Remote Data Access — pandas 0.15.2 documentation (original) (raw)

Functions from pandas.io.data and pandas.io.ga extract data from various Internet sources into a DataFrame. Currently the following sources are supported:

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.

Yahoo! Finance

In [1]: import pandas.io.data as web

In [2]: import datetime

In [3]: start = datetime.datetime(2010, 1, 1)

In [4]: end = datetime.datetime(2013, 1, 27)

In [5]: f=web.DataReader("F", 'yahoo', start, end)

In [6]: f.ix['2010-01-04'] Out[6]: Open 10.17 High 10.28 Low 10.05 Close 10.28 Volume 60855800.00 Adj Close 9.52 Name: 2010-01-04 00:00:00, dtype: float64

Yahoo! Finance Options

*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.

In [7]: from pandas.io.data import Options

In [8]: aapl = Options('aapl', 'yahoo')

In [9]: data = aapl.get_all_data()

In [10]: data.iloc[0:5, 0:5] Out[10]: Last Bid Ask Chg PctChg Strike Expiry Type Symbol
27.86 2015-01-17 call AAPL150117C00027860 84.35 84.5 85.70 0 0.00% put AAPL150117P00027860 0.02 0.0 0.01 0 0.00% 28.57 2015-01-17 call AAPL150117C00028570 87.25 83.6 84.65 0 0.00% put AAPL150117P00028570 0.01 0.0 0.01 0 0.00% 29.29 2015-01-17 call AAPL150117C00029290 83.05 82.8 84.25 0 0.00%

#Show the $100 strike puts at all expiry dates: In [11]: data.loc[(100, slice(None), 'put'),:].iloc[0:5, 0:5] Out[11]: Last Bid Ask Chg PctChg Strike Expiry Type Symbol
100 2014-12-12 put AAPL141212P00100000 0.01 0.00 0.01 0.00 0.00% 2014-12-20 put AAPL141220P00100000 0.09 0.08 0.09 -0.02 -15.38% 2014-12-26 put AAPL141226P00100000 0.18 0.17 0.19 0.00 0.00% 2015-01-02 put AAPL150102P00100000 0.33 0.23 0.27 0.00 0.00% 2015-01-09 put AAPL150109P00100000 0.47 0.32 0.38 0.00 0.00%

#Show the volume traded of $100 strike puts at all expiry dates: In [12]: data.loc[(100, slice(None), 'put'),'Vol'].head() Out[12]: Strike Expiry Type Symbol
100 2014-12-12 put AAPL141212P00100000 529 2014-12-20 put AAPL141220P00100000 194 2014-12-26 put AAPL141226P00100000 467 2015-01-02 put AAPL150102P00100000 172 2015-01-09 put AAPL150109P00100000 16 Name: Vol, dtype: int64

If you don’t want to download all the data, more specific requests can be made.

In [13]: import datetime

In [14]: expiry = datetime.date(2016, 1, 1)

In [15]: data = aapl.get_call_data(expiry=expiry)

In [16]: data.iloc[0:5:, 0:5] Out[16]: Last Bid Ask Chg PctChg Strike Expiry Type Symbol
34.29 2016-01-15 call AAPL160115C00034290 76.85 76.65 81.0 0 0.00% 35.71 2016-01-15 call AAPL160115C00035710 72.65 75.25 79.8 0 0.00% 37.14 2016-01-15 call AAPL160115C00037140 72.55 73.80 78.0 0 0.00% 38.57 2016-01-15 call AAPL160115C00038570 58.35 72.40 76.9 0 0.00% 40.00 2016-01-15 call AAPL160115C00040000 72.00 70.95 75.0 0 0.00%

Note that if you call get_all_data first, this second call will happen much faster, as the data is cached.

If a given expiry date is not available, data for the next available expiry will be returned (January 15, 2015 in the above example).

Available expiry dates can be accessed from the expiry_dates property.

In [17]: aapl.expiry_dates Out[17]: [datetime.date(2014, 12, 12), datetime.date(2014, 12, 20), datetime.date(2014, 12, 26), datetime.date(2015, 1, 2), datetime.date(2015, 1, 9), datetime.date(2015, 1, 17), datetime.date(2015, 1, 23), datetime.date(2015, 2, 20), datetime.date(2015, 3, 20), datetime.date(2015, 4, 17), datetime.date(2015, 7, 17), datetime.date(2016, 1, 15), datetime.date(2017, 1, 20)]

In [18]: data = aapl.get_call_data(expiry=aapl.expiry_dates[0])

In [19]: data.iloc[0:5:, 0:5] Out[19]: Last Bid Ask Chg PctChg Strike Expiry Type Symbol
90 2014-12-12 call AAPL141212C00090000 21.95 23.15 23.35 0 0.00% 92 2014-12-12 call AAPL141212C00092000 20.22 21.15 21.35 0 0.00% 93 2014-12-12 call AAPL141212C00093000 19.05 20.15 20.35 0 0.00% 94 2014-12-12 call AAPL141212C00094000 17.70 19.15 19.35 0 0.00% 95 2014-12-12 call AAPL141212C00095000 16.65 18.15 18.35 0 0.00%

A list-like object containing dates can also be passed to the expiry parameter, returning options data for all expiry dates in the list.

In [20]: data = aapl.get_near_stock_price(expiry=aapl.expiry_dates[0:3])

In [21]: data.iloc[0:5:, 0:5] Out[21]: Last Bid Ask Chg PctChg Strike Expiry Type Symbol
113 2014-12-20 call AAPL141220C00113000 1.95 1.93 1.96 0.00 0.00% 2014-12-26 call AAPL141226C00113000 2.45 2.44 2.46 0.07 +3.29% 114 2014-12-12 call AAPL141212C00114000 0.46 0.46 0.48 0.01 +3.03% 2014-12-20 call AAPL141220C00114000 1.49 1.43 1.45 0.02 +1.68% 2014-12-26 call AAPL141226C00114000 1.96 1.94 1.97 0.13 +7.83%

The month and year parameters can be used to get all options data for a given month.

Google Finance

In [22]: import pandas.io.data as web

In [23]: import datetime

In [24]: start = datetime.datetime(2010, 1, 1)

In [25]: end = datetime.datetime(2013, 1, 27)

In [26]: f=web.DataReader("F", 'google', start, end)

In [27]: f.ix['2010-01-04'] Out[27]: Open 10.17 High 10.28 Low 10.05 Close 10.28 Volume 60855796.00 Name: 2010-01-04 00:00:00, dtype: float64

FRED

In [28]: import pandas.io.data as web

In [29]: import datetime

In [30]: start = datetime.datetime(2010, 1, 1)

In [31]: end = datetime.datetime(2013, 1, 27)

In [32]: gdp=web.DataReader("GDP", "fred", start, end)

In [33]: gdp.ix['2013-01-01'] Out[33]: GDP 16502.4 Name: 2013-01-01 00:00:00, dtype: float64

Multiple series:

In [34]: inflation = web.DataReader(["CPIAUCSL", "CPILFESL"], "fred", start, end)

In [35]: inflation.head() Out[35]: CPIAUCSL CPILFESL DATE
2010-01-01 217.466 220.543 2010-02-01 217.251 220.662 2010-03-01 217.305 220.753 2010-04-01 217.376 220.817 2010-05-01 217.299 221.026

Fama/French

Dataset names are listed at Fama/French Data Library.

In [36]: import pandas.io.data as web

In [37]: ip=web.DataReader("5_Industry_Portfolios", "famafrench")

In [38]: ip[4].ix[192607] Out[38]: 1 Cnsmr 5.43 2 Manuf 2.73 3 HiTec 1.83 4 Hlth 1.64 5 Other 2.15 Name: 192607, dtype: float64

World Bank

pandas users can easily access thousands of panel data series from theWorld Bank’s World Development Indicatorsby using the wb I/O functions.

Indicators

Either from exploring the World Bank site, or using the search function included, every world bank indicator is accessible.

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 42516.3934699993

The 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: float64

Now 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 becausepandas 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 72.4

Finally, 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

Country Codes

New in version 0.15.1.

The country argument accepts a string or list of mixedtwo or three character ISO country codes, as well as dynamic World Bank exceptions to the ISO standards.

For a list of the the hard-coded country codes (used solely for error handling logic) see pandas.io.wb.country_codes.

Problematic Country Codes & Indicators

Note

The World Bank’s country list and indicators are dynamic. As of 0.15.1,wb.download() is more flexible. To achieve this, the warning and exception logic changed.

The world bank converts some country codes, in their response, which makes error checking by pandas difficult. Retired indicators still persist in the search.

Given the new flexibility of 0.15.1, improved error handling by the user may be necessary for fringe cases.

To help identify issues:

There are at least 4 kinds of country codes:

  1. Standard (2/3 digit ISO) - returns data, will warn and error properly.
  2. Non-standard (WB Exceptions) - returns data, but will falsely warn.
  3. Blank - silently missing from the response.
  4. Bad - causes the entire response from WB to fail, always exception inducing.

There are at least 3 kinds of indicators:

  1. Current - Returns data.
  2. Retired - Appears in search results, yet won’t return data.
  3. Bad - Will not return data.

Use the errors argument to control warnings and exceptions. Setting errors to ignore or warn, won’t stop failed responses. (ie, 100% bad indicators, or a single “bad” (#4 above) country code).

See docstrings for more info.

Google Analytics

The ga module provides a wrapper forGoogle Analytics APIto simplify retrieving traffic data. Result sets are parsed into a pandas DataFrame with a shape and data types derived from the source table.

Configuring Access to Google Analytics

The first thing you need to do is to setup accesses to Google Analytics API. Follow the steps below:

  1. In the Google Developers Console
    1. enable the Analytics API
    2. create a new project
    3. create a new Client ID for an “Installed Application” (in the “APIs & auth / Credentials section” of the newly created project)
    4. download it (JSON file)
  2. On your machine
    1. rename it to client_secrets.json
    2. move it to the pandas/io module directory

The first time you use the read_ga() funtion, a browser window will open to ask you to authentify to the Google API. Do proceed.

Using the Google Analytics API

The following will fetch users and pageviews (metrics) data per day of the week, for the first semester of 2014, from a particular property.

import pandas.io.ga as ga ga.read_ga( account_id = "2360420", profile_id = "19462946", property_id = "UA-2360420-5", metrics = ['users', 'pageviews'], dimensions = ['dayOfWeek'], start_date = "2014-01-01", end_date = "2014-08-01", index_col = 0, filters = "pagePath=~aboutus;ga:country==France", )

The only mandatory arguments are metrics, dimensions and start_date. We can only strongly recommend you to always specify the account_id, profile_id and property_id to avoid accessing the wrong data bucket in Google Analytics.

The index_col argument indicates which dimension(s) has to be taken as index.

The filters argument indicates the filtering to apply to the query. In the above example, the page has URL has to contain aboutus AND the visitors country has to be France.

Detailed informations in the followings: