Python自动化办公--Pandas玩转Excel数据分析【二】

相关文章：

Python自动化办公--Pandas玩转Excel【一】

Python自动化办公--Pandas玩转Excel数据分析【三】

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python处理Excel实现自动化办公教学（含实战）【一】

python处理Excel实现自动化办公教学（含实战）【二】

python处理Excel实现自动化办公教学（数据筛选、公式操作、单元格拆分合并、冻结窗口、图表绘制等）【三】

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python入门之后须掌握的知识点（模块化编程、时间模块）【一】

python入门之后须掌握的知识点（excel文件处理+邮件发送+实战：批量化发工资条）【二】

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相关码源

pandas玩转excel码源.zip-数据挖掘文档类资源-CSDN下载

1.多表联合

​​

merge 和join区别：

join没有：left_on right_on

import pandas as pd

students = pd.read_excel('Student_score.xlsx',
                         sheet_name='Students')  # 此处ID未被设置成index
scores = pd.read_excel('Student_score.xlsx', sheet_name='Scores')
table = students.merge(scores, how='left', on='ID').fillna(
    0)  # how='left'无论条件是否成立左边数据表都要保存
# left_on right_on
table.Score = table.Score.astype(int)
print(table)

# 此处ID被设置成index两种方法
# students = pd.read_excel('Student_score.xlsx', sheet_name='Students', index_col='ID')
# scores = pd.read_excel('Student_score.xlsx', sheet_name='Scores', index_col='ID')
# table = students.merge(scores, how='left', left_on=students.index, right_on=scores.index).fillna(0)
# table.Score = table.Score.astype(int)
# print(table)

students = pd.read_excel('Student_score.xlsx',
                         sheet_name='Students', index_col='ID')
scores = pd.read_excel('Student_score.xlsx',
                       sheet_name='Scores', index_col='ID')
table = students.join(scores, how='left').fillna(0)
table.Score = table.Score.astype(int)
print(table)

      Name  Score
ID
1   Student_001     81
3   Student_003     83
5   Student_005     85
7   Student_007     87
9   Student_009     89
11  Student_011     91
13  Student_013     93
15  Student_015     95
17  Student_017     97
19  Student_019     99
21  Student_021      0
23  Student_023      0
25  Student_025      0
27  Student_027      0
29  Student_029      0
31  Student_031      0
33  Student_033      0
35  Student_035      0
37  Student_037      0
39  Student_039      0

 通常情况mysql用的比较多，可以考虑和python联用，可以参考我的MySQL专栏

2. 数据校验，轴的概念

​

​

python会捕获到try中的异常，并且当try中某一行出现异常后，后面的代码将不会再被执行；而是直接调用except中的代码

try...except语句的执行流程非常简单，可分为两步：

1. 执行try语句中的代码，如果出现异常，Python会得到异常的类型
2. Python将出现的异常类型和except语句中的异常类型做对比，调用对应except语句中的代码块

else的功能：当try中的代码没有异常时，会调用else中的代码。

1. try...except..else的使用和try...except相同，只不过多了else代码，else中的代码只有当try中的代码块没有发现异常的时候才会调用。
2. else中的代码只有当try中的代码没有出现异常时才会被执行；并且else要和try…except配合使用，如果使用了else，则代码中不能没有except，否则会报错

finally的功能：不管try中的代码是否有异常，最终都会调用finally中的代码

1. finally可以结合try...except，try...except...else使用，也可以仅有try和finally。

Python assert（断言）用于判断一个表达式，在表达式条件为 false 的时候触发异常。

断言可以在条件不满足程序运行的情况下直接返回错误，而不必等待程序运行后出现崩溃的情况，例如我们的代码只能在 Linux 系统下运行，可以先判断当前系统是否符合条件。

import pandas as pd

def score_valication(row):
    try:
        assert 0 <= row.Score <= 100
    except:
        print(f'#{row.ID}\tstudent {row.Name} has an invalid score {row.Score}')

students = pd.read_excel('Students4.xlsx')
# print(students)
students.apply(score_valication, axis=1)

#1      student Student_001 has an invalid score -40
#2      student Student_002 has an invalid score -30
#3      student Student_003 has an invalid score -20
#4      student Student_004 has an invalid score -10
#16     student Student_016 has an invalid score 110
#17     student Student_017 has an invalid score 120
#18     student Student_018 has an invalid score 130
#19     student Student_019 has an invalid score 140
#20     student Student_020 has an invalid score 150

3.把一列数据分割成两列

​

进行分割：

split()函数讲解：

split`(*self*, *pat=None*, *n=-1*, *expand=False*)

pat：分列的依据，可以是空格，符号，字符串等等。默认为空格 “ ” ； “,”

n：分列的次数，不指定的话就会根据符号的个数全部分列。n=-1 or 0 全部保留

expand：为True可以直接将分列后的结果转换成DataFrame。

如果想要从最右边开始分列，可以使用rsplit()，rsplit()和split()的用法类似，一个从右边开始，一个从左边开始。

import pandas as pd

employees = pd.read_excel('Employees.xlsx', index_col='ID')
df = employees['Full Name'].str.split(expand=True)
# df = employees['Full Name'].str.split(expand=False)
print(df)
employees['First Name'] = df[0]
employees['Last Name'] = df[1]
#employees['Last Name'] = df[1].str.upper() 全变成大写，相关函数可以查询字符串功能
print(employees)

ID
1           [Syed, Abbas]
2       [Catherine, Abel]
3      [Kim, Abercrombie]
4      [Kim, Abercrombie]
5      [Kim, Abercrombie]
6       [Hazem, Abolrous]
7         [Sam, Abolrous]
8     [Humberto, Acevedo]
9       [Gustavo, Achong]
10      [Pilar, Ackerman]
11      [Pilar, Ackerman]
12         [Aaron, Adams]
13          [Adam, Adams]
14          [Alex, Adams]
15     [Alexandra, Adams]
16       [Allison, Adams]
17        [Amanda, Adams]
18         [Amber, Adams]
19        [Andrea, Adams]
20         [Angel, Adams]

df = employees['Full Name'].str.split(expand=True)
            0            1
ID
1        Syed        Abbas
2   Catherine         Abel
3         Kim  Abercrombie
4         Kim  Abercrombie
5         Kim  Abercrombie
6       Hazem     Abolrous
7         Sam     Abolrous
8    Humberto      Acevedo
9     Gustavo       Achong
10      Pilar     Ackerman
11      Pilar     Ackerman
12      Aaron        Adams
13       Adam        Adams
14       Alex        Adams
15  Alexandra        Adams
16    Allison        Adams
17     Amanda        Adams
18      Amber        Adams
19     Andrea        Adams
20      Angel        Adams

           Full Name First Name    Last Name
ID
1         Syed Abbas       Syed        Abbas
2     Catherine Abel  Catherine         Abel
3    Kim Abercrombie        Kim  Abercrombie
4    Kim Abercrombie        Kim  Abercrombie
5    Kim Abercrombie        Kim  Abercrombie
6     Hazem Abolrous      Hazem     Abolrous
7       Sam Abolrous        Sam     Abolrous
8   Humberto Acevedo   Humberto      Acevedo
9     Gustavo Achong    Gustavo       Achong
10    Pilar Ackerman      Pilar     Ackerman
11    Pilar Ackerman      Pilar     Ackerman
12       Aaron Adams      Aaron        Adams
13        Adam Adams       Adam        Adams
14        Alex Adams       Alex        Adams
15   Alexandra Adams  Alexandra        Adams
16     Allison Adams    Allison        Adams
17      Amanda Adams     Amanda        Adams
18       Amber Adams      Amber        Adams
19      Andrea Adams     Andrea        Adams
20       Angel Adams      Angel        Adams

4.求和求平均！等统计

​

import pandas as pd

students = pd.read_excel('Students5.xlsx', index_col='ID')

row_sum = students[['Test_1', 'Test_2', 'Test_3']].sum(axis=1) #拿到子集求和。默认sum是axis=0
row_mean = students[['Test_1', 'Test_2', 'Test_3']].mean(axis=1)

students['Total'] = row_sum
students['Average'] = row_mean

col_mean = students[['Test_1', 'Test_2', 'Test_3', 'Total', 'Average']].mean()#所有列的平均值
print(col_mean)
col_mean['Name'] = 'Summary'#进行命名
students = students.append(col_mean, ignore_index=True)#增加一行，向量拼接
print(students)

Test_1      72.95
Test_2      78.95
Test_3      73.10
Total      225.00
Average     75.00
dtype: float64
           Name  Test_1  Test_2  Test_3  Total    Average
0   Student_001   62.00   86.00    83.0  231.0  77.000000
1   Student_002   77.00   97.00    78.0  252.0  84.000000
2   Student_003   57.00   96.00    46.0  199.0  66.333333
3   Student_004   57.00   87.00    80.0  224.0  74.666667
4   Student_005   95.00   59.00    87.0  241.0  80.333333
5   Student_006   56.00   97.00    61.0  214.0  71.333333
6   Student_007   64.00   91.00    67.0  222.0  74.000000
7   Student_008   96.00   70.00    48.0  214.0  71.333333
8   Student_009   77.00   73.00    48.0  198.0  66.000000
9   Student_010   90.00   94.00    67.0  251.0  83.666667
10  Student_011   62.00   55.00    63.0  180.0  60.000000
11  Student_012   83.00   76.00    81.0  240.0  80.000000
12  Student_013   68.00   60.00    90.0  218.0  72.666667
13  Student_014   82.00   68.00    98.0  248.0  82.666667
14  Student_015   61.00   67.00    91.0  219.0  73.000000
15  Student_016   59.00   63.00    46.0  168.0  56.000000
16  Student_017   62.00   83.00    93.0  238.0  79.333333
17  Student_018   90.00   75.00    80.0  245.0  81.666667
18  Student_019  100.00   95.00    55.0  250.0  83.333333
19  Student_020   61.00   87.00   100.0  248.0  82.666667
20      Summary   72.95   78.95    73.1  225.0  75.000000

5.定位、消除重复数据

​

df.duplicated(subset=None, keep=‘first’) # 指定列数据重复项判断；
# 返回：指定列重复行boolean Series
df.drop_duplicates(subset=None, keep=‘first’, # 删除重复数据
inplace=False) # 返回：副本或替代

参数：
subset=None：列标签或标签序列，可选# 只考虑某些列来识别重复项;默认使用所有列
keep=‘first’：{‘first’，‘last’，False}
# - first：将第一次出现重复值标记为True
# - last：将最后一次出现重复值标记为True
# - False：将所有重复项标记为True

import pandas as pd

students = pd.read_excel('Students_Duplicates.xlsx')
dupe = students.duplicated(subset='Name')
#多列
# dupe = students.duplicated(subset=['Name',"ID"])
print(dupe.any())#判断有没有重复，有返回true
dupe = dupe[dupe == True]  # 过滤，筛选出重复值，dupe = dupe[dupe]
print(students.iloc[dupe.index]) #定位得到重复数据
print("=========")
students.drop_duplicates(subset='Name', inplace=True, keep='last') #保存最后一次的，前面重复的删除
print(students)

# keep=‘first’：{‘first’，‘last’，False}
# - first：将第一次出现重复值标记为True
# - last：将最后一次出现重复值标记为True
# - False：将所有重复项标记为True

True
    ID         Name  Test_1  Test_2  Test_3
20  21  Student_001      62      86      83
21  22  Student_002      77      97      78
22  23  Student_003      57      96      46
23  24  Student_004      57      87      80
24  25  Student_005      95      59      87
=========
    ID         Name  Test_1  Test_2  Test_3
5    6  Student_006      56      97      61
6    7  Student_007      64      91      67
7    8  Student_008      96      70      48
8    9  Student_009      77      73      48
9   10  Student_010      90      94      67
10  11  Student_011      62      55      63
11  12  Student_012      83      76      81
12  13  Student_013      68      60      90
13  14  Student_014      82      68      98
14  15  Student_015      61      67      91
15  16  Student_016      59      63      46
16  17  Student_017      62      83      93
17  18  Student_018      90      75      80
18  19  Student_019     100      95      55
19  20  Student_020      61      87     100
20  21  Student_001      62      86      83
21  22  Student_002      77      97      78
22  23  Student_003      57      96      46
23  24  Student_004      57      87      80
24  25  Student_005      95      59      87

6.旋转数据表（行&转换）---转置

import pandas as pd

pd.options.display.max_columns = 999
videos = pd.read_excel('Videos.xlsx', index_col='Month')
table = videos.transpose()#转置
print(table)

table = videos.T #简单写法
print(table)

Month         Jan   Feb   Mar    Apr    May    Jun    Jul    Aug    Sep  \
Active User  1000  1500  2250   3375   5063   7595  11393  17090  25635
Page View    3500  5250  7875  11813  17721  26583  39876  59815  89723

Month           Oct     Nov     Dec
Active User   38453   57680   86520
Page View    134586  201880  302820

7.读取CSV、TSV、TXT中数据

import pandas as pd

students1 = pd.read_csv('Students.csv', index_col='ID')
# students2 = pd.read_csv('Students.tsv', sep='\t', index_col='ID') #制表符分割
students3 = pd.read_csv('Students.txt', sep='|', index_col='ID')  #分割符

print(students1)
# print(students2)
print(students3)

           Name  Age
ID
1   Student_001   21
2   Student_002   22
3   Student_003   23
4   Student_004   24
5   Student_005   25
6   Student_006   26
7   Student_007   27
8   Student_008   28
9   Student_009   29
10  Student_010   30
11  Student_011   31
12  Student_012   32
13  Student_013   33
14  Student_014   34
15  Student_015   35
16  Student_016   36
17  Student_017   37
18  Student_018   38
19  Student_019   39
20  Student_020   40

8.透视表！！、分组、聚合

import pandas as pd
from datetime import date
import numpy as np

orders = pd.read_excel('Orders1.xlsx', dtype={'Date': date})
orders['Year'] = pd.DatetimeIndex(orders.Date).year #以年份去做透视表
groups = orders.groupby(['Category', 'Year'])
s = groups['Total'].sum()
c = groups['ID'].count()
pt1 = pd.DataFrame({'Sum': s, 'Count': c})
#两种方法做透视表
pt2 = orders.pivot_table(index='Category', columns='Year', values='Total',
      aggfunc=np.sum)#聚合方式

print(pt1)
print(pt2)

                           Sum  Count
Category    Year
Accessories 2011  2.082077e+04    360
            2012  1.024398e+05   1339
            2013  6.750247e+05  20684
            2014  4.737876e+05  18811
Bikes       2011  1.194565e+07   3826
            2012  2.898552e+07  10776
            2013  3.626683e+07  16485
            2014  1.745318e+07   8944
Clothing    2011  3.603148e+04    655
            2012  5.555877e+05   4045
            2013  1.067690e+06  10266
            2014  4.612336e+05   6428
Components  2011  6.391730e+05    875
            2012  3.880758e+06   5529
            2013  5.612935e+06   9138
            2014  1.669727e+06   3156

Year                 2011          2012          2013          2014
Category
Accessories  2.082077e+04  1.024398e+05  6.750247e+05  4.737876e+05
Bikes        1.194565e+07  2.898552e+07  3.626683e+07  1.745318e+07
Clothing     3.603148e+04  5.555877e+05  1.067690e+06  4.612336e+05
Components   6.391730e+05  3.880758e+06  5.612935e+06  1.669727e+06