Python Pandas Library for Data Science

Pandas Background:

When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean and process your data. In pandas, a data table is called a DataFrame. Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,. . . ). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.

Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in pandas. There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. Adding a column to a DataFrame based on existing data in other columns is straightforward.

Pandas has great support for time series and has an extensive set of tools for working with dates, times, and timeindexed data. Data sets do not only contain numerical data. pandas provides a wide range of functions to cleaning textual data and extract useful information from it.

In this course we cover:

Basics of Pandas Library

Pandas Data structures - Series & Dataframes

Playing with Dataframes, Selecting columns & rows from a dataframe

Subsetting of dataframes - df[start_index:end_index]

Indexing

Dataframes merging and concatenating

Python programming has become one of the most sought after programming languages in the world, with its extensive amount of features and the sheer amount of productivity it provides. Therefore, being able to code Pandas in Python, enables you to tap into the power of the various other features and libraries which will use with Python. Some of these libraries are NumPy, SciPy, MatPlotLib, etc.