獲取數據

通過指定列的方式獲取數據

選擇單獨一列，通過這種方式獲取一個Series,等價于df.A

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("------------獲取一列數據,包括index的值------------") print(df['A'])

輸出結果：

A B C D 2013-01-01 -1.254715 0.208110 -0.961949 -0.370242 2013-01-02 0.080537 0.262347 1.113138 -2.134468 2013-01-03 1.021890 -0.701764 1.200650 2.187048 2013-01-04 -1.061324 -2.099654 -0.036560 1.200454 2013-01-05 -1.517845 0.198959 1.660779 -0.749684 2013-01-06 0.789730 -0.066244 0.622702 -0.876385 ---------------獲取一列數據-------------- 2013-01-01 -1.254715 2013-01-02 0.080537 2013-01-03 1.021890 2013-01-04 -1.061324 2013-01-05 -1.517845 2013-01-06 0.789730 Freq: D, Name: A, dtype: float64

通過[]來選擇一些行的數據

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("------------通過[]獲取一些行的數據------------") print(df[0:3])

輸出結果：

A B C D 2013-01-01 -0.472598 0.634512 -1.154252 0.650860 2013-01-02 -1.020034 2.215081 0.152721 -1.132280 2013-01-03 -0.976544 -0.292899 0.022719 -0.468406 2013-01-04 -1.367970 -0.087343 -0.590131 -0.279336 2013-01-05 -1.295620 -1.630914 -1.304561 -1.334444 2013-01-06 -0.790731 1.043622 -0.772808 0.564674 ------------通過[]獲取一些行的數據------------A B C D 2013-01-01 -0.472598 0.634512 -1.154252 0.650860 2013-01-02 -1.020034 2.215081 0.152721 -1.132280 2013-01-03 -0.976544 -0.292899 0.022719 -0.468406

再如：

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("------------通過[]獲取一些行的數據------------") print(df['20130102':'20130104'] )

輸出結果為：

A B C D 2013-01-01 -1.253099 0.378643 -0.119608 1.971239 2013-01-02 -0.425589 -0.689410 -1.373875 -1.027356 2013-01-03 -1.302570 -0.182313 1.564062 0.059378 2013-01-04 0.637397 0.330284 0.607045 -0.928848 2013-01-05 0.106358 1.431572 0.789533 1.483123 2013-01-06 2.089771 0.823074 0.362509 -0.206203 ------------通過[]獲取一些行的數據------------A B C D 2013-01-02 -0.425589 -0.689410 -1.373875 -1.027356 2013-01-03 -1.302570 -0.182313 1.564062 0.059378 2013-01-04 0.637397 0.330284 0.607045 -0.928848

通過label的值查找loc函數，at函數

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("------------Selected by label------------") print(df.loc[dates[0]])

輸出結果為：

A B C D 2013-01-01 1.280903 0.512179 -0.231190 0.036669 2013-01-02 -0.540552 -0.878910 0.918104 1.878132 2013-01-03 -1.106968 -0.176439 -1.247349 0.994883 2013-01-04 0.260098 -0.885501 0.346970 2.435448 2013-01-05 1.110520 1.756852 -2.025549 0.566382 2013-01-06 1.405610 -1.663422 -0.071328 -0.778265 ------------Selected by label------------ A 1.280903 B 0.512179 C -0.231190 D 0.036669 Name: 2013-01-01 00:00:00, dtype: float64

選擇多行多列的值：

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("----------Selecting on a multi-axis by label:--------------") print(df.loc[:,['A','B']])

輸出結果為：

A B C D 2013-01-01 1.595626 -0.564944 -0.668520 0.809201 2013-01-02 0.151399 0.647508 -1.653127 -1.161560 2013-01-03 -0.530367 0.024272 0.799212 -1.079254 2013-01-04 1.060353 0.047422 1.886798 0.254401 2013-01-05 0.064728 -1.073669 -1.503000 -1.328167 2013-01-06 2.126969 -0.535444 0.078469 1.392002 ----------Selecting on a multi-axis by label:--------------A B 2013-01-01 1.595626 -0.564944 2013-01-02 0.151399 0.647508 2013-01-03 -0.530367 0.024272 2013-01-04 1.060353 0.047422 2013-01-05 0.064728 -1.073669 2013-01-06 2.126969 -0.535444

再如：

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("----------Showing label slicing, both endpoints are included:--------------") print(df.loc['20130102':'20130104', ['A', 'B']] )print("----------Reduction in the dimensions of the returned object:--------------") print(df.loc['20130102', ['A', 'B']])print("----------For getting a scalar value:--------------") print(df.loc[dates[0],'A'])print("----------For getting fast access to a scalar (equivalent to the prior method):--------------") print(df.at[dates[0],'A'])

輸出結果為：

A B C D 2013-01-01 0.417995 -0.026418 -1.027092 -0.042631 2013-01-02 0.184714 1.289753 2.064584 -2.566320 2013-01-03 0.155585 -0.043835 -0.204965 -0.502541 2013-01-04 -1.698213 0.914601 -0.576331 -0.722310 2013-01-05 0.023068 2.280261 2.014296 -1.111354 2013-01-06 0.456681 0.202428 0.612209 -0.525896 ----------Showing label slicing, both endpoints are included:--------------A B 2013-01-02 0.184714 1.289753 2013-01-03 0.155585 -0.043835 2013-01-04 -1.698213 0.914601 ----------Reduction in the dimensions of the returned object:-------------- A 0.184714 B 1.289753 Name: 2013-01-02 00:00:00, dtype: float64 ----------For getting a scalar value:-------------- 0.4179945574941799 ----------For getting fast access to a scalar (equivalent to the prior method):-------------- 0.4179945574941799

通過指定位置獲取具體的值 iloc iat

import numpy as np import pandas as pd# 通過設置開始時間，并設置間隔了多少月 dates = pd.date_range('20130101',periods=6)# 隨機生成一個6行4列的值 # print(np.random.randn(6,4))# 設置dates為行，ABCD為列的標題值，np.random.randn(6, 4)為行和列中的值 df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) print("----------Select via the position of the passed integers:----------") print(df.iloc[3])print("----------By integer slices, acting similar to numpy/python:----------") print(df.iloc[3:5,0:2])print("----------By lists of integer position locations, similar to the numpy/python style:----------") print(df.iloc[[1,2,4],[0,2]])print("----------For slicing rows explicitly:----------") print(df.iloc[1:3,:])print("----------For getting a value explicitly:----------") print(df.iloc[1,1])print("----------For getting fast access to a scalar (equivalent to the prior method)::----------") print(df.iat[1, 1])

輸出結果為：

A B C D 2013-01-01 0.156959 -0.026162 -0.687972 -0.631711 2013-01-02 -0.331658 0.069760 0.820701 1.330417 2013-01-03 -0.842929 0.240588 1.073343 1.231400 2013-01-04 -0.433373 1.680165 -1.624269 -0.568833 2013-01-05 -0.023643 0.766268 0.210271 -0.107263 2013-01-06 0.856357 0.932945 -1.367324 0.766313 ----------Select via the position of the passed integers:---------- A -0.433373 B 1.680165 C -1.624269 D -0.568833 Name: 2013-01-04 00:00:00, dtype: float64 ----------By integer slices, acting similar to numpy/python:----------A B 2013-01-04 -0.433373 1.680165 2013-01-05 -0.023643 0.766268 ----------By lists of integer position locations, similar to the numpy/python style:----------A C 2013-01-02 -0.331658 0.820701 2013-01-03 -0.842929 1.073343 2013-01-05 -0.023643 0.210271 ----------For slicing rows explicitly:----------A B C D 2013-01-02 -0.331658 0.069760 0.820701 1.330417 2013-01-03 -0.842929 0.240588 1.073343 1.231400 ----------For getting a value explicitly:---------- 0.06976023059625595 ----------For getting fast access to a scalar (equivalent to the prior method)::---------- 0.06976023059625595

總結

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