Pandas
The analyst who lives in spreadsheets and wants the same work to be repeatable, auditable and thirty times faster.
12 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Create Series from lists, dicts, arrays, and scalars, with explicit indexes
- Explain what the index is and why it makes a Series more than an array
- Select values by label (.loc), by position (.iloc), and by boolean mask
- Predict the result of arithmetic between Series with mismatched indexes
- Handle missing data with isna, fillna, dropna, and understand NaN propagation
- Apply the essential methods: value_counts, sort_values, rank, describe, map
- Use the .str and .dt accessors for text and date columns
- Distinguish .map, .apply, and vectorised operations, and choose correctly
Curriculum
12 sections · 12 lessons
Section 1
12 modules
Section 1
- SeriesThe labelled one-dimensional array — and the automatic alignment that makes pandas fundamentally different from NumPy.~120 min
- DataFramesCommand of the two-dimensional labelled table you will use for the rest of this course.~110 min
- Reading CSV, Excel & JSONReliable ingestion from every format you will meet — and the parameters that prevent silent corruption.~110 min
- Selecting & Filtering DataPrecise, safe access to any subset of a DataFrame — and a definitive answer to the warning that confuses people for years.~110 min
- Data CleaningA repeatable cleaning pipeline that reports every change it makes.~120 min
- Missing ValuesDefensible handling of missing data — and the ability to tell when imputation is honest and when it is fabrication.~120 min
- Data TransformationTurning raw columns into the features an analysis actually needs.~110 min
- GroupBy & AggregationSplit-apply-combine — the single most powerful idea in pandas.~120 min
- Merge, Join & ConcatenateCombining datasets without silently duplicating or losing rows.~110 min
- Pivot TablesCross-tabulation that turns a transaction log into a summary a manager can read.~100 min
- Date & Time HandlingTime series operations that turn 'monthly totals' or 'rolling 30-day average' into a single line.~110 min
- Real-world Case StudyOne continuous analysis from messy multi-table data to a written recommendation — Section 3's capstone.~180 min
About this course
12 modules from the Python for Data Analysis curriculum.
This course covers Series, DataFrames, Reading CSV, Excel & JSON, Selecting & Filtering Data, Data Cleaning and Missing Values, and 6 more.
About 23h 40m of material across 12 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- The foundations covered earlier in this curriculum, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start Pandas today
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