📜  如何确定 Pandas 中频率的周期范围?

📅  最后修改于: 2022-05-13 01:55:17.541000             🧑  作者: Mango

如何确定 Pandas 中频率的周期范围?

在 pandas 中,我们可以借助 period_range() 确定带频率的周期范围。 pandas.period_range()是 Pandas 中的通用函数之一,用于返回固定频率 PeriodIndex,默认频率为 day(日历)。

示例 1:

Python3
import pandas as pd
 
 
# initialize country
country = ["India", "Australia", "Pak", "Sri Lanka",
           "England", "Bangladesh"]
 
# perform period_range() function
match_date = pd.period_range('8/1/2020', '8/6/2020', freq='D')
 
# generates dataframes
df = pd.DataFrame(country, index=match_date, columns=['Country'])
 
df


Python3
import pandas as pd
 
# initialize country
Course = ["DSA", "OOPS", "DBMS", "Computer Network",
          "System design", ]
 
# perform period_range() function
webinar_month = pd.period_range('8/1/2020', '12/1/2020', freq='M')
 
# generates dataframes
df = pd.DataFrame(Course, index=webinar_month, columns=['Course'])
 
df


Python3
import pandas as pd
 
 
# initialize gold price
gold_price = ["32k", "34k", "37k", "33k", "38k", "39k", "35k",
              "32k", "42k", "52k", "62k", "52k", "38k", "39k",
              "35k", "33k"]
 
# perform period_range() function
price_month = pd.period_range(start=pd.Period('2019Q1', freq='Q'),
                              end=pd.Period('2020Q2', freq='Q'),
                              freq='M')
 
# generates dataframes
df = pd.DataFrame(gold_price, index=price_month, columns=['Price'])
 
df


输出:

例子

Python3

import pandas as pd
 
# initialize country
Course = ["DSA", "OOPS", "DBMS", "Computer Network",
          "System design", ]
 
# perform period_range() function
webinar_month = pd.period_range('8/1/2020', '12/1/2020', freq='M')
 
# generates dataframes
df = pd.DataFrame(Course, index=webinar_month, columns=['Course'])
 
df

输出:

示例 3:

Python3

import pandas as pd
 
 
# initialize gold price
gold_price = ["32k", "34k", "37k", "33k", "38k", "39k", "35k",
              "32k", "42k", "52k", "62k", "52k", "38k", "39k",
              "35k", "33k"]
 
# perform period_range() function
price_month = pd.period_range(start=pd.Period('2019Q1', freq='Q'),
                              end=pd.Period('2020Q2', freq='Q'),
                              freq='M')
 
# generates dataframes
df = pd.DataFrame(gold_price, index=price_month, columns=['Price'])
 
df

输出: