Data Manipulation with pandas
Master pandas for cleaning, aggregating, and visualizing data
Data Manipulation with pandas is a concise, hands-on introduction to using the pandas library in Python to clean, transform, and inspect tabular datasets for analysis. The course emphasizes practical DataFrame operations—indexing and subsetting, grouped aggregation, conditional transforms, and basic plotting—so learners can apply reproducible data-manipulation workflows.
Targeted at beginners and practitioners moving from spreadsheets to code, the instruction uses real-world examples to teach importing data, handling missing values, deriving new features, and producing visual summaries. The emphasis on short, interactive exercises makes the material immediately applicable for analysts, students, and professionals who need reliable data-cleaning and aggregation skills.
At a Glance
Data Manipulation with pandas is an introductory course on using the pandas library to clean, transform, and visualize tabular data, offered through DataCamp.
The course is taught by DataCamp instructors and covers inspecting and modifying DataFrames, grouping and aggregation, slicing and filtering, conditional operations, and basic plotting using real-world datasets.
| Level | Beginner |
| Rating | 4.8 out of 5 |
| Duration | 4+ hours |
| Languages | English |
| Certificate | Statement of Accomplishment |
| Access | Access for as long as your subscription is active |
| Course includes |
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| Price | Subscription-based, monthly or annual plans |
What This Course Teaches
The course frames outcomes as concrete, demonstrable tasks so learners can show they can manipulate and analyze tabular data using pandas in reproducible Python code.
By the end, learners should be able to inspect and subset DataFrames, implement grouped aggregations and pivot tables, create and transform columns while handling missing values, apply precise filtering with Boolean logic, and produce common pandas visualizations.
How the Course Is Structured
The course is organized into four focused modules that move from aggregation to transformation, then filtering, and finish with conditional operations, delivered as short lessons paired with interactive exercises.
The full course runs about four hours in total across these modules.
01 Data Aggregation ▾
Summarizes data using totals, averages, and counts and shows how to break down statistics by one or multiple grouping columns with groupby and pivot tables.
02 Data Transformation ▾
Covers creating new columns, setting indexes, and performing both basic and multi-step transformations to derive metrics and percentages of totals.
03 Data Filtering ▾
Introduces row selection techniques including simple and compound Boolean conditions, handling missing values, and strategies to simplify complex filters.
04 Conditional Operations ▾
Teaches conditional transformations and conditional aggregation to apply calculations or compute summaries that depend on specific criteria.
Audience & Requirements
The course is aimed at beginners and early-career professionals who need practical, job-relevant skills for cleaning, transforming, and summarizing tabular data with pandas.
Typical learners include aspiring data analysts, business analysts, students or researchers working with datasets, and spreadsheet users who want to transition to Python-based workflows.
- Career changers preparing for data-analysis roles.
- Analysts and business professionals who need to clean and aggregate datasets.
- Students or researchers working with empirical CSV/Excel data.
- Spreadsheet users who want to adopt Python/pandas workflows.
- No special prerequisites; designed for beginners.
- Basic familiarity with Python is helpful but not required.
- Willingness to complete interactive, hands-on exercises (in-browser coding environment provided).
Final Verdict
Data Manipulation with pandas is a practical, beginner-focused short course that delivers useful, job-relevant pandas skills and is a sensible choice for learners who want hands-on data-cleaning and aggregation experience.
Given its strong platform rating and broad learner community, the course is especially appropriate for people who prefer interactive exercises and applied problem sets over lengthy theoretical treatment.
The included Statement of Accomplishment and the platform’s subscription access model increase the course’s value for regular users of the service; if you only need a single brief primer, the subscription model may be less cost-efficient than a one-off purchase elsewhere, so weigh ongoing access needs against cost before enrolling.
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