- Python数据科学简介
- Python数据科学开发环境
- Python Pandas库
- Python Numpy库
- Python Scipy库
- Python Matplotlib库
- Python数据处理
- Python数据可视化
- 统计数据分析
Python数据聚合
Python有几种方法可用于对数据执行聚合。 它使用Pandas和numpy库完成。 数据必须可用或转换为数据框才能应用聚合功能。
在DataFrame上应用聚合
创建一个DataFrame并在其上应用聚合。
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print df
r = df.rolling(window=3,min_periods=1)
print (r)
执行上面示例代码，得到以下结果 -
A B C D 2000-01-01 1.088512 -0.650942 -2.547450 -0.566858 2000-01-02 0.790670 -0.387854 -0.668132 0.267283 2000-01-03 -0.575523 -0.965025 0.060427 -2.179780 2000-01-04 1.669653 1.211759 -0.254695 1.429166 2000-01-05 0.100568 -0.236184 0.491646 -0.466081 2000-01-06 0.155172 0.992975 -1.205134 0.320958 2000-01-07 0.309468 -0.724053 -1.412446 0.627919 2000-01-08 0.099489 -1.028040 0.163206 -1.274331 2000-01-09 1.639500 -0.068443 0.714008 -0.565969 2000-01-10 0.326761 1.479841 0.664282 -1.361169 Rolling [window=3,min_periods=1,center=False,axis=0]
可以通过将函数传递给整个DataFrame进行聚合，或者通过标准的get item方法选择一个列。
在整个数据框上应用聚合-
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print df
r = df.rolling(window=3,min_periods=1)
print (r.aggregate(np.sum))
执行上面示例代码，得到以下结果 -
A B C D 2000-01-01 1.088512 -0.650942 -2.547450 -0.566858 2000-01-02 0.790670 -0.387854 -0.668132 0.267283 2000-01-03 -0.575523 -0.965025 0.060427 -2.179780 2000-01-04 1.669653 1.211759 -0.254695 1.429166 2000-01-05 0.100568 -0.236184 0.491646 -0.466081 2000-01-06 0.155172 0.992975 -1.205134 0.320958 2000-01-07 0.309468 -0.724053 -1.412446 0.627919 2000-01-08 0.099489 -1.028040 0.163206 -1.274331 2000-01-09 1.639500 -0.068443 0.714008 -0.565969 2000-01-10 0.326761 1.479841 0.664282 -1.361169 Rolling [window=3,min_periods=1,center=False,axis=0]
可以通过将函数传递给整个DataFrame进行聚合，或者通过标准的get item方法选择一个列。
在整个数据帧上应用聚合
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print (df)
r = df.rolling(window=3,min_periods=1)
print (r.aggregate(np.sum))
执行上面示例代码，得到以下结果 -
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
在数据帧的单个列上应用聚合
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print (df)
r = df.rolling(window=3,min_periods=1)
print (r['A'].aggregate(np.sum))
执行上面示例代码，得到以下结果 -
A B C D 2000-01-01 1.088512 -0.650942 -2.547450 -0.566858 2000-01-02 1.879182 -1.038796 -3.215581 -0.299575 2000-01-03 1.303660 -2.003821 -3.155154 -2.479355 2000-01-04 1.884801 -0.141119 -0.862400 -0.483331 2000-01-05 1.194699 0.010551 0.297378 -1.216695 2000-01-06 1.925393 1.968551 -0.968183 1.284044 2000-01-07 0.565208 0.032738 -2.125934 0.482797 2000-01-08 0.564129 -0.759118 -2.454374 -0.325454 2000-01-09 2.048458 -1.820537 -0.535232 -1.212381 2000-01-10 2.065750 0.383357 1.541496 -3.201469 2000-01-01 1.088512 2000-01-02 1.879182 2000-01-03 1.303660 2000-01-04 1.884801 2000-01-05 1.194699 2000-01-06 1.925393 2000-01-07 0.565208 2000-01-08 0.564129 2000-01-09 2.048458 2000-01-10 2.065750 Freq: D, Name: A, dtype: float64
在DataFrame的多列上应用聚合
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
print (df)
r = df.rolling(window=3,min_periods=1)
print (r[['A','B']].aggregate(np.sum))
执行上面示例代码，得到以下结果-
A B C D
2000-01-01 1.088512 -0.650942 -2.547450 -0.566858
2000-01-02 1.879182 -1.038796 -3.215581 -0.299575
2000-01-03 1.303660 -2.003821 -3.155154 -2.479355
2000-01-04 1.884801 -0.141119 -0.862400 -0.483331
2000-01-05 1.194699 0.010551 0.297378 -1.216695
2000-01-06 1.925393 1.968551 -0.968183 1.284044
2000-01-07 0.565208 0.032738 -2.125934 0.482797
2000-01-08 0.564129 -0.759118 -2.454374 -0.325454
2000-01-09 2.048458 -1.820537 -0.535232 -1.212381
2000-01-10 2.065750 0.383357 1.541496 -3.201469
A B
2000-01-01 1.088512 -0.650942
2000-01-02 1.879182 -1.038796
2000-01-03 1.303660 -2.003821
2000-01-04 1.884801 -0.141119
2000-01-05 1.194699 0.010551
2000-01-06 1.925393 1.968551
2000-01-07 0.565208 0.032738
2000-01-08 0.564129 -0.759118
2000-01-09 2.048458 -1.820537
2000-01-10 2.065750 0.383357
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