2. 查询
查看每列的数据类型
# 查看df数据类型df.dtypesdate objectopen float64close float64high float64low float64volume float64code objectdtype: object查看指定指定数量的行：head函数默认查看前5行，tail函数默认查看后5行，可以传递指定的数值用于查看指定行数。
查看前5行df.head()date open close high low volume code0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 0000011 2015-12-24 9.919 9.823 9.998 9.744 640229.0 0000012 2015-12-25 9.855 9.879 9.927 9.815 399845.0 0000013 2015-12-28 9.895 9.537 9.919 9.537 822408.0 0000014 2015-12-29 9.545 9.624 9.632 9.529 619802.0 000001# 查看后5行df.tail()date open close high low volume code636 2018-08-01 9.42 9.15 9.50 9.11 814081.0 000001637 2018-08-02 9.13 8.94 9.15 8.88 931401.0 000001638 2018-08-03 8.93 8.91 9.10 8.91 476546.0 000001639 2018-08-06 8.94 8.94 9.11 8.89 554010.0 000001640 2018-08-07 8.96 9.17 9.17 8.88 690423.0 000001# 查看前10行df.head(10)date open close high low volume code0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 0000011 2015-12-24 9.919 9.823 9.998 9.744 640229.0 0000012 2015-12-25 9.855 9.879 9.927 9.815 399845.0 0000013 2015-12-28 9.895 9.537 9.919 9.537 822408.0 0000014 2015-12-29 9.545 9.624 9.632 9.529 619802.0 0000015 2015-12-30 9.624 9.632 9.640 9.513 532667.0 0000016 2015-12-31 9.632 9.545 9.656 9.537 491258.0 0000017 2016-01-04 9.553 8.995 9.577 8.940 563497.0 0000018 2016-01-05 8.972 9.075 9.210 8.876 663269.0 0000019 2016-01-06 9.091 9.179 9.202 9.067 515706.0 000001查看某一行或多行,某一列或多列
# 查看第一行df[0:1] date open close high low volume code0 2015-12-23 9.927 9.935 10.174 9.871 1039018.0 000001 # 查看 10到20行df[10:21] date open close high low volume code10 2016-01-07 9.083 8.709 9.083 8.685 174761.0 00000111 2016-01-08 8.924 8.852 8.987 8.677 747527.0 00000112 2016-01-11 8.757 8.566 8.820 8.502 732013.0 00000113 2016-01-12 8.621 8.605 8.685 8.470 561642.0 00000114 2016-01-13 8.669 8.526 8.709 8.518 391709.0 00000115 2016-01-14 8.430 8.574 8.597 8.343 666314.0 00000116 2016-01-15 8.486 8.327 8.597 8.295 448202.0 00000117 2016-01-18 8.231 8.287 8.406 8.199 421040.0 00000118 2016-01-19 8.319 8.526 8.582 8.287 501109.0 00000119 2016-01-20 8.518 8.390 8.597 8.311 603752.0 00000120 2016-01-21 8.343 8.215 8.558 8.215 606145.0 000001 # 查看看Date列前5个数据df["date"].head() # 或者df.date.head()0 2015-12-231 2015-12-242 2015-12-253 2015-12-284 2015-12-29Name: date, dtype: object # 查看看Date列,code列, open列前5个数据df[["date","code", "open"]].head() date code open0 2015-12-23 000001 9.9271 2015-12-24 000001 9.9192 2015-12-25 000001 9.8553 2015-12-28 000001 9.8954 2015-12-29 000001 9.545
使用行列组合条件查询
# 查看date, code列的第10行df.loc[10, ["date", "code"]] date 2016-01-07code 000001Name: 10, dtype: object# 查看date, code列的第10行到20行df.loc[10:20, ["date", "code"]] date code10 2016-01-07 00000111 2016-01-08 00000112 2016-01-11 00000113 2016-01-12 00000114 2016-01-13 00000115 2016-01-14 00000116 2016-01-15 00000117 2016-01-18 00000118 2016-01-19 00000119 2016-01-20 00000120 2016-01-21 000001 # 查看第一行,open列的数据df.loc[0, "open"]9.9269999999999996
通过位置查询：值得注意的是上面的索引值就是特定的位置。
# 查看第1行()df.iloc[0]date 2015-12-24open 9.919close 9.823high 9.998low 9.744volume 640229code 000001Name: 0, dtype: object# 查看最后一行df.iloc[-1]date 2018-08-08open 9.16close 9.12high 9.16low 9.1volume 29985code 000001Name: 640, dtype: object# 查看第一列，前5个数值df.iloc[:,0].head()0 2015-12-241 2015-12-252 2015-12-283 2015-12-294 2015-12-30Name: date, dtype: object # 查看前2到4行，第1，3列df.iloc[2:4,[0,2]] date close2 2015-12-28 9.5373 2015-12-29 9.624
通过条件筛选：
查看open列大于10的前5行df[df.open > 10].head() date open close high low volume code378 2017-07-14 10.483 10.570 10.609 10.337 1722570.0 000001379 2017-07-17 10.619 10.483 10.987 10.396 3273123.0 000001380 2017-07-18 10.425 10.716 10.803 10.299 2349431.0 000001381 2017-07-19 10.657 10.754 10.851 10.551 1933075.0 000001382 2017-07-20 10.745 10.638 10.880 10.580 1537338.0 000001 # 查看open列大于10且open列小于10.6的前五行df[(df.open > 10) & (df.open < 10.6)].head() date open close high low volume code378 2017-07-14 10.483 10.570 10.609 10.337 1722570.0 000001380 2017-07-18 10.425 10.716 10.803 10.299 2349431.0 000001387 2017-07-27 10.550 10.422 10.599 10.363 1194490.0 000001388 2017-07-28 10.441 10.569 10.638 10.412 819195.0 000001390 2017-08-01 10.471 10.865 10.904 10.432 2035709.0 000001 # 查看open列大于10或open列小于10.6的前五行df[(df.open > 10) | (df.open < 10.6)].head() date open close high low volume code0 2015-12-24 9.919 9.823 9.998 9.744 640229.0 0000011 2015-12-25 9.855 9.879 9.927 9.815 399845.0 0000012 2015-12-28 9.895 9.537 9.919 9.537 822408.0 0000013 2015-12-29 9.545 9.624 9.632 9.529 619802.0 0000014 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001
3. 增加
在前面已经简单的说明Series, DataFrame的创建，这里说一些常用有用的创建方式。
# 创建2018-08-08到2018-08-15的时间序列，默认时间间隔为Days2 = pd.date_range("20180808", periods=7)print(s2) DatetimeIndex(['2018-08-08', '2018-08-09', '2018-08-10', '2018-08-11', '2018-08-12', '2018-08-13', '2018-08-14'], dtype='datetime64[ns]', freq='D')# 指定2018-08-08 00：00 到2018-08-09 00:00 时间间隔为小时# freq参数可使用参数, 参考: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases s3 = pd.date_range("20180808", "20180809", freq="H")print(s2) DatetimeIndex(['2018-08-08 00:00:00', '2018-08-08 01:00:00', '2018-08-08 02:00:00', '2018-08-08 03:00:00', '2018-08-08 04:00:00', '2018-08-08 05:00:00', '2018-08-08 06:00:00', '2018-08-08 07:00:00', '2018-08-08 08:00:00', '2018-08-08 09:00:00', '2018-08-08 10:00:00', '2018-08-08 11:00:00', '2018-08-08 12:00:00', '2018-08-08 13:00:00', '2018-08-08 14:00:00', '2018-08-08 15:00:00', '2018-08-08 16:00:00', '2018-08-08 17:00:00', '2018-08-08 18:00:00', '2018-08-08 19:00:00', '2018-08-08 20:00:00', '2018-08-08 21:00:00', '2018-08-08 22:00:00', '2018-08-08 23:00:00', '2018-08-09 00:00:00'], dtype='datetime64[ns]', freq='H')# 通过已有序列创建时间序列s4 = pd.to_datetime(df.date.head())print(s4) 0 2015-12-241 2015-12-252 2015-12-283 2015-12-294 2015-12-30Name: date, dtype: datetime64[ns]
4. 修改
# 将df 的索引修改为date列的数据,并且将类型转换为datetime类型df.index = pd.to_datetime(df.date)df.head() date open close high low volume code date 2015-12-24 2015-12-24 9.919 9.823 9.998 9.744 640229.0 0000012015-12-25 2015-12-25 9.855 9.879 9.927 9.815 399845.0 0000012015-12-28 2015-12-28 9.895 9.537 9.919 9.537 822408.0 0000012015-12-29 2015-12-29 9.545 9.624 9.632 9.529 619802.0 0000012015-12-30 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001# 修改列的字段df.columns = ["Date", "Open","Close","High","Low","Volume","Code"]print(df.head()) Date Open Close High Low Volume Code date2015-12-24 2015-12-24 9.919 9.823 9.998 9.744 640229.0 0000012015-12-25 2015-12-25 9.855 9.879 9.927 9.815 399845.0 0000012015-12-28 2015-12-28 9.895 9.537 9.919 9.537 822408.0 0000012015-12-29 2015-12-29 9.545 9.624 9.632 9.529 619802.0 0000012015-12-30 2015-12-30 9.624 9.632 9.640 9.513 532667.0 000001# 将Open列每个数值加1, apply方法并不直接修改源数据，所以需要将新值复制给dfdf.Open = df.Open.apply(lambda x: x+1)df.head() Date Open Close High Low Volume Code date2015-12-24 2015-12-24 10.919 9.823 9.998 9.744 640229.0 0000012015-12-25 2015-12-25 10.855 9.879 9.927 9.815 399845.0 0000012015-12-28 2015-12-28 10.895 9.537 9.919 9.537 822408.0 0000012015-12-29 2015-12-29 10.545 9.624 9.632 9.529 619802.0 0000012015-12-30 2015-12-30 10.624 9.632 9.640 9.513 532667.0 000001# 将Open，Close列都数值上加1,如果多列，apply接收的对象是整个列df[["Open", "Close"]].head().apply(lambda x: x.apply(lambda x: x+1)) Open Closedate 2015-12-24 11.919 10.8232015-12-25 11.855 10.8792015-12-28 11.895 10.5372015-12-29 11.545 10.6242015-12-30 11.624 10.632
5. 删除
通过drop方法drop指定的行或者列。
注意: drop方法并不直接修改源数据，如果需要使源dataframe对象被修改，需要传入inplace=True，通过之前的axis图解，知道行的值(或者说label)在axis=0，列的值(或者说label)在axis=1。
# 删除指定列,删除Open列df.drop("Open", axis=1).head() #或者df.drop(df.columns[1]) Date Close High Low Volume Code date 2015-12-24 2015-12-24 9.823 9.998 9.744 640229.0 0000012015-12-25 2015-12-25 9.879 9.927 9.815 399845.0 0000012015-12-28 2015-12-28 9.537 9.919 9.537 822408.0 0000012015-12-29 2015-12-29 9.624 9.632 9.529 619802.0 0000012015-12-30 2015-12-30 9.632 9.640 9.513 532667.0 000001# 删除第1，3列. 即Open,High列df.drop(df.columns[[1,3]], axis=1).head() # 或df.drop(["Open", "High], axis=1).head() Date Close Low Volume Code date 2015-12-24 2015-12-24 9.823 9.744 640229.0 000001 2015-12-25 2015-12-25 9.879 9.815 399845.0 000001 2015-12-28 2015-12-28 9.537 9.537 822408.0 000001 2015-12-29 2015-12-29 9.624 9.529 619802.0 000001 2015-12-30 2015-12-30 9.632 9.513 532667.0 000001