Description

get the index of a row pandas ?

# How to Get the Index of a Row in Pandas?

When working with pandas DataFrames, it’s frequently necessary to retrieve the index of a specific row. This article explains how to get the index of a row in pandas using various methods.

## Method 1: Using the `index` Attribute

You can fetch the index of a row by utilizing the `index` attribute of the row in question. Here is how to do it:

“`python
row_index = df.loc[row].index
“`

In this instance, `df` is your DataFrame and `row` is the row for which you want to find the index.

## Method 2: Using the `index.get_loc` Method

The `index.get_loc` method allows you to get the index of a row based on its value. Here is how you can use this method:

“`python
row_index = df.index.get_loc(row)
“`

In this example, `df` is your DataFrame and `row` is the value of the row for which you want to find the index.

## Method 3: Using the `index.tolist` Method

The `index.tolist` method allows you to get a list of indices for all the rows in the DataFrame. Here is how you can use this method:

“`python
row_indices = df.index.tolist()
“`

In this example, `df` is your DataFrame.

## Example Code

Here’s an example of code that uses the methods mentioned above:

“`python
import pandas as pd

# Creating a DataFrame
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})

# Getting the index of the row 1
row_index = df.loc[1].index
print(row_index) # Output: Int64Index([1], dtype=’int64′)

# Getting the index of the row 1 using the index.get_loc method
row_index = df.index.get_loc(1)
print(row_index) # Output: 1

# Getting a list of indices of all rows
row_indices = df.index.tolist()
print(row_indices) # Output: [0, 1, 2]
“`

In summary, you can get the index of a row in pandas using the `index`, `index.get_loc` and `index.tolist` methods. Each of these methods has its own advantages and disadvantages, and it’s always advisable to refer to the [official pandas documentation](https://pandas.pydata.org/docs/reference/operations/indexing.html) for more information.

Feel free to experiment and adapt these methods based on your specific DataFrame structure and requirements. Happy coding with pandas!