Subset Large Dataframes for Efficient Computation Using Python and Pandas Library

Subset Large Dataframes for Efficient Computation

When working with large datasets, efficient computation is crucial to avoid performance issues. In this article, we will explore how to subset many dataframes efficiently using Python and the pandas library.

Introduction

The original code provided a clear example of a problem that arises when working with large datasets. The loop through each day’s data was slow due to the need to prevent “look ahead bias” by only returning subsets of the data up to the current datapoint. We will delve into this issue and explore ways to improve performance.

Understanding the Issue

The original code used the loc method to subset the dataframe, which returns a new dataframe containing only the rows specified by the index labels. However, this approach can be slow for large datasets because it involves creating a new dataframe. Another option was using the iloc method, but again, this results in a new dataframe.

To improve performance, we need to find a way to subset the data without creating new dataframes.

Using Ranges

One approach is to use a range of dates instead of individual datapoints. By doing so, we can avoid the overhead of indexing each datapoint individually. We will explore how to create this range using the pd.date_range function.

from datetime import datetime
import pandas as pd

freq = pd.tseries.offsets.BDay()

index = pd.date_range(datetime(2000,1,3), datetime(2011,12,31), freq=freq)
df = pd.DataFrame(index=index, columns=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'])

Creating a Range of Dates

To create a range of dates that covers the entire period, we can use the pd.date_range function. We will set the start date to 2000-01-03 and the end date to the current date.

# Create a range of dates from 2000-01-03 to the current date
datapoint_range = pd.date_range(datetime(2000,1,3), datetime.now(), freq=freq)

# Get the last date in the range (current date)
datapoint = datapoint_range[-1]

Subseting Data using Ranges

Now that we have created a range of dates, we can use it to subset our data. We will create a new dataframe containing only the rows within the specified range.

# Create a new dataframe containing only the rows within the specified range
x = df.loc[datapoint_range]

Conclusion

In this article, we explored ways to subset many dataframes efficiently using Python and the pandas library. By creating a range of dates instead of individual datapoints, we can avoid the overhead of indexing each datapoint individually. We demonstrated how to use the pd.date_range function to create this range and then used it to subset our data.

Example Use Case

Suppose you are working with historical financial data and need to compute daily returns for a specific stock. You have 180 dataframes, each containing a day’s worth of data from 2000-01-03 to 2011-12-31. To avoid the performance issues associated with individual datapoints, you can use the approach described in this article.

# Create the range of dates
datapoint_range = pd.date_range(datetime(2000,1,3), datetime.now(), freq=freq)

# Initialize an empty list to store the results
results = []

for i, datapoint in enumerate(datapoint_range):
    # Get the corresponding dataframe from the original dataframes
    df_i = df.iloc[i]

    # Compute the daily returns for the current day
    x = df_i['A'] / df_i['B']

    # Append the result to the list
    results.append(x)

In this example, we create a range of dates using pd.date_range. We then iterate over each date in the range and get the corresponding dataframe from the original dataframes. Finally, we compute the daily returns for the current day and append the result to a list.

Performance Benefits

The approach described in this article provides several performance benefits:

Reduced memory usage: By creating a range of dates instead of individual datapoints, we can reduce the amount of memory required.
Improved computation speed: Computing on ranges of data is generally faster than computing on individual datapoints.
Better scalability: This approach makes it easier to scale computations for large datasets.