Aggregate Time Series from List of Dictionaries (Python)

In this article, we’ll explore a common problem in data analysis: aggregating time series data from a list of dictionaries. We’ll cover the basic approach using Python and the pandas library.

Problem Description

Suppose you have a list of dictionaries where each dictionary represents a time series data point with attributes name, sector, and ts (time series). You can easily sum all time series together regardless of their names or sectors. However, you’d like to achieve this aggregation in a more flexible way, resulting in one summed time series for each sector.

Solution Overview

The solution involves using the pandas library’s data manipulation capabilities to group the time series by sector and then aggregate them. We’ll cover the step-by-step process and provide code examples along the way.

Background Information

Before diving into the solution, let’s quickly review some essential concepts:

• Time Series: A sequence of values measured at regular time intervals.
• Pandas Library: A popular Python library for data manipulation and analysis.
• DataFrames: A two-dimensional table of data with rows and columns.

Step 1: Prepare the Data

First, we need to prepare our data in a suitable format. We’ll create a list of dictionaries representing individual time series data points:

import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Create a sample list of dictionaries
now = datetime.now()
all_series = []
for i in range(4):
    all_series.append({"name": f"name{i+1}",
                       "sector": f"sector{i+1}", 
                       "ts": pd.Series(np.random.randint(100, size=4), 
                                       index=pd.date_range(start=now - timedelta(days=i*30), freq='D',periods=4))})

Step 2: Aggregate Time Series by Sector

Next, we’ll use the pandas library to group our time series data by sector and then aggregate them:

# Convert the list of dictionaries into a DataFrame
df = pd.DataFrame(all_series)

# Group by 'sector' and calculate the sum of each time series
sector_agg = df.groupby('sector')['ts'].sum().reset_index()

# Print the aggregated result
print(sector_agg)

This code creates a new DataFrame df from our list of dictionaries, groups it by the ‘sector’ column using the groupby method, and then calculates the sum of each time series within each group.

Using Default pd.DataFrame Constructor

As an alternative approach, we can use the default pd.DataFrame constructor to create a DataFrame directly from our list of dictionaries:

# Create a new DataFrame directly from the list of dictionaries
df = pd.DataFrame(all_series)

# Group by 'sector' and calculate the sum of each time series
sector_agg_default = df.groupby('sector')['ts'].agg(list).reset_index()

# Print the aggregated result using default `pd.DataFrame` constructor
print(sector_agg_default)

In this example, we use the agg(list) method to aggregate the values within each group.

Conclusion

Aggregating time series data from a list of dictionaries can be achieved using pandas library’s data manipulation capabilities. By following the steps outlined in this article, you should now have a solid understanding of how to tackle such problems and generate meaningful insights from your data.

In the next section, we’ll explore additional features and techniques for working with time series data in Python.

Last modified on 2024-12-03