Fundamentals of Pandas

Explore data analysis with pandas in Python using an Olympic medal winners dataset. Learn to uncover insights and crunch numbers as you get started with pandas fundamentals.

Use Google Colab as a code editor for pandas essential training, open notebooks from GitHub, and run cells in order to view code, commentary, and visualizations in one workspace.

Learn to work with pandas for tabular data using series and data frames, filtering, grouping, handling missing and date-time data, and loading csv files.

Learn to read csv data into pandas with read_csv, skip the first five rows, and store the result in a dataframe. Explore downloading and uploading datasets in Google Colab.

Install pandas, load the Olympics data with read_csv, and explore shape, head, tail, sample, info, and describe to understand columns such as year, city, sport, discipline, and medal.

Learn to select a column from a dataframe using dot or square bracket notation, handle spaces in column names with brackets, and view unique values and value counts with normalization.

Explore the Olympics dataset with pandas by answering questions about its time range, missing years, medal types and counts, and the role of NOCs, including what NOC codes represent.

Explore the Olympics dataset with Pandas by querying unique years, medal types, and value counts, and understand how world wars, ties, and team events shape medal distributions.

Learn how Python lists and dictionaries work, using square brackets to create ordered lists, handling duplicates, and mapping keys to values for quick lookups with a position dictionary.

Learn to rename pandas columns with a mapping dictionary and the rename method, producing headers like athlete_name and event_gender, and to drop columns or rows using axis.

Learn to filter rows in pandas by applying conditions, producing boolean series, and selecting matching data; master single and multiple conditions with brackets.

Learn to clean and transform text data in pandas using str methods such as upper and capitalize, and use contains for conditional filtering of rows.

Sort dataframes and series with the sort_values method to order by athlete name, year, event, and medals, control ascending and descending orders, and understand Unicode implications.

Understand how pandas handles data types, convert columns to category for limited values, and order categories. Compare memory between category and object, and apply dtype mapping when loading CSV files.

Learn Python functions to automate data preprocessing, including defining a preprocess function, reading CSV files, returning data frames, and applying dtype mappings while dropping the position column.

Discover how pandas uses indexes to reference rows and columns, switch from numeric to named indices, and select specific rows and columns—like Carl Lewis’s Olympic events—while preserving data integrity.

Learn practical pandas productivity tips for immediate feedback, data validation with asserts, and common patterns like chaining, is in checks, and handling missing values.

Learn to create series and data frames from scratch using lists, dictionaries, and zip, including naming columns and aligning cities with start dates.

Pandas converts mixed date formats to date time, allowing start and end dates to be converted and a 16-day duration to be computed.

Combine data frames with pandas using concat and merge, align axes and city/date columns, perform inner, outer, left, or right joins to unify start and end data.

Detect and handle missing values in a pandas data frame using isna, fillna, and dropna, illustrated with the 2008 Olympics data (Beijing city, year 2008).

Explore finding and removing duplicates in pandas dataframes using the duplicated method and drop duplicates, illustrated with athlete medal records across events.

Validate data by checking edge cases and unique values for event gender and gender, clean via preprocessing, and automate cross-dataset tests with sorting and normalization while preserving the original CSV.

Update data types in pandas and combine two datasets by confirming column formats, splitting names to verify surname capitalization, and aligning dtypes across datasets.

Learn to visualize pandas data with matplotlib by plotting series and data frames, adjusting figure size, and creating bar and line graphs to reveal sports counts across Olympics.

Explore seaborn's visualization capabilities, color maps, and palette options while comparing seaborn with matplotlib, and learn how to plot categorical data using hue, order, and color schemes.

Learn how pandas groupby splits data into groups, applies a function, and combines results, illustrated by Olympic sprint data grouped by country, year, and event.

Explore how stacking and unstacking reshape data in pandas, using a multi-indexed sprint data example to count by NOC, gender, and event, and apply fill values for missing combos.

Use pandas to plot a line graph of USA gold medals for male (blue) and female (pink) Olympians, then a bar chart of five medalists with silver and bronze tie-breakers.

Filters USA gold medalists, groups by year and gender for a blue and pink line plot, then creates a bar chart of top five athletes by gold, silver, and bronze.

Create a custom color map in matplotlib using the listed color map class with gold, silver, and bronze hex values, then apply it to enhance the bar graph.

Identify, for each Olympic year in the dataset, the US athlete with the most medals using gold, silver, bronze tiebreakers and reverse-surname order, and return name, sport, total.

Use pandas to filter by the NOC USA, group and unstack to compute medals, then sort, reset indexes, and concatenate results to reveal top medalists by year.

Develop problem solving through coding by seeking immediate feedback and thinking clearly, step by step, for continuous improvement. Embrace coding in any language, Python with Pandas included, and keep coding.