pandas 快速入门

分類 database, pandas

pandas 快速入门#

这是 pandas 的一个简短介绍，主要面向新用户。你可以在实用代码片段中查看更复杂的示例。

通常，我们按如下方式导入

1
2
3
In [1]: import numpy as np

In [2]: import pandas as pd

pandas 中的基本数据结构#

Pandas 提供了两种用于处理数据的类

  1. Series：一个一维的带标签数组，可容纳任何类型的数据

    例如整数、字符串、Python 对象等。

  2. DataFrame：一个二维数据结构，像二维数组或带有行和列的表格一样容纳数据。

对象创建#

请参阅数据结构简介部分。

通过传入值列表创建Series，让 pandas 创建默认的RangeIndex。

1
2
3
4
5
6
7
8
9
10
11
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

通过传入 NumPy 数组，使用date_range()创建带有日期时间索引和标签列的DataFrame

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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

通过传入一个对象字典创建DataFrame，其中键是列标签，值是列值。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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

生成的DataFrame的列具有不同的dtypes

1
2
3
4
5
6
7
8
9
In [11]: df2.dtypes
Out[11]: 
A          float64
B    datetime64[s]
C          float32
D            int32
E         category
F           object
dtype: object

如果你使用 IPython，列名（以及公共属性）的 Tab 自动补全功能会自动启用。以下是部分将自动补全的属性：

1
2
3
4
5
6
7
8
9
10
11
12
13
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

如你所见，列 A、B、C 和 D 会自动 Tab 补全。E 和 F 也在其中；其余属性为简洁起见已被截断。

查看数据#

请参阅基本核心功能部分。

使用DataFrame.head()和DataFrame.tail()分别查看框架的顶部和底部行。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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

1
2
3
4
5
6
7
8
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，不包含索引或列标签。

1
2
3
4
5
6
7
8
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 数组的整个数组只有一个 dtype，而 pandas DataFrames 的每列有一个 dtype。当你调用DataFrame.to_numpy()时，pandas 会找到能够容纳 DataFrame 中 所有 dtypes 的 NumPy dtype。如果共同的数据类型是 object，则DataFrame.to_numpy()将需要复制数据。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
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

1
2
3
4
5
6
7
8
9
10
11
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

1
2
3
4
5
6
7
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

1
2
3
4
5
6
7
8
9
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

1
2
3
4
5
6
7
8
9
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#

请参阅索引文档索引与选择数据和MultiIndex / 高级索引。

Getitem ([])#

对于DataFrame，传入单个标签会选择一列并生成一个Series，等同于 df.A

1
2
3
4
5
6
7
8
9
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，传入切片 : 会选择匹配的行。

1
2
3
4
5
6
7
8
9
10
11
12
13
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#

更多信息请参阅按标签选择，使用DataFrame.loc()或DataFrame.at()。

选择与标签匹配的行

1
2
3
4
5
6
7
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 (:) 和选定的列标签

1
2
3
4
5
6
7
8
9
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

1
2
3
4
5
6
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会返回一个标量

1
2
In [30]: df.loc[dates[0], "A"]
Out[30]: 0.4691122999071863

为了快速访问标量（等同于之前的方法）

1
2
In [31]: df.at[dates[0], "A"]
Out[31]: 0.4691122999071863

按位置选择#

更多信息请参阅按位置选择，使用DataFrame.iloc()或DataFrame.iat()。

通过传入整数的位置进行选择

1
2
3
4
5
6
7
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

整数切片行为类似于 NumPy/Python

1
2
3
4
5
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

1
2
3
4
5
6
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

1
2
3
4
5
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

1
2
3
4
5
6
7
8
9
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

1
2
In [37]: df.iloc[1, 1]
Out[37]: -0.17321464905330858

为了快速访问标量（等同于之前的方法）

1
2
In [38]: df.iat[1, 1]
Out[38]: -0.17321464905330858

布尔索引#

选择 df.A 大于 0 的行。

1
2
3
4
5
6
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

从满足布尔条件的DataFrame中选择值

1
2
3
4
5
6
7
8
9
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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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#

设置新列会自动按索引对齐数据

1
2
3
4
5
6
7
8
9
10
11
12
13
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

1
In [48]: df.at[dates[0], "A"] = 0

按位置设置值

1
In [49]: df.iat[0, 1] = 0

通过分配 NumPy 数组进行设置

1
In [50]: df.loc[:, "D"] = np.array([5] * len(df))

先前的设置操作的结果

1
2
3
4
5
6
7
8
9
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

带设置的 where 操作

1
2
3
4
5
6
7
8
9
10
11
12
13
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。

重新索引允许你更改/添加/删除指定轴上的索引。这会返回数据的副本。

1
2
3
4
5
6
7
8
9
10
11
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

1
2
3
4
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

1
2
3
4
5
6
7
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

1
2
3
4
5
6
7
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

操作#

请参阅二元运算基本部分。

统计#

操作通常排除缺失数据。

计算每列的平均值

1
2
3
4
5
6
7
8
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

1
2
3
4
5
6
7
8
9
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

与具有不同索引或列的另一个Series或DataFrame进行操作时，结果将与索引或列标签的并集对齐。此外，pandas 会沿指定维度自动广播，并将未对齐的标签填充为np.nan。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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()分别应用一个用户定义函数，该函数会减少或广播其结果。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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#

更多信息请参阅直方图和离散化。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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，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。更多信息请参阅矢量化字符串方法。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
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#

拼接#

pandas 提供了各种功能，可以轻松地将 Series 和 DataFrame 对象与各种集合逻辑（用于索引）和关系代数功能（用于连接/合并类型操作）结合在一起。

请参阅合并部分。

使用concat()按行拼接 pandas 对象

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
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()支持沿着特定列进行 SQL 风格的连接。请参阅数据库风格连接部分。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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() 基于唯一键进行合并

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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”指的是一个或多个以下步骤的过程

  • 根据某些条件将数据分拆成组

  • 对每个组独立地应用一个函数

  • 将结果组合成一个数据结构

请参阅分组部分。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
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

1
2
3
4
5
6
In [89]: df.groupby("A")[["C", "D"]].sum()
Out[89]: 
            C         D
A                      
bar  1.732707  1.073134
foo  2.824590 -0.574779

按多列标签分组会形成MultiIndex。

1
2
3
4
5
6
7
8
9
10
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#

请参阅层次化索引和重塑部分。

堆叠#

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
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

stack()方法“压缩”了 DataFrame 列中的一个层

1
2
3
4
5
6
7
8
9
10
11
12
13
14
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

对于一个“堆叠”的 DataFrame 或 Series（其 index 为 MultiIndex），stack() 的逆操作是 unstack()，它默认会解除堆叠最底层

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
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()通过指定values、index和columns来透视DataFrame

1
2
3
4
5
6
7
8
9
10
11
12
13
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 分钟级数据）。这在金融应用中非常常见，但不仅限于此。请参阅时间序列部分。

1
2
3
4
5
6
7
8
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某个时区

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
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()将时区感知的时间序列转换为另一个时区

1
2
3
4
5
6
7
8
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

向时间序列添加非固定时长（BusinessDay）

1
2
3
4
5
6
7
8
9
10
11
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)

分类数据#

pandas 可以在DataFrame中包含分类数据。有关完整文档，请参阅分类简介和API 文档。

1
2
3
4
In [115]: df = pd.DataFrame(
   .....:     {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
   .....: )
   .....:

将原始等级转换为分类数据类型

1
2
3
4
5
6
7
8
9
10
11
12
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

1
2
3
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）

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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']

排序是按类别顺序，而不是字典顺序

1
2
3
4
5
6
7
8
9
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 按分类列分组也会显示空类别

1
2
3
4
5
6
7
8
9
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

1
2
3
In [124]: import matplotlib.pyplot as plt

In [125]: plt.close("all")

plt.close 方法用于关闭图形窗口

1
2
3
4
5
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();

../_images/series_plot_basic.png

plot()绘制所有列

1
2
3
4
5
6
7
8
9
10
11
12
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');

../_images/frame_plot_basic.png

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()

1
2
3
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()

1
2
3
4
5
6
7
8
9
10
11
12
13
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

1
In [137]: df.to_parquet("foo.parquet")

使用read_parquet()从 Parquet 文件读取

1
2
3
4
5
6
7
8
9
10
11
12
13
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#

读写Excel。

使用DataFrame.to_excel()写入 Excel 文件

1
In [139]: df.to_excel("foo.xlsx", sheet_name="Sheet1")

使用read_excel()从 Excel 文件读取

1
2
3
4
5
6
7
8
9
10
11
12
13
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

常见陷阱#

如果您尝试对Series或DataFrame执行布尔操作，可能会看到类似以下的异常：

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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().

请参阅比较和常见陷阱以获取解释和解决方法。

留言與分享

  • 第 1 頁 共 1 頁
作者的圖片

Kein Chan

這是獨立全棧工程師Kein Chan的技術博客
分享一些技術教程,命令備忘(cheat-sheet)等


全棧工程師
資深技術顧問
數據科學家
Hit廣島觀光大使


Tokyo/Macau