[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 EmpAge0 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 == operatorAge 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 operatorAge 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 iterrowsJohn -- 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 Occupation0 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 City0 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