十分钟了解Pandas
目录
对象创建 (Objiect Creation)
数据查看(Viewing Data)
数据选取 (Selection)
数据获取(Getting)
按列标签选取 (Selection bt Lable)
按位置选择数据 (Selection by Position)
布尔值索引 (Boolean Indexing)
数据设置 (Setting)
缺失值(Missing Data)
元操作(Operations)
统计(Stats)
Apply
Histogramming
字符方法(String Methods)
数据合并(Merge)
Concat
Join
Append
数据分组 (Grouping)
数据重整型(Reshaping)
Stack
透视表(Pivot Tables)
时间序列 (Time Series)
数据分类(Categoricals)
可视化(Plotting)
数据的读取和输出(Getting Data In/Out)
csv
HDF5
Excel
本文简要的介绍Pandas.目的是快速的了解Pandas的主要功能.特定的功能的都会在其他的博文中进行针对性详细讲解.
因Pandas涉及内容较多,其他关于Pandas特性介绍的博文还在持续更新中......
导入Pandas有一个习惯性的规定:
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: import matplotlib.pyplot as plt对象创建 (Objiect Creation)
关于Pandas对象的详细信息,可以参见另外一篇博文:Pandas的数据结构.
从列表创建一个Series,Pandas会默认为其创建整型的索引:
In [4]: s = pd.Series([1,3,5,np.nan,6,8])
In [5]: s
Out[5]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64由Numpy数组创建以时间序列为索引和带有列标签的DataFrame:
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 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将一个字典对象转换为DataFrame对象:
In [10]: 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 [11]: df2
Out[11]:
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这个由字典创建的DataFrame对象每一列的属性类型都不一样:
In [12]: df2.dtypes
Out[12]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object数据查看(Viewing Data)
关于数据查看的详细信息,可以参见另外一篇博文,Pandas必要的基础功能.
查看DataFrame的头部和尾部数据:
In [14]: df.head()
Out[14]:
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 [15]: df.tail(3)
Out[15]:
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查看index(索引),columns(列标签),values(值)属性:
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 ]])使用describe()方法可以查看DataFrame在统计学意义上的摘要数据:
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 1.071804DataFrame的转置:
In [20]: df.T
Out[20]:
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
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DataFrame:
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按数据的值进行排序:
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数据选取 (Selection)
数据获取(Getting)
关于数据选择的详细介绍可以查阅另一篇博文,Pandas索引和数据选取.
选择单一的列标签会返回一个Series,下面例子也等同是df.A的返回结果
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使用'[ ]'进行切片的话,则选择的是行数据:
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按列标签选取 (Selection bt Lable)
使用标签选取横截面:
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选取多个列标签的所有横截面:
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使用索引切片和类标签两个维度选取数据:
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选取列标签的指定行:
In [29]: df.loc['20130102',['A','B']]
Out[29]:
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64获取标量数据:
In [30]: df.loc[dates[0],'A']
Out[30]: 0.46911229990718628下面这个at方法同样是获取标量数据,但性能上相对于之前的loc方法更快:
In [31]: df.at[dates[0],'A']
Out[31]: 0.46911229990718628按位置选择数据 (Selection by Position)
通过数据位置信息选择数据:
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类似于Numpy或Python中的整型切片:
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使用列表的方法表示选择指定的多行,多列的数据:
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选择行切片的所有列数据:
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选择列切片的所有行数据:
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通过位置获取指定行/列的标量:
In [37]: df.iloc[1,1]
Out[37]: -0.17321464905330858等同于之前的标量获取方法,但访问性能更快的方法:
In [38]: df.iat[1,1]
Out[38]: -0.17321464905330858布尔值索引 (Boolean Indexing)
通过单列标签的布尔值选取数据:
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对DataFrame中的每一个数据进行bool判断:
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方法进行包含筛选:
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数据设置 (Setting)
设置或者说添加新的列数据会根据行索引自动对齐:
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:
In [48]: df.at[dates[0],'A'] = 0按位置信息进行设置:
In [49]: df.iat[0,1] = 0使用Numpy数组进行赋值:
In [50]: df.loc[:,'D'] = np.array([5] * len(df))之前几次操作后的df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结合bool值进行赋值:
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(Missing Data)
Pandas中默认使用np.nan来代表缺失值,但主要注意该缺失值不能进行任何计算.
关于缺失值处理的详细信息可以参阅另一篇博文:Pandas的缺失值处理.
重新设置行索引(reindex方法) 可以修改/增加/删除指定行的索引,同时返回的是一个原数据的拷贝.
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丢弃任意一列含有缺失值的行数据:
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获取缺失值的bool判断值:
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元操作(Operations)
关于元操作的详细信息,科参阅另一篇博文: Pandas必要的基础功能.
统计(Stats)
在描述性的统计中,是排除了缺失值的.
执行描述性统计的均值:
In [61]: df.mean()
Out[61]:
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64也可以指定统计均值的轴:
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对不同维度或者尺寸的数据进行元操作需要进行索引对齐,同时Pandas会自动按指定的维度进行广播.
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 NaNApply
对DtaFrame应用指定的函数方法:
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: float64Histogramming
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字符方法(String Methods)
Pandas的Series结构提供了专门处理字符属性的方法,以便快速操作处理数组中的字符.
字符处理中使用的匹配模式是正则的模式,更多的关于字符处理的信息,参阅另一篇博文:Pandas中字符的处理.
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数据合并(Merge)
Panda提供了各种工具和方法，可以轻松地将Series、DataFrame和Pannel对象组合在一起.
更多信息信息请参阅另一篇博文:Pandas的数据结合和连接.
Concat
concat()方法可以合并Pandas中的数据对象.
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 -0.862495Join
SQL风格形式的数据连接.
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 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再看下另一个例子:
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 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 5Append
为DataFrame添加行数据:
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数据分组 (Grouping)
数据分组(group by)过程可以视为三个步骤:
1.按准则分组数据
2.对每组数据进行方法计算
3.整合数据
更多关于数学分组的信息可以参阅另一篇博文:Pandas的group by方法详解
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对数据进行分组并并每个分组数据进行求和运算:
In [93]: df.groupby('A').sum()
Out[93]:
C D
A
bar -2.802588 2.42611
foo 3.146492 -0.63958对多列且分层索引的数据进行分组并对每组进行求和运算:
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数据重整型(Reshaping)
更多关于重整型的信息可以参阅另外一篇博文:Pandas的数据重整型和透视表.
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 1.100230stack()方法将按列标签进行'压缩'和堆叠数据:
In [100]: stacked = df2.stack()
In [101]: stacked
Out[101]:
first second
bar one A 0.029399
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对于进行过stack方法的数据,unstack方法是其逆操作:
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我们可以很容易生成上面这个数据的透视表:
In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
Out[107]:
C bar foo
A B
one A -0.773723 1.418757
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 (Time Series)
Pandas具有简单,强大,高校的功能,可以在频率计算中进行重采样.
更多关于时间序列的信息,请参阅另一篇博文:Pandas的时间序列和日期功能.
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转换为另一个时区:
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时间跨度之间的转换:
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 1.523962
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时间周期和时间戳之间的转换需要使用一些算法函数,下面的例子中,我们将季度周期转换为该季度结束后第一天的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数据分类(Categoricals)
Pandas的DataFrame数据中可以包含分类数据.
更多分类数据的信息请参阅另一篇博文:Pandas中的分类数据.
In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})将原始的数据等级转换为分类数据类型:
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]重命名类名:
In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]重新排列类别并添加缺失的类别:
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]按类别排序:
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按类别分组时,空类别也会被显示:
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(Plotting)
更多绘图信息请参阅另一篇博文:Pandas的可视化.
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]: 
数据的读取和输出(Getting Data In/Out)
更多数据的读取和输出信息请参阅另一篇博文:Pandas的I/O操作.
csv
保存为csv文件:
In [141]: df.to_csv('foo.csv')读取csv文件:
In [142]: pd.read_csv('foo.csv')
Out[142]:
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
保存为HDF5文件:
In [143]: df.to_hdf('foo.h5','df')读书HDF5文件:
In [144]: pd.read_hdf('foo.h5','df')
Out[144]:
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
保存为excel文件:
In [145]: df.to_excel('foo.xlsx', sheet_name='Sheet1')读取Excel文件:
In [146]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
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]