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)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | #-*- 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) | cs |
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.
1 | $ pip install pandas-datareader | cs |
ln[4] 와 같이 임포트한다.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #-*- 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) | cs |
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 |