CODEWITHSUNDEEP
pythonpandasfinancegoogle-finance
Lakhan Pahuja
Lakhan Pahuja

Reputation: 47

google finance pandas read table to pandas dataframe

How to convert that html table to pandas dataframe?

https://www.google.com/finance/getprices?q=HINDALCO&i=60&p=15d&f=d,o,h,l,c,v

Example data:

enter image description here

Upvotes: 1

Views: 1137

Answers (3)

dmdip
dmdip

Reputation: 1795

@jezrael] has answered your question for one minute data for Hindalco. Here's more or less the same answer, tweaked a bit :

import pandas as pd
period = 60 #one minute
days = 2 # 2 days of data
ticker = 'HINDALCO'
url = 'http://www.google.com/finance/getprices?i={}&p={}d&f=d,o,h,l,c,v&df=cpct&q={}'.format(period, days, ticker)
cols = ['minute', 'open', 'high', 'low', 'close', 'volume']
df = pd.read_csv(url, skiprows=8, header=None, names=cols)
df.head()

Output:

  minute    open    high     low   close  volume
0      1  194.35  194.35  193.80  193.85   25785
1      2  194.15  194.40  194.00  194.35   64580
2      3  193.95  194.25  193.85  194.15   42980
3      4  193.80  193.95  193.75  193.90   33936
4      5  193.90  193.90  193.60  193.80   57088

Upvotes: 0

dmdip
dmdip

Reputation: 1795

If you are not particular about getting your data only from Google, this will work with Yahoo :

import pandas as pd
import pandas_datareader.data as web
from datetime import datetime

start = datetime(2014, 6, 2)
end = datetime(2014, 9, 5)

hindalco = web.DataReader('HINDALCO.NS', 'yahoo', start, end)

In [15]: hindalco.head(5)
Out[15]: 
             Open    High     Low   Close    Volume  Adj Close
Date                                                          
2014-06-02  147.3  151.15  146.35  150.05  13844600   146.2012
2014-06-03  150.7  155.70  149.35  155.20  23276100   151.2191
2014-06-04  156.0  161.75  155.20  160.70  15948200   156.5780
2014-06-05  160.6  171.00  159.95  169.85  20296900   165.4934
2014-06-06  172.0  172.10  165.30  169.25  13769100   164.9087

You will need to install pandas-datareader. 

sudo -H pip install pandas-datareader (ubuntu)
pip install pandas-datareader (windows)

Upvotes: 0

jezrael
jezrael

Reputation: 863256

You can use read_csv with parameters skiprows and names for new column names:

url = 'https://www.google.com/finance/getprices?q=HINDALCO&i=60&p=15d&f=d,o,h,l,c,v'
df = pd.read_csv(url, skiprows=[0,1,2,3,5,6]).rename(columns={'COLUMNS=DATE':'DATE'})

print (df.head())
          DATE   CLOSE    HIGH     LOW    OPEN  VOLUME
0  a1490154300  194.80  194.80  194.80  194.80    2600
1            1  193.55  194.70  193.00  194.15  339142
2            2  193.80  193.95  193.55  193.60  242687
3            3  194.20  194.40  193.80  193.90  119874
4            4  193.80  194.20  193.80  194.20  121355

Upvotes: 2

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