@@ -77,10 +77,10 @@ prices are available for up to 5 years:
7777
7878 import pandas_datareader.data as web
7979 from datetime import datetime
80- start = datetime(2015 , 2 , 9 )
81- end = datetime(2017 , 5 , 24 )
80+ start = datetime(2016 , 9 , 1 )
81+ end = datetime(2018 , 9 , 1 )
8282 f = web.DataReader(' F' , ' iex' , start, end)
83- f.loc[' 2015-02-09 ' ]
83+ f.loc[' 2018-08-31 ' ]
8484
8585 There are additional interfaces to this API that are
8686directly exposed: tops (`'iex-tops' `) and last (`'iex-lasts' `).
@@ -109,8 +109,7 @@ year relative to today.
109109 f = web.DataReader(' F' , ' robinhood' )
110110 f.head()
111111
112-
113- .. _remote_data.alphavantage
112+ .. _remote_data.alphavantage :
114113
115114Alpha Vantage
116115=============
270269 start = datetime.datetime(2010 , 1 , 1 )
271270 end = datetime.datetime(2013 , 1 , 27 )
272271 gdp = web.DataReader(' GDP' , ' fred' , start, end)
273- gdp.ix [' 2013-01-01' ]
272+ gdp.loc [' 2013-01-01' ]
274273
275274 # Multiple series:
276275 inflation = web.DataReader([' CPIAUCSL' , ' CPILFESL' ], ' fred' , start, end)
@@ -305,7 +304,7 @@ World Bank
305304by using the ``wb `` I/O functions.
306305
307306Indicators
308- ----------
307+ ^^^^^^^^^^
309308
310309Either from exploring the World Bank site, or using the search function included,
311310every world bank indicator is accessible.
@@ -316,7 +315,7 @@ constant dollars in North America, you would use the ``search`` function:
316315.. code-block :: python
317316
318317 In [1 ]: from pandas_datareader import wb
319- In [2 ]: mathces = wb.search(' gdp.*capita.*const' )
318+ In [2 ]: matches = wb.search(' gdp.*capita.*const' )
320319
321320
322321 Then you would use the ``download `` function to acquire the data from the World
@@ -417,7 +416,7 @@ populations in rich countries tend to use cellphones at a higher rate:
417416 ==============================================================================
418417
419418Country Codes
420- ------------ -
419+ ^^^^^^^^^^^^^
421420
422421The `` country`` argument accepts a string or list of mixed
423422`two < http:// en.wikipedia.org/ wiki/ ISO_3166 - 1_alpha - 2 > ` __ or `three < http:// en.wikipedia.org/ wiki/ ISO_3166 - 1_alpha - 3 > ` __ character
@@ -426,7 +425,7 @@ ISO country codes, as well as dynamic `World Bank exceptions <https://datahelpde
426425For a list of the the hard- coded country codes (used solely for error handling logic) see `` pandas_datareader.wb.country_codes`` .
427426
428427Problematic Country Codes & Indicators
429- --------------------------------------
428+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
430429
431430.. note::
432431
@@ -533,7 +532,7 @@ available. More information on the `field <http://www.nasdaqtrader.com/trader.as
533532
534533 In [12 ]: from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
535534 In [13 ]: symbols = get_nasdaq_symbols()
536- In [14 ]: print (symbols.ix [' IBM' ])
535+ In [14 ]: print (symbols.loc [' IBM' ])
537536 Nasdaq Traded True
538537 Security Name International Business Machines Corporation Co...
539538 Listing Exchange N
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