pandas快速入门
Pandas的主要数据结构：
| Dimensions | Name | Description |
|---|---|---|
| 1 | Series | 1D labeled homogeneously-typed array |
| 2 | DataFrame | General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns |
| 3 | Panel | General 3D labeled, also size-mutable array |
一、引入
import pandas as pd //数据分析，代码基于numpyimport numpy as np //处理数据，代码基于ndarrayimport matplotlib.pyplot as plt //画图123
matplotlib图库具有大量代码案例，可直接使用
pandas 官网教程
二、创建对象
Series字典对象
>>>s = pd.Series([1,3,5,np.nan,6,8]) //默认以数字从0开始作为键值,使用np.nan表示不参与计算 >>>s0 1.01 3.02 5.03 NaN4 6.05 8.0dtype: float64123456789
>>> s = pd.Series(data=[1,2,3,4],index = ['a','b','c','d']) //传入键和值方式>>> s a 1b 2c 3d 4dtype: int64 >>> s.index //获取键列表Index(['a', 'b', 'c', 'd'], dtype='object') >>> s.values //获取值列表array([1, 2, 3, 4], dtype=int64)1234567891011
DataFrame表格对象
In [10]: df2 = pd.DataFrame({ 'A' : 1., 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1,index=list(range(4)),dtype='float32'), //生成Series对象,取的是value
'D' : np.array([3] * 4,dtype='int32'), //生成numpy对象
'E' : pd.Categorical(["test","train","test","train"]), 'F' : 'foo' })
In [11]: df2
Out[11]: // 默认以数字从0开始作为行键,以字典键为列键
A B C D E F0 1.0 2013-01-02 1.0 3 test foo1 1.0 2013-01-02 1.0 3 train foo2 1.0 2013-01-02 1.0 3 test foo3 1.0 2013-01-02 1.0 3 train foo123456789101112131415In [6]: dates = pd.date_range('20130101', periods=6)In [7]: datesOut[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')) //np.random.randn(6,4)返回一个样本，具有标准正态分布In [9]: dfOut[9]: // 指定dates为行键，columns为列键
A B C D2013-01-01 0.469112 -0.282863 -1.509059 -1.1356322013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929 1.0718042013-01-04 0.721555 -0.706771 -1.039575 0.2718602013-01-05 -0.424972 0.567020 0.276232 -1.0874012013-01-06 -0.673690 0.113648 -1.478427 0.524988In [12]: df2.dtypes //查看列数据类型Out[12]:
A float64
B datetime64[ns]
C float32
D int32
E category
F objectdtype: object123456789101112131415161718192021222324252627282930三、查看数据
查看头尾数据：
In [14]: df.head() //默认值5Out[14]: A B C D2013-01-01 0.469112 -0.282863 -1.509059 -1.1356322013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929 1.0718042013-01-04 0.721555 -0.706771 -1.039575 0.2718602013-01-05 -0.424972 0.567020 0.276232 -1.087401In [15]: df.tail(3) //默认值5Out[15]: A B C D2013-01-04 0.721555 -0.706771 -1.039575 0.2718602013-01-05 -0.424972 0.567020 0.276232 -1.0874012013-01-06 -0.673690 0.113648 -1.478427 0.524988123456789101112131415
查看行键、列键、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([u'A', u'B', u'C', u'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 ]])1234567891011121314151617
查看数据整体概况，和、平均值、最大、最小等：
In [19]: df.describe()Out[19]: A B C Dcount 6.000000 6.000000 6.000000 6.000000mean 0.073711 -0.431125 -0.687758 -0.233103std 0.843157 0.922818 0.779887 0.973118min -0.861849 -2.104569 -1.509059 -1.13563225% -0.611510 -0.600794 -1.368714 -1.07661050% 0.022070 -0.228039 -0.767252 -0.38618875% 0.658444 0.041933 -0.034326 0.461706max 1.212112 0.567020 0.276232 1.0718041234567891011
train_df.info()
print('_'*40)
<class 'pandas.core.frame.DataFrame'>RangeIndex: 891 entries, 0 to 890Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64Name 891 non-null objectSex 891 non-null objectAge 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null objectFare 891 non-null float64
Cabin 204 non-null objectEmbarked 889 non-null objectdtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
________________________________________1234567891011121314151617181920train_df.describe(include=['O'])Name Sex Ticket Cabin Embarkedcount 891 891 891 204 889unique 891 2 681 147 3top Chronopoulos, Mr. Apostolos male CA. 2343 G6 Sfreq 1 577 7 4 6441234567
行或列平均值：
In [61]: df.mean()Out[61]: A -0.004474B -0.383981C -0.687758D 5.000000F 3.000000dtype: float6412345678
In [62]: df.mean(1)Out[62]: 2013-01-01 0.8727352013-01-02 1.4316212013-01-03 0.7077312013-01-04 1.3950422013-01-05 1.8836562013-01-06 1.592306Freq: D, dtype: float64123456789
转置：
In [20]: df.TOut[20]: 2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06A 0.469112 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 0.5249881234567
根据行、列排序：
In [21]: df.sort_index(axis=1, ascending=False) //根据轴，可以.sort_index(axis=0, by=None, ascending=True)。by参数只能对列Out[21]: D C B A2013-01-01 -1.135632 -1.509059 -0.282863 0.4691122013-01-02 -1.044236 0.119209 -0.173215 1.2121122013-01-03 1.071804 -0.494929 -2.104569 -0.8618492013-01-04 0.271860 -1.039575 -0.706771 0.7215552013-01-05 -1.087401 0.276232 0.567020 -0.4249722013-01-06 0.524988 -1.478427 0.113648 -0.673690Sorting by valuesIn [22]: df.sort_values(by='B') //根据值Out[22]: A B C D2013-01-03 -0.861849 -2.104569 -0.494929 1.0718042013-01-04 0.721555 -0.706771 -1.039575 0.2718602013-01-01 0.469112 -0.282863 -1.509059 -1.1356322013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-06 -0.673690 0.113648 -1.478427 0.5249882013-01-05 -0.424972 0.567020 0.276232 -1.0874011234567891011121314151617181920
四、选择数据
选择单列：
In [23]: df['A'] //可使用df.AOut[23]: 2013-01-01 0.4691122013-01-02 1.2121122013-01-03 -0.8618492013-01-04 0.7215552013-01-05 -0.4249722013-01-06 -0.673690Freq: D, Name: A, dtype: float64123456789
选择局部：
In [24]: df[0:3]Out[24]: A B C D2013-01-01 0.469112 -0.282863 -1.509059 -1.1356322013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-03 -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 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929 1.0718042013-01-04 0.721555 -0.706771 -1.039575 0.27186012345678910111213
标签选择：
通过行键，列键
In [26]: df.loc[dates[0]] //选择一行，会降维Out[26]: A 0.469112B -0.282863C -1.509059D -1.135632Name: 2013-01-01 00:00:00, dtype: float641234567
In [27]: df.loc[:,['A','B']] //局部选择Out[27]: A B2013-01-01 0.469112 -0.2828632013-01-02 1.212112 -0.1732152013-01-03 -0.861849 -2.1045692013-01-04 0.721555 -0.7067712013-01-05 -0.424972 0.5670202013-01-06 -0.673690 0.113648123456789
In [28]: df.loc['20130102':'20130104',['A','B']] //局部选择Out[28]: A B2013-01-02 1.212112 -0.1732152013-01-03 -0.861849 -2.1045692013-01-04 0.721555 -0.706771123456
In [29]: df.loc['20130102',['A','B']] //选择一行，会降维Out[29]: A 1.212112B -0.173215Name: 2013-01-02 00:00:00, dtype: float6412345
In [30]: df.loc[dates[0],'A'] //选择具体某个元素，会降维Out[30]: 0.4691122999071862812
In [31]: df.at[dates[0],'A'] //选择具体某个元素，会降维Out[31]: 0.4691122999071862812
位置选择：
存在一个从0开始类似于数组
In [32]: df.iloc[3]Out[32]: A 0.721555B -0.706771C -1.039575D 0.271860Name: 2013-01-04 00:00:00, dtype: float641234567
In [33]: df.iloc[3:5,0:2]Out[33]: A B2013-01-04 0.721555 -0.7067712013-01-05 -0.424972 0.56702012345
In [34]: df.iloc[[1,2,4],[0,2]]Out[34]: A C2013-01-02 1.212112 0.1192092013-01-03 -0.861849 -0.4949292013-01-05 -0.424972 0.276232123456
In [35]: df.iloc[1:3,:]Out[35]: A B C D2013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-03 -0.861849 -2.104569 -0.494929 1.07180412345
In [36]: df.iloc[:,1:3]Out[36]: B C2013-01-01 -0.282863 -1.5090592013-01-02 -0.173215 0.1192092013-01-03 -2.104569 -0.4949292013-01-04 -0.706771 -1.0395752013-01-05 0.567020 0.2762322013-01-06 0.113648 -1.478427123456789
In [37]: df.iloc[1,1]Out[37]: -0.1732146490533085812
In [38]: df.iat[1,1]Out[38]: -0.1732146490533085812
布尔索引：
In [39]: df[df.A > 0]Out[39]: A B C D2013-01-01 0.469112 -0.282863 -1.509059 -1.1356322013-01-02 1.212112 -0.173215 0.119209 -1.0442362013-01-04 0.721555 -0.706771 -1.039575 0.271860123456
In [40]: df[df > 0] Out[40]: A B C D2013-01-01 0.469112 NaN NaN NaN2013-01-02 1.212112 NaN 0.119209 NaN2013-01-03 NaN NaN NaN 1.0718042013-01-04 0.721555 NaN NaN 0.2718602013-01-05 NaN 0.567020 0.276232 NaN2013-01-06 NaN 0.113648 NaN 0.524988123456789
In [41]: df2 = df.copy()In [42]: df2['E'] = ['one', 'one','two','three','four','three']In [43]: df2Out[43]: A B C D E2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four2013-01-06 -0.673690 0.113648 -1.478427 0.524988 threeIn [44]: df2[df2['E'].isin(['two','four'])]Out[44]: A B C D E2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four12345678910111213141516171819
五、修改数据
读取时将多列并成一列：
def parse(x):
return datetime.strptime(x, '%Y %m %d %H')
dataset = read_csv('raw.csv', parse_dates = [['year', 'month', 'day', 'hour']], index_col=0, date_parser=parse)123Series赋值列：
In [45]: s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20130102', periods=6))In [46]: s1Out[46]:
2013-01-02 12013-01-03 22013-01-04 32013-01-05 42013-01-06 52013-01-07 6Freq: D, dtype: int64In [47]: df['F'] = s1 //通过Series赋值列12345678910111213赋值单个元素：
df.at[dates[0],'A'] = 0df.iat[0,1] = 0123
df.loc[:,'D'] = np.array([5] * len(df)) //通过numpy赋值列In [51]: dfOut[51]: A B C D F2013-01-01 0.000000 0.000000 -1.509059 5 NaN2013-01-02 1.212112 -0.173215 0.119209 5 1.02013-01-03 -0.861849 -2.104569 -0.494929 5 2.02013-01-04 0.721555 -0.706771 -1.039575 5 3.02013-01-05 -0.424972 0.567020 0.276232 5 4.02013-01-06 -0.673690 0.113648 -1.478427 5 5.012345678910
In [52]: df2 = df.copy()In [53]: df2[df2 > 0] = -df2 //为每个数据赋值In [54]: df2Out[54]: A B C D F2013-01-01 0.000000 0.000000 -1.509059 -5 NaN2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.02013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.02013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.02013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.02013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.012345678910111213
修改索引：
In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E']) //修改DataFrame的键In [56]: df1.loc[dates[0]:dates[1],'E'] = 1In [57]: df1Out[57]: A B C D F E2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.02013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.02013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN1234567891011
六、缺失值处理
pandas用numpy.nan表示缺失值，不参与计算。
去掉缺失行：
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 1.01234
填充缺失值：
In [59]: df1.fillna(value=5) //对缺失值处进行填充Out[59]: A B C D F E2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.02013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.02013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.02013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.01234567
判断何处缺失：
In [60]: pd.isnull(df1) //判断位置元素是否为缺失值Out[60]: A B C D F E2013-01-01 False False False False True False2013-01-02 False False False False False False2013-01-03 False False False False False True2013-01-04 False False False False False True1234567
七、操作
偏移（对齐）元素：
In [63]: s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2) //序列元素偏移两位In [64]: s Out[64]: 2013-01-01 NaN2013-01-02 NaN2013-01-03 1.02013-01-04 3.02013-01-05 5.02013-01-06 NaNFreq: D, dtype: float64 In [65]: df.sub(s, axis='index') Out[65]: A B C D F2013-01-01 NaN NaN NaN NaN NaN2013-01-02 NaN NaN NaN NaN NaN2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.02013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.02013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.02013-01-06 NaN NaN NaN NaN NaN123456789101112131415161718192021
对元素应用函数：
In [66]: df.apply(np.cumsum) //对对象每个元素应用函数Out[66]: A B C D F2013-01-01 0.000000 0.000000 -1.509059 5 NaN2013-01-02 1.212112 -0.173215 -1.389850 10 1.02013-01-03 0.350263 -2.277784 -1.884779 15 3.02013-01-04 1.071818 -2.984555 -2.924354 20 6.02013-01-05 0.646846 -2.417535 -2.648122 25 10.02013-01-06 -0.026844 -2.303886 -4.126549 30 15.0In [67]: df.apply(lambda x: x.max() - x.min())Out[67]: A 2.073961B 2.671590C 1.785291D 0.000000F 4.000000dtype: float64123456789101112131415161718
直方图：
In [68]: s = pd.Series(np.random.randint(0, 7, size=10))In [69]: sOut[69]: 0 41 22 13 24 65 46 47 68 49 4dtype: int64In [70]: s.value_counts() //统计值以数字格式显示直方图Out[70]: 4 56 22 21 1dtype: int641234567891011121314151617181920212223
字符串操作：
In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])In [72]: s.str.lower() //序列字符串转成小写字母Out[72]: 0 a1 b2 c3 aaba4 baca5 NaN6 caba7 dog8 cat dtype: object1234567891011121314
八、合并
Comcat：
In [73]: df = pd.DataFrame(np.random.randn(10, 4))In [74]: dfOut[74]: 0 1 2 30 -0.548702 1.467327 -1.015962 -0.4830751 1.637550 -1.217659 -0.291519 -1.7455052 -0.263952 0.991460 -0.919069 0.2660463 -0.709661 1.669052 1.037882 -1.7057754 -0.919854 -0.042379 1.247642 -0.0099205 0.290213 0.495767 0.362949 1.5481066 -1.131345 -0.089329 0.337863 -0.9458677 -0.932132 1.956030 0.017587 -0.0166928 -0.575247 0.254161 -1.143704 0.2158979 1.193555 -0.077118 -0.408530 -0.862495# break it into piecesIn [75]: pieces = [df[:3], df[3:7], df[7:]]In [76]: pd.concat(pieces)Out[76]: 0 1 2 30 -0.548702 1.467327 -1.015962 -0.4830751 1.637550 -1.217659 -0.291519 -1.7455052 -0.263952 0.991460 -0.919069 0.2660463 -0.709661 1.669052 1.037882 -1.7057754 -0.919854 -0.042379 1.247642 -0.0099205 0.290213 0.495767 0.362949 1.5481066 -1.131345 -0.089329 0.337863 -0.9458677 -0.932132 1.956030 0.017587 -0.0166928 -0.575247 0.254161 -1.143704 0.2158979 1.193555 -0.077118 -0.408530 -0.8624951234567891011121314151617181920212223242526272829303132
Join：
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
In [79]: left
Out[79]:
key lval0 foo 11 foo 2In [80]: rightOut[80]:
key rval0 foo 41 foo 5In [81]: pd.merge(left, right, on='key')Out[81]:
key lval rval0 foo 1 41 foo 1 52 foo 2 43 foo 2 512345678910111213141516171819202122In [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 lval0 foo 11 bar 2In [85]: rightOut[85]:
key rval0 foo 41 bar 5In [86]: pd.merge(left, right, on='key')Out[86]:
key lval rval0 foo 1 41 bar 2 5123456789101112131415161718192021Append：
In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])In [88]: dfOut[88]: A B C D0 1.346061 1.511763 1.627081 -0.9905821 -0.441652 1.211526 0.268520 0.0245802 -1.577585 0.396823 -0.105381 -0.5325323 1.453749 1.208843 -0.080952 -0.2646104 -0.727965 -0.589346 0.339969 -0.6932055 -0.339355 0.593616 0.884345 1.5914316 0.141809 0.220390 0.435589 0.1924517 -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 D0 1.346061 1.511763 1.627081 -0.9905821 -0.441652 1.211526 0.268520 0.0245802 -1.577585 0.396823 -0.105381 -0.5325323 1.453749 1.208843 -0.080952 -0.2646104 -0.727965 -0.589346 0.339969 -0.6932055 -0.339355 0.593616 0.884345 1.5914316 0.141809 0.220390 0.435589 0.1924517 -0.096701 0.803351 1.715071 -0.7087588 1.453749 1.208843 -0.080952 -0.26461012345678910111213141516171819202122232425262728
九、分组
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 D0 foo one -1.202872 -0.0552241 bar one -1.814470 2.3959852 foo two 1.018601 1.5528253 bar three -0.595447 0.1665994 foo two 1.395433 0.0476095 bar two -0.392670 -0.1364736 foo one 0.007207 -0.5617577 foo three 1.928123 -1.623033In [93]: df.groupby('A').sum() //对键index A分组进行并对每个组执行sum函数Out[93]:
C D
A
bar -2.802588 2.42611foo 3.146492 -0.639581234567891011121314151617181920212223242526In [94]: df.groupby(['A','B']).sum() //对index A B进行分组并对每个组执行sum函数Out[94]: C D A B bar one -1.814470 2.395985 three -0.595447 0.166599 two -0.392670 -0.136473foo one -1.195665 -0.616981 three 1.928123 -1.623033 two 2.414034 1.60043412345678910
十、重切片
stack：压缩DataFrame列
In [99]: df2 Out[99]: A Bfirst second bar one 0.029399 -0.542108 two 0.282696 -0.087302baz one -1.575170 1.771208 two 0.816482 1.100230In [100]: stacked = df2.stack() In [101]: stacked = df2.stack() Out[101]: stackedfirst second bar one A 0.029399 B -0.542108 two A 0.282696 B -0.087302baz one A -1.575170 B 1.771208 two A 0.816482 B 1.100230dtype: float6412345678910111213141516171819202122
unstack反解压到上一层，不同参数解压层不同
In [102]: stacked.unstack() Out[102]: A Bfirst second bar one 0.029399 -0.542108 two 0.282696 -0.087302baz one -1.575170 1.771208 two 0.816482 1.100230In [103]: stacked.unstack(1) Out[103]: second one twofirst bar A 0.029399 0.282696 B -0.542108 -0.087302baz A -1.575170 0.816482 B 1.771208 1.100230In [104]: stacked.unstack(0) Out[104]: first bar bazsecond one A 0.029399 -1.575170 B -0.542108 1.771208two A 0.282696 0.81648212345678910111213141516171819202122232425
透视Pivot表：
In [106]: dfOut[106]: A B C D E0 one A foo 1.418757 -0.1796661 one B foo -1.879024 1.2918362 two C foo 0.536826 -0.0096143 three A bar 1.006160 0.3921494 one B bar -0.029716 0.2645995 one C bar -1.146178 -0.0574096 two A foo 0.100900 -1.4256387 three B foo -1.035018 1.0240988 one C foo 0.314665 -0.1060629 one A bar -0.773723 1.82437510 two B bar -1.170653 0.59597411 three C bar 0.648740 1.167115In [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 0.314665three A 1.006160 NaN B NaN -1.035018 C 0.648740 NaN two A NaN 0.100900 B -1.170653 NaN C NaN 0.53682612345678910111213141516171819202122232425262728
十一、时间序列
生成：
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: int6412345678In [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]: tsOut[113]:
2012-03-06 0.4640002012-03-07 0.2273712012-03-08 -0.4969222012-03-09 0.3063892012-03-10 -2.290613Freq: D, dtype: float64In [114]: ts_utc = ts.tz_localize('UTC')In [115]: ts_utcOut[115]:
2012-03-06 00:00:00+00:00 0.4640002012-03-07 00:00:00+00:00 0.2273712012-03-08 00:00:00+00:00 -0.4969222012-03-09 00:00:00+00:00 0.3063892012-03-10 00:00:00+00:00 -2.290613Freq: D, dtype: float641234567891011121314151617181920212223转换时间区：
In [116]: ts_utc.tz_convert('US/Eastern')Out[116]:
2012-03-05 19:00:00-05:00 0.4640002012-03-06 19:00:00-05:00 0.2273712012-03-07 19:00:00-05:00 -0.4969222012-03-08 19:00:00-05:00 0.3063892012-03-09 19:00:00-05:00 -2.290613Freq: D, dtype: float6412345678显示格式转换：
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]: tsOut[119]:
2012-01-31 -1.1346232012-02-29 -1.5618192012-03-31 -0.2608382012-04-30 0.2819572012-05-31 1.523962Freq: M, dtype: float64In [120]: ps = ts.to_period()In [121]: psOut[121]:
2012-01 -1.1346232012-02 -1.5618192012-03 -0.2608382012-04 0.2819572012-05 1.523962Freq: M, dtype: float64In [122]: ps.to_timestamp()Out[122]:
2012-01-01 -1.1346232012-02-01 -1.5618192012-03-01 -0.2608382012-04-01 0.2819572012-05-01 1.523962Freq: MS, dtype: float641234567891011121314151617181920212223242526272829303132In [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 -0.9029371990-06-01 09:00 0.0681591990-09-01 09:00 -0.0578731990-12-01 09:00 -0.3682041991-03-01 09:00 -1.144073Freq: H, dtype: float641234567891011121314十二、categoricals
version 0.15后DataFrame能够包含categorical
In [127]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "raw_grade":['a', 'b', 'b', 'a', 'a', 'e']})In [128]: df["grade"] = df["raw_grade"].astype("category")In [129]: df["grade"]Out[129]:
0 a1 b2 b3 a4 a5 eName: grade, dtype: category
Categories (3, object): [a, b, e]12345678910111213重命名categorical：
df["grade"].cat.categories = ["very good", "good", "very bad"]1
重排categorical并加入缺失categorical：
In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium", "good", "very good"])In [132]: df["grade"]Out[132]: 0 very good1 good2 good3 very good4 very good5 very badName: grade, dtype: category Categories (5, object): [very bad, bad, medium, good, very good]123456789101112
根据categorical排序：
In [133]: df.sort_values(by="grade") Out[133]: id raw_grade grade5 6 e very bad1 2 b good2 3 b good0 1 a very good3 4 a very good12345678
分组categorical：
In [134]: df.groupby("grade").size()Out[134]:
grade
very bad 1bad 0medium 0good 2very good 3dtype: int64123456789十三、画图
官方文档
一般不使用pandas的画图功能，而使用其他如matplotlib等。
十四、读取存储
CSV：
写入：
df.to_csv('foo.csv')
读取：
In [142]: pd.read_csv('foo.csv')
Out[142]:
Unnamed: 0 A B C D0 2000-01-01 0.266457 -0.399641 -0.219582 1.1868601 2000-01-02 -1.170732 -0.345873 1.653061 -0.2829532 2000-01-03 -1.734933 0.530468 2.060811 -0.5155363 2000-01-04 -1.555121 1.452620 0.239859 -1.1568964 2000-01-05 0.578117 0.511371 0.103552 -2.4282025 2000-01-06 0.478344 0.449933 -0.741620 -1.9624096 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753.. ... ... ... ... ...993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368[1000 rows x 5 columns]1234567891011121314151617181920212223HDF5：
df.to_hdf('foo.h5','df')
In [144]: pd.read_hdf('foo.h5','df')
Out[144]:
A B C D2000-01-01 0.266457 -0.399641 -0.219582 1.1868602000-01-02 -1.170732 -0.345873 1.653061 -0.2829532000-01-03 -1.734933 0.530468 2.060811 -0.5155362000-01-04 -1.555121 1.452620 0.239859 -1.1568962000-01-05 0.578117 0.511371 0.103552 -2.4282022000-01-06 0.478344 0.449933 -0.741620 -1.9624092000-01-07 1.235339 -0.091757 -1.543861 -1.084753... ... ... ... ...2002-09-20 -10.628548 -9.153563 -7.883146 28.3139402002-09-21 -10.390377 -8.727491 -6.399645 30.9141072002-09-22 -8.985362 -8.485624 -4.669462 31.3677402002-09-23 -9.558560 -8.781216 -4.499815 30.5184392002-09-24 -9.902058 -9.340490 -4.386639 30.1055932002-09-25 -10.216020 -9.480682 -3.933802 29.7585602002-09-26 -11.856774 -10.671012 -3.216025 29.369368[1000 rows x 4 columns]123456789101112131415161718192021EXCEL：
df.to_excel('foo.xlsx', sheet_name='Sheet1')
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 1.1868602000-01-02 -1.170732 -0.345873 1.653061 -0.2829532000-01-03 -1.734933 0.530468 2.060811 -0.5155362000-01-04 -1.555121 1.452620 0.239859 -1.1568962000-01-05 0.578117 0.511371 0.103552 -2.4282022000-01-06 0.478344 0.449933 -0.741620 -1.9624092000-01-07 1.235339 -0.091757 -1.543861 -1.084753... ... ... ... ...2002-09-20 -10.628548 -9.153563 -7.883146 28.3139402002-09-21 -10.390377 -8.727491 -6.399645 30.9141072002-09-22 -8.985362 -8.485624 -4.669462 31.3677402002-09-23 -9.558560 -8.781216 -4.499815 30.5184392002-09-24 -9.902058 -9.340490 -4.386639 30.1055932002-09-25 -10.216020 -9.480682 -3.933802 29.7585602002-09-26 -11.856774 -10.671012 -3.216025 29.369368[1000 rows x 4 columns]123456789101112131415161718192021
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