  1293400   
2010-01-05   39.389999   39.520000   39.029999   39.430000  1261400   
2010-01-06   39.549999   40.700001   39.020000   40.250000  1879800   
2010-01-07   40.090000   40.349998   39.910000   40.090000   836400   
2010-01-08   40.139999   40.310001   39.720001   40.290001   654600   
2010-01-11   40.209999   40.520000   40.040001   40.290001   963600   
2010-01-12   40.160000   40.340000   39.279999   39.980000  1012800   
2010-01-13   39.930000   40.669998   39.709999   40.560001  1773400   
2010-01-14   40.490002   40.970001   40.189999   40.520000  1240600   
2010-01-15   40.570000   40.939999   40.099998   40.450001  1244200   

What I intend to do is to merge it into weekly-based data. After grouping:

1. the Date should be every Monday (at this point, holidays scenario should be considered when Monday is not a trading day, we should apply the first trading day in current week as the Date).
2. Open should be Monday's (or the first trading day of current week) Open.
3. Close should be Friday's (or the last trading day of current week) Close.
4. High should be the highest High of trading days in current week.
5. Low should be the lowest Low of trading days in current week.
6. Volumn should be the sum of all Volumes of trading days in current week.

which should look like this:

Date              Open        High         Low       Close   Volume
2010-01-04   38.660000   40.700001   38.509998   40.290001  5925600   
2010-01-11   40.209999   40.970001   39.279999   40.450001  6234600   

Currently, my code snippet is as below, which function should I use to mapping daily-based data to the expected weekly-based data? Many thanks!

import pandas_datareader.data as web

start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2016, 12, 31)
f = web.DataReader("MNST", "yahoo", start, end, session=session)
print f

Solution 1

You can resample (to weekly), offset (shift), and apply aggregation rules as follows:

logic = {'Open'  : 'first',
         'High'  : 'max',
         'Low'   : 'min',
         'Close' : 'last',
         'Volume': 'sum'}

offset = pd.offsets.timedelta(days=-6)

f = pd.read_clipboard(parse_dates=['Date'], index_col=['Date'])
f.resample('W', loffset=offset).apply(logic)

to get:

                 Open       High        Low      Close   Volume
Date                                                           
2010-01-04  38.660000  40.700001  38.509998  40.290001  5925600
2010-01-11  40.209999  40.970001  39.279999  40.450001  6234600

Solution 2

In general, assuming that you have the dataframe in the form you specified, you need to do the following steps:

1. put Date in the index
2. resample the index.

What you have is a case of applying different functions to different columns. See.

You can resample in various ways. for e.g. you can take the mean of the values or count or so on. check pandas resample.

You can also apply custom aggregators (check the same link). With that in mind, the code snippet for your case can be given as:

f['Date'] = pd.to_datetime(f['Date'])
f.set_index('Date', inplace=True)
f.sort_index(inplace=True)

def take_first(array_like):
    return array_like[0]

def take_last(array_like):
    return array_like[-1]

output = f.resample('W',                                 # Weekly resample
                    how={'Open': take_first, 
                         'High': 'max',
                         'Low': 'min',
                         'Close': take_last,
                         'Volume': 'sum'}, 
                    loffset=pd.offsets.timedelta(days=-6))  # to put the labels to Monday

output = output[['Open', 'High', 'Low', 'Close', 'Volume']]

Here, W signifies a weekly resampling which by default spans from Monday to Sunday. To keep the labels as Monday, loffset is used. There are several predefined day specifiers. Take a look at pandas offsets. You can even define custom offsets (see).

Coming back to the resampling method. Here for Open and Close you can specify custom methods to take the first value or so on and pass the function handle to the how argument.

This answer is based on the assumption that the data seems to be daily, i.e. for each day you have only 1 entry. Also, no data is present for the non-business days. i.e. Sat and Sun. So taking the last data point for the week as the one for Friday is ok. If you so want you can use business week instead of 'W'. Also, for more complex data you may want to use groupby to group the weekly data and then work on the time indices within them.

btw a gist for the solution can be found at: https://gist.github.com/prithwi/339f87bf9c3c37bb3188

Solution 3

I had the exact same question and found a great solution here.

https://www.techtrekking.com/how-to-convert-daily-time-series-data-into-weekly-and-monthly-using-pandas-and-python/

The weekly code is posted below.

import pandas as pd
import numpy as np

print('*** Program Started ***')

df = pd.read_csv('15-06-2016-TO-14-06-2018HDFCBANKALLN.csv')

# ensuring only equity series is considered
df = df.loc[df['Series'] == 'EQ']

# Converting date to pandas datetime format
df['Date'] = pd.to_datetime(df['Date'])
# Getting week number
df['Week_Number'] = df['Date'].dt.week
# Getting year. Weeknum is common across years to we need to create unique index by using year and weeknum
df['Year'] = df['Date'].dt.year

# Grouping based on required values
df2 = df.groupby(['Year','Week_Number']).agg({'Open Price':'first', 'High Price':'max', 'Low Price':'min', 'Close Price':'last','Total Traded Quantity':'sum'})
# df3 = df.groupby(['Year','Week_Number']).agg({'Open Price':'first', 'High Price':'max', 'Low Price':'min', 'Close Price':'last','Total Traded Quantity':'sum','Average Price':'avg'})
df2.to_csv('Weekly_OHLC.csv')
print('*** Program ended ***')

Solution 4

Adding to @Stefan 's answer with recent pandas API as loffset was deprecated since version 1.1.0 and later removed.

df = pd.read_clipboard(parse_dates=['Date'], index_col=['Date'])
logic = {'Open'  : 'first',
         'High'  : 'max',
         'Low'   : 'min',
         'Close' : 'last',
         'Volume': 'sum'}

dfw = df.resample('W').apply(logic)
# set the index to the beginning of the week
dfw.index = dfw.index - pd.tseries.frequencies.to_offset("6D")

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