Adventures in Machine Learning

Effortlessly Merge DataFrames with Pandas VLOOKUP Function

VLOOKUP Function in Pandas: How to Use It to Merge DataFrames

Have you ever struggled to find information in a large dataset, or spent hours manually matching data from two different tables? If so, you might want to consider using the VLOOKUP function in Pandas.

This powerful tool can help you quickly search for matching data in different data frames, eliminating the need for tedious manual matching. In this article, well show you how to use the VLOOKUP function in Pandas to merge two data frames.

Well also provide you with additional resources to help you master this skill.

Creating Two DataFrames

To get started, we first need to create two data frames with data that we want to merge. Lets assume that we have a data frame of basketball players and another data frame of their corresponding statistics.

The player data frame might look something like this:

| Player ID | Name |

|———–|————–|

| 1 | Michael |

| 2 | LeBron |

| 3 | Kobe |

| 4 | Shaquille |

| 5 | Magic |

The statistics data frame might look like this:

| Player ID | Points/Game | Assists/Game | Rebounds/Game |

|———–|————-|————–|—————-|

| 1 | 30 | 5 | 7 |

| 2 | 28 | 7 | 8 |

| 3 | 25 | 4 | 5 |

| 4 | 20 | 3 | 10 |

| 5 | 18 | 6 | 7 |

Our goal is to merge these two tables so we can easily see a players name and stats in one table.

Performing VLOOKUP Function

Now that we have our two data frames, lets perform the VLOOKUP function to merge them. We can accomplish this using the Pandas Merge() function.

To merge these two tables, well use the following code:

“`

merged_df = pd.merge(players_df, stats_df, on=’Player ID’)

“`

The on parameter specifies the column that both data frames share. In this case, we are using the Player ID column to match the data.

After running this code, we should have a new data frame that looks like this:

| Player ID | Name | Points/Game | Assists/Game | Rebounds/Game |

|———–|————–|————-|————–|—————-|

| 1 | Michael | 30 | 5 | 7 |

| 2 | LeBron | 28 | 7 | 8 |

| 3 | Kobe | 25 | 4 | 5 |

| 4 | Shaquille | 20 | 3 | 10 |

| 5 | Magic | 18 | 6 | 7 |

We can see that the two data frames have been merged successfully, and we now have a single table that shows both player names and statistics. Congratulations, you have successfully used the VLOOKUP function in Pandas to merge two data frames.

Additional Resources

If you want to learn more about the Pandas Merge() function and other related topics, check out the Pandas documentation. The documentation provides full details on the syntax and usage of the merge() function, as well as other useful Pandas functions.

In conclusion, the VLOOKUP function in Pandas is a powerful tool that can help you quickly merge data from two different tables. By using Pandas Merge() function, you can easily match data based on a shared column.

With the additional resources available, you can continue to learn more about how to make the most out of this function and other useful Pandas features. In summary, the article explained how to use the VLOOKUP function in Pandas to merge data frames.

The process requires creating two data frames and using the Pandas Merge() function to perform the VLOOKUP operation. The article also provided a link to the Pandas documentation for further information on the topic.

This skill is essential for data analysis and can save time by eliminating the need for manual data matching. Overall, Pandas’ VLOOKUP function is a powerful and valuable tool to have in your toolkit when working with data.

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