Pandas Concat() Function

Pandas concat() Function Syntax

When working with data in Python, the Pandas package comes in handy with its diverse features. For example, the concat() function is a useful mechanism for combining data from many sources. The syntax of the concat() method is rather simple. This is how it goes:

Here's a quick breakdown:

• dataframe1, dataframe2, and so on are the DataFrames you want to concatenate.
• axis=0 indicates that the concatenation should happen along the rows. You can also use axis=1 to concatenate along columns.
• ignore_index=True resets the index of the resulting DataFrame, ensuring a continuous index without repeated values.

Essentially, concat() joins DataFrames based on your chosen axis. It's a useful method for stacking data on top of each other or side by side. So, whether you're merging datasets or adding extra rows/columns, the concat() method is your go-to tool for making data manipulation simple.

Pandas concat() Function Parameters

When it comes to merging data in Pandas, the concat() method proves to be incredibly useful. This method facilitates the combination and concatenation of datasets along various axes with remarkable ease. To make the most of this capability, it's essential to understand the key arguments provided by the concat() function.

obj

This is where you input the data items you intend to concatenate. These items could include DataFrames, Series, or even a mix of both. The crucial aspect here is the method's adaptability to different data types.

axis*

The axis argument comes into play, governing the axis along which the concatenation occurs. To stack data horizontally, set it to 1; for vertical stacking, set it to 0.

inner and outer

The join argument provides control over how the data aligns. Options like inner and outer mimic database-style joins, whereas ignore_index re-indexes the concatenated data. As the name suggests, the ignore_index argument discards the existing indices and generates a new index for the concatenated data.

keys

It creates a hierarchical index. This proves handy for distinguishing between sources after the concatenation process.

Pandas concat() Function Returns

When the concat() method is called, it processes the input DataFrames and produces a new DataFrame with the combined data. The precise result depends on the parameters you enter. By default, the function concatenates DataFrames row by row, stacking them on each other. Some of its important points are:

• The axis is a helpful argument that lets you define whether you wish to concatenate along rows (axis=0) or columns (axis=1).
• Because of its versatility, you may adjust the function's behavior to your data arrangement requirements.
• The Pandas concat() method functions as a data stacker, allowing you to combine several datasets into a coherent whole efficiently.
• It's a strong data integration and aggregation tool that makes complicated data manipulation jobs easier.

Pandas concat() Examples

In this section, we'll walk you through a series of basic yet illuminating examples to demonstrate the adaptability of Pandas's concat() function.

Concatenating 2 Series with Default Parameters in Pandas

Let's start with the fundamentals. Assume you have two Series that you wish to concatenate vertically. This is where concat() comes into play:

Output:

Concatenating 2 Series Horizontally with "index = 1"

Assume you have two Series with distinct indices you want to concatenate horizontally. Here's how it works:

Output:

Concatenating 2 DataFrames and Assigning Keys

When working with DataFrames, consider labeling the source of each piece of data. That is where keys come in:

Output:

Concatenating 2 DataFrames Horizontally with "axis = 1"

Assume you have two DataFrames with distinct columns that you wish to merge:

Output:

Concatenating 2 DataFrames with "ignore_index = True"

At times, maintaining the original indices is unnecessary. You can reset the index for the concatenated DataFrame:

Output:

Concatenating a DataFrame with a Series

You may even combine a DataFrame with a Series, treating the Series as a new row:

Output:

By Supplying the Inner to the Join Keyword Argument, you may Combine DataFrame Objects with Overlapping Columns and Return Just those Shared

When you have DataFrames with common columns, an inner join might be handy:

Output:

Append a Single Row to the End of a DataFrame Object

A single row can be appended to an existing DataFrame via the append() function:

Output: