在Pandas中确定DataFrame的周期索引和列

在Pandas中，为了确定数据框的周期指数和列，我们将使用pandas.period_range()方法。它是Pandas中的一个通用函数，用于返回一个固定频率的PeriodIndex，日（日历）为默认频率。

语法: pandas.period_range(start=None, end=None, periods=None, freq=None, name=None)

参数:

start：生成周期的左边界
end：生成周期的右边界
periods : 产生的周期数
freq :频率别名
name : 产生的PeriodIndex的名称

返回: PeriodIndex

示例 1:

import pandas as pd
 
 
 
course = ["DBMS", "DSA", "OOPS",
          "System Design", "CN", ]
 
# pass the period and starting index
webinar_date = pd.period_range('2020-08-15', periods=5)
 
# Determine Period Index and Column
# for DataFrame
df = pd.DataFrame(course, index=webinar_date, columns=['Course'])
 
df

输出 :

在Pandas中确定数据框架的周期索引和列

示例 2:

import pandas as pd
 
day = ["Sun", "Mon", "Tue",
       "Wed", "Thurs", "Fri", "Sat"]
 
# pass the period and starting index
daycode = pd.period_range('2020-08-15', periods=7)
 
# Determine Period Index and Column for DataFrame
df = pd.DataFrame(day, index=daycode, columns=['day'])
 
df

输出:

在Pandas中确定数据框架的周期索引和列

示例 3:

import pandas as pd
 
Team = ["Ind", "Pak", "Aus"]
 
# pass the period and starting index
match_date = pd.period_range('2020-08-01', periods=3)
 
# Determine Period Index and Column for DataFrame
df = pd.DataFrame(Team, index=match_date, columns=['Team'])
 
df