Python Pandas - Indicate whether the date in DateTimeIndex is the first day of the quarter

To check whether the date in DateTimeIndex is the first day of the quarter, use the DateTimeIndex.is_quarter_start property. This property returns a boolean array indicating which dates fall on the first day of their respective quarters.

Understanding Quarters

In Pandas, a year is divided into four quarters ?

• Quarter 1: January 1 to March 31
• Quarter 2: April 1 to June 30
• Quarter 3: July 1 to September 30
• Quarter 4: October 1 to December 31

Syntax

DateTimeIndex.is_quarter_start

Example

Let's create a DateTimeIndex and check which dates are the first day of a quarter ?

import pandas as pd

# Create DatetimeIndex with period 6 and frequency of 30 days
datetimeindex = pd.date_range('2021-10-1 02:30:50', periods=6, tz='Australia/Adelaide', freq='30D')

# Display DateTimeIndex
print("DateTimeIndex...")
print(datetimeindex)

# Display DateTimeIndex frequency
print("\nDateTimeIndex frequency...")
print(datetimeindex.freq)

# Check whether the date in DateTimeIndex is the first day of the quarter
print("\nCheck whether the date in DateTimeIndex is the first day of the quarter...")
print(datetimeindex.is_quarter_start)

DateTimeIndex...
DatetimeIndex(['2021-10-01 02:30:50+09:30', '2021-10-31 02:30:50+10:30',
               '2021-11-30 02:30:50+10:30', '2021-12-30 02:30:50+10:30',
               '2022-01-29 02:30:50+10:30', '2022-02-28 02:30:50+10:30'],
              dtype='datetime64[ns, Australia/Adelaide]', freq='30D')

DateTimeIndex frequency...
<30 * Days>

Check whether the date in DateTimeIndex is the first day of the quarter...
[ True False False False False False]

Example with Specific Quarter Start Dates

Let's create another example with dates that include multiple quarter start dates ?

import pandas as pd

# Create DatetimeIndex with specific dates including quarter starts
dates = ['2023-01-01', '2023-04-01', '2023-07-01', '2023-10-01', '2023-11-15']
datetimeindex = pd.DatetimeIndex(dates)

print("DateTimeIndex...")
print(datetimeindex)

print("\nIs quarter start...")
print(datetimeindex.is_quarter_start)

# Show the actual dates that are quarter starts
quarter_starts = datetimeindex[datetimeindex.is_quarter_start]
print("\nDates that are quarter starts:")
print(quarter_starts)

DateTimeIndex...
DatetimeIndex(['2023-01-01', '2023-04-01', '2023-07-01', '2023-10-01',
               '2023-11-15'],
              dtype='datetime64[ns]', freq=None)

Is quarter start...
[ True  True  True  True False]

Dates that are quarter starts:
DatetimeIndex(['2023-01-01', '2023-04-01', '2023-07-01', '2023-10-01'], dtype='datetime64[ns]', freq=None)

Key Points

• Returns a boolean array with True for quarter start dates
• Quarter starts are always the 1st day of January, April, July, and October
• Works with timezone-aware DateTimeIndex objects
• Useful for financial and business analytics where quarterly reporting is common

Conclusion

The is_quarter_start property provides an efficient way to identify quarter start dates in a DateTimeIndex. This is particularly useful for financial analysis and quarterly business reporting where you need to filter or group data by quarter boundaries.