Pandas — Zero to Hero — Part 3

Pandas — Zero to Hero — Part 3

Welcome back to part 3 of Pandas zero to hero, if you like to start from scratch I would recommend to go through the blog part 1 & part 2. Let's explore it with us now.

Welcome back to part 3 of Pandas zero to hero, if you like to start from scratch I would recommend to go through the blog [part 1 _](https://medium.com/analytics-vidhya/pandas-zero-to-hero-series-1-5f6ee546dc53?sk=9b8db77d53907295c4a8423b0d8a3a0b)&[ part 2_](https://medium.com/analytics-vidhya/pandas-zero-to-hero-part-2-9af4fe28cd65?sk=81987bd7d593626e8bf76a66ba000842). In this article I will be covering about some of the activities that help us achieve data munging

If you are new in Data engineering, then you must be saying I never heard about Data munging or Wrangling ?

Well, it is an art of turning the raw data into desired shape so that it can be used. Lot of times we perform these operations using excel or using code which depends upon the size of data.

It is important step in every data problem, so we must learn new ways performing operations on data. Let’s start, as now we have a fair idea of what we are doing.

*Join *— Official documentation or *Merge * Official documentation

How does SQL join works ? we all know join is SQL clause for joining one or more tables together based on key or keys.

We are so used to perform SQL joins that we think it works in the same way in programming but that’s not true. However to make this compatible in order to perform SQL like join we use pandas merge but lets understand these complex things slowly

In a SQL table the primary key column must be unique .i.e. it uniquely defines a row in a table, however in data frame we have index which may or may not be unique that identifies a row. we use loc and iloc on dataframe index to access a row.

pandas-dataframe pandas data-wrangling python sql-joins

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