.. currentmodule:: pandas
.. ipython:: python
:suppress:
import os
import csv
import pandas as pd
import numpy as np
np.random.seed(123456)
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.close('all')
from pandas import *
options.display.max_rows=15
import pandas.util.testing as tm
Functions from :mod:`pandas.io.data` extract data from various Internet sources into a DataFrame. Currently the following sources are supported:
- Yahoo! Finance
- Google Finance
- St. Louis FED (FRED)
- Kenneth French's data library
- World Bank
It should be noted, that various sources support different kinds of data, so not all sources implement the same methods and the data elements returned might also differ.
.. ipython:: python
import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f=web.DataReader("F", 'yahoo', start, end)
f.ix['2010-01-04']
*Experimental*
The Options class allows the download of options data from Yahoo! Finance.
The get_all_data method downloads and caches option data for all expiry months
and provides a formatted DataFrame with a hierarchical index, so its easy to get
to the specific option you want.
.. ipython:: python
from pandas.io.data import Options
aapl = Options('aapl', 'yahoo')
data = aapl.get_all_data()
data.iloc[0:5, 0:5]
#Show the $100 strike puts at all expiry dates:
data.loc[(100, slice(None), 'put'),:].iloc[0:5, 0:5]
#Show the volume traded of $100 strike puts at all expiry dates:
data.loc[(100, slice(None), 'put'),'Vol'].head()
If you don't want to download all the data, more specific requests can be made.
.. ipython:: python
import datetime
expiry = datetime.date(2016, 1, 1)
data = aapl.get_call_data(expiry=expiry)
data.iloc[0:5:, 0:5]
Note that if you call get_all_data first, this second call will happen much faster, as the data is cached.
.. ipython:: python
import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f=web.DataReader("F", 'google', start, end)
f.ix['2010-01-04']
.. ipython:: python
import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
gdp=web.DataReader("GDP", "fred", start, end)
gdp.ix['2013-01-01']
# Multiple series:
inflation = web.DataReader(["CPIAUCSL", "CPILFESL"], "fred", start, end)
inflation.head()
Dataset names are listed at Fama/French Data Library.
.. ipython:: python
import pandas.io.data as web
ip=web.DataReader("5_Industry_Portfolios", "famafrench")
ip[4].ix[192607]
pandas users can easily access thousands of panel data series from the
World Bank's World Development Indicators
by using the wb I/O functions.
For example, if you wanted to compare the Gross Domestic Products per capita in
constant dollars in North America, you would use the search function:
In [1]: from pandas.io import wb
In [2]: wb.search('gdp.*capita.*const').iloc[:,:2]
Out[2]:
id name
3242 GDPPCKD GDP per Capita, constant US$, millions
5143 NY.GDP.PCAP.KD GDP per capita (constant 2005 US$)
5145 NY.GDP.PCAP.KN GDP per capita (constant LCU)
5147 NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2005 internation...Then you would use the download function to acquire the data from the World
Bank's servers:
In [3]: dat = wb.download(indicator='NY.GDP.PCAP.KD', country=['US', 'CA', 'MX'], start=2005, end=2008)
In [4]: print(dat)
NY.GDP.PCAP.KD
country year
Canada 2008 36005.5004978584
2007 36182.9138439757
2006 35785.9698172849
2005 35087.8925933298
Mexico 2008 8113.10219480083
2007 8119.21298908649
2006 7961.96818458178
2005 7666.69796097264
United States 2008 43069.5819857208
2007 43635.5852068142
2006 43228.111147107
2005 42516.3934699993The resulting dataset is a properly formatted DataFrame with a hierarchical
index, so it is easy to apply .groupby transformations to it:
In [6]: dat['NY.GDP.PCAP.KD'].groupby(level=0).mean()
Out[6]:
country
Canada 35765.569188
Mexico 7965.245332
United States 43112.417952
dtype: float64Now imagine you want to compare GDP to the share of people with cellphone contracts around the world.
In [7]: wb.search('cell.*%').iloc[:,:2]
Out[7]:
id name
3990 IT.CEL.SETS.FE.ZS Mobile cellular telephone users, female (% of ...
3991 IT.CEL.SETS.MA.ZS Mobile cellular telephone users, male (% of po...
4027 IT.MOB.COV.ZS Population coverage of mobile cellular telepho...Notice that this second search was much faster than the first one because
pandas now has a cached list of available data series.
In [13]: ind = ['NY.GDP.PCAP.KD', 'IT.MOB.COV.ZS']
In [14]: dat = wb.download(indicator=ind, country='all', start=2011, end=2011).dropna()
In [15]: dat.columns = ['gdp', 'cellphone']
In [16]: print(dat.tail())
gdp cellphone
country year
Swaziland 2011 2413.952853 94.9
Tunisia 2011 3687.340170 100.0
Uganda 2011 405.332501 100.0
Zambia 2011 767.911290 62.0
Zimbabwe 2011 419.236086 72.4Finally, we use the statsmodels package to assess the relationship between
our two variables using ordinary least squares regression. Unsurprisingly,
populations in rich countries tend to use cellphones at a higher rate:
In [17]: import numpy as np
In [18]: import statsmodels.formula.api as smf
In [19]: mod = smf.ols("cellphone ~ np.log(gdp)", dat).fit()
In [20]: print(mod.summary())
OLS Regression Results
==============================================================================
Dep. Variable: cellphone R-squared: 0.297
Model: OLS Adj. R-squared: 0.274
Method: Least Squares F-statistic: 13.08
Date: Thu, 25 Jul 2013 Prob (F-statistic): 0.00105
Time: 15:24:42 Log-Likelihood: -139.16
No. Observations: 33 AIC: 282.3
Df Residuals: 31 BIC: 285.3
Df Model: 1
===============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
-------------------------------------------------------------------------------
Intercept 16.5110 19.071 0.866 0.393 -22.384 55.406
np.log(gdp) 9.9333 2.747 3.616 0.001 4.331 15.535
==============================================================================
Omnibus: 36.054 Durbin-Watson: 2.071
Prob(Omnibus): 0.000 Jarque-Bera (JB): 119.133
Skew: -2.314 Prob(JB): 1.35e-26
Kurtosis: 11.077 Cond. No. 45.8
==============================================================================