Python for Data Analysis

Create an isolated environment for each project, write functions and lambdas you will pass to pandas, and learn how notebooks remember what you ran.

1. pip and Virtual Environments12 min
2. Functions and Lambdas for Data Work14 min
3. Notebooks: JupyterLab, Colab and VS Code12 min

Build arrays and read their shape and dtype, do element-wise arithmetic and aggregations, pick values by position, range and mask, then make repeatable random data and use the dot product.

1. Arrays and the ndarray12 min
2. Array Operations14 min
3. Indexing and Slicing14 min
4. Random Numbers and Linear Algebra Basics14 min

Meet the Series and DataFrame, select with loc and iloc, filter rows with conditions, summarise with groupby, and join tables with merge — all on one small bookshop sales table.

1. Series and DataFrame14 min
2. Selecting with loc and iloc14 min
3. Filtering and Querying14 min
4. Groupby and Aggregation15 min
5. Merging and Joining15 min

Load files with the right options and check what arrived, handle the one question each format asks, and query databases straight into DataFrames.

1. Reading CSV Files14 min
2. Excel, JSON and Parquet14 min
3. From a SQL Database into pandas14 min

Count missing values, decide between dropping and filling, fold messy text into clean categories, and give every column the type that matches its meaning.

1. Finding Missing Values12 min
2. Dropping, Imputing and Filling Gaps15 min
3. Cleaning Text Columns14 min
4. Fixing Types and Parsing Dates15 min

Choose between mean, median and mode, measure spread and flag outliers, read correlations, and draw clear line, bar, scatter, histogram and box plots with Matplotlib.

1. Mean, Median and Mode12 min
2. Spread and Outliers15 min
3. Correlation and Covariance14 min
4. First Plots with Matplotlib14 min
5. Histograms, Box Plots and Saving Figures15 min