Python Pandas: How to Get Quarter from Date (e.g., 2025Q1, 1)

Extracting the calendar quarter (Q1, Q2, Q3, Q4) from a date is a common requirement in time series analysis, financial reporting, and business intelligence using Pandas. Whether your dates are strings or already datetime objects, Pandas provides several convenient ways to determine the quarter.

This guide explains how to get the quarter from a date column in a Pandas DataFrame using Series.dt.quarter, Series.dt.to_period('Q'), and pd.PeriodIndex.

Understanding Quarters in Dates

A calendar year is typically divided into four quarters:

• Q1: January, February, March
• Q2: April, May, June
• Q3: July, August, September
• Q4: October, November, December

Pandas can represent the quarter either as an integer (1, 2, 3, or 4) or as a Period object (e.g., '2025Q1').

Example DataFrame:

import pandas as pd

data = {
    'OrderID': [101, 102, 103, 104, 105],
    'OrderDate': ['2025-01-15', '2025-05-20', '2025-08-10', '2025-11-05', '2026-02-20'],
    'Amount': [100, 150, 200, 50, 120]
}
df_original = pd.DataFrame(data)
print("Original DataFrame:")
print(df_original)
print()

print("Original dtypes:")
print(df_original.dtypes)

Output:

Original DataFrame:
   OrderID   OrderDate  Amount
0      101  2025-01-15     100
1      102  2025-05-20     150
2      103  2025-08-10     200
3      104  2025-11-05      50
4      105  2026-02-20     120

Original dtypes:
OrderID       int64
OrderDate    object
Amount        int64
dtype: object

Prerequisite: Ensuring Date Column is Datetime Type

Most Pandas date/time operations require the column to be of datetime64 (datetime) type. If your date column is currently stored as strings (object type), convert it first using pd.to_datetime().

import pandas as pd

df = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'OrderDate': ['2025-01-15', '2025-05-20', '2025-08-10', '2025-11-05', '2026-02-20'],
    'Amount': [100, 150, 200, 50, 120]
})

# ✅ Convert 'OrderDate' column to datetime objects
df['OrderDate'] = pd.to_datetime(df['OrderDate'])

print("DataFrame after converting 'OrderDate' to datetime:")
print(df)
print()
print("Dtypes after conversion:")
print(df.dtypes)

Output:

DataFrame after converting 'OrderDate' to datetime:
   OrderID  OrderDate  Amount
0      101 2025-01-15     100
1      102 2025-05-20     150
2      103 2025-08-10     200
3      104 2025-11-05      50
4      105 2026-02-20     120

Dtypes after conversion:
OrderID               int64
OrderDate    datetime64[ns]
Amount                int64
dtype: object

Method 1: Using Series.dt.quarter (Returns Integer 1-4)

Once your date column is of datetime64 type, you can access the .dt accessor, which provides many date/time properties and methods. The .dt.quarter attribute returns the quarter of the date as an integer (1, 2, 3, or 4).

import pandas as pd

df = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'OrderDate': pd.to_datetime(['2025-01-15', '2025-05-20', '2025-08-10', '2025-11-05', '2026-02-20']),
    'Amount': [100, 150, 200, 50, 120]
})

# ✅ Get the quarter as an integer
df['Quarter_Int'] = df['OrderDate'].dt.quarter

print("DataFrame with integer quarter:")
print(df)

Output:

DataFrame with integer quarter:
   OrderID  OrderDate  Amount  Quarter_Int
0      101 2025-01-15     100            1
1      102 2025-05-20     150            2
2      103 2025-08-10     200            3
3      104 2025-11-05      50            4
4      105 2026-02-20     120            1

note

This is a very direct and common way to get the numerical quarter.

Method 2: Using Series.dt.to_period('Q') (Returns Period Object like '2025Q1')

The Series.dt.to_period(freq) method converts a Series of datetime objects into Period objects representing a span of time (like a quarter, month, or year). Using freq='Q' converts each date to its corresponding quarter period.

import pandas as pd

df = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'OrderDate': pd.to_datetime(['2025-01-15', '2025-05-20', '2025-08-10', '2025-11-05', '2026-02-20']),
    'Amount': [100, 150, 200, 50, 120]
})

# ✅ Get the quarter as a Period object (e.g., '2025Q1')
df['Quarter_Period'] = df['OrderDate'].dt.to_period('Q')

print("DataFrame with Period quarter:")
print(df)

Output:

DataFrame with Period quarter:
   OrderID  OrderDate  Amount Quarter_Period
0      101 2025-01-15     100         2025Q1
1      102 2025-05-20     150         2025Q2
2      103 2025-08-10     200         2025Q3
3      104 2025-11-05      50         2025Q4
4      105 2026-02-20     120         2026Q1

note

The dtype of the 'Quarter_Period' column will be period[Q-DEC], indicating quarterly periods ending in December. This format is often useful for time series analysis and grouping by quarter.

Method 3: Using pd.PeriodIndex(series, freq='Q')

This method is similar to dt.to_period('Q') but operates at the Pandas top level, creating a PeriodIndex directly. This PeriodIndex can then be assigned as a new column.

import pandas as pd

df = pd.DataFrame({
    'OrderID': [101, 102, 103, 104, 105],
    'OrderDate': pd.to_datetime(['2025-01-15', '2025-05-20', '2025-08-10', '2025-11-05', '2026-02-20']),
    'Amount': [100, 150, 200, 50, 120]
})

# ✅ Create a PeriodIndex for quarters and assign as a new column
df['Quarter_PeriodIndex'] = pd.PeriodIndex(df['OrderDate'], freq='Q')

print("DataFrame with quarter from PeriodIndex:")
print(df)

Output:

DataFrame with quarter from PeriodIndex:
   OrderID  OrderDate  Amount Quarter_PeriodIndex
0      101 2025-01-15     100              2025Q1
1      102 2025-05-20     150              2025Q2
2      103 2025-08-10     200              2025Q3
3      104 2025-11-05      50              2025Q4
4      105 2026-02-20     120              2026Q1

note

Both dt.to_period('Q') and pd.PeriodIndex(..., freq='Q') produce the same type of quarterly period representation.

Getting the Quarter from a Single Date Object

If you have a single Python datetime.date or datetime.datetime object, or a Pandas Timestamp object, you can get its quarter directly.

import pandas as pd
from datetime import date, datetime

# Python datetime object
py_dt_object = datetime(2025, 7, 25) # July 25th, 2025
quarter_from_py_dt = pd.Timestamp(py_dt_object).quarter
print(f"Quarter from Python datetime {py_dt_object}: {quarter_from_py_dt}")

# Pandas Timestamp object
pd_ts_object = pd.Timestamp('2025-12-01') # December 1st, 2025
quarter_from_pd_ts = pd_ts_object.quarter
print(f"Quarter from Pandas Timestamp {pd_ts_object}: {quarter_from_pd_ts}")

# Get Period object from a single Timestamp
quarter_period_single = pd_ts_object.to_period('Q')
print(f"Quarter Period from Pandas Timestamp: {quarter_period_single}")

Output:

Quarter from Python datetime 2025-07-25 00:00:00: 3
Quarter from Pandas Timestamp 2025-12-01 00:00:00: 4
Quarter Period from Pandas Timestamp: 2025Q4

• Convert Python datetime objects to pd.Timestamp first to access the .quarter attribute or .to_period() method.

Choosing the Right Method

• For an integer representation (1, 2, 3, 4): Use your_datetime_series.dt.quarter. This is simple and direct for numerical quarter values.
• For a Period object representation (e.g., '2025Q1', '2025Q2'): Use your_datetime_series.dt.to_period('Q') or pd.PeriodIndex(your_datetime_series, freq='Q'). This format is excellent for time-based indexing, grouping, and resampling by quarter.

Conclusion

Extracting quarter information from dates is a straightforward process in Pandas:

1. Ensure your date column is of datetime64 type using pd.to_datetime().
2. Use Series.dt.quarter to get the quarter as an integer (1-4).
3. Use Series.dt.to_period('Q') or pd.PeriodIndex(series, freq='Q') to get the quarter as a Pandas Period object (e.g., '2025Q1'), which includes the year.

The choice between an integer quarter and a Period object depends on your subsequent analysis needs. Period objects are particularly powerful for time series operations that group by or align with calendar quarters.