这是我的代码
from pandas_datareader import data as pdr
import datetime
import fix_yahoo_finance as yf
yf.pdr_override() # <== that's all it takes :-)
start = datetime.datetime(2008, 1, 1)
end = datetime.datetime(2018, 1, 1)
data = yf.download("CGG", start, end)
print (type(data))
data.to_csv('cgg.csv', sep='\t')
我正在使用yahoo修复程序来获取雅虎数据。这些是来自我的csv文件的50行
head -50 cgg.csv
Date Open High Low Close Adj Close Volume
2007-12-31 1828.160034 1828.47998 1793.5999760000002 1793.5999760000002 1339.275269 2100
2008-01-02 1821.119995 1840.319946 1801.5999760000002 1819.52002 1358.629639 5100
2008-01-03 1844.160034 1871.359985 1841.280029 1857.9200440000002 1387.302856 6100
2008-01-04 1859.199951 1861.439941 1783.040039 1806.0799559999998 1348.5939939999998 12600
2008-01-07 1793.5999760000002 1795.839966 1744.319946 1767.680054 1319.920898 6700
2008-01-08 1786.880005 1808.319946 1758.0799559999998 1762.880005 1316.336792 6000
2008-01-09 1775.040039 1812.47998 1772.47998 1804.800049 1347.638306 10800
2008-01-10 1681.9200440000002 1744.0 1676.800049 1730.2399899999998 1291.9646 19900
2008-01-11 1626.560059 1656.319946 1619.839966 1643.839966 1227.449951 25700
2008-01-14 1691.52002 1733.119995 1650.880005 1720.0 1284.3183589999999 9900
2008-01-15 1652.47998 1665.280029 1593.280029 1603.52002 1197.3432619999999 14400
2008-01-16 1555.199951 1600.319946 1533.439941 1547.199951 1155.2891849999999 13300
2008-01-17 1620.47998 1641.599976 1553.280029 1567.359985 1170.342651 40200
2008-01-18 1590.400024 1590.400024 1504.0 1540.47998 1150.271362 25100
2008-01-22 1449.599976 1470.079956 1344.0 1462.719971 1092.208252 13600
2008-01-23 1339.199951 1409.920044 1312.0 1406.079956 1049.915283 16900
2008-01-24 1427.52002 1479.359985 1415.680054 1477.439941 1103.199585 12000
2008-01-25 1534.079956 1544.319946 1474.23999 1484.800049 1108.6953130000002 10900
2008-01-28 1500.47998 1528.0 1464.319946 1528.0 1140.9526369999999 7200
2008-01-29 1555.52002 1560.959961 1531.839966 1549.439941 1156.961792 9600
2008-01-30 1509.76001 1552.0 1496.0 1513.599976 1130.200195 7200
2008-01-31 1424.319946 1503.680054 1424.319946 1484.800049 1108.6953130000002 9500
2008-02-01 1496.640015 1539.52002 1494.079956 1538.23999 1148.598755 5200
2008-02-04 1553.920044 1556.800049 1537.280029 1540.160034 1150.032471 7300
2008-02-05 1504.959961 1507.839966 1475.52002 1477.76001 1103.438599 9000
2008-02-06 1445.76001 1457.280029 1422.079956 1423.359985 1062.818237 9400
2008-02-07 1412.160034 1431.040039 1393.920044 1418.560059 1059.2341310000002 7500
2008-02-08 1408.0 1424.319946 1400.0 1419.839966 1060.189941 13800
2008-02-11 1429.119995 1429.119995 1399.040039 1424.319946 1063.535034 8200
2008-02-12 1466.880005 1490.560059 1442.23999 1458.560059 1089.102051 9100
2008-02-13 1511.040039 1548.160034 1504.319946 1544.319946 1153.138672 7000
2008-02-14 1522.880005 1522.880005 1471.359985 1471.359985 1098.659668 9100
2008-02-15 1507.839966 1507.839966 1458.23999 1478.719971 1104.155396 9900
2008-02-19 1546.880005 1557.76001 1527.359985 1537.280029 1147.881958 6200
2008-02-20 1548.160034 1587.839966 1544.0 1578.560059 1178.705688 7100
2008-02-21 1657.280029 1661.119995 1598.400024 1603.199951 1197.104126 16100
2008-02-22 1619.199951 1634.23999 1599.040039 1633.599976 1219.803711 9700
2008-02-25 1647.359985 1687.040039 1633.280029 1682.2399899999998 1256.123047 5700
2008-02-26 1664.640015 1707.839966 1654.400024 1704.959961 1273.088013 8600
2008-02-27 1687.040039 1720.959961 1675.52002 1704.640015 1272.8491210000002 9800
2008-02-28 1632.959961 1634.560059 1577.920044 1588.800049 1186.351807 19400
2008-02-29 1577.920044 1587.839966 1555.839966 1555.839966 1161.740601 7800
2008-03-03 1534.719971 1547.839966 1516.800049 1540.800049 1150.510376 7100
2008-03-04 1527.040039 1536.640015 1508.160034 1532.47998 1144.297852 16400
2008-03-05 1543.680054 1596.47998 1543.680054 1592.640015 1189.219116 9500
2008-03-06 1601.920044 1601.920044 1550.400024 1552.640015 1159.351196 7000
2008-03-07 1491.839966 1511.680054 1467.199951 1483.199951 1107.50061 6400
2008-03-10 1485.76001 1502.079956 1448.959961 1458.880005 1089.340942 6800
2008-03-11 1552.640015 1560.319946 1520.959961 1555.199951 1161.262817 9700
如何格式化五个浮点列并保留第一个和最后一个未更改？类似
pd.options.display.float_format = '${:,.2f}'.format
除了第一个和最后一个。如何将这些浮点数舍入到两个小数位？ 如果我尝试@shrumm建议我得到
Date Open High Low Close Adj Close Volume
2007-12-31 $1,828.16 $1,828.48 $1,793.60 $1,793.60 $1,339.28 2100
2008-01-02 $1,821.12 $1,840.32 $1,801.60 $1,819.52 $1,358.63 5100
2008-01-03 $1,844.16 $1,871.36 $1,841.28 $1,857.92 $1,387.30 6100
2008-01-04 $1,859.20 $1,861.44 $1,783.04 $1,806.08 $1,348.59 12600
我想摆脱$ sign。
答案 0 :(得分：2)
你应该可以做到
df['Open'] = df['Open'].map('{:,.2f}'.format)
df['High'] = df['High'].map('{:,.2f}'.format)
df['Low'] = df['Low'].map('{:,.2f}'.format)
df['Close'] = df['Close'].map('{:,.2f}'.format)
df['Adj Close'] = df['Adj Close'].map('{:,.2f}'.format)
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