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What is Pandas?
Pandas is a Python library used for working with data sets.
It has functions for analyzing, cleaning, exploring, and manipulating data.
The name "Pandas" has a reference to both "Panel Data", and "Python Data Analysis" and was created by Wes McKinney in 2008.
Why Use Pandas?
Pandas allow us to analyze big data and make conclusions based on statistical theories.
Pandas can clean messy data sets, and make them readable and relevant.
Relevant data is very important in data science.
What Can Pandas Do?
Pandas give you answers about the data. Like:
• Is there a correlation between two or more columns?
• What is the average value?
• Max value?
• Min value?
Pandas are also able to delete rows that are not relevant or contain wrong values, like empty or NULL values. This is called cleaning the data.
Package overview
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
• Any other form of observational/statistical data sets. The data need not be labelled at all to be placed into a panda data structure