pandas 计算工具
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pandas 计算工具
Claroja 发表于11个月前
pandas 计算工具
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统计函数

增长率pct_change

序列(Series)、数据框(DataFrame)和Panel(面板)都有pct_change方法来计算增长率(需要先使用fill_method来填充空值)
Series.pct_change(periods=1, fill_method=’pad’, limit=None, freq=None, **kwargs)
periods参数控制步长

In [1]: ser = pd.Series(np.random.randn(8))

In [2]: ser.pct_change()
Out[2]: 
0         NaN
1   -1.602976
2    4.334938
3   -0.247456
4   -2.067345
5   -1.142903
6   -1.688214
7   -9.759729
dtype: float64

协方差Covariance

序列Series对象有cov方法来计算协方差
Series.cov(other, min_periods=None)

In [5]: s1 = pd.Series(np.random.randn(1000))

In [6]: s2 = pd.Series(np.random.randn(1000))

In [7]: s1.cov(s2)
Out[7]: 0.00068010881743108746

数据框DataFrame对象的cov方法
DataFrame.cov(min_periods=None)

In [8]: frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])

In [9]: frame.cov()
Out[9]: 
          a         b         c         d         e
a  1.000882 -0.003177 -0.002698 -0.006889  0.031912
b -0.003177  1.024721  0.000191  0.009212  0.000857
c -0.002698  0.000191  0.950735 -0.031743 -0.005087
d -0.006889  0.009212 -0.031743  1.002983 -0.047952
e  0.031912  0.000857 -0.005087 -0.047952  1.042487

相关系数Correlation

相关系数有三种计算方法

Method name Description
pearson?(default) Standard correlation coefficient
kendall Kendall Tau correlation coefficient
spearman Spearman rank correlation coefficient

Series.corr(other, method=’pearson’, min_periods=None)

DataFrame.corr(method=’pearson’, min_periods=1)

In [15]: frame = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd', 'e'])
In [19]: frame.corr()
Out[19]: 
          a         b         c         d         e
a  1.000000  0.013479 -0.049269 -0.042239 -0.028525
b  0.013479  1.000000 -0.020433 -0.011139  0.005654
c -0.049269 -0.020433  1.000000  0.018587 -0.054269
d -0.042239 -0.011139  0.018587  1.000000 -0.017060
e -0.028525  0.005654 -0.054269 -0.017060  1.000000

DataFrame.corrwith(other, axis=0, drop=False)

数据排名

Series.rank(axis=0, method=’average’, numeric_only=None, na_option=’keep’, ascending=True, pct=False)

In [31]: s = pd.Series(np.random.np.random.randn(5), index=list('abcde'))

In [32]: s['d'] = s['b'] # so there's a tie

In [33]: s.rank()
Out[33]: 
a    5.0
b    2.5
c    1.0
d    2.5
e    4.0
dtype: float64

DataFrame.rank(axis=0, method=’average’, numeric_only=None, na_option=’keep’, ascending=True, pct=False)
axis=0则是按行排序,axis=1按列排序
ascending=True为升序,False为降序

In [34]: df = pd.DataFrame(np.random.np.random.randn(10, 6))

In [35]: df[4] = df[2][:5] # some ties

In [36]: df
Out[36]: 
 0 1 2 3 4 5
0 -0.904948 -1.163537 -1.457187  0.135463 -1.457187  0.294650
1 -0.976288 -0.244652 -0.748406 -0.999601 -0.748406 -0.800809
2  0.401965  1.460840  1.256057  1.308127  1.256057  0.876004
3  0.205954  0.369552 -0.669304  0.038378 -0.669304  1.140296
4 -0.477586 -0.730705 -1.129149 -0.601463 -1.129149 -0.211196
5 -1.092970 -0.689246  0.908114  0.204848       NaN  0.463347
6  0.376892  0.959292  0.095572 -0.593740       NaN -0.069180
7 -1.002601  1.957794 -0.120708  0.094214       NaN -1.467422
8 -0.547231  0.664402 -0.519424 -0.073254       NaN -1.263544
9 -0.250277 -0.237428 -1.056443  0.419477       NaN  1.375064

In [37]: df.rank(1)
Out[37]: 
 0 1 2 3 4 5
0  4.0  3.0  1.5  5.0  1.5  6.0
1  2.0  6.0  4.5  1.0  4.5  3.0
2  1.0  6.0  3.5  5.0  3.5  2.0
3  4.0  5.0  1.5  3.0  1.5  6.0
4  5.0  3.0  1.5  4.0  1.5  6.0
5  1.0  2.0  5.0  3.0  NaN  4.0
6  4.0  5.0  3.0  1.0  NaN  2.0
7  2.0  5.0  3.0  4.0  NaN  1.0
8  2.0  5.0  3.0  4.0  NaN  1.0
9  2.0  3.0  1.0  4.0  NaN  5.0

窗口函数

窗口函数介绍rolling

Series.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0)
window:移动窗口的大小
min_periods:??
center:是否在中间设置标签,默认False
win type=??

In [38]: s = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
r = s.rolling(window=60)
In [42]: r
Out[42]: Rolling [window=60,center=False,axis=0]
In [43]: r.mean()
Out[43]: 
2000-01-01          NaN
2000-01-02          NaN
2000-01-03          NaN
2000-01-04          NaN
2000-01-05          NaN
2000-01-06          NaN
2000-01-07          NaN
                ...    
2002-09-20   -62.694135
2002-09-21   -62.812190
2002-09-22   -62.914971
2002-09-23   -63.061867
2002-09-24   -63.213876
2002-09-25   -63.375074
2002-09-26   -63.539734
Freq: D, dtype: float64
In [44]: s.plot(style='k--')
Out[44]: <matplotlib.axes._subplots.AxesSubplot at 0x7ff282080dd0>

In [45]: r.mean().plot(style='k')
Out[45]: <matplotlib.axes._subplots.AxesSubplot at 0x7ff282080dd0>

这里写图片描述
在数据框汇总将会作用于每一列
DataFrame.rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0)

In [46]: df = pd.DataFrame(np.random.randn(1000, 4),
   ....:                   index=pd.date_range('1/1/2000', periods=1000),
   ....:                   columns=['A', 'B', 'C', 'D'])
   ....: 

In [47]: df = df.cumsum()

In [48]: df.rolling(window=60).sum().plot(subplots=True)

这里写图片描述

计算方法总结

Method Description
count() Number of non-null observations
sum() Sum of values
mean() Mean of values
median() Arithmetic median of values
min() Minimum
max() Maximum
std() Bessel-corrected sample standard deviation
var() Unbiased variance
skew() Sample skewness (3rd moment)
kurt() Sample kurtosis (4th moment)
quantile() Sample quantile (value at %)
apply() Generic apply
cov() Unbiased covariance (binary)
corr() Correlation (binary)

apply()方法可以应用在滚动窗口中。apply()的参数函数必须是指产生一个值,假设我们需要计算均值绝对离差:

In [49]: mad = lambda x: np.fabs(x - x.mean()).mean()

In [50]: s.rolling(window=60).apply(mad).plot(style='k')

这里写图片描述

使用聚合函数(Aggregation)

拓展窗口(Expanding Windows)

指数加权窗口(Exponentially Weighted Windows)

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