Python | Pandas dataframe.resample()

Resampling is the process of changing the frequency of time-indexed data for example, converting daily data into weekly, monthly, or quarterly intervals. In Pandas, resample() is used to perform such time-based grouping and aggregation.

During resampling, functions like .mean() are used to combine all values within each new time interval and return their average. This helps summarize time-grouped data efficiently.

Syntax

The syntax below shows how the resample() function is applied on a DataFrame or Series:

DataFrame.resample(rule, axis=0, closed=None, label=None, on=None, level=None)

Parameters:

• rule: Frequency string (e.g., 'D', 'W', 'ME', 'QE') defining target time interval.
• axis (optional): Axis to resample. Default is 0 (index).
• closed (optional): Interval side that is closed - 'left' or 'right'.
• label (optional): Whether labels represent left or right interval boundary.
• on (optional): Column to use instead of index for resampling. Must be datetime-like.
• level (optional): For MultiIndex - datetime like level to use for resampling.

Examples

We will use this dataset in all examples below.

import pandas as pd
df = pd.read_csv("apple.csv")
print(df.head())

Output

To download the file "apple.csv" used in this article, click here

Example 1: This example groups daily closing prices into monthly buckets and calculates the monthly average using "ME" (Month-End).

import pandas as pd
df = pd.read_csv("apple.csv", parse_dates=["date"], dayfirst=True, index_col="date")
res = df["close"].resample("ME").mean()
print(res)

Output

Explanation:

• df["close"] selects the closing price column
• resample("ME") groups data by month-end
• mean() computes average closing price for each month

Example 2: This example resamples opening prices into weekly groups and computes the average opening price per week.

import pandas as pd
df = pd.read_csv("apple.csv", parse_dates=["date"], dayfirst=True, index_col="date")
res = df["open"].resample("W").mean()
print(res)

Output

Explanation:

• .resample("W") groups data by week
• .mean() returns average opening price per week

Example 3: This example converts daily opening prices into quarterly groups and calculates the quarterly averages using "QE" (Quarter-End).

import pandas as pd
df = pd.read_csv("apple.csv", parse_dates=["date"], dayfirst=True, index_col="date")
res = df["open"].resample("QE").mean()
print(res.head(10))

Output

Explanation:

• resample("QE") forms quarterly time blocks (quarter-end)
• mean() computes the average opening price for each quarter