Identifying Quarter Starts with pandas.tseries.offsets.MonthBegin.is_quarter_start

Data Offsets in pandas

pandas provides a powerful mechanism for working with time series data through data offsets. These offsets represent relative changes in time units (e.g., days, weeks, months, quarters) and are used to shift dates or date ranges.

MonthBegin Offset

The MonthBegin offset specifically refers to the beginning of a month. It's a subclass of the more general DateOffset class. When applied to a date, it moves the date to the first day of the same month.

is_quarter_start Method

The is_quarter_start method is associated with the MonthBegin offset. It's a boolean function that takes a timestamp (ts) as input and returns True if the timestamp falls on the first day of a quarter, and False otherwise.

How it Works

1. Month Identification
   The MonthBegin offset internally extracts the month number (1-12) from the provided timestamp.
2. Quarter Calculation
   Based on the month number, it determines the corresponding quarter (1 for January-March, 2 for April-June, and so on).
3. Start of Quarter Check
   It verifies if the day of the month in the timestamp is 1 (the first day).
4. Return Value
   
   • If the day is 1 and the month falls within a quarter's starting range (1st, 4th, 7th, or 10th month), it returns True.
   • Otherwise, it returns False.

Example

import pandas as pd

# Sample date
date = pd.to_datetime('2024-06-01')  # First day of June 2024

# Create MonthBegin offset
month_begin = pd.offsets.MonthBegin()

# Check if it's a quarter start
is_quarter_start = month_begin.is_quarter_start(date)

print(is_quarter_start)  # Output: True (June 1st is the beginning of Q2)

• It's useful when working with quarterly data or when you need to filter or group dates based on quarters.
• MonthBegin.is_quarter_start helps identify dates that align with the start of a quarter based on the month information.

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Example 1: Checking Multiple Dates

This code checks if several dates fall on the start of a quarter and prints the results:

import pandas as pd

dates = ['2024-03-31', '2024-04-01', '2024-07-15', '2024-10-01']  # Mix of quarter start and non-start dates
month_begin = pd.offsets.MonthBegin()

for date in dates:
  ts = pd.to_datetime(date)
  is_quarter_start = month_begin.is_quarter_start(ts)
  print(f"{date}: {is_quarter_start}")

This will output:

2024-03-31: False (Not first day of the month or quarter)
2024-04-01: True (First day of Q2)
2024-07-15: False (Not first day of the month or quarter)
2024-10-01: True (First day of Q4)

Example 2: Filtering Quarter Start Dates from a Series

This code creates a pandas Series with dates and filters out only the dates that are quarter starts:

import pandas as pd

dates = pd.to_datetime(['2024-02-15', '2024-04-01', '2024-06-30', '2024-09-01'])
month_begin = pd.offsets.MonthBegin()

quarter_starts = dates[dates.apply(lambda x: month_begin.is_quarter_start(x))]

print(quarter_starts)

0    2024-04-01
3    2024-09-01
dtype: datetime64[ns]

Example 3: Grouping Data by Quarter

This code demonstrates how you can use is_quarter_start to group data by quarter:

import pandas as pd

# Sample data with timestamps and some values
data = {
  'date': pd.to_datetime(['2024-03-10', '2024-04-15', '2024-05-20', '2024-07-01']),
  'value': [10, 20, 30, 40]
}

df = pd.DataFrame(data)

# Create MonthBegin offset
month_begin = pd.offsets.MonthBegin()

# Define custom function to check if it's a quarter start based on the date and offset
def is_quarter_start_with_offset(ts):
  return month_begin.is_quarter_start(ts - month_begin)  # Adjust date by one offset

# Group data by quarter using the custom function
df_grouped = df.groupby(df['date'].dt.to_period('Q-START', func=is_quarter_start_with_offset))

# Print the grouped data (showing quarter and corresponding values)
print(df_grouped['value'].sum())

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Using MonthBegin Offset and Arithmetic

• Check if the resulting date's month is within the first month of each quarter (1, 4, 7, or 10) and the day is 1 (first day of the month).
• Subtract the offset from the date (ts - month_begin). This will move the date back to the previous month's beginning.
• Create a MonthBegin offset (month_begin = pd.offsets.MonthBegin())

import pandas as pd

def is_quarter_start_alt(ts):
  month_begin = pd.offsets.MonthBegin()
  prev_month_start = ts - month_begin
  return prev_month_start.month in (1, 4, 7, 10) and prev_month_start.day == 1

# Sample date
date = pd.to_datetime('2024-06-01')

is_quarter_start = is_quarter_start_alt(date)
print(is_quarter_start)  # Output: True

Using QuarterEnd Offset and Arithmetic

• Check if the original date is less than the adjusted QuarterEnd offset.
• Add 1 day to the offset (quarter_end + pd.offsets.Day(1)) to shift it to the first day of the next quarter.
• Create a QuarterEnd offset (quarter_end = pd.offsets.QuarterEnd(offset=0)) which points to the end of the current quarter.

import pandas as pd

def is_quarter_start_alt2(ts):
  quarter_end = pd.offsets.QuarterEnd(offset=0)
  next_quarter_start = quarter_end + pd.offsets.Day(1)
  return ts < next_quarter_start

# Sample date
date = pd.to_datetime('2024-06-01')

is_quarter_start = is_quarter_start_alt2(date)
print(is_quarter_start)  # Output: True

Using dt.is_quarter_start (pandas >= 1.1.0)

• If you're using pandas version 1.1.0 or later, you can take advantage of the dt.is_quarter_start attribute directly on the datetime object:

import pandas as pd

date = pd.to_datetime('2024-06-01')

is_quarter_start = date.dt.is_quarter_start

print(is_quarter_start)  # Output: True

• If you're using pandas 1.1.0 or later, dt.is_quarter_start is a concise option.
• If performance is a concern and you're dealing with large datasets, using arithmetic with offsets might be slightly more efficient.
• For readability and clarity, pandas.tseries.offsets.MonthBegin.is_quarter_start might be preferred.