Data Science · Beginner level

Beginner level · Module 2 of 5 · 11 guided hours

Exploratory Data Analysis with pandas

Summarise, group and visualise data to find patterns, problems and questions worth investigating.

Learning objectives

  • Summarise numeric and categorical variables
  • Compare groups with groupby and cross-tabulations
  • Find outliers, errors and patterns

Topics

  • value_counts and describe
  • groupby and aggregation
  • cross-tabulation
  • missing values
  • outliers and impossible values
  • quick plots

Lessons

  1. Lesson 1 Summarising Variables

    Describe each variable's distribution, missing values and categories before analysing relationships.

    describe()value_counts()missing valuesdistribution

  2. Lesson 2 Comparing Groups

    Compare groups with groupby and cross-tabulations to find differences worth explaining.

    groupbyaggpd.crosstabrow and column percentages

  3. Lesson 3 Patterns, Outliers and Data Problems

    Use plots and checks to find relationships, outliers and data problems, and decide how to handle them.

    scatter plotbox plotoutliercleaning log

Practical activity

Explore the water survey: compare access by district and household size, find impossible values and list questions for the data collectors.

Expected outcomes

  • A systematic exploration routine
  • A list of data problems and interesting patterns

Assessment

Exploration notebook marked on coverage, correctness and the quality of questions raised.

Free taster lesson Exploratory Data Analysis (EDA) Read a free self-study lesson on this topic in the Free Course Library.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.