pandas 案例系列3[color=rgba(0, 0, 0, 0.3)]以下文章来源于萝卜大杂烩[color=rgba(0, 0, 0, 0.3)] [color=rgba(0, 0, 0, 0.3)]，作者周萝卜
[color=rgba(0, 0, 0, 0.3)]10如何重命名 DataFrame 的列名称import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp00'],
    'Name': ['John Doe', 'William Spark'],
    'Occupation': ['Chemist', 'Statistician'],
    'Date Of Join': ['2018-01-25', '2018-01-26'],
    'Age': [23, 24]})

employees.columns = ['EmpCode', 'EmpName', 'EmpOccupation', 'EmpDOJ', 'EmpAge']

print(employees)

Output:

   EmpCode     EmpName EmpOccupation         EmpDOJ        EmpAge
0       23  2018-01-25        Emp001       John Doe       Chemist
1       24  2018-01-26         Emp00  William Spark  Statistician
11如何根据 Pandas 列中的值从 DataFrame 中选择或过滤行import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})

print("\nUse == operator\n")
print(employees.loc[employees['Age'] == 23])

print("\nUse < operator\n")
print(employees.loc[employees['Age'] < 30])

print("\nUse != operator\n")
print(employees.loc[employees['Occupation'] != 'Statistician'])

print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] != 'Statistician') &
                    (employees['Name'] == 'John')])

Output:

Use == operator

   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist

Use < operator

   Age Date Of Join EmpCode   Name    Occupation
0   23   2018-01-25  Emp001   John       Chemist
1   24   2018-01-26  Emp002    Doe  Statistician
3   29   2018-02-26  Emp004  Spark  Statistician

Use != operator

   Age Date Of Join EmpCode  Name  Occupation
0   23   2018-01-25  Emp001  John     Chemist
4   40   2018-03-16  Emp005  Mark  Programmer

Multiple Conditions

   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist

12在 DataFrame 中使用“isin”过滤多行import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})

print("\nUse isin operator\n")
print(employees.loc[employees['Occupation'].isin(['Chemist','Programmer'])])

print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] == 'Chemist') |
                    (employees['Name'] == 'John') &
                    (employees['Age'] < 30)])

Output:

Use isin operator

   Age Date Of Join EmpCode  Name  Occupation
0   23   2018-01-25  Emp001  John     Chemist
4   40   2018-03-16  Emp005  Mark  Programmer

Multiple Conditions

   Age Date Of Join EmpCode  Name Occupation
0   23   2018-01-25  Emp001  John    Chemist
13迭代 DataFrame 的行和列import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})

print("\n Example iterrows \n")
for index, col in employees.iterrows():
    print(col['Name'], "--", col['Age'])


print("\n Example itertuples \n")
for row in employees.itertuples(index=True, name='Pandas'):
    print(getattr(row, "Name"), "--", getattr(row, "Age"))

Output:

Example iterrows

John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40

Example itertuples

John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40
14如何通过名称或索引删除 DataFrame 的列import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})

print(employees)

print("\n Drop Column by Name \n")
employees.drop('Age', axis=1, inplace=True)
print(employees)

print("\n Drop Column by Index \n")
employees.drop(employees.columns[[0,1]], axis=1, inplace=True)
print(employees)

Output:

   Age Date Of Join EmpCode     Name    Occupation
0   23   2018-01-25  Emp001     John       Chemist
1   24   2018-01-26  Emp002      Doe  Statistician
2   34   2018-01-26  Emp003  William  Statistician
3   29   2018-02-26  Emp004    Spark  Statistician
4   40   2018-03-16  Emp005     Mark    Programmer

Drop Column by Name

  Date Of Join EmpCode     Name    Occupation
0   2018-01-25  Emp001     John       Chemist
1   2018-01-26  Emp002      Doe  Statistician
2   2018-01-26  Emp003  William  Statistician
3   2018-02-26  Emp004    Spark  Statistician
4   2018-03-16  Emp005     Mark    Programmer

Drop Column by Index

      Name    Occupation
0     John       Chemist
1      Doe  Statistician
2  William  Statistician
3    Spark  Statistician
4     Mark    Programmer
15向 DataFrame 中新增列import pandas as pd

employees = pd.DataFrame({
    'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
    'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
    'Occupation': ['Chemist', 'Statistician', 'Statistician',
                   'Statistician', 'Programmer'],
    'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
                     '2018-03-16'],
    'Age': [23, 24, 34, 29, 40]})

employees['City'] = ['London', 'Tokyo', 'Sydney', 'London', 'Toronto']

print(employees)

Output:

   Age Date Of Join EmpCode     Name    Occupation     City
0   23   2018-01-25  Emp001     John       Chemist   London
1   24   2018-01-26  Emp002      Doe  Statistician    Tokyo
2   34   2018-01-26  Emp003  William  Statistician   Sydney
3   29   2018-02-26  Emp004    Spark  Statistician   London
4   40   2018-03-16  Emp005     Mark    Programmer  Toronto