Pandas is an open source Python package that is used for data science/data analysis and machine learning tasks. It is built using Numpy and provides support for multi-dimensional arrays, dataframes etc. Pandas makes it simple to do many of the time consuming, repetitive tasks associated with working with data. Pandas happens to play important role in Data Science / Data Analysis.

Data Science is an essential part of many industries, given the massive amounts of data that are produced, and is one of the most topic. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. The data used for analysis can come from many different sources and presented in various formats.

The following topics are covered using pandas:

• What is Data Science

• How to get (real) stock data , how to plot data in Jupyter

• SQL and MySQL interaction using Pandas

• read csv file and do various operations. Stocks data is considered as example

• write csv file and associated operations

• Handling missing data,

• Data filtering / Wrangling using Pandas

• Group by support in Pandas

• Concat support in Pandas

• Merge of Dataframes

• Pivot Table support in Pandas

• Stack / Unstack support in Pandas

• Reshape with Pandas

• Cross Tab with Pandas

• excel support

• Time series Support for calendar ( Business Days, Holidays)

• Time series Support for resampling, indexing time series data

• Hands-on / Practical with various datasets like: stock /share data, weather data etc