April 2018
Beginner to intermediate
300 pages
7h 34m
English
You may often see df appearing on Python-based data science resources and literature. It is a conventional way to denote the pandas DataFrame structure. pandas lets us perform the otherwise tedious operations on tables (data frames) with simple commands, such as dropna(), merge(), pivot(), and set_index().
pandas is designed to streamline handling processes of common data types, such as time series. While NumPy is more specialized in mathematical calculations, pandas has built-in string manipulation functions and also allows custom functions to be applied to each cell via apply().
Before use, we import the module with the conventional shorthand as:
pd.DataFrame(my_list_or_array)
To read data from existing files, just use ...
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