easymerge 0.4

A simple Python library for merging dataframes accommodating variations in language, spelling, and typos.

EasyMerge: DataFrame Merger

Overview

The merge function in this Python library facilitates the iterative merging of multiple DataFrames based on a specified column. This functionality is particularly useful when dealing with diverse datasets that may contain variations in language, spelling, or typos.

Installation

You can install the library using pip:

pip install easymerge

Usage

Consider the following example where we have multiple DataFrames containing information about countries:

Dataframe 1:

Country	Capital
USA	Washington DC
China	Beijing
India	New Delhi
FrAnce	Paris
Canada	Ottawa

Dataframe 2:

Country	Population (in Millions)
Canada	38.25
United States	331.90
China	1410.78
Indía	1417.00

Dataframe 3:

Country	Continent
UsA	North America
ChinA	Asia
indIa	Asia
france	Europe
CaNada	North America

We can merge these DataFrames using the merge function:

from easymerge import merger
import pandas as pd

# Define the DataFrames
df1 = pd.DataFrame({
'Country': ['USA', 'China', 'India','FrAnce', 'Canada'],
'Capital': ['Washington DC', 'Beijing', 'New Delhi', 'Paris', 'Ottawa']
})

df2 = pd.DataFrame({
'Country': ['canada','United States', 'China', 'Indía'],=
'Population (in Millions)': [38.25, 331.9, 1410.78, 1417]
})

df3 = pd.DataFrame({
'Country': ['UsA', 'ChinA', 'indIa','france', 'CaNada'],
'Continent ': ['North America', 'Asia', 'Asia', 'Europe', 'North America']
})

# Perform iterative merging
merged_df = merger.merge(df1, df2, df3, column_name='Country')

print(merged_df)

This code will merge the provided DataFrames based on the "Country" column, accounting for variations in case and spelling. The resulting DataFrame will contain all the information merged based on the specified column.

Merged Dataframe:

Country	Continent	Population (in Millions)	Capital
USA	North America	331.90	Washington DC
China	Asia	1410.78	Beijing
India	Asia	1417.00	New Delhi
FrAnce	Europe	NaN	Paris
Canada	North America	38.25	Ottawa