5 Best Ways to Write a Python Program to Resample Time Series Data and Find Maximum Month End Frequency

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πŸ’‘ Problem Formulation: Working with time series data often involves resampling to conform to different time frequencies. This article provides Python programmers with various methods to resample time series data and calculate the maximum value at the end of each month. Imagine having a dataset that logs daily sales figures, and you wish to determine the peak sales day values at the end of each month.

Method 1: Using pandas resample and last()

This method involves the pandas library, which is a powerful tool for time series analysis in Python. The resample() function is used to group data into specific time intervals, while last() retrieves the last value from each group. This is ideal for finding the final value in each monthly frequency.

Here’s an example:

import pandas as pd

# Sample time series data
data = {'date': pd.date_range(start='1/1/2020', periods=90, freq='D'),
        'value': range(90)}

df = pd.DataFrame(data)
df.set_index('date', inplace=True)

# Resampling to month end frequency and finding max values
month_end_max = df.resample('M').last()
print(month_end_max)

Output:

            value
date             
2020-01-31     30
2020-02-29     59
2020-03-31     89

In this snippet, the DataFrame df is resampled to a month-end frequency using 'M'. The last() method is then called to select the last observation of each month. The resulting DataFrame month_end_max contains the maximum values for the end of each month.

Method 2: Using pandas Grouper with agg()

The pandas Grouper class allows more flexibility when grouping time series data. When combined with the agg() method, you can apply multiple aggregation functions simultaneously. For month-end frequency analysis, one can use the ‘max’ aggregation after grouping by month.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
   'date': pd.date_range(start='1/1/2020', periods=90, freq='D'),
   'value': range(90)
})

df.set_index('date', inplace=True)

# Grouping by month using Grouper and aggregating with max
month_end_max = df.groupby(pd.Grouper(freq='M')).aggregate('max')
print(month_end_max)

Output:

            value
date             
2020-01-31     30
2020-02-29     59
2020-03-31     89

In this code, pd.Grouper(freq='M') groups the DataFrame by month end. Then the agg('max') function finds the maximum value in each monthly group. The result is the same as Method 1, but this code illustrates an alternate approach that’s useful when applying multiple aggregate functions.

Method 3: Using resample with custom lambda function

Another versatile feature of pandas is the ability to pass custom functions to resample() via a lambda function. This method provides personalized control over the aggregation process, which is particularly useful for complex data manipulations.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
   'date': pd.date_range(start='1/1/2020', periods=90, freq='D'),
   'value': range(90)
})

df.set_index('date', inplace=True)

# Using a lambda function to get the max value
month_end_max = df.resample('M').apply(lambda x: x.max())
print(month_end_max)

Output:

            value
date             
2020-01-31     30
2020-02-29     59
2020-03-31     89

The lambda function passed to apply() simply calls x.max(), which computes the maximum value for each resampled group. While this approach gives the same result as the previous methods, it demonstrates how you might extend this to more complex custom operations.

Method 4: Using resample with ohlc()

Financial time series data often require open-high-low-close (OHLC) resampling, where for each period you get the first (open), highest (high), lowest (low), and last (close) values. The ohlc() method in pandas does exactly that.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
   'date': pd.date_range(start='1/1/2020', periods=90, freq='D'),
   'value': range(90)
})

df.set_index('date', inplace=True)

# Applying OHLC resampling
month_end_ohlc = df['value'].resample('M').ohlc()
print(month_end_ohlc['close'])

Output:

date
2020-01-31    30
2020-02-29    59
2020-03-31    89
Name: close, dtype: int64

The ohlc() method generates a DataFrame with columns for open, high, low, and close. By selecting the ‘close’ column, we effectively retrieve the month-end frequency maximums, similar to the last() function in Method 1.

Bonus One-Liner Method 5: Using at_time() with last()

For users looking for a one-liner that doesn’t explicitly use resampling, you can first filter the DataFrame to only consider end-of-day times using at_time() and then apply last() to get the final entry for each month.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
   'datetime': pd.date_range(start='1/1/2020', periods=90, freq='D') + pd.Timedelta(hours=23, minutes=59),
   'value': range(90)
})

df.set_index('datetime', inplace=True)

# Using at_time with last
month_end_max = df.at_time('23:59').resample('M').last()
print(month_end_max)

Output:

                      value
datetime                 
2020-01-31 23:59:00     30
2020-02-29 23:59:00     59
2020-03-31 23:59:00     89

This one-liner takes advantage of at_time('23:59') to target the last minute of each day, assuming the time series includes end-of-day data points. Following that, .resample('M').last() obtains the last data point for each month.

Summary/Discussion

  • Method 1: Using resample and last(). Strengths: Straightforward and concise. Weaknesses: Limited to last value, not a custom aggregation.
  • Method 2: Using Grouper with agg(). Strengths: Offers flexibility, especially for multiple aggregations. Weaknesses: Slightly more verbose than Method 1.
  • Method 3: Using resample with custom lambda function. Strengths: Highly customizable. Weaknesses: Could be overkill for simpler tasks.
  • Method 4: Using resample with ohlc(). Strengths: Provides full OHLC data in one go. Weaknesses: More suited for financial time series.
  • Bonus Method 5: Using at_time() with last(). Strengths: Elegant one-liner. Weaknesses: Assumes specific time format and presence of end-of-day data points.