5 Pandas Fundamentals | Python

Pandas library build on top of Python language is, by no doubt, the most powerful tool currently for data manipulation and analysis. For this reason, getting a good understanding of this library is a step towards the right direction of being a better data scientist, statistician, analyst and more.

In this article we will cover 5 essentials in Pandas.

1. Merging / Joining

Joining of two or more DataFrames / tables in Pandas is done using mergefunction.

Pandas.DataFrame.merge(Documentation):

DataFrame.merge(left, right, how=inner, on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=_x, _y, copy=True, indicator=False, validate=None) →DataFrame[source]

Lets go through some parameters:

• left and right — These are left and right tables you want to merge respectively.
• how — There are several options for this parameter: { left, right, outer, inner}, default: inner.

Synonymous to SQL JOIN operator, “how” parameter brings 4 types of joins:

— Inner : Returns records that have matching values in both left and right tables.