IT Skills     Software Design and Development     Python     Python for Data Science

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Explore advanced data manipulation and analysis with Pandas DataFrames, a Python library that shares similarities with relational databases. To take this course, prior basic experience is needed with Pandas DataFrames, data loading, and Jupyter Notebook data manipulation. You will learn to iterate data in your DataFrame. See how to export data to Excel files, JSON (JavaScript Object Notation) files, and CSV (comma separated values) files. Sort the contents of a DataFrame and manage missing data. Group data with a multi-index. Merge disparate data into a single DataFrame through join and concatenate operations. Finally, you will determine when and where to integrate data with structured queries, similar to SQL.

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Objectives

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames Course Overview learn how to iterate over a DataFrame's rows and columns export the contents of a DataFrame into files of various formats describe and apply the different techniques involved in handling datasets where some information is missing describe and apply the different techniques involved in handling datasets where some information is missing implement a hierarchical index and access the DataFrame's contents based on that index combine two similar DataFrames using the concat operation apply a join operation on two related but dissimilar DataFrames using the merge function load data into a Pandas DataFrame from a table in a relational database use Pandas for advanced tabular data manipulation