当前位置: 代码迷 >> 综合 >> pandas 常用的数学统计方法 cumsum()
  详细解决方案

pandas 常用的数学统计方法 cumsum()

热度:32   发布时间:2024-01-26 22:18:30.0

                                          pandas 常用的数学统计方法 cumsum()

 

1、定义:样本值的累计和。

2、示例:

import pandas as pdstudent_info = pd.read_csv("F:/人工智能/科学计算库/files/student_info.csv")
print(student_info)
print("============================")
# cumsum() 样本数值的累计和
print(student_info.cumsum())# 运行结果:
Chinese   Math  English
0        88   11.0     22.0
1        33    NaN     30.0
2        85   32.0     90.0
3        45   39.0      NaN
4        11  100.0    103.0
5        88   33.0     74.0
6        85   39.0     90.0
7        88   11.0     22.0
8        33    NaN     30.0
9        85   32.0     90.0
10       45   39.0      NaN
11       11  100.0    103.0
12       88   33.0     74.0
13       85   39.0     90.0
14       88   11.0     22.0
15       33    NaN     30.0
16       85   32.0     90.0
17       45   39.0      NaN
18       11  100.0    103.0
19       88   33.0     74.0
20       85   39.0     90.0
21       88   11.0     22.0
22       33    NaN     30.0
23       85   32.0     90.0
24       45   39.0      NaN
25       11  100.0    103.0
26       88   33.0     74.0
27       85   39.0     90.0
============================Chinese    Math  English
0      88.0    11.0     22.0
1     121.0     NaN     52.0
2     206.0    43.0    142.0
3     251.0    82.0      NaN
4     262.0   182.0    245.0
5     350.0   215.0    319.0
6     435.0   254.0    409.0
7     523.0   265.0    431.0
8     556.0     NaN    461.0
9     641.0   297.0    551.0
10    686.0   336.0      NaN
11    697.0   436.0    654.0
12    785.0   469.0    728.0
13    870.0   508.0    818.0
14    958.0   519.0    840.0
15    991.0     NaN    870.0
16   1076.0   551.0    960.0
17   1121.0   590.0      NaN
18   1132.0   690.0   1063.0
19   1220.0   723.0   1137.0
20   1305.0   762.0   1227.0
21   1393.0   773.0   1249.0
22   1426.0     NaN   1279.0
23   1511.0   805.0   1369.0
24   1556.0   844.0      NaN
25   1567.0   944.0   1472.0
26   1655.0   977.0   1546.0
27   1740.0  1016.0   1636.0

 

  相关解决方案