十分钟掌握 Pandas#
本指南是 Pandas 的简短介绍，主要面向新用户。您可以在 食谱 中查看更复杂的示例。
通常，我们按以下方式导入
In [1]: import numpy as np
In [2]: import pandas as pd
Pandas 中的基本数据结构#
Pandas 提供两种类型的类来处理数据
对象创建#
请参阅数据结构简介部分。
通过传递一个值列表来创建一个Series，让 pandas 创建一个默认的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
通过传递一个 NumPy 数组，使用date_range()创建日期时间索引并标记列，来创建一个DataFrame。
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
通过传递一个对象字典来创建一个DataFrame，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
In [11]: df2.dtypes
Out[11]:
A float64
B datetime64[s]
C float32
D int32
E category
F object
dtype: object
如果您使用的是 IPython，则会自动启用列名（以及公共属性）的制表符补全。以下是将要完成的属性子集
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
如您所见，列A、B、C和D会自动进行制表符补全。E和F也在其中；为了简洁起见，其他属性已被截断。
查看数据#
请参阅基本功能部分。
使用 DataFrame.head() 和 DataFrame.tail() 分别查看框架的顶部和底部行
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')
使用 DataFrame.to_numpy() 返回底层数据的 NumPy 表示形式，不包含索引或列标签
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 ]])
注意
NumPy 数组对整个数组使用一个 dtype，而 pandas DataFrame 对每列使用一个 dtype。当您调用 DataFrame.to_numpy() 时，pandas 将找到可以容纳 DataFrame 中所有 dtype 的 NumPy dtype。如果公共数据类型是 object，DataFrame.to_numpy() 将需要复制数据。
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
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
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#
注意
虽然标准的 Python/NumPy 表达式在选择和设置方面直观且便于交互式工作，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()，DataFrame.loc() 和 DataFrame.iloc()。
获取项 ([])#
对于 DataFrame，传递单个标签将选择一列并生成一个等效于 df.A 的 Series。
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 DataFrame，传递切片 : 将选择匹配的行。
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#
在 按标签选择 中了解更多信息，使用 DataFrame.loc() 或 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
选择所有行 (:) 并使用选定的列标签。
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，两个端点都包含在内。
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（等同于先前方法）
In [31]: df.at[dates[0], "A"]
Out[31]: 0.4691122999071863
按位置选择#
更多内容请参见 按位置选择，使用 DataFrame.iloc() 或 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
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
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
显式获取值
In [37]: df.iloc[1, 1]
Out[37]: -0.17321464905330858
为了快速访问标量（等同于先前方法）
In [38]: df.iat[1, 1]
Out[38]: -0.17321464905330858
布尔索引#
选择 df.A 大于 0 的行。
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
从 DataFrame 中选择满足布尔条件的值
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
使用 isin() 方法进行过滤
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
设置#
设置新列会根据索引自动对齐数据
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
使用 NumPy 数组进行赋值设置
In [50]: df.loc[:, "D"] = np.array([5] * len(df))
先前设置操作的结果
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 where 操作
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。请参见 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() 删除任何包含缺失数据的行
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() 用于填充缺失数据
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() 获取值为 nan 的布尔掩码
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
运算#
参见 二元运算基础部分.
统计#
一般情况下，运算会 *排除* 缺失数据。
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
使用另一个具有不同索引或列的 Series 或 DataFrame 进行运算，结果将与索引或列标签的并集对齐。此外，pandas 会自动沿指定维度广播，并用 np.nan 填充未对齐的标签。
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
用户定义函数#
DataFrame.agg() 和 DataFrame.transform() 分别应用用户定义的函数，该函数可以减少或广播其结果。
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
值计数#
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 在 str 属性中配备了一组字符串处理方法，使您可以轻松地对数组的每个元素进行操作，如下面的代码片段所示。有关更多信息，请参阅 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，可以轻松地将 Series 和 DataFrame 对象组合在一起，并使用各种索引集逻辑和关系代数功能来进行连接/合并类型的操作。
请参阅 合并部分。
使用 concat() 将 pandas 对象按行连接在一起
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
连接#
merge() 允许根据特定列进行 SQL 风格的联接类型。请参阅 数据库风格联接 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#
“分组”指的是一个包含以下一个或多个步骤的过程
拆分数据，根据某些标准将其分成组
应用函数到每个组，独立进行
合并结果到一个数据结构中
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
根据列标签进行分组，选择列标签，然后将 DataFrameGroupBy.sum() 函数应用到结果组
In [89]: df.groupby("A")[["C", "D"]].sum()
Out[89]:
C D
A
bar 1.732707 1.073134
foo 2.824590 -0.574779
根据多个列标签进行分组会形成 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#
堆叠#
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
The stack() 方法将 DataFrame 的列中的一个级别“压缩”
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 MultiIndex 作为 index），stack() 的逆操作是 unstack()，它默认情况下会取消堆叠最后一个级别
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
透视表#
请参阅透视表部分。
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() 透视一个DataFrame，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 提供简单、强大且高效的功能，用于在频率转换期间执行重采样操作（例如，将秒级数据转换为 5 分钟数据）。这在金融应用中非常常见，但并不局限于此。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() 将时间序列本地化为时区
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
将非固定持续时间（BusinessDay）添加到时间序列
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 可以将分类数据包含在 DataFrame 中。有关完整文档，请参阅 分类数据介绍 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)
重新排序类别并同时添加缺失的类别（Series.cat() 下的方法默认返回一个新的 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']
排序是根据类别中的顺序进行的，而不是按字母顺序
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
使用 observed=False 对分类列进行分组也会显示空类别
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")
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();
注意
在使用 Jupyter 时，绘图将使用 plot() 显示。否则，使用 matplotlib.pyplot.show 显示它，或使用 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")
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
Parquet#
写入 Parquet 文件
In [137]: df.to_parquet("foo.parquet")
使用 read_parquet() 从 Parquet 文件存储读取数据
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 DataFrame.to_excel() 写入 Excel 文件
In [139]: df.to_excel("foo.xlsx", sheet_name="Sheet1")
使用 read_excel() 从 Excel 文件读取数据
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
注意事项#
如果您尝试对 Series 或 DataFrame 执行布尔运算，您可能会看到类似的异常
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)
1574 @final
1575 def __nonzero__(self) -> NoReturn:
-> 1576 raise ValueError(
1577 f"The truth value of a {type(self).__name__} is ambiguous. "
1578 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
1579 )
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().