IT Skills     Software Design and Development     Python     Python for Data Science

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Simplify data analysis with Pandas DataFrames. Pandas is a Python library that enables you to work with series and tabular data, including initialization, and population. For this course, learners do not need prior experience working with Pandas, but should be familiar with Python3, and Jupyter Notebooks. Topics include the following: Define your own index for a Pandas series object; load data from a CSV (comma separated values) file, to create a Pandas DataFrame; Add and remove data from your Pandas DataFrame; Analyze a portion of your DataFrame; Examine how to reshape or reorient data, and to create a pivot table. Finally, represent multidimensional data in two-dimensional DataFrames, with multi or hierarchical indexes.

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Objectives

Python for Data Science: Introduction to Pandas Course Overview understand the various applications of Pandas and why it is a building block in the field of data science install Pandas and create a Pandas Series work with Pandas Series by accessing elements using the default and a custom index define a Pandas DataFrame and describe how data can be stored and accessed in these data structures initialize and populate a simple Pandas DataFrame load data into a DataFrame from a CSV file edit individual cells and entire rows and columns in a Pandas DataFrame access specific rows and columns of a Pandas DataFrame using the index and labels access parts of a Pandas DataFrame based on specific conditions describe the concept of hierarchical index or multi-index and why can be useful re-orient a DataFrame as a pivot table to better visualize data apply a multi-index to a DataFrame and reshape it using the stack and melt operations work with Pandas for basic tabular data manipulation