Importing Financial Data form the Web

Using pandas_datareader to import financial data from the web to a dataframe

It is convinient to use pandas_datareader to import fiancial data directly into a dataframe. The process is very straight-forward, there is minimal code required and the data is available from different sources such as Google Finance, Yahoo!, the Federal Reserve, the World Bank, etc.

import pandas as pd
import numpy as np
import pandas_datareader as pdr 
from datetime import datetime
#import datetime
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline

#Set times, -1 = yearg ago from now
end = datetime.now()
print (end)

start = datetime(end.year -1, end.month, end.day)
print (start)

2018-10-23 21:29:25.592488
2017-10-23 00:00:00

stock_data = pdr.DataReader('AAPL', 'yahoo', start, end)
stock_data.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 253 entries, 2017-10-23 to 2018-10-23
Data columns (total 6 columns):
Open         253 non-null float64
High         253 non-null float64
Low          253 non-null float64
Close        253 non-null float64
Adj Close    253 non-null float64
Volume       253 non-null int64
dtypes: float64(5), int64(1)
memory usage: 13.8 KB

stock_data.head()

	Open	High	Low	Close	Adj Close	Volume
Date
2017-10-23	156.889999	157.690002	155.500000	156.169998	153.843857	21984300
2017-10-24	156.289993	157.419998	156.199997	157.100006	154.760010	17757200
2017-10-25	156.910004	157.550003	155.270004	156.410004	154.080307	21207100
2017-10-26	157.229996	157.830002	156.779999	157.410004	155.065399	17000500
2017-10-27	159.289993	163.600006	158.699997	163.050003	160.621384	44454200

stock_data.tail()

	Open	High	Low	Close	Adj Close	Volume
Date
2018-10-17	222.300003	222.639999	219.339996	221.190002	221.190002	22885400
2018-10-18	217.860001	219.740005	213.000000	216.020004	216.020004	32581300
2018-10-19	218.059998	221.259995	217.429993	219.309998	219.309998	33078700
2018-10-22	219.789993	223.360001	218.940002	220.649994	220.649994	28792100
2018-10-23	215.830002	223.250000	214.699997	222.729996	222.729996	38616200

#View historical trend of the closing price Adj Close column
stock_data['Adj Close'].plot(legend=True, figsize=(10,4))

<matplotlib.axes._subplots.AxesSubplot at 0x11f3f0160>