Pandas Concat, Guid To Merging Dataframes

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Pandas Concat: A Beginner's Guide to Merging DataFrames

Pandas is a powerful data analysis library in Python that provides easy-to-use data structures and data analysis tools for handling and manipulating numerical tables and time series data.

One of the most commonly used functions in Pandas is concat, which is used to concatenate or join two or more dataframes into a single dataframe.

The concat function in Pandas allows you to combine multiple dataframes in different ways.

You can concatenate dataframes vertically, which means adding rows to the bottom of the dataframe, or horizontally, which means adding columns to the right of the dataframe.

The concat function is flexible and can handle dataframes with different shapes and columns.

Concat Two Dataframes Vertically

Here is a simple example of how to concatenate two dataframes vertically using the concat function:

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 1, 2, 3])

df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']},
                   index=[4, 5, 6, 7])

df = pd.concat([df1, df2])

print(df)

The resulting dataframe df will have 8 rows, with the rows from df2 added to the bottom of df1.

Concat Two Dataframes Horizontally

You can also concatenate dataframes horizontally using the axis parameter.

To concatenate dataframes horizontally, you need to set the axis parameter to 1.

Here is an example:

df3 = pd.DataFrame({'E': ['E0', 'E1', 'E2', 'E3'],
                    'F': ['F0', 'F1', 'F2', 'F3'],
                    'G': ['G0', 'G1', 'G2', 'G3'],
                    'H': ['H0', 'H1', 'H2', 'H3']},
                   index=[0, 1, 2, 3])

df = pd.concat([df1, df3], axis=1)

print(df)

In this example, the resulting dataframe df will have the columns from df3 added to the right of df1.

Pandas Concat vs Append: What's the Difference?

When it comes to merging dataframes in Pandas, two common functions that you'll come across are concat and append.

Both functions are used to combine dataframes, but they differ in their syntax, functionality, and the types of merge they support.

Concat is a general-purpose function that can merge dataframes both vertically and horizontally.

It supports concatenating multiple dataframes at once, and it can handle dataframes with different shapes and columns.

The syntax for concat is:

pd.concat([df1, df2, df3], axis=0)

Append, on the other hand, is a more specialized function that only supports merging dataframes vertically.

It's often used to append a single dataframe to another, and its syntax is much simpler than concat:

df1.append(df2)

In summary, concat is a more versatile function that can handle a wider range of merge scenarios, while append is a simpler option for appending a single dataframe to another.

When choosing between the two, consider your specific requirements and what type of merge you need to perform. In most cases, concat will be the better choice, but append can be a useful option for specific use cases.

Summary

In conclusion, the concat function in Pandas is a powerful tool for merging dataframes in Python.

Whether you want to concatenate dataframes vertically or horizontally, the concat function makes it easy to join dataframes of different shapes and sizes into a single, unified dataframe.

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