[Update Links] Programming with Data: Python and Pandas | LiveLessons

Learn how to use Pandas and Python to load and transform tabular data and perform your own analyses.

In Programming with Data: Python and Pandas LiveLessons, data scientist Daniel Gerlanc prepares learners who have no experience working with tabular data to perform their own analyses. The video course focuses on both the distinguishing features of Pandas and the commonalities Pandas shares with other data analysis environments.

In this LiveLesson, Dan starts by introducing univariate and multivariate data structures in Pandas and describes how to understand them both in the context of the Pandas framework and in relation to other libraries and environments for tabular data like R and relational databases. Next, Dan covers reading and writing to external file formats, split-apply-combine computations, introductory and advanced time series, and merging and reshaping datasets. After watching this video, Python programmers will gain a deep understanding of the Pandas framework through exposures to all of its APIs and feature sets.

Beginner
Intermediate

Avoid common pitfalls and “gotchas” in Pandas by understanding the conceptual underpinnings common to most data manipulation libraries and environments
Create univariate (Series) and multivariate (DataFrame) data structures in Pandas
Read from and write to external data sources like text and binary files and databases
Use the Split-Apply-Combine technique to calculate grouped summary statistics like mean, median, and standard deviation on your data
Handle time series data; apply lead, lag, and rolling computations to them; and interpolate missing data
Merge and reshape datasets
Understand how data alignment is a central concept of Pandas

People with a solid understanding of Python programming who want to learn how to load and transform tabular data using Pandas and understand general principles and requirements common to tabular data manipulation frameworks

Intermediate-level programming ability in Python. You should know the difference between a dict, list, and tuple. Familiarity with control-flow (if/else/for/while) and error handling (try/catch) are required.
No statistics background is required.

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