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course content

Course Content

Unveiling the Power of Data Manipulation with Pandas

Merging in PandasMerging in Pandas

Imagine you have two tables of data with some common columns, and you want to combine them into a single table for easier analysis and manipulation. This is where merging proves to be useful.

Merging can be performed in several ways, depending on how you want to handle the data present in one DataFrame but not in the other. Here’s a brief overview of different types of merging:

  • Inner Merge: Combines rows that have matching values in both DataFrames.
  • Outer Merge: Includes all rows from both DataFrames, filling in NaNs where there are no matches.
  • Left/Right Merge: Includes all rows from the left (or right) DataFrame and the matched rows from the opposite DataFrame.

Each type of merge is suited to different scenarios and requirements, allowing for flexible data manipulation to meet specific analysis needs.

Task

  1. Create two DataFrames from dictionaries.
  2. Merge these two DataFrames using the 'key' column as the common column:
    • Perform the first merge as an inner join.
    • Perform the second merge as an outer join.

Congratulations

Congratulations on completing our Pandas Project! You have made an excellent investment in your skills and knowledge, and you are now well-equipped to work with and analyze data using this powerful tool. Keep up the good work, continue learning, and you will surely succeed in your endeavors. See you next time!

Mark tasks as Completed

Everything was clear?

Section 1. Chapter 7
AVAILABLE TO ULTIMATE ONLY
course content

Course Content

Unveiling the Power of Data Manipulation with Pandas

Merging in PandasMerging in Pandas

Imagine you have two tables of data with some common columns, and you want to combine them into a single table for easier analysis and manipulation. This is where merging proves to be useful.

Merging can be performed in several ways, depending on how you want to handle the data present in one DataFrame but not in the other. Here’s a brief overview of different types of merging:

  • Inner Merge: Combines rows that have matching values in both DataFrames.
  • Outer Merge: Includes all rows from both DataFrames, filling in NaNs where there are no matches.
  • Left/Right Merge: Includes all rows from the left (or right) DataFrame and the matched rows from the opposite DataFrame.

Each type of merge is suited to different scenarios and requirements, allowing for flexible data manipulation to meet specific analysis needs.

Task

  1. Create two DataFrames from dictionaries.
  2. Merge these two DataFrames using the 'key' column as the common column:
    • Perform the first merge as an inner join.
    • Perform the second merge as an outer join.

Congratulations

Congratulations on completing our Pandas Project! You have made an excellent investment in your skills and knowledge, and you are now well-equipped to work with and analyze data using this powerful tool. Keep up the good work, continue learning, and you will surely succeed in your endeavors. See you next time!

Mark tasks as Completed

Everything was clear?

Section 1. Chapter 7
AVAILABLE TO ULTIMATE ONLY
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