Labelled data in one and two dimensions: importing and exporting, selecting and filtering rows, handling missing values, transforming columns, and grouped aggregation.
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The 9 lessons in this unit. Expand any one to see what it covers. Each unlocks when its own prerequisites are passed.
01
Pandas Series - 1D labelled data
10 questions
Learn how Pandas Series store one-dimensional labelled data, how to create Series from lists, dictionaries or arrays, and how to access elements by position or by label using .iloc and .loc.
Covers
What is a Series?
Creating Series (from lists, dicts, NumPy arrays)
Basic Series indexing
02
Pandas DataFrames - 2D labelled data
10 questions
Learn how to create Pandas DataFrames from dicts or lists, inspect their content and structure using built-in methods, and access key metadata such as shape, columns and data types.
Covers
Creating a DataFrame (from dicts, lists)
Inspecting DataFrames (head(), tail(), info(), describe())
DataFrame shape, columns, dtypes
03
Importing & exporting data in Pandas
6 questions
Learn how to load tabular data from CSV files into Pandas DataFrames using pd.read_csv(), and how to export DataFrames back to CSV format using .to_csv().
Covers
Reading CSV files (pd.read_csv())
Writing DataFrames to CSV (.to_csv())
04
Indexing and selecting Pandas DataFrames
10 questions
Learn how to select columns from a Pandas DataFrame using label-based access, use .iloc for positional indexing and .loc for label-based indexing of rows and columns.
Covers
Selecting columns
Positional indexing with .iloc
Label-based indexing with .loc
05
Conditional selection & filtering in Pandas
7 questions
Learn how to filter Pandas DataFrames by applying single or multiple conditions using boolean masks and logical operators to select specific rows and columns.
Covers
Filtering rows by a single condition
Combining multiple conditions with &, |, and ~
06
Missing data in Pandas
9 questions
Learn how to detect, count and locate missing values in Pandas DataFrames, then handle them by either dropping incomplete rows or columns or by filling missing entries with appropriate replacement values using built-in methods and parameters.
Covers
Detecting missing values
Dropping missing values
Filling missing values
07
Basic DataFrame transformations in Pandas
9 questions
Learn how to rename columns, set DataFrame indices, change column data types for analytical correctness and efficiency, and create or transform columns using arithmetic and string operations in Pandas.
Covers
Renaming columns and setting the index
Changing column data types
Creating and transforming columns
08
Custom transformations with Pandas .apply()
9 questions
Learn how to use the Pandas .apply() method to execute custom functions on DataFrame columns or rows, including with lambda functions, enabling flexible transformations that go beyond built-in vectorised operations.
Covers
Column-wise application with .apply()
Row-wise application with axis=1
Using .apply() with lambda functions
09
Pandas aggregations and groupby operations
9 questions
Learn how to quickly summarise pandas DataFrames using built-in aggregation methods, and use .groupby() to compute statistics for single or multiple groups.
Covers
Simple aggregations on DataFrames and Series
Using .groupby() operations with single groups
Multi-column grouping
This unit is part of our Essential Python for Data Science and ML learning path, which contains 117 lessons. Every one of them is drawn below.
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9 lessons in this unit
90 prerequisite lessons across the unit, counting every step back to the start
18 other lessons in the learning path - after this unit, or alongside it
Builds on: NumPy, User-defined functions
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