Tutorial: Indexing DataFrames in Pandas

In this tutorial, you will learn how to index DataFrames in Pandas. You will learn about the different types of indexing, such as label-based indexing, position-based indexing, and boolean indexing. You will also learn how to use these different types of indexing to select rows, columns, and individual cells from a DataFrame.

Label-based Indexing

Label-based indexing is the most common type of indexing in Pandas. It allows you to select rows or columns by their labels. For example, to select the first row of a DataFrame, you would use the following code:

```python df.loc[0] ```

This code will return a Series object containing the values from the first row of the DataFrame.

To select a column by its label, you would use the following code:

```python df['column_name'] ```

This code will return a Series object containing the values from the column named `column_name`.

Position-based Indexing

Position-based indexing allows you to select rows or columns by their position. For example, to select the first row of a DataFrame, you would use the following code:

```python df.iloc[0] ```

This code will return a Series object containing the values from the first row of the DataFrame.

To select a column by its position, you would use the following code:

```python df.iloc[:, 0] ```

This code will return a Series object containing the values from the first column of the DataFrame.

Boolean Indexing

Boolean indexing allows you to select rows or columns based on a Boolean condition. For example, to select all rows where the `value` column is greater than 10, you would use the following code:

```python df[df['value'] > 10] ```

This code will return a DataFrame containing all rows where the `value` column is greater than 10.

Boolean indexing can also be used to select columns. For example, to select all columns where the values are not null, you would use the following code:

```python df[df.notnull()] ```

This code will return a DataFrame containing all columns where the values are not null.

I hope this tutorial has been helpful. For more information on indexing DataFrames in Pandas, please refer to the documentation: <https://pandas.pydata.org/pandas-docs/stable/indexing.html>