Python data analysis — Series, DataFrames, CSV/JSON, cleaning and plotting — written by Aivasu Tech Institute's mentors. Free to read, structured to actually help you learn.
Pandas notes by Aivasu Tech Institute cover 15 topics — from pandas home to pandas plotting — each explained with short definitions, syntax and runnable code examples. They are 100% free, need no signup, and work as quick revision for college exams, Pandas interviews and Aivasu Tech Institute's mentor-led Pandas course in Pitampura, Delhi.
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We have created 14 tutorial pages for you to learn more about Pandas.
Pandas is a Python library used for working with data sets.
If you have Python and PIP already installed on a system, then installation of Pandas is very easy.
A Pandas Series is like a column in a table.
A Pandas DataFrame is a 2 dimensional data structure, like a 2 dimensional array, or a table with rows and columns.
A simple way to store big data sets is to use CSV files (comma separated files).
Big data sets are often stored, or extracted as JSON.
One of the most used method for getting a quick overview of the DataFrame, is the head() method.
Data cleaning means fixing bad data in your data set.
Empty cells can potentially give you a wrong result when you analyze data.
Cells with data of wrong format can make it difficult, or even impossible, to analyze data.
"Wrong data" does not have to be "empty cells" or "wrong format", it can just be wrong, like if someone registered "199" instead of "1.99".
Duplicate rows are rows that have been registered more than one time.
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