Pandas Datareader To Download Stocks Data From Google And Yahoo Finance

Datareader package can be used to access data from multiple sources such as yahoo and google finance…

To install the package, use pip

In [ ]:

!pip install pandas_datareader

Use DataReader to access data from yahoo finance

In [ ]:

import numpy as np
import pandas as pd

from pandas_datareader import data as wb
aapl = wb.DataReader('AAPL', data_source='yahoo', start='1995-1-1')

At the time of writing this notebook, there is a bug in DataReader because of which You might run in to following error

TypeError: string indices must be integers

How To Fix DataReader TypeError: string indices must be integers

Use the following snippet to work around the above error.

In [9]:

import numpy as np
import pandas as pd
import yfinance as yf
yf.pdr_override()
from pandas_datareader import data as wb

[*********************100%***********************]  1 of 1 completed

In [11]:

aapl = wb.DataReader('AAPL', data_source='yahoo', start='1995-1-1')

[*********************100%***********************]  1 of 1 completed

In [15]:

aapl

Out[15]:

	Open	High	Low	Close	Adj Close	Volume
Date
1995-01-03	0.347098	0.347098	0.338170	0.342634	0.288771	103868800
1995-01-04	0.344866	0.353795	0.344866	0.351563	0.296296	158681600
1995-01-05	0.350446	0.351563	0.345982	0.347098	0.292533	73640000
1995-01-06	0.371652	0.385045	0.367188	0.375000	0.316049	1076622400
1995-01-09	0.371652	0.373884	0.366071	0.367885	0.310052	274086400
…	…	…	…	…	…	…
2023-01-09	130.470001	133.410004	129.889999	130.149994	130.149994	70790800
2023-01-10	130.259995	131.259995	128.119995	130.729996	130.729996	63896200
2023-01-11	131.250000	133.509995	130.460007	133.490005	133.490005	69458900
2023-01-12	133.880005	134.259995	131.440002	133.410004	133.410004	71379600
2023-01-13	132.029999	134.919998	131.660004	134.759995	134.759995	57758000

7059 rows × 6 columns

Use DataReader to access data from google finance

In [13]:

googl = wb.DataReader('GOOGL', data_source='googl', start='1995-1-1')

[*********************100%***********************]  1 of 1 completed

In [14]:

googl

Out[14]:

	Open	High	Low	Close	Adj Close	Volume
Date
2004-08-19	2.502503	2.604104	2.401401	2.511011	2.511011	893181924
2004-08-20	2.527778	2.729730	2.515015	2.710460	2.710460	456686856
2004-08-23	2.771522	2.839840	2.728979	2.737738	2.737738	365122512
2004-08-24	2.783784	2.792793	2.591842	2.624374	2.624374	304946748
2004-08-25	2.626627	2.702703	2.599600	2.652653	2.652653	183772044
…	…	…	…	…	…	…
2023-01-09	88.360001	90.050003	87.860001	88.019997	88.019997	29003900
2023-01-10	85.980003	88.669998	85.830002	88.419998	88.419998	30467800
2023-01-11	89.180000	91.599998	89.010002	91.519997	91.519997	26862000
2023-01-12	91.480003	91.870003	89.750000	91.129997	91.129997	30258100
2023-01-13	90.849998	92.190002	90.129997	92.120003	92.120003	26309900

4634 rows × 6 columns

Note – The pandas_datareader library does not have a built-in function for retrieving options data. However, there are other libraries and APIs you can use to obtain options data, such as yfinance or alpha_vantage for Yahoo Finance and Alpha Vantage, iex for IEX Cloud API.

Check out following tutorial if you want to use yfinance
How To Use yfinance Package

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