10分钟上手pythonpandas_【译】10分钟学会Pandas

十分钟学会Pandas

这是关于Pandas的简短介绍主要面向新用户。你可以参考Cookbook了解更复杂的使用方法

习惯上，我们这样导入：

In [1]: importpandas as pd

In [2]: importnumpy as np

In [3]: import matplotlib.pyplot as plt

创建对象

通过传递一个列表的值创建一个Series，让Pandas创建一个默认的整数索引：

In [4]: s = pd.Series([1,3,5,np.nan,6,8])

In [5]: s

Out[5]:

01.0

1 3.0

2 5.0

3NaN4 6.0

5 8.0dtype: float64

通过传递的numpy数组创建一个DataFrame，并使用DataFrame索引和标记列：

In [6]: dates = pd.date_range('20130101', periods=6)

In [7]: dates

Out[7]:

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 [8]: df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))

In [9]: df

Out[9]:

A B C D2013-01-01 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

通过传递可转换成类似序列结构的字典序列来创建DataFrame。

查看不同列的数据类型

In [12]: df2.dtypes

Out[12]:

A float64

B datetime64[ns]

C float32

D int32

E category

F object

dtype: object

如果你使用的是IPython，可以使用Tab自动补全列名称(以及公共属性)。以下是将要完成的属性的一个子集：

In [13]: df2.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.as_blocks df2.convert_objects

df2.asfreq df2.copy

df2.as_matrix df2.corr

df2.astype df2.corrwith

df2.at df2.count

df2.at_time df2.cov

df2.axes df2.cummax

df2.B df2.cummin

df2.between_time df2.cumprod

df2.bfill df2.cumsum

df2.blocks df2.D

像你见到的那样，A、B、C、D都是使用Tab自动补全的。E也是如此；为了简洁其它的属性被截断了。

查看数据

查看frame中头部和尾部的行

In [14]: df.head()

Out[14]:

A B C D2013-01-01 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 -1.087401In [15]: df.tail(3)

Out[15]:

A B C D2013-01-04 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、行和底层numpy数据

In [16]: df.index

Out[16]:

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 [17]: df.columns

Out[17]: Index(['A', 'B', 'C', 'D'], dtype='object')

In [18]: df.values

Out[18]:

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 ]])

显示您的数据的快速统计摘要

In [19]: df.describe()

Out[19]:

A B C D

count6.000000 6.000000 6.000000 6.000000mean0.073711 -0.431125 -0.687758 -0.233103std0.843157 0.922818 0.779887 0.973118min-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

数据转置

In [20]: df.T

Out[20]:2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06A0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690B-0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648C-1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427D-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 A2013-01-01 -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 D2013-01-03 -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

读取

选择一个单独的列，返回一个Series，等同于 df.A

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 -0.673690Freq: D, Name: A, dtype: float64

使用[]选择，对行进行切片。

In [24]: df[0:3]

Out[24]:

A B C D2013-01-01 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 1.071804In [25]: df['20130102':'20130104']

Out[25]:

A B C D2013-01-02 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

通过标签选择

详情参阅标签选择

使用标签获取交叉区域

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

Out[26]:

A0.469112B-0.282863C-1.509059D-1.135632Name:2013-01-01 00:00:00, dtype: float64

通过标签选择多轴

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

Out[27]:

A B2013-01-01 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

显示标签切片，包含端点

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

Out[28]:

A B2013-01-02 1.212112 -0.173215

2013-01-03 -0.861849 -2.104569

2013-01-04 0.721555 -0.706771

降低返回对象维度

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

Out[29]:

A1.212112B-0.173215Name:2013-01-02 00:00:00, dtype: float64

获取标量值

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

Out[30]: 0.46911229990718628

快速访问标量(同上一方法等价)

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

Out[31]: 0.46911229990718628

按位置选择

通过传递整数选择位置

In [32]: df.iloc[3]

Out[32]:

A0.721555B-0.706771C-1.039575D0.271860Name:2013-01-04 00:00:00, dtype: float64

通过整数切片，类似于numpy/python

In [33]: df.iloc[3:5,0:2]

Out[33]:

A B2013-01-04 0.721555 -0.706771

2013-01-05 -0.424972 0.567020

通过列表指定位置，类似于numpy/python样式

In [34]: df.iloc[[1,2,4],[0,2]]

Out[34]:

A C2013-01-02 1.212112 0.119209

2013-01-03 -0.861849 -0.494929

2013-01-05 -0.424972 0.276232

对行切片

In [35]: df.iloc[1:3,:]

Out[35]:

A B C D2013-01-02 1.212112 -0.173215 0.119209 -1.044236

2013-01-03 -0.861849 -2.104569 -0.494929 1.071804

对列切片

In [36]: df.iloc[:,1:3]

Out[36]:

B C2013-01-01 -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

获取指定值

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

Out[37]: -0.17321464905330858

快速访问标量(同上一方法等价)

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

Out[38]: -0.17321464905330858

布尔索引

使用单列值选择数据。

In [39]: df[df.A >0]

Out[39]:

A B C D2013-01-01 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。

In [40]: df[df >0]

Out[40]:

A B C D2013-01-01 0.469112NaN NaN NaN2013-01-02 1.212112 NaN 0.119209NaN2013-01-03 NaN NaN NaN 1.071804

2013-01-04 0.721555 NaN NaN 0.271860

2013-01-05 NaN 0.567020 0.276232NaN2013-01-06 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 E2013-01-01 0.469112 -0.282863 -1.509059 -1.135632one2013-01-02 1.212112 -0.173215 0.119209 -1.044236one2013-01-03 -0.861849 -2.104569 -0.494929 1.071804two2013-01-04 0.721555 -0.706771 -1.039575 0.271860three2013-01-05 -0.424972 0.567020 0.276232 -1.087401four2013-01-06 -0.673690 0.113648 -1.478427 0.524988three

In [44]: df2[df2['E'].isin(['two','four'])]

Out[44]:

A B C D E2013-01-03 -0.861849 -2.104569 -0.494929 1.071804two2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four

设置

设置一个新列会自动使索引对齐数据

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 6Freq: D, dtype: int64

In [47]: df['F'] = s1

按标签切片

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

按位置设置值

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

通过numpy数组设置

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

设置结果如下

In [51]: df

Out[51]:

A B C D F2013-01-01 0.000000 0.000000 -1.509059 5NaN2013-01-02 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

where操作赋值

In [52]: df2 =df.copy()

In [53]: df2[df2 > 0] = -df2

In [54]: df2

Out[54]:

A B C D F2013-01-01 0.000000 0.000000 -1.509059 -5NaN2013-01-02 -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

缺失数据

Pandas主要使用np.nan来表示缺失数据。默认情况下不包括在计算中。请参阅缺失数据部分

重建索引允许修改/添加/删除指定轴的索引，并返回数据副本。

In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])

In [56]: df1.loc[dates[0]:dates[1],'E'] = 1In [57]: df1

Out[57]:

A B C D F E2013-01-01 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 2.0NaN2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN

删除所有缺少数据的行。

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

Out[58]:

A B C D F E2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0

填写缺失的数据行

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

Out[59]:

A B C D F E2013-01-01 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

获取值为nan的布尔值

In [60]: pd.isnull(df1)

Out[60]:

A B C D F E2013-01-01False False False False True False2013-01-02False False False False False False2013-01-03False False False False False True2013-01-04 False False False False False True

运算

统计

运算一般排除丢失的数据。

执行描述性统计

In [61]: df.mean()

Out[61]:

A-0.004474B-0.383981C-0.687758D5.000000F3.000000dtype: float64

在其他轴上执行相同的运算

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 1.592306Freq: D, dtype: float64

运算具有不同维度和需要对齐的对象。此外，Pandas会沿着指定维度运算。

In [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)

In [64]: s

Out[64]:2013-01-01NaN2013-01-02NaN2013-01-03 1.0

2013-01-04 3.0

2013-01-05 5.0

2013-01-06NaN

Freq: D, dtype: float64

In [65]: df.sub(s, axis='index')

Out[65]:

A B C D F2013-01-01NaN NaN NaN NaN NaN2013-01-02NaN NaN NaN NaN NaN2013-01-03 -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

应用

将函数应用于数据

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

Out[66]:

A B C D F2013-01-01 0.000000 0.000000 -1.509059 5NaN2013-01-02 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 15.0In [67]: df.apply(lambda x: x.max() -x.min())

Out[67]:

A2.073961B2.671590C1.785291D0.000000F4.000000dtype: float64

直方图

In [68]: s = pd.Series(np.random.randint(0, 7, size=10))

In [69]: s

Out[69]:

1 2

2 1

3 2

4 6

5 4

6 4

7 6

8 4

9 4dtype: int64

In [70]: s.value_counts()

Out[70]:4 5

6 2

2 2

1 1dtype: int64

字符串方法

Series在字符串中设置了一组字符串处理方法，可以方便地对数组中每个元素进行操作，如下面代码片段所示。请注意，字符串中的模式匹配默认使用正则表达式。(在某些情况下总是使用它们)。详情请参阅矢量字符串方法。

In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])

In [72]: s.str.lower()

Out[72]:

0 a1b2c3aaba4baca5NaN6caba7dog8cat

dtype: object

合并

连接

在连接/合并类型操作的情况下，Pandas提供了一些具有用于索引和关系代数的各种函数合并Series、DataFrame和Panel对象的方法

使用concat()把Pandas对象连接：

In [73]: df = pd.DataFrame(np.random.randn(10, 4))

In [74]: df

Out[74]:

01 2 30-0.548702 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]:

01 2 30-0.548702 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

SQL风格合并。请参阅数据库风格连接

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 foo1

1 foo 2In [80]: right

Out[80]:

key rval

0 foo4

1 foo 5In [81]: pd.merge(left, right, on='key')

Out[81]:

key lval rval

0 foo1 4

1 foo 1 5

2 foo 2 4

3 foo 2 5

给出另一个例子：

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 foo1

1 bar 2In [85]: right

Out[85]:

key rval

0 foo4

1 bar 5In [86]: pd.merge(left, right, on='key')

Out[86]:

key lval rval

0 foo1 4

1 bar 2 5

追加

添加行到DataFrame。请参阅追加

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

In [88]: df

Out[88]:

A B C D

01.346061 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 -0.708758In [89]: s = df.iloc[3]

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

Out[90]:

A B C D

01.346061 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

"分组"我们指的是涉及一个或多个以下步骤的过程

Splitting：根据一些标准将数据分组

Applying：将功能独立应用于每个组

Combining：将结果合并成数据结构

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

分组，然后将sum()函数应用于生成的组。

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

Out[93]:

C D

bar-2.802588 2.42611foo3.146492 -0.63958

按多列分组形成层次索引，然后应用该函数。

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

Out[94]:

C D

A B

bar one-1.814470 2.395985three-0.595447 0.166599two-0.392670 -0.136473foo one-1.195665 -0.616981three1.928123 -1.623033two2.414034 1.600434

重塑

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 one0.029399 -0.542108two0.282696 -0.087302baz one-1.575170 1.771208two0.816482 1.100230

堆

stack()方法将"压缩"DataFrame列中的一个级别。

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

In [101]: stacked

Out[101]:

first second

bar one A0.029399B-0.542108two A0.282696B-0.087302baz one A-1.575170B1.771208two A0.816482B1.100230dtype: float64

使用"stacked"DataFrame或Series(有一个MultilIndex作为索引)stack()的反向操作是unstack()，它默认情况下解除最后一个级别。

In [102]: stacked.unstack()

Out[102]:

A B

first second

bar one0.029399 -0.542108two0.282696 -0.087302baz one-1.575170 1.771208two0.816482 1.100230In [103]: stacked.unstack(1)

Out[103]:

second one two

first

bar A0.029399 0.282696B-0.542108 -0.087302baz A-1.575170 0.816482B1.771208 1.100230In [104]: stacked.unstack(0)

Out[104]:

first bar baz

second

one A0.029399 -1.575170B-0.542108 1.771208two A0.282696 0.816482B-0.087302 1.100230

数据透视表

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 foo1.418757 -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

我们可以轻松地从这些数据中快速生成数据透视表：

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

Out[107]:

C bar foo

A B

one A-0.773723 1.418757B-0.029716 -1.879024C-1.146178 0.314665three A1.006160NaN

B NaN-1.035018C0.648740NaN

two A NaN0.100900B-1.170653NaN

C NaN0.536826

时间序列

Pandas具有简单、强大和高效的功能，用于在变频期间执行重采样操作。这在金融应用中非常常见，但是不限于此。请参阅时间序列部分

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 25083Freq: 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 -2.290613Freq: 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 -2.290613Freq: D, dtype: float64

转换到另一时区

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 -2.290613Freq: D, dtype: float64

在时间跨度之间转换

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 1.523962Freq: 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 1.523962Freq: 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 1.523962Freq: MS, dtype: float64

在时间和时间戳之间转换，可以使用一些方便的算术函数。在下面例子中：

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') + 9In [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 -1.144073Freq: H, dtype: float64

明确的

自0.15版本以来，Pandas可以在DataFrame中包含分类数据。有关完整文档，请参阅分类介绍和API文档。

In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})

将原始数据转换为分类数据。

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

In [129]: df["grade"]

Out[129]:

0 a1b2b3a4a5e

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

重新排列类别并同时添加丢失的类别(Series.cat下的方法返回一个默认的新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 good1good2good3very good4very good5very bad

Name: grade, dtype: category

Categories (5, object): [very bad, bad, medium, good, very good]

排序是按类别中的顺序排序的，而不是词法顺序。

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

Out[133]:

id raw_grade grade5 6e very bad1 2b good2 3b good

01a very good3 4a very good4 5 a very good

按分类列分组还显示空类别。

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

Out[134]:

grade

very bad1bad 0

medium 0

good2very good3dtype: int64

绘图

绘图文档

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]:

在DataFrame中，plot()可以方便绘制带标签的所有列。

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(); df.plot(); plt.legend(loc='best')

Out[140]:

获取数据输入/输出

CSV

In [141]: df.to_csv('foo.csv')

In [142]: pd.read_csv('foo.csv')

Out[142]:

Unnamed: 0 A B C D

02000-01-01 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存储

In [143]: df.to_hdf('foo.h5','df')

从HDF5存储中读取

In [144]: pd.read_hdf('foo.h5','df')

Out[144]:

A B C D2000-01-01 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 [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')

从Excel文件读取

In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])

Out[146]:

A B C D2000-01-01 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]

陷阱

如果你尝试以下操作，可以看到如下异常：

>>> ifpd.Series([False, True, False]):print("I was true")

Traceback

...

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

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