Adding New Column to Existing DataFrame in Pandas

Determine Period Index and Column for DataFrame in Pandas

Last Updated : 11 Jun, 2021

In Pandas to determine Period Index and Column for Data Frame, we will use the pandas.period_range() method. It is one of the general functions in Pandas that is used to return a fixed frequency PeriodIndex, with day (calendar) as the default frequency.

Syntax: pandas.to_numeric(arg, errors=’raise’, downcast=None)

Parameters:
start : Left bound for generating periods
end : Right bound for generating periods
periods : Number of periods to generate
freq : Frequency alias
name : Name of the resulting PeriodIndex

Returns: PeriodIndex

Example 1:

Python3

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

Output:

Example 2:

Python3

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

Output:

Example 3:

Python3

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

Output:

Adding New Column to Existing DataFrame in Pandas

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