In this article, I try to show how to vlookup in Pandas.

The VLOOKUP function in Excel is a popular function that allows you to search for a value in a table and return the corresponding value from another column. The equivalent of a VLOOKUP in Pandas is the `merge`

method, which allows you to join two DataFrames based on a common column.

## How to Use theĀ `merge`

Method

The `merge`

method takes three arguments:

- The first argument is the first DataFrame.
- The second argument is the second DataFrame.
- The third argument is the name of the column that the two DataFrames have in common.

The `merge`

method returns a new DataFrame that contains the rows from both DataFrames that have matching values in the common column.

### Example

The following code shows how to use the `merge`

method to achieve the same results as the VLOOKUP function:

import pandas as pd df1 = pd.DataFrame({'key': [1, 2, 3], 'value': ['A', 'B', 'C']}) df2 = pd.DataFrame({'key': [2, 3, 4], 'value': ['D', 'E', 'F']}) result = df1.merge(df2, on='key', how='left') print(result)

In this example, the merge method will return a DataFrame with the following values:

key value_x value_y 0 1 A NaN 1 2 B D 2 3 C E

## Advantages of using the merge method to perform a VLOOKUP

There are several advantages to using the merge method to perform a VLOOKUP:

- The merge method is more efficient than using a VLOOKUP formula, especially when working with large DataFrames.
- The merge method is more flexible than using a VLOOKUP formula. For example, you can use the merge method to perform a VLOOKUP on multiple columns or to return multiple values from the lookup table.
- The merge method is more robust than using a VLOOKUP formula. For example, the merge method will handle missing values in the lookup table gracefully.

The merge method in Pandas is the equivalent of the VLOOKUP function in Excel.