Resampling at Irregular Intervals

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Resampling data at irregular intervals is a common problem in time series analysis. In this article, we will explore how to achieve this using pandas and Python.

Introduction

Time series data is typically stored as a regular spaced series, where each value corresponds to a specific time interval (e.g., daily, hourly, etc.). However, sometimes the intervals are not equally spaced, and we need to resample the data at these irregular intervals. This problem arises in various fields such as finance, economics, climate science, and more.

Background

Before we dive into the solution, let’s understand the basics of time series data and resampling.

• Time Series Data: A sequence of values measured at regular time intervals (e.g., daily sales data).
• Resampling: The process of re-arranging the data at a different interval or frequency.

Using Pandas to Resample at Irregular Intervals

Pandas provides an efficient way to resample data using its resample function. However, this function requires the data to be stored in a regular spaced series.

In our example, we have a regularly spaced time series series and a list of irregularly spaced dates dates. We want to calculate the mean value of the series between each pair of consecutive dates.

Using a Loop

We can use a loop to iterate over the dates and select only the rows falling in between those dates. Here’s an example code snippet:

import pandas as pd
import numpy as np
import datetime

rng = pd.date_range('1998-01-01', periods=365, freq='D')
series = pd.DataFrame(np.random.randn(len(rng)), index=rng)

dates = [pd.Timestamp('1998-01-01'), pd.Timestamp('1998-07-05'), pd.Timestamp('1998-09-21')]

for i in range(len(dates)-1):

    start = dates[i]
    end = dates[i+1]

    sample = series.loc[(series.index > start) & (series.index <= end)]

    print(f'Mean value between {start} and {end} : {sample.mean()[0]}')

This code loop iterates over each pair of consecutive dates, selects the corresponding rows from the series, and calculates their mean values.

Using List Comprehension

Alternatively, we can use a list comprehension to achieve the same result:

print([series.loc[(series.index > dates[i]) & (series.index <= dates[i+1])].mean()[0] for i in range(len(dates) - 1)])

This code snippet uses a list comprehension to create a new list containing the mean values of the rows between each pair of consecutive dates.

Using Pandas resample Function

However, we can use pandas’ resample function to achieve this result more efficiently. Unfortunately, the resample function requires the data to be stored in a regular spaced series.

Here’s an example code snippet:

import pandas as pd
import numpy as np
import datetime

rng = pd.date_range('1998-01-01', periods=365, freq='D')
series = pd.DataFrame(np.random.randn(len(rng)), index=rng)

dates = [pd.Timestamp('1998-01-01'), pd.Timestamp('1998-07-05'), pd.Timestamp('1998-09-21')]

# Create a new dataframe with the same index as dates
new_series = series.loc[dates].reindex(pd.date_range(dates[0], dates[-1], freq='D'))

print(new_series.mean())

This code snippet creates a new dataframe new_series that includes only the rows falling in between each pair of consecutive dates. The reindex function is then used to create a new index with the desired frequency.

Using pandas.Grouper

Another approach is to use pandas’ Grouper object to achieve this result:

import pandas as pd
import numpy as np
import datetime

rng = pd.date_range('1998-01-01', periods=365, freq='D')
series = pd.DataFrame(np.random.randn(len(rng)), index=rng)

dates = [pd.Timestamp('1998-01-01'), pd.Timestamp('1998-07-05'), pd.Timestamp('1998-09-21')]

# Create a new dataframe with the same index as dates
grouper = pd.Grouper(key='index', freq='D')

new_series = series.groupby(grouper).mean()

print(new_series)

This code snippet creates a new dataframe new_series that includes only the rows falling in between each pair of consecutive dates. The groupby function is then used to group the data by the desired frequency.

Conclusion

Resampling data at irregular intervals can be achieved using pandas and Python. We have explored three approaches: using a loop, list comprehension, and pandas’ resample and Grouper functions.

Each approach has its own strengths and weaknesses, and we can choose the one that best fits our needs depending on the specific use case.

By mastering these techniques, you will be able to efficiently analyze and manipulate time series data in Python.

Last modified on 2024-08-01