dataframes don t merge but concat using pandas python

Title: Concatenating DataFrames in Pandas: A Comprehensive Tutorial
Introduction:
Pandas is a powerful data manipulation library in Python, widely used for working with structured data. When dealing with multiple datasets, combining them is a common task. While merging is one way to combine DataFrames, another approach is concatenation. In this tutorial, we'll explore how to use the concat function in Pandas to concatenate DataFrames along different axes.
Before we start, make sure you have Pandas installed. You can install it using the following command:
Concatenation involves combining two or more DataFrames along a particular axis. The concat function in Pandas allows us to concatenate DataFrames vertically or horizontally.
Concatenating along the rows (vertical concatenation) can be achieved by stacking DataFrames on top of each other. Let's create two sample DataFrames and concatenate them vertically:
In this example, the concat function takes a list of DataFrames ([df1, df2]) and concatenates them vertically. The ignore_index=True argument resets the index of the resulting DataFrame.
Concatenating along columns (horizontal concatenation) involves aligning DataFrames along their columns. Let's illustrate this with an example:
Here, we use the axis=1 argument to specify concatenation along columns.
If your DataFrames have the same index values, you may encounter issues when concatenating. To avoid this, you can use the ignore_index parameter or reset the index after concatenation.
Concatenation in Pandas is a powerful tool for combining DataFrames vertically or horizontally. Whether you're working with large datasets or small ones, understanding how to use the concat function will enhance your data manipulation capabilities.
Remember to refer to the official Pandas documentation for more details and advanced usage: Pandas Concatenate Documentation.
ChatGPT