ImportError: The pandas.io.data module is moved to a separate package (pandas-datareader)

ImportError: The pandas.io.data module is moved to a separate package (pandas-datareader)

#-*- coding: utf-8 -*-
 
import pandas as pd
import pandas.io.data as web
import datetime
 
def download_stock_data(file_name, company_code, year1, month1, date1, year2, month2, date2):
    start = datetime.datetime(year1, month1, date1)
    end = datetime.datetime(year2, month2, date2)
    df = web.DataReader("%s.KS" % (company_code), "yahoo", start, end)
    
    df.to_pickle(file_name)
    
    return df
 
download_stock_date('samsung.data', '005930', 2016, 1, 1, 2016, 1)
 

ImportError: The pandas.io.data module is moved to a separate package (pandas-datareader). After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.

pandas-datareader 패키지를 설치한다.

$ pip install pandas-datareader

ln[4] 와 같이 임포트한다.

#-*- coding: utf-8 -*-
 
import pandas as pd
import pandas_datareader.data as web
import datetime
 
def download_stock_data(file_name, company_code, year1, month1, date1, year2, month2, date2):
    start = datetime.datetime(year1, month1, date1)
    end = datetime.datetime(year2, month2, date2)
    df = web.DataReader("%s.KS" % (company_code), "yahoo", start, end)
    
    df.to_pickle(file_name)
    
    return df
 
download_stock_data('samsung.data', '005930', 2016, 10, 1, 2016, 10, 31)

실행결과

	Open	High	Low	Close	Volume	Adj Close
Date
2016-10-03	1598000.0	1598000.0	1598000.0	1598000.0	0	1598000.0
2016-10-04	1610000.0	1624000.0	1606000.0	1614000.0	261000	1614000.0
2016-10-05	1601000.0	1626000.0	1597000.0	1619000.0	249400	1619000.0
2016-10-06	1696000.0	1700000.0	1667000.0	1691000.0	591100	1691000.0
2016-10-07	1700000.0	1716000.0	1690000.0	1706000.0	521000	1706000.0
2016-10-10	1650000.0	1689000.0	1628000.0	1680000.0	505800	1680000.0
2016-10-11	1600000.0	1625000.0	1545000.0	1545000.0	768500	1545000.0
2016-10-12	1495000.0	1545000.0	1494000.0	1535000.0	781700	1535000.0
2016-10-13	1550000.0	1581000.0	1545000.0	1557000.0	437200	1557000.0
2016-10-14	1548000.0	1588000.0	1547000.0	1577000.0	283100	1577000.0
2016-10-17	1565000.0	1602000.0	1538000.0	1590000.0	255700	1590000.0
2016-10-18	1572000.0	1595000.0	1572000.0	1589000.0	207600	1589000.0
2016-10-19	1579000.0	1643000.0	1575000.0	1625000.0	308800	1625000.0
2016-10-20	1626000.0	1651000.0	1609000.0	1620000.0	206600	1620000.0
2016-10-21	1606000.0	1613000.0	1588000.0	1589000.0	209500	1589000.0
2016-10-24	1593000.0	1608000.0	1590000.0	1608000.0	185700	1608000.0
2016-10-25	1600000.0	1604000.0	1592000.0	1597000.0	197300	1597000.0
2016-10-26	1597000.0	1599000.0	1562000.0	1567000.0	210600	1567000.0
2016-10-27	1571000.0	1617000.0	1556000.0	1573000.0	282300	1573000.0
2016-10-28	1580000.0	1614000.0	1580000.0	1614000.0	204200	1614000.0
2016-10-31	1616000.0	1639000.0	1611000.0	1639000.0	241300	1639000.0