10分钟到熊猫# 这是对 pandas 的简短介绍，主要面向新用户。您可以在食谱中看到更复杂的食谱。 通常，我们导入如下： In [1]: import numpy as np In [2]: import pandas as pd pandas 的基本数据结构# Pandas 提供了两种类型的类来处理数据： Series：保存任何类型数据的一维标记数组例如整数、字符串、Python 对象等。 DataFrame：一种二维数据结构，用于保存数据，例如二维数组或具有行和列的表格。 对象创建# 请参阅数据结构简介部分。 Series通过传递值列表来创建，让 pandas 创建默认的RangeIndex. In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 DataFrame通过使用带标签的列传递带有日期时间索引的 NumPy 数组来创建date_range() ： In [5]: dates = pd.date_range("20130101", periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list("ABCD")) In [8]: df Out[8]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 DataFrame通过传递对象字典来创建一个对象，其中键是列标签，值是列值。 In [9]: df2 = pd.DataFrame( ...: { ...: "A": 1.0, ...: "B": pd.Timestamp("20130102"), ...: "C": pd.Series(1, index=list(range(4)), dtype="float32"), ...: "D": np.array([3] * 4, dtype="int32"), ...: "E": pd.Categorical(["test", "train", "test", "train"]), ...: "F": "foo", ...: } ...: ) ...: In [10]: df2 Out[10]: A B C D E F 0 1.0 2013-01-02 1.0 3 test foo 1 1.0 2013-01-02 1.0 3 train foo 2 1.0 2013-01-02 1.0 3 test foo 3 1.0 2013-01-02 1.0 3 train foo 结果的列DataFrame具有不同的 dtypes： In [11]: df2.dtypes Out[11]: A float64 B datetime64[s] C float32 D int32 E category F object dtype: object 如果您使用 IPython，则会自动启用列名称（以及公共属性）的制表符补全。以下是将完成的属性的子集： In [12]: df2.<TAB> # noqa: E225, E999 df2.A df2.bool df2.abs df2.boxplot df2.add df2.C df2.add_prefix df2.clip df2.add_suffix df2.columns df2.align df2.copy df2.all df2.count df2.any df2.combine df2.append df2.D df2.apply df2.describe df2.applymap df2.diff df2.B df2.duplicated 正如您所看到的，A、B、C和列D会自动按 Tab 键完成。E也在F那里；为简洁起见，其余属性已被截断。 查看数据# 请参阅本质上的基础功能部分。 使用DataFrame.head()和DataFrame.tail()分别查看框架的顶行和底行： In [13]: df.head() Out[13]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 In [14]: df.tail(3) Out[14]: A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 显示DataFrame.index或DataFrame.columns： In [15]: df.index Out[15]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D') In [16]: df.columns Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object') DataFrame.to_numpy() 返回不带索引或列标签的基础数据的 NumPy 表示形式： In [17]: df.to_numpy() Out[17]: array([[ 0.4691, -0.2829, -1.5091, -1.1356], [ 1.2121, -0.1732, 0.1192, -1.0442], [-0.8618, -2.1046, -0.4949, 1.0718], [ 0.7216, -0.7068, -1.0396, 0.2719], [-0.425 , 0.567 , 0.2762, -1.0874], [-0.6737, 0.1136, -1.4784, 0.525 ]]) 笔记 NumPy 数组的整个数组有一种数据类型，而 pandas DataFrame 的每列有一种数据类型。当您调用 时，pandas 会找到可以容纳DataFrame 中所有DataFrame.to_numpy()数据类型的 NumPy 数据类型。如果通用数据类型为，则需要复制数据。objectDataFrame.to_numpy() In [18]: df2.dtypes Out[18]: A float64 B datetime64[s] C float32 D int32 E category F object dtype: object In [19]: df2.to_numpy() Out[19]: array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'], [1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object) describe()显示数据的快速统计摘要： In [20]: df.describe() Out[20]: A B C D count 6.000000 6.000000 6.000000 6.000000 mean 0.073711 -0.431125 -0.687758 -0.233103 std 0.843157 0.922818 0.779887 0.973118 min -0.861849 -2.104569 -1.509059 -1.135632 25% -0.611510 -0.600794 -1.368714 -1.076610 50% 0.022070 -0.228039 -0.767252 -0.386188 75% 0.658444 0.041933 -0.034326 0.461706 max 1.212112 0.567020 0.276232 1.071804 转置您的数据： In [21]: df.T Out[21]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06 A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690 B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648 C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427 D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988 DataFrame.sort_index()按轴排序： In [22]: df.sort_index(axis=1, ascending=False) Out[22]: D C B A 2013-01-01 -1.135632 -1.509059 -0.282863 0.469112 2013-01-02 -1.044236 0.119209 -0.173215 1.212112 2013-01-03 1.071804 -0.494929 -2.104569 -0.861849 2013-01-04 0.271860 -1.039575 -0.706771 0.721555 2013-01-05 -1.087401 0.276232 0.567020 -0.424972 2013-01-06 0.524988 -1.478427 0.113648 -0.673690 DataFrame.sort_values()按值排序： In [23]: df.sort_values(by="B") Out[23]: A B C D 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 选择# 笔记 虽然用于选择和设置的标准 Python / NumPy 表达式非常直观，并且对于交互式工作非常有用，但对于生产代码，我们建议使用优化的 pandas 数据访问方法DataFrame.at()、DataFrame.iat()和 。DataFrame.loc()DataFrame.iloc() 请参阅索引文档索引和选择数据以及MultiIndex/Advanced Indexing。 获取项目 ( []) # 对于 a DataFrame，传递单个标签会选择一列并产生Series相当于 的结果df.A： In [24]: df["A"] Out[24]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64 对于 a DataFrame，传递切片:会选择匹配的行： In [25]: df[0:3] Out[25]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [26]: df["20130102":"20130104"] Out[26]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 按标签选择# 请参阅使用或按标签选择的更多内容。DataFrame.loc()DataFrame.at() 选择与标签匹配的行： In [27]: df.loc[dates[0]] Out[27]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64 :选择带有选择列标签的所有行 ( )： In [28]: df.loc[:, ["A", "B"]] Out[28]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648 对于标签切片，两个端点都包括在内： In [29]: df.loc["20130102":"20130104", ["A", "B"]] Out[29]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 选择单个行和列标签将返回一个标量： In [30]: df.loc[dates[0], "A"] Out[30]: 0.4691122999071863 为了快速访问标量（相当于之前的方法）： In [31]: df.at[dates[0], "A"] Out[31]: 0.4691122999071863 按位置#选择 请参阅使用或按位置选择的更多内容。DataFrame.iloc()DataFrame.iat() 通过传递的整数的位置进行选择： In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64 整数切片的作用类似于 NumPy/Python： In [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 整数位置列表： In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232 对于显式切片行： In [35]: df.iloc[1:3, :] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 对于显式切片列： In [36]: df.iloc[:, 1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427 为了显式获取值： In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858 为了快速访问标量（相当于之前的方法）： In [38]: df.iat[1, 1] Out[38]: -0.17321464905330858 布尔索引# df.A选择大于 的行0。 In [39]: df[df["A"] > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 DataFrame从满足布尔条件的地方选择值： In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988 isin()过滤的使用方法： In [41]: df2 = df.copy() In [42]: df2["E"] = ["one", "one", "two", "three", "four", "three"] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2["E"].isin(["two", "four"])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 环境＃ 设置新列会自动按索引对齐数据： In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df["F"] = s1 按标签设置值： In [48]: df.at[dates[0], "A"] = 0 按位置设置值： In [49]: df.iat[0, 1] = 0 通过使用 NumPy 数组进行赋值来设置： In [50]: df.loc[:, "D"] = np.array([5] * len(df)) 前面设置操作的结果： In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5.0 NaN 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 2013-01-05 -0.424972 0.567020 0.276232 5.0 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5.0 5.0 带有设置的操作where： In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5.0 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5.0 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5.0 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5.0 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5.0 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5.0 -5.0 缺失数据＃ 对于 NumPy 数据类型，np.nan表示缺失数据。默认情况下，它不包含在计算中。请参阅缺失数据部分。 重新索引允许您更改/添加/删除指定轴上的索引。这将返回数据的副本： In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ["E"]) In [56]: df1.loc[dates[0] : dates[1], "E"] = 1 In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5.0 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 NaN DataFrame.dropna()删除任何缺少数据的行： In [58]: df1.dropna(how="any") Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0 DataFrame.fillna()填充缺失数据： In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5.0 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 5.0 isna()获取布尔掩码，其中值为nan： In [60]: pd.isna(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True 运营＃ 请参阅有关二进制操作的基本部分。 统计数据# 一般来说，操作会排除缺失的数据。 计算每列的平均值： In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64 计算每行的平均值： In [62]: df.mean(axis=1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64 使用另一个Series或DataFrame不同的索引或列进行操作会将结果与索引或列标签的并集对齐。此外，pandas 会自动沿着指定的维度进行广播，并且会用 填充未对齐的标签np.nan。 In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis="index") Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN 用户定义的函数# DataFrame.agg()并DataFrame.transform()应用用户定义的函数来分别减少或广播其结果。 In [66]: df.agg(lambda x: np.mean(x) * 5.6) Out[66]: A -0.025054 B -2.150294 C -3.851445 D 28.000000 F 16.800000 dtype: float64 In [67]: df.transform(lambda x: x * 101.2) Out[67]: A B C D F 2013-01-01 0.000000 0.000000 -152.716721 506.0 NaN 2013-01-02 122.665737 -17.529322 12.063922 506.0 101.2 2013-01-03 -87.219115 -212.982405 -50.086843 506.0 202.4 2013-01-04 73.021382 -71.525239 -105.204988 506.0 303.6 2013-01-05 -43.007200 57.382459 27.954680 506.0 404.8 2013-01-06 -68.177398 11.501219 -149.616767 506.0 506.0 价值很重要# 更多信息请参见直方图和离散化。 In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 2 2 6 2 1 1 Name: count, dtype: int64 字符串方法# Series属性中配备了一组字符串处理方法str ，可以方便地对数组的每个元素进行操作，如下面的代码片段所示。更多信息请参见矢量化字符串方法。 In [71]: s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"]) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object 合并# 连接# pandas 提供了各种工具，可以在连接/合并类型操作的情况下轻松地将对象与索引和关系代数功能的各种集合逻辑Series组合 在一起。DataFrame 请参阅合并部分。 将 pandas 对象按行连接在一起concat()： In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 笔记 向a 添加列DataFrame相对较快。但是，添加行需要副本，并且可能很昂贵。我们建议将预先构建的记录列表传递给DataFrame构造函数，而不是DataFrame通过迭代地向其追加记录来构建 a。 加入＃ merge()启用沿特定列的 SQL 样式联接类型。请参阅数据库样式连接部分。 In [77]: left = pd.DataFrame({"key": ["foo", "foo"], "lval": [1, 2]}) In [78]: right = pd.DataFrame({"key": ["foo", "foo"], "rval": [4, 5]}) In [79]: left Out[79]: key lval 0 foo 1 1 foo 2 In [80]: right Out[80]: key rval 0 foo 4 1 foo 5 In [81]: pd.merge(left, right, on="key") Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5 merge()在唯一键上： In [82]: left = pd.DataFrame({"key": ["foo", "bar"], "lval": [1, 2]}) In [83]: right = pd.DataFrame({"key": ["foo", "bar"], "rval": [4, 5]}) In [84]: left Out[84]: key lval 0 foo 1 1 bar 2 In [85]: right Out[85]: key rval 0 foo 4 1 bar 5 In [86]: pd.merge(left, right, on="key") Out[86]: key lval rval 0 foo 1 4 1 bar 2 5 分组# 我们所说的“分组依据”是指涉及以下一个或多个步骤的过程： 根据某些标准将数据分组 独立地将函数应用于每个组 将结果组合成数据结构 请参阅分组部分。 In [87]: df = pd.DataFrame( ....: { ....: "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"], ....: "B": ["one", "one", "two", "three", "two", "two", "one", "three"], ....: "C": np.random.randn(8), ....: "D": np.random.randn(8), ....: } ....: ) ....: In [88]: df Out[88]: A B C D 0 foo one 1.346061 -1.577585 1 bar one 1.511763 0.396823 2 foo two 1.627081 -0.105381 3 bar three -0.990582 -0.532532 4 foo two -0.441652 1.453749 5 bar two 1.211526 1.208843 6 foo one 0.268520 -0.080952 7 foo three 0.024580 -0.264610 按列标签分组，选择列标签，然后将 DataFrameGroupBy.sum()函数应用于结果组： In [89]: df.groupby("A")[["C", "D"]].sum() Out[89]: C D A bar 1.732707 1.073134 foo 2.824590 -0.574779 按多列标签形式分组MultiIndex。 In [90]: df.groupby(["A", "B"]).sum() Out[90]: C D A B bar one 1.511763 0.396823 three -0.990582 -0.532532 two 1.211526 1.208843 foo one 1.614581 -1.658537 three 0.024580 -0.264610 two 1.185429 1.348368 重塑# 请参阅有关分层索引和 重塑的部分。 堆＃ In [91]: arrays = [ ....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], ....: ["one", "two", "one", "two", "one", "two", "one", "two"], ....: ] ....: In [92]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"]) In [93]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=["A", "B"]) In [94]: df2 = df[:4] In [95]: df2 Out[95]: A B first second bar one -0.727965 -0.589346 two 0.339969 -0.693205 baz one -0.339355 0.593616 two 0.884345 1.591431 该stack()方法“压缩”DataFrame 列中的一个级别： In [96]: stacked = df2.stack(future_stack=True) In [97]: stacked Out[97]: first second bar one A -0.727965 B -0.589346 two A 0.339969 B -0.693205 baz one A -0.339355 B 0.593616 two A 0.884345 B 1.591431 dtype: float64 对于“堆叠”的 DataFrame 或 Series（具有 aMultiIndex作为 ）， is index的逆运算，默认情况下会取消堆叠最后一层：stack()unstack() In [98]: stacked.unstack() Out[98]: A B first second bar one -0.727965 -0.589346 two 0.339969 -0.693205 baz one -0.339355 0.593616 two 0.884345 1.591431 In [99]: stacked.unstack(1) Out[99]: second one two first bar A -0.727965 0.339969 B -0.589346 -0.693205 baz A -0.339355 0.884345 B 0.593616 1.591431 In [100]: stacked.unstack(0) Out[100]: first bar baz second one A -0.727965 -0.339355 B -0.589346 0.593616 two A 0.339969 0.884345 B -0.693205 1.591431 数据透视表# 请参阅有关数据透视表的部分。 In [101]: df = pd.DataFrame( .....: { .....: "A": ["one", "one", "two", "three"] * 3, .....: "B": ["A", "B", "C"] * 4, .....: "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2, .....: "D": np.random.randn(12), .....: "E": np.random.randn(12), .....: } .....: ) .....: In [102]: df Out[102]: A B C D E 0 one A foo -1.202872 0.047609 1 one B foo -1.814470 -0.136473 2 two C foo 1.018601 -0.561757 3 three A bar -0.595447 -1.623033 4 one B bar 1.395433 0.029399 5 one C bar -0.392670 -0.542108 6 two A foo 0.007207 0.282696 7 three B foo 1.928123 -0.087302 8 one C foo -0.055224 -1.575170 9 one A bar 2.395985 1.771208 10 two B bar 1.552825 0.816482 11 three C bar 0.166599 1.100230 pivot_table()旋转 aDataFrame指定values,index和columns In [103]: pd.pivot_table(df, values="D", index=["A", "B"], columns=["C"]) Out[103]: C bar foo A B one A 2.395985 -1.202872 B 1.395433 -1.814470 C -0.392670 -0.055224 three A -0.595447 NaN B NaN 1.928123 C 0.166599 NaN two A NaN 0.007207 B 1.552825 NaN C NaN 1.018601 时间序列＃ pandas具有简单、强大、高效的功能，可以在变频过程中进行重采样操作（例如将秒数数据转换为5分钟数据）。这在（但不限于）金融应用中极为常见。请参阅时间序列部分。 In [104]: rng = pd.date_range("1/1/2012", periods=100, freq="s") In [105]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng) In [106]: ts.resample("5Min").sum() Out[106]: 2012-01-01 24182 Freq: 5min, dtype: int64 Series.tz_localize()将时间序列本地化到时区： In [107]: rng = pd.date_range("3/6/2012 00:00", periods=5, freq="D") In [108]: ts = pd.Series(np.random.randn(len(rng)), rng) In [109]: ts Out[109]: 2012-03-06 1.857704 2012-03-07 -1.193545 2012-03-08 0.677510 2012-03-09 -0.153931 2012-03-10 0.520091 Freq: D, dtype: float64 In [110]: ts_utc = ts.tz_localize("UTC") In [111]: ts_utc Out[111]: 2012-03-06 00:00:00+00:00 1.857704 2012-03-07 00:00:00+00:00 -1.193545 2012-03-08 00:00:00+00:00 0.677510 2012-03-09 00:00:00+00:00 -0.153931 2012-03-10 00:00:00+00:00 0.520091 Freq: D, dtype: float64 Series.tz_convert()将时区感知时间序列转换为另一个时区： In [112]: ts_utc.tz_convert("US/Eastern") Out[112]: 2012-03-05 19:00:00-05:00 1.857704 2012-03-06 19:00:00-05:00 -1.193545 2012-03-07 19:00:00-05:00 0.677510 2012-03-08 19:00:00-05:00 -0.153931 2012-03-09 19:00:00-05:00 0.520091 Freq: D, dtype: float64 BusinessDay向时间序列添加非固定持续时间 ( )： In [113]: rng Out[113]: DatetimeIndex(['2012-03-06', '2012-03-07', '2012-03-08', '2012-03-09', '2012-03-10'], dtype='datetime64[ns]', freq='D') In [114]: rng + pd.offsets.BusinessDay(5) Out[114]: DatetimeIndex(['2012-03-13', '2012-03-14', '2012-03-15', '2012-03-16', '2012-03-16'], dtype='datetime64[ns]', freq=None) 分类# pandas 可以在DataFrame.有关完整文档，请参阅 分类介绍和API 文档。 In [115]: df = pd.DataFrame( .....: {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]} .....: ) .....: 将原始成绩转换为分类数据类型： In [116]: df["grade"] = df["raw_grade"].astype("category") In [117]: df["grade"] Out[117]: 0 a 1 b 2 b 3 a 4 a 5 e Name: grade, dtype: category Categories (3, object): ['a', 'b', 'e'] 将类别重命名为更有意义的名称： In [118]: new_categories = ["very good", "good", "very bad"] In [119]: df["grade"] = df["grade"].cat.rename_categories(new_categories) 对类别重新排序并同时添加缺少的类别（默认情况下Series.cat()返回新类别的方法Series）： In [120]: df["grade"] = df["grade"].cat.set_categories( .....: ["very bad", "bad", "medium", "good", "very good"] .....: ) .....: In [121]: df["grade"] Out[121]: 0 very good 1 good 2 good 3 very good 4 very good 5 very bad Name: grade, dtype: category Categories (5, object): ['very bad', 'bad', 'medium', 'good', 'very good'] 排序是按照类别中的顺序，而不是词汇顺序： In [122]: df.sort_values(by="grade") Out[122]: id raw_grade grade 5 6 e very bad 1 2 b good 2 3 b good 0 1 a very good 3 4 a very good 4 5 a very good 按分类列分组observed=False还显示空类别： In [123]: df.groupby("grade", observed=False).size() Out[123]: grade very bad 1 bad 0 medium 0 good 2 very good 3 dtype: int64 绘图# 请参阅绘图文档。 我们使用标准约定来引用 matplotlib API： In [124]: import matplotlib.pyplot as plt In [125]: plt.close("all") 该plt.close方法用于关闭图形窗口： In [126]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000)) In [127]: ts = ts.cumsum() In [128]: ts.plot(); 笔记 使用 Jupyter 时，绘图将使用 出现plot()。否则使用 matplotlib.pyplot.show显示它或 matplotlib.pyplot.savefig将其写入文件。 plot()绘制所有列： In [129]: df = pd.DataFrame( .....: np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"] .....: ) .....: In [130]: df = df.cumsum() In [131]: plt.figure(); In [132]: df.plot(); In [133]: plt.legend(loc='best'); 导入和导出数据# 请参阅IO 工具部分。 CSV # 写入 csv 文件：使用DataFrame.to_csv() In [134]: df = pd.DataFrame(np.random.randint(0, 5, (10, 5))) In [135]: df.to_csv("foo.csv") 从 csv 文件读取：使用read_csv() In [136]: pd.read_csv("foo.csv") Out[136]: Unnamed: 0 0 1 2 3 4 0 0 4 3 1 1 2 1 1 1 0 2 3 2 2 2 1 4 2 1 2 3 3 0 4 0 2 2 4 4 4 2 2 3 4 5 5 4 0 4 3 1 6 6 2 1 2 0 3 7 7 4 0 4 4 4 8 8 4 4 1 0 1 9 9 0 4 3 0 3 实木复合地板# 写入 Parquet 文件： In [137]: df.to_parquet("foo.parquet") 使用以下命令从 Parquet 文件存储中读取read_parquet()： In [138]: pd.read_parquet("foo.parquet") Out[138]: 0 1 2 3 4 0 4 3 1 1 2 1 1 0 2 3 2 2 1 4 2 1 2 3 0 4 0 2 2 4 4 2 2 3 4 5 4 0 4 3 1 6 2 1 2 0 3 7 4 0 4 4 4 8 4 4 1 0 1 9 0 4 3 0 3 Excel # 读取和写入Excel。 使用以下命令写入 Excel 文件DataFrame.to_excel()： In [139]: df.to_excel("foo.xlsx", sheet_name="Sheet1") 使用以下命令从 Excel 文件中读取read_excel()： In [140]: pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"]) Out[140]: Unnamed: 0 0 1 2 3 4 0 0 4 3 1 1 2 1 1 1 0 2 3 2 2 2 1 4 2 1 2 3 3 0 4 0 2 2 4 4 4 2 2 3 4 5 5 4 0 4 3 1 6 6 2 1 2 0 3 7 7 4 0 4 4 4 8 8 4 4 1 0 1 9 9 0 4 3 0 3 陷阱# 如果您尝试对Seriesor执行布尔运算，DataFrame 您可能会看到如下异常： In [141]: if pd.Series([False, True, False]): .....: print("I was true") .....: --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-141-b27eb9c1dfc0> in ?() ----> 1 if pd.Series([False, True, False]): 2 print("I was true") ~/work/pandas/pandas/pandas/core/generic.py in ?(self) 1575 @final 1576 def __nonzero__(self) -> NoReturn: -> 1577 raise ValueError( 1578 f"The truth value of a {type(self).__name__} is ambiguous. " 1579 "Use a.empty, a.bool(), a.item(), a.any() or a.all()." 1580 ) ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). 请参阅比较和陷阱以获取解释和操作方法。