一：数据集准备

可以创建一个txt，并放置pycharm工程目录下

下面是博主的数据集测试，所用数据，需要的自取

1001,Chinese,1,80
1001,Chinese,2,81
1001,Chinese,3,79
1001,Chinese,4,86
1001,Math,1,69
1001,Math,2,70
1001,Math,3,79
1001,Math,4,90
1001,English,1,90
1001,English,2,89
1001,English,3,92
1001,English,4,96
1002,Chinese,1,85
1002,Chinese,2,86
1002,Chinese,3,88
1002,Chinese,4,71
1002,Math,1,63
1002,Math,2,96
1002,Math,3,68
1002,Math,4,72
1002,English,1,63
1002,English,2,93
1002,English,3,86
1002,English,4,75
1003,Chinese,1,87
1003,Chinese,2,81
1003,Chinese,3,82
1003,Chinese,4,77
1003,Math,1,69
1003,Math,2,91
1003,Math,3,61
1003,Math,4,79
1003,English,1,68
1003,English,2,82
1003,English,3,87
1003,English,4,96
1004,Chinese,1,81
1004,Chinese,2,77
1004,Chinese,3,92
1004,Chinese,4,68
1004,Math,1,96
1004,Math,2,85
1004,Math,3,85
1004,Math,4,74
1004,English,1,67
1004,English,2,63
1004,English,3,96
1004,English,4,77

二：加载文件

加载文件 read_csv

import numpy as np
import pandas as pd

# 列名
columns = ['sno', 'subject', 'unit', 'score']
# 1 加载文件
df = pd.read_csv("student.txt", sep=',', names=columns)
print(df.head())
print(df.shape)

    sno  subject  unit  score
0  1001  Chinese     1     80
1  1001  Chinese     2     81
2  1001  Chinese     3     79
3  1001  Chinese     4     86
4  1001     Math     1     69
(48, 4)

如上结果

加载读取数据集head 头部数据，

shape 规格形状为4列，分别为学号、科目、次数、分数

三：分组操作进行统计

1 按照科目分组

# 根据科目进行分组
df_subject = df.groupby('subject')
for i in df_subject:
print(i)

import numpy as np
import pandas as pd

# 列名
columns = ['sno', 'subject', 'unit', 'score']
# 1 加载文件
df = pd.read_csv("student.txt", sep=',', names=columns, index_col=0)
# 分组操作进行统计
# 根据科目进行分组、只获取分数
df_subject = df.groupby('subject')
for i in df_subject:
    print(i)

('Chinese',       subject  unit  score
sno                       
1001  Chinese     1     80
1001  Chinese     2     81
1001  Chinese     3     79
1001  Chinese     4     86
1002  Chinese     1     85
1002  Chinese     2     86
1002  Chinese     3     88
1002  Chinese     4     71
1003  Chinese     1     87
1003  Chinese     2     81
1003  Chinese     3     82
1003  Chinese     4     77
1004  Chinese     1     81
1004  Chinese     2     77
1004  Chinese     3     92
1004  Chinese     4     68)
('English',       subject  unit  score
sno                       
1001  English     1     90
1001  English     2     89
1001  English     3     92
1001  English     4     96
1002  English     1     63
1002  English     2     93
1002  English     3     86
1002  English     4     75
1003  English     1     68
1003  English     2     82
1003  English     3     87
1003  English     4     96
1004  English     1     67
1004  English     2     63
1004  English     3     96
1004  English     4     77)
('Math',      subject  unit  score
sno                      
1001    Math     1     69
1001    Math     2     70
1001    Math     3     79
1001    Math     4     90
1002    Math     1     63
1002    Math     2     96
1002    Math     3     68
1002    Math     4     72
1003    Math     1     69
1003    Math     2     91
1003    Math     3     61
1003    Math     4     79
1004    Math     1     96
1004    Math     2     85
1004    Math     3     85
1004    Math     4     74)

2 各个科目只取分数

# 根据科目进行分组、只获取分数
df_subject = df.groupby('subject')['score']
for i in df_subject:
print(i)

import numpy as np
import pandas as pd

# 列名
columns = ['sno', 'subject', 'unit', 'score']
# 1 加载文件
df = pd.read_csv("student.txt", sep=',', names=columns, index_col=0)
# 分组操作进行统计
# 根据科目进行分组、只获取分数
df_subject = df.groupby('subject')['score']
for i in df_subject:
    print(i)

('Chinese', sno
1001    80
1001    81
1001    79
1001    86
1002    85
1002    86
1002    88
1002    71
1003    87
1003    81
1003    82
1003    77
1004    81
1004    77
1004    92
1004    68
Name: score, dtype: int64)
('English', sno
1001    90
1001    89
1001    92
1001    96
1002    63
1002    93
1002    86
1002    75
1003    68
1003    82
1003    87
1003    96
1004    67
1004    63
1004    96
1004    77
Name: score, dtype: int64)
('Math', sno
1001    69
1001    70
1001    79
1001    90
1002    63
1002    96
1002    68
1002    72
1003    69
1003    91
1003    61
1003    79
1004    96
1004    85
1004    85
1004    74
Name: score, dtype: int64)

3 各个科目的平均成绩

df_subject_mean = df.groupby('subject')['score'].mean()
print(df_subject_mean)

import numpy as np
import pandas as pd

# 列名
columns = ['sno', 'subject', 'unit', 'score']
# 1 加载文件
df = pd.read_csv("student.txt", sep=',', names=columns, index_col=0)
# 分组操作进行统计
# 根据科目进行分组、只获取分数
# df_subject = df.groupby('subject')['score']
# for i in df_subject:
#     print(i)

df_subject_mean = df.groupby('subject')['score'].mean()
print(df_subject_mean)

subject
Chinese    81.3125
English    82.5000
Math       77.9375
Name: score, dtype: float64

4 各个同学，各个科目的平均成绩
# 分组因素学号科目
df_mean = df.groupby(['sno', 'subject'])['score'].mean()
print(df_mean)

import numpy as np
import pandas as pd

# 列名
columns = ['sno', 'subject', 'unit', 'score']
# 1 加载文件
df = pd.read_csv("student.txt", sep=',', names=columns, index_col=0)
# 分组操作进行统计
# 每个同学 每个科目的平均成绩
# 分组因素 学号 科目
df_mean = df.groupby(['sno', 'subject'])['score'].mean()
print(df_mean)

sno   subject
1001  Chinese    81.50
      English    91.75
      Math       77.00
1002  Chinese    82.50
      English    79.25
      Math       74.75
1003  Chinese    81.75
      English    83.25
      Math       75.00
1004  Chinese    79.50
      English    75.75
      Math       85.00
Name: score, dtype: float64

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