append dataframe pandas how to add rows and columns like a

## Appending DataFrames in Pandas: A Comprehensive Guide to Adding Rows and Columns

Pandas DataFrames are incredibly versatile data structures, but their power is truly unleashed when you learn how to manipulate and grow them. Appending, in the context of DataFrames, refers to the process of adding data – either rows or columns – to an existing DataFrame. This is a crucial skill for data cleaning, merging data from different sources, and building DataFrames incrementally.

This tutorial will cover various techniques for appending to DataFrames, with detailed explanations and code examples, along with important considerations for performance and best practices.

**1. Adding Rows to a DataFrame (Appending Rows):**

The most common scenario involves adding rows to a DataFrame. Pandas provides several methods to achieve this, each with its own use case and performance characteristics.

The `append()` method was a popular way to add rows. However, it's officially deprecated and will be removed in a future version of Pandas. While you shouldn't use it in new code, understanding how it *worked* helps understand *why* `concat()` is the preferred method.

**Explanation of `append()` (and Why It's Deprecated):**

* `df2` is appended to `df1`.
* `ignore_index=True` is crucial. If set to `False` (the default), the index labels of `df2` will be preserved, potentially leading to duplicate index labels in the resulting DataFrame. This is often undesirable, so `ignore_index=True` is used to renumber the index from 0 to n-1.

* **Important:** `append()` creates a *new* DataFrame. It does *not* modify `df1` in place. This means that repeatedly using `append()` within a loop is extremely inefficient because it creates a new DataFrame in each iteration. This is the main reason for its deprecation. For loop-like scenarios, using list accumula ...

#python #python #python