linux pandas教程_十分钟入门 Pandas

# 十分钟入门 Pandas

本节是帮助 Pandas 新手快速上手的简介。烹饪指南里介绍了更多实用案例。

本节以下列方式导入 Pandas 与 NumPy：

In [1]: import numpy as np

In [2]: import pandas as pd

# 生成对象

用值列表生成 Series(opens new window) 时，Pandas 默认自动生成整数索引：

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

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

用 Series 字典对象生成 DataFrame:

In [9]: df2 = pd.DataFrame({'A': 1.,

...: '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[ns]

C float32

D int32

E category

F object

dtype: object

IPython支持 tab 键自动补全列名与公共属性。下面是部分可自动补全的属性：

In [12]: df2. # noqa: E225, E999

df2.A df2.bool

df2.abs df2.boxplot

df2.add df2.C

df2.add_prefix df2.clip

df2.add_suffix df2.clip_lower

df2.align df2.clip_upper

df2.all df2.columns

df2.any df2.combine

df2.append df2.combine_first

df2.apply df2.compound

df2.applymap df2.consolidate

df2.D

列 A、B、C、D 和 E 都可以自动补全；为简洁起见，此处只显示了部分属性。

# 查看数据

下列代码说明如何查看 DataFrame 头部和尾部数据：

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

显示索引与列名：

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()(opens new window) 输出底层数据的 NumPy 对象。注意，DataFrame(opens new window) 的列由多种数据类型组成时，该操作耗费系统资源较大，这也是 Pandas 和 NumPy 的本质区别：NumPy 数组只有一种数据类型，DataFrame 每列的数据类型各不相同。调用 DataFrame.to_numpy()(opens new window) 时，Pandas 查找支持 DataFrame 里所有数据类型的 NumPy 数据类型。还有一种数据类型是 object，可以把 DataFrame 列里的值强制转换为 Python 对象。

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 ]])

In [18]: df2.to_numpy()

Out[18]:

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)

In [19]: df.describe()

Out[19]:

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 [20]: df.T

Out[20]:

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

按轴排序：

In [21]: df.sort_index(axis=1, ascending=False)

Out[21]:

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

按值排序：

In [22]: df.sort_values(by='B')

Out[22]:

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

提醒

选择、设置标准 Python / Numpy 的表达式已经非常直观，交互也很方便，但对于生产代码，我们还是推荐优化过的 Pandas 数据访问方法：.at、.iat、.loc 和 .iloc。

# 获取数据

选择单列，产生 Series，与 df.A 等效：

In [23]: df['A']

Out[23]:

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

用 [ ] 切片行：

In [24]: df[0:3]

Out[24]:

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 [25]: df['20130102':'20130104']

Out[25]:

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

用标签提取一行数据：

In [26]: df.loc[dates[0]]

Out[26]:

A 0.469112

B -0.282863

C -1.509059

D -1.135632

Name: 2013-01-01 00:00:00, dtype: float64

用标签选择多列数据：

In [27]: df.loc[:, ['A', 'B']]

Out[27]:

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

用标签切片，包含行与列结束点：

In [28]: df.loc['20130102':'20130104', ['A', 'B']]

Out[28]:

A B

2013-01-02 1.212112 -0.173215

2013-01-03 -0.861849 -2.104569

2013-01-04 0.721555 -0.706771

返回对象降维：

In [29]: df.loc['20130102', ['A', 'B']]

Out[29]:

A 1.212112

B -0.173215

Name: 2013-01-02 00:00:00, dtype: float64

提取标量值：

In [30]: df.loc[dates[0], 'A']

Out[30]: 0.46911229990718628

快速访问标量，与上述方法等效：

In [31]: df.at[dates[0], 'A']

Out[31]: 0.46911229990718628

# 按位置选择

用整数位置选择：

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

类似 NumPy / Python，用整数切片：

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

类似 NumPy / Python，用整数列表按位置切片：

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

显式整行切片：

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

显式整列切片：

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

显式提取值：

In [37]: df.iloc[1, 1]

Out[37]: -0.17321464905330858

快速访问标量，与上述方法等效：

In [38]: df.iat[1, 1]

Out[38]: -0.17321464905330858

# 布尔索引

用单列的值选择数据：

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

选择 DataFrame 里满足条件的值：

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

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

# 赋值

用索引自动对齐新增列的数据：

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

按标签赋值：

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

按位置赋值：

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

按 NumPy 数组赋值：

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

上述赋值结果：

In [51]: df

Out[51]:

A B C D F

2013-01-01 0.000000 0.000000 -1.509059 5 NaN

2013-01-02 1.212112 -0.173215 0.119209 5 1.0

2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0

2013-01-04 0.721555 -0.706771 -1.039575 5 3.0

2013-01-05 -0.424972 0.567020 0.276232 5 4.0

2013-01-06 -0.673690 0.113648 -1.478427 5 5.0

用 where 条件赋值：

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 NaN

2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0

2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0

2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0

2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0

2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0

# 缺失值

Pandas 主要用 np.nan 表示缺失数据。计算时，默认不包含空值。详见缺失数据(opens new window)。

重建索引(reindex)可以更改、添加、删除指定轴的索引，并返回数据副本，即不更改原数据。

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 NaN 1.0

2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0

2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN

2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN

删除所有含缺失值的行：

In [58]: df1.dropna(how='any')

Out[58]:

A B C D F E

2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0

填充缺失值：

In [59]: df1.fillna(value=5)

Out[59]:

A B C D F E

2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0

2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0

2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0

2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0

提取 nan 值的布尔掩码：

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

# 统计

一般情况下，运算时排除缺失值。

描述性统计：

In [61]: df.mean()

Out[61]:

A -0.004474

B -0.383981

C -0.687758

D 5.000000

F 3.000000

dtype: float64

在另一个轴(即，行)上执行同样的操作：

In [62]: df.mean(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

不同维度对象运算时，要先对齐。此外，Pandas 自动沿指定维度广播。

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

# Apply 函数

Apply 函数处理数据：

In [66]: df.apply(np.cumsum)

Out[66]:

A B C D F

2013-01-01 0.000000 0.000000 -1.509059 5 NaN

2013-01-02 1.212112 -0.173215 -1.389850 10 1.0

2013-01-03 0.350263 -2.277784 -1.884779 15 3.0

2013-01-04 1.071818 -2.984555 -2.924354 20 6.0

2013-01-05 0.646846 -2.417535 -2.648122 25 10.0

2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0

In [67]: df.apply(lambda x: x.max() - x.min())

Out[67]:

A 2.073961

B 2.671590

C 1.785291

D 0.000000

F 4.000000

dtype: float64

# 直方图

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

6 2

2 2

1 1

dtype: int64

# 字符串方法

Series 的 str 属性包含一组字符串处理功能，如下列代码所示。注意，str 的模式匹配默认使用正则表达式(opens new window)。详见矢量字符串方法(opens new window)。

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(Merge)

# 结合(Concat)

Pandas 提供了多种将 Series、DataFrame 对象组合在一起的功能，用索引与关联代数功能的多种设置逻辑可执行连接(join)与合并(merge)操作。

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

# 分解为多组

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(join)

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

这里还有一个例子：

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

# 追加(Append)

为 DataFrame 追加行。详见追加(opens new window)文档。

In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])

In [88]: df

Out[88]:

A B C D

0 1.346061 1.511763 1.627081 -0.990582

1 -0.441652 1.211526 0.268520 0.024580

2 -1.577585 0.396823 -0.105381 -0.532532

3 1.453749 1.208843 -0.080952 -0.264610

4 -0.727965 -0.589346 0.339969 -0.693205

5 -0.339355 0.593616 0.884345 1.591431

6 0.141809 0.220390 0.435589 0.192451

7 -0.096701 0.803351 1.715071 -0.708758

In [89]: s = df.iloc[3]

In [90]: df.append(s, ignore_index=True)

Out[90]:

A B C D

0 1.346061 1.511763 1.627081 -0.990582

1 -0.441652 1.211526 0.268520 0.024580

2 -1.577585 0.396823 -0.105381 -0.532532

3 1.453749 1.208843 -0.080952 -0.264610

4 -0.727965 -0.589346 0.339969 -0.693205

5 -0.339355 0.593616 0.884345 1.591431

6 0.141809 0.220390 0.435589 0.192451

7 -0.096701 0.803351 1.715071 -0.708758

8 1.453749 1.208843 -0.080952 -0.264610

# 分组(Grouping)

“group by” 指的是涵盖下列一项或多项步骤的处理流程：分割：按条件把数据分割成多组；

应用：为每组单独应用函数；

组合：将处理结果组合成一个数据结构。

In [91]: 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 [92]: df

Out[92]:

A B C D

0 foo one -1.202872 -0.055224

1 bar one -1.814470 2.395985

2 foo two 1.018601 1.552825

3 bar three -0.595447 0.166599

4 foo two 1.395433 0.047609

5 bar two -0.392670 -0.136473

6 foo one 0.007207 -0.561757

7 foo three 1.928123 -1.623033

In [93]: df.groupby('A').sum()

Out[93]:

C D

bar -2.802588 2.42611

foo 3.146492 -0.63958

多列分组后，生成多层索引，也可以应用 sum 函数：

In [94]: df.groupby(['A', 'B']).sum()

Out[94]:

C D

A B

bar one -1.814470 2.395985

three -0.595447 0.166599

two -0.392670 -0.136473

foo one -1.195665 -0.616981

three 1.928123 -1.623033

two 2.414034 1.600434

# 重塑(Reshaping)

# 堆叠(Stack)

In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',

....: 'foo', 'foo', 'qux', 'qux'],

....: ['one', 'two', 'one', 'two',

....: 'one', 'two', 'one', 'two']]))

....:

In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])

In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])

In [98]: df2 = df[:4]

In [99]: df2

Out[99]:

A B

first second

bar one 0.029399 -0.542108

two 0.282696 -0.087302

baz one -1.575170 1.771208

two 0.816482 1.100230

In [100]: stacked = df2.stack()

In [101]: stacked

Out[101]:

first second

B -0.542108

two A 0.282696

B -0.087302

baz one A -1.575170

B 1.771208

two A 0.816482

B 1.100230

dtype: float64

In [102]: stacked.unstack()

Out[102]:

A B

first second

bar one 0.029399 -0.542108

two 0.282696 -0.087302

baz one -1.575170 1.771208

two 0.816482 1.100230

In [103]: stacked.unstack(1)

Out[103]:

second one two

first

bar A 0.029399 0.282696

B -0.542108 -0.087302

baz A -1.575170 0.816482

B 1.771208 1.100230

In [104]: stacked.unstack(0)

Out[104]:

first bar baz

second

one A 0.029399 -1.575170

B -0.542108 1.771208

two A 0.282696 0.816482

B -0.087302 1.100230

# 数据透视表(Pivot Tables)

In [105]: 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 [106]: df

Out[106]:

A B C D E

0 one A foo 1.418757 -0.179666

1 one B foo -1.879024 1.291836

2 two C foo 0.536826 -0.009614

3 three A bar 1.006160 0.392149

4 one B bar -0.029716 0.264599

5 one C bar -1.146178 -0.057409

6 two A foo 0.100900 -1.425638

7 three B foo -1.035018 1.024098

8 one C foo 0.314665 -0.106062

9 one A bar -0.773723 1.824375

10 two B bar -1.170653 0.595974

11 three C bar 0.648740 1.167115

用上述数据生成数据透视表非常简单：

In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])

Out[107]:

C bar foo

A B

one A -0.773723 1.418757

B -0.029716 -1.879024

C -1.146178 0.314665

three A 1.006160 NaN

B NaN -1.035018

C 0.648740 NaN

two A NaN 0.100900

B -1.170653 NaN

C NaN 0.536826

# 时间序列(TimeSeries)

Pandas 为频率转换时重采样提供了虽然简单易用，但强大高效的功能，如，将秒级的数据转换为 5 分钟为频率的数据。这种操作常见于财务应用程序，但又不仅限于此。详见时间序列(opens new window)。

In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S')

In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)

In [110]: ts.resample('5Min').sum()

Out[110]:

2012-01-01 25083

Freq: 5T, dtype: int64

时区表示：

In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')

In [112]: ts = pd.Series(np.random.randn(len(rng)), rng)

In [113]: ts

Out[113]:

2012-03-06 0.464000

2012-03-07 0.227371

2012-03-08 -0.496922

2012-03-09 0.306389

2012-03-10 -2.290613

Freq: D, dtype: float64

In [114]: ts_utc = ts.tz_localize('UTC')

In [115]: ts_utc

Out[115]:

2012-03-06 00:00:00+00:00 0.464000

2012-03-07 00:00:00+00:00 0.227371

2012-03-08 00:00:00+00:00 -0.496922

2012-03-09 00:00:00+00:00 0.306389

2012-03-10 00:00:00+00:00 -2.290613

Freq: D, dtype: float64

转换成其它时区：

In [116]: ts_utc.tz_convert('US/Eastern')

Out[116]:

2012-03-05 19:00:00-05:00 0.464000

2012-03-06 19:00:00-05:00 0.227371

2012-03-07 19:00:00-05:00 -0.496922

2012-03-08 19:00:00-05:00 0.306389

2012-03-09 19:00:00-05:00 -2.290613

Freq: D, dtype: float64

转换时间段：

In [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M')

In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng)

In [119]: ts

Out[119]:

2012-01-31 -1.134623

2012-02-29 -1.561819

2012-03-31 -0.260838

2012-04-30 0.281957

2012-05-31 1.523962

Freq: M, dtype: float64

In [120]: ps = ts.to_period()

In [121]: ps

Out[121]:

2012-01 -1.134623

2012-02 -1.561819

2012-03 -0.260838

2012-04 0.281957

2012-05 1.523962

Freq: M, dtype: float64

In [122]: ps.to_timestamp()

Out[122]:

2012-01-01 -1.134623

2012-02-01 -1.561819

2012-03-01 -0.260838

2012-04-01 0.281957

2012-05-01 1.523962

Freq: MS, dtype: float64

Pandas 函数可以很方便地转换时间段与时间戳。下例把以 11 月为结束年份的季度频率转换为下一季度月末上午 9 点：

In [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')

In [124]: ts = pd.Series(np.random.randn(len(prng)), prng)

In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9

In [126]: ts.head()

Out[126]:

1990-03-01 09:00 -0.902937

1990-06-01 09:00 0.068159

1990-09-01 09:00 -0.057873

1990-12-01 09:00 -0.368204

1991-03-01 09:00 -1.144073

Freq: H, dtype: float64

# 类别型(Categoricals)

In [127]: df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],

.....: "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})

.....:

将 grade 的原生数据转换为类别型数据：

In [128]: df["grade"] = df["raw_grade"].astype("category")

In [129]: df["grade"]

Out[129]:

0 a

1 b

2 b

3 a

4 a

5 e

Name: grade, dtype: category

Categories (3, object): [a, b, e]

用有含义的名字重命名不同类型，调用 Series.cat.categories。

In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]

重新排序各类别，并添加缺失类，Series.cat 的方法默认返回新 Series。

In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",

.....: "good", "very good"])

.....:

In [132]: df["grade"]

Out[132]:

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]

注意，这里是按生成类别时的顺序排序，不是按词汇排序：

In [133]: df.sort_values(by="grade")

Out[133]:

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

按类列分组(groupby)时，即便某类别为空，也会显示：

In [134]: df.groupby("grade").size()

Out[134]:

grade

very bad 1

bad 0

medium 0

good 2

very good 3

dtype: int64

# 可视化

In [135]: ts = pd.Series(np.random.randn(1000),

.....: index=pd.date_range('1/1/2000', periods=1000))

.....:

In [136]: ts = ts.cumsum()

In [137]: ts.plot()

Out[137]:

DataFrame 的 plot()(opens new window) 方法可以快速绘制所有带标签的列：

In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,

.....: columns=['A', 'B', 'C', 'D'])

.....:

In [139]: df = df.cumsum()

In [140]: plt.figure()

Out[140]:

In [141]: df.plot()

Out[141]:

In [142]: plt.legend(loc='best')

Out[142]:

# 数据输入 / 输出

# CSV

In [143]: df.to_csv('foo.csv')

读取 CSV 文件数据：

In [144]: pd.read_csv('foo.csv')

Out[144]:

Unnamed: 0 A B C D

0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860

1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953

2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536

3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896

4 2000-01-05 0.578117 0.511371 0.103552 -2.428202

5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409

6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753

.. ... ... ... ... ...

993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940

994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107

995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740

996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439

997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593

998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560

999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368

[1000 rows x 5 columns]

# HDF5

写入 HDF5 Store：

In [145]: df.to_hdf('foo.h5', 'df')

读取 HDF5 Store：

In [146]: pd.read_hdf('foo.h5', 'df')

Out[146]:

A B C D

2000-01-01 0.266457 -0.399641 -0.219582 1.186860

2000-01-02 -1.170732 -0.345873 1.653061 -0.282953

2000-01-03 -1.734933 0.530468 2.060811 -0.515536

2000-01-04 -1.555121 1.452620 0.239859 -1.156896

2000-01-05 0.578117 0.511371 0.103552 -2.428202

2000-01-06 0.478344 0.449933 -0.741620 -1.962409

2000-01-07 1.235339 -0.091757 -1.543861 -1.084753

... ... ... ... ...

2002-09-20 -10.628548 -9.153563 -7.883146 28.313940

2002-09-21 -10.390377 -8.727491 -6.399645 30.914107

2002-09-22 -8.985362 -8.485624 -4.669462 31.367740

2002-09-23 -9.558560 -8.781216 -4.499815 30.518439

2002-09-24 -9.902058 -9.340490 -4.386639 30.105593

2002-09-25 -10.216020 -9.480682 -3.933802 29.758560

2002-09-26 -11.856774 -10.671012 -3.216025 29.369368

[1000 rows x 4 columns]

# Excel

写入 Excel 文件：

In [147]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

读取 Excel 文件：

In [148]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])

Out[148]:

Unnamed: 0 A B C D

0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860

1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953

2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536

3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896

4 2000-01-05 0.578117 0.511371 0.103552 -2.428202

5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409

6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753

.. ... ... ... ... ...

993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940

994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107

995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740

996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439

997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593

998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560

999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368

[1000 rows x 5 columns]

# 各种坑(Gotchas)

执行某些操作，将触发异常，如:

>>> if pd.Series([False, True, False]):

... print("I was true")

Traceback

...

ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

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