Intermediate Data Analysis Techniques with Pandas

This Packt continuation concentrates on working with DataFrames. Selection and filtering lead into text operations, hierarchical indexes, aggregation and merging. The provider’s roughly thirteen-hour estimate makes it a substantial practice route after basic Python and pandas.

What you will learn

• Select and filter rows by position, labels or conditions.
• Reshape and aggregate data using indexes and groups.
• Join related DataFrames while checking matching keys.

Who this suits

Analysts who already know basic Python and pandas objects.

Before you enroll

Those foundations are explicit prerequisites. A successful join can still duplicate or discard records, so inspect row counts and key uniqueness during the exercises.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Tool subscriptions, model API usage and cloud services may cost extra.

Put it into practice

Combine an orders table with fictional customer records. Test duplicate keys and missing customers, then compare the results of inner and left joins.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

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