流动的水没有形状，漂流的风找不到踪迹，变化的规律无处可寻，这节博客带来的是数据分析工具Pandas。
包括tushare在内的很多财经工具包，都使用pandas进行数据分析。在tushare中，获取数据的格式主要就是pandas中的DataFrame二维表格。
0 Pandas主要数据结构
Pandas有三种数据结构
- Series：1D labeled homogeneously-typed array
- DataFrame：General 2D labeled， size-mutable tabular structure with potentially heterogeneously-typed columns
- Panel: General 3D labeled, also size-mutable array
1 数据存储
CSV
写入：df.to_cvs(‘foo.csv’)
读取：df.read_csv(‘foo.csv’)
HDF5
读入：df.to_hdf(‘foo.h5′,’df’)
读取：df.read_hdf(‘foo.h5′,’df’)
Excel
读入：df.to_hdf(‘foo.xlsx’,sheet_name=’Sheet1′)
读取：df.read_hdf(‘foo.xlsx’,’Sheet1′,index_col=None, na_values=[‘NA’])
2 引入
import pandas as pd # 数据分析，代码基于numpy import numpy as np # 处理数据，代码基于ndarray import matplotlib.pyplot as plt # 画图
matplotlib官网，pandas官网 ，pandas快速入门
3 查看数据
3.1 查看头尾数据
In [14]: df.head() //默认值5
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) //默认值5
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
3.2 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 ]])
3.3 查看数据整体概况，和、平均值、最大、最小值等
查看数据整体情况
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
查看数据信息
train_df.info()
print('_'*40)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId 891 non-null int64
Survived 891 non-null int64
Pclass 891 non-null int64
Name 891 non-null object
Sex 891 non-null object
Age 714 non-null float64
SibSp 891 non-null int64
Parch 891 non-null int64
Ticket 891 non-null object
Fare 891 non-null float64
Cabin 204 non-null object
Embarked 889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB
行或列的平均值
In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64
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
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
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 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
Sorting by values
In [22]: df.sort_values(by='B') //根据值
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
4 选择数据
选择单列
In [23]: df['A'] //可使用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
选择局部
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
标签选择，通过行键、列键进行选择
In [26]: df.loc[dates[0]] //选择一行，会降维 Out[26]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64
In [27]: df.loc[:,['A','B']] //局部选择
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
In [28]: df.loc['20130102':'20130104',['A','B']] //局部选择
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
In [29]: df.loc['20130102',['A','B']] //选择一行，会降维 Out[29]: A 1.212112 B -0.173215 Name: 2013-01-02 00:00:00, dtype: float64
In [30]: df.loc[dates[0],'A'] //选择具体某个元素，会降维 Out[30]: 0.46911229990718628
In [31]: df.at[dates[0],'A'] //选择具体某个元素，会降维 Out[31]: 0.46911229990718628
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
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
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
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
布尔索引
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
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
5 修改数据
读取时将多列并成一排
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)
Series赋值列
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 //通过Series赋值列
赋值单个元素
df.at[dates[0],'A'] = 0 df.iat[0,1] = 0
df.loc[:,'D'] = np.array([5] * len(df)) //通过numpy赋值列
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
In [52]: df2 = df.copy()
In [53]: df2[df2 > 0] = -df2 //为每个数据赋值
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
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'] = 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
后面的内容不再粘完整结果，如果需要的话参见：Pandas快速入门
6 缺省值处理
去掉缺省值
df1.dropna(how=’any’)
填充缺省值
df1.fillna(value= 5)
判断何处缺失
pd.isnull(df1)
7 操作
偏移，对齐操作
s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2) df.sub(s, axis='index')
对元素应用函数
df.apply(np.cumsum) //对对象每个元素应用函数 df.apply(lambda x: x.max() - x.min())
直方图统计
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() //统计值以数字格式显示直方图 Out[70]: 4 5 6 2 2 2 1 1 dtype: int64
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 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
8 合并
Concat
In [73]: df = pd.DataFrame(np.random.randn(10, 4))
In [74]: df
Out[74]:
0 1 2 3
0 -0.548702 1.467327 -1.015962 -0.483075
1 1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952 0.991460 -0.919069 0.266046
3 -0.709661 1.669052 1.037882 -1.705775
4 -0.919854 -0.042379 1.247642 -0.009920
5 0.290213 0.495767 0.362949 1.548106
6 -1.131345 -0.089329 0.337863 -0.945867
7 -0.932132 1.956030 0.017587 -0.016692
8 -0.575247 0.254161 -1.143704 0.215897
9 1.193555 -0.077118 -0.408530 -0.862495
# break it into pieces
In [75]: pieces = [df[:3], df[3:7], df[7:]]
In [76]: pd.concat(pieces)
Out[76]:
0 1 2 3
0 -0.548702 1.467327 -1.015962 -0.483075
1 1.637550 -1.217659 -0.291519 -1.745505
2 -0.263952 0.991460 -0.919069 0.266046
3 -0.709661 1.669052 1.037882 -1.705775
4 -0.919854 -0.042379 1.247642 -0.009920
5 0.290213 0.495767 0.362949 1.548106
6 -1.131345 -0.089329 0.337863 -0.945867
7 -0.932132 1.956030 0.017587 -0.016692
8 -0.575247 0.254161 -1.143704 0.215897
9 1.193555 -0.077118 -0.408530 -0.862495
Join
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
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
Append
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
9 分组
对键A分组 并对每个组执行sum操作
df.groupby(‘A’).sum()
df.groupby([‘A’ ‘B’]).sum()