Comparing DataFrames Columns Based on Ids Using Pandas in Python

Comparing DataFrames Columns Based on Ids

In this article, we will explore the process of comparing columns in two dataframes based on their ids. We will use Python and its popular libraries Pandas to achieve this.

Introduction

When working with data, it is often necessary to compare data from different sources or transformations. In our case, we have an input dataframe and an output dataframe that contain the same dataset but are transformed differently. Our goal is to identify rows where both the id and first two columns (offset and predicted_feature) match between the two dataframes.

Background

The Pandas library in Python provides efficient data structures and operations for working with structured data, including tabular data such as tables, spreadsheets, or SQL results. The DataFrame class is the primary data structure used to store and manipulate data in a Pandas object.

What are DataFrames?

A DataFrame is a two-dimensional labeled data structure that can be thought of as an excel spreadsheet or table. It consists of rows and columns, similar to an Excel spreadsheet. Each value in the DataFrame has a row and column index, similar to how each cell in an Excel spreadsheet does.

The key features of DataFrames are:

Rows: Represented by the index label.
Columns: Represented by the column label.
Cells: Contain values from both rows and columns.

Creating DataFrames

To create a DataFrame, you can use the pd.DataFrame constructor, passing in data from any iterable (such as a list of lists) or a dictionary.

import pandas as pd

data = {
    'Document_ID': [0, 0, 0, 0, 1, 1, 1],
    'OFFSET': [0, 8, 16, 23, 0, 5, 7],
    'PredictedFeature': [2000, 2000, 2200, 2200, 2100, 2100, 2100]
}

df_input = pd.DataFrame(data)

DataFrames Comparison

When comparing two DataFrames, we need to consider the following:

Row Matching: We want to identify rows that have matching ids between both dataframes.
Column Matching: We also want to ensure that the first two columns (offset and predicted_feature) match between both dataframes for each row where the id matches.

To perform this comparison, we can use Pandas’ merge method, which performs an inner join on the dataframes based on a common column. In our case, we will merge the DataFrames on the ‘Document_ID’ and first two columns (‘OFFSET’ and ‘PredictedFeature’).

# Merge DataFrames on id and first 2 columns
df_output = pd.merge(df_input, df_output, left_on='Document_ID', right_on='Document_ID')
df_output = pd.merge(df_output, df_output[['Document_ID', 'OFFSET', 'PredictedFeature']], left_on=['Document_ID', 'OFFSET'], right_on=['Document_ID', 'OFFSET'])

Note that this approach may result in a lot of false positives if the DataFrames do not match exactly. A better approach might be to use apply and create a boolean mask to identify matching rows.

# Create a boolean mask for matching rows
mask = (df_input['Document_ID'] == df_output['Document_ID']) & \
       (df_input['OFFSET'].eq(df_output['OFFSET'])) & \
       (df_input['PredictedFeature'] == df_output['PredictedFeature'])

# Apply the mask to create a new column with True/False values
df_input['is_match'] = mask.apply(lambda row: 1 if all(x==y for x, y in zip(row[:3], df_output.loc[df_output['Document_ID']==row[0], :][:3])) else 0)

However, the apply method is generally slower than a vectorized operation. Pandas operations are designed to be element-wise, meaning they work on each individual value within the data structure.

Final Solution

The final solution involves identifying rows where both the id and first two columns (offset and predicted_feature) match between the two dataframes.

# Create a boolean mask for matching rows
mask = df_input['Document_ID'].isin(df_output['Document_ID']) & \
       df_input['OFFSET'].eq(df_output['OFFSET']) & \
       df_input['PredictedFeature'].eq(df_output['PredictedFeature'])

# Apply the mask to create a new column with True/False values
df_input['is_match'] = mask.apply(lambda row: 1 if all(x==y for x, y in zip(row[:3], df_output.loc[df_output['Document_ID']==row[0], :][:3])) else 0)

Code Block

import pandas as pd

# Define DataFrames
data = {
    'Document_ID': [0, 0, 0, 0, 1, 1, 1],
    'OFFSET': [0, 8, 16, 23, 0, 5, 7],
    'PredictedFeature': [2000, 2000, 2200, 2200, 2100, 2100, 2100]
}

df_input = pd.DataFrame(data)

data_output = {
    'Document_ID': [0, 0, 0, 0, 1, 1, 1],
    'OFFSET': [0, 8, 16, 23, 0, 5, 7],
    'PredictedFeature': [2000, 2000, 2100, 2200, 2000, 2100, 2100]
}

df_output = pd.DataFrame(data_output)

# Create a boolean mask for matching rows
mask = df_input['Document_ID'].isin(df_output['Document_ID']) & \
       df_input['OFFSET'].eq(df_output['OFFSET']) & \
       df_input['PredictedFeature'].eq(df_output['PredictedFeature'])

# Apply the mask to create a new column with True/False values
df_input['is_match'] = mask.apply(lambda row: 1 if all(x==y for x, y in zip(row[:3], df_output.loc[df_output['Document_ID']==row[0], :][:3])) else 0)

print(df_input)

Last modified on 2023-12-17