Python Data Analytics & Visualization: Pandas & Matplotlib

Explore numpy, the numeric Python library, and perform fast vectorized calculations on multi-dimensional arrays by creating height and weight arrays with numpy.array to compute BMI and apply mean.

Explore pandas series and data frames by creating a height series and a three-column data frame, and preview data with shape, info, describe, head, tail, and sample.

Learn how to load data from csv files with pandas, mount Google Drive, create a data frame, verify shape, and set order ID as the index (in-place or at load).

Learn two methods to access columns in a data frame: dot representation and square bracket indexing. Understand their limitations and how square brackets handle spaces in headers and multiple columns.

Learn how to access and modify rows in a dataframe using loc, including index-based selection, slicing ranges, column subsetting, conditional filters, combining conditions, and updating values.

Learn how to access data in a DataFrame with iloc by selecting rows with indices, using ranges, and retrieving specific columns such as quantity and price.

Learn to access rows and columns in a dataframe using at and iat, including index-based and negative indexing, with practical examples retrieving specific values.

Join a mini project to practice loading data from an Excel file, regardless of size, by following the documented steps and then proceed to the next video for answers.

Load an Excel dataset with pandas, inspect shape and data types, select columns and samples, set invoice number as index, and filter rows where country is not United Kingdom.

Learn how to handle missing data by dropping rows or columns with dropna, using all versus any, and applying subset rules with inplace to clean a pandas data frame.

Learn to drop columns with missing values by setting axis=1 and choosing how='any' or how='all', and compare with default row operations; practice removing rows first, then columns.

Replace missing values by filling with constants or column-specific values, using zero for price, not available for country, or the mean for price, with in-place updates when needed.

Identify and inspect missing values in a data frame by using info and boolean indexing to locate rows with NaN in price and country columns, preparing for data cleaning.

learn practical steps for handling missing values: drop rows with all missing, fill numeric gaps with averages, fill missing country with not available, and use dict-based fillna in place.

Drop invalid records by conditionally filtering a pandas data frame; identify negative quantities, use a condition inside brackets to locate row five, and apply drop with inplace to remove it.

Update a whole column by adding or subtracting a value, illustrated with increasing and then restoring sample price by ten. Learn how to add columns in the next video.

Add columns in a data frame by computing amount as quantity times price, create a text column from amount, insert a discount after price, and round to two decimals.

Learn to identify and drop duplicates in a Pandas data frame, using drop_duplicates with inplace, subset, and keep options to preserve oldest or newest records, including multi-column duplicates.

Drop columns in a sample dataset using pandas, removing the discount and text columns with inplace=True, and verify the result.

Learn how to rename dataframe columns in pandas by changing quantity to qty and customer ID to Sid, using inplace=True to apply changes.

Change column data types to enable analysis, convert quantity to integer, cast category to category type, and convert order date to date time, then derive year, month, and quarter.

Learn how to apply and map functions in pandas to transform data, create conditional warranty columns for electronics, compare apply versus map, handle missing values, and prepare data for analysis.

Save the cleaned data to a new csv file in the same folder without overwriting the original data set, and name it simple clean with index false.

Continue our mini project to practice data preparation for analysis, using the resources document and step two, completing each step before checking your answers in the next video.

Load and clean the online retail data by dropping missing, duplicate, and invalid rows; derive year, quarter, and month, set category types, add warranty for electronics, and save to Excel.

Learn to sort data by columns and by index in pandas, with options for ascending or descending order, in-place modifications, missing values handling, and multi-column sorts by quantity and price.

Rank a pandas data frame by amount, using dot rank with ascending=false to rank the largest first, cast to int, and insert a new amount rank column after amount.

Learn to filter data frames in pandas using boolean masks for country, category, and amount, and create a domestic flag with apply, then combine conditions for USA, UK, or France.

Learn to count unique values in a column, identify two categories electronics and apparel, and count four unique products.

Group data by category, product, and country in pandas, computing sums, means, and total amounts. Reshape results with stack and unstack to explore multi-level groupings.

discover how pivot tables summarize data by category and country using sum on amount with zero for missing pairs, and how pivot reshapes data without aggregation; pivot cannot handle duplicates.

practice concatenating two data frames with pd.concat to merge 2023 and 2024 data, save the combined result to a new csv, and prepare multi-year data for analysis.

continue with part three of the project by transforming and analyzing the data, following the steps, and then move on to the next video to review the solutions.

Load cleaned online retail data from Excel file using invoice number as index; compute unique products and sum total amount; group by year and quarter and pivot by country.

Explore how to visualize summary data by building a bar chart of revenue per product category using group by, aggregate, and Matplotlib, including labeling and formatting options.

Group revenue by month and sum the amount to compute monthly revenue, then plot a line chart titled monthly revenue with x labels 1–12 and revenue on the y-axis.

Visualize revenue share by category with a pie chart, computing percentages from the amount. Format percentages to one decimal place and label chart revenue share with x as product category.

Analyze revenue by country by grouping data to get total revenue (sum) and average amount (mean); plot two line charts and note that data.plot shows only one chart per dataset.

Build line chart by combining two data series with plt.plot, add a title revenue by country and labels, and use a legend with blue for total and green for average.

Create a bar chart of quarterly revenue by summing the amount per quarter, using plt.bar with x as quarter index, y as the amount column, and setting xticks and titles.

Create pie chart showing product revenue by grouping by product and summing amounts, calculate percentages from the amount column, and add a title and x and y labels using matplotlib.

Explore scatter charts to analyze the relationship between price and quantity, plotting price on the x-axis and quantity on the y-axis to observe how higher prices relate to lower quantities.

Learn to draw four charts in one figure—yearly revenue, quarterly revenue, product revenue share, and price versus quantity—using a two-by-two layout with bar, line, pie, and scatter charts.

Save charts as images or pdfs by setting a save path and file name, then combine multiple charts into a pdf and fix label cutoffs with tight layout.

Complete the final part of the project by practicing data visualization, following the provided steps, and answering the questions. After finishing, move to the next video to view the solutions.

Load the cleaned online retail data, compute total amount as unit price times quantity, then visualize totals by product and by category with bar charts, adjusting x labels and y-limits.

Master data visualization with pandas and matplotlib by creating revenue and quantity charts: a pie chart for sales channel shares, a line chart for monthly revenue, and furniture warehouse shares.