Erby Jr
Grade 6 | Points: 63460 | Rank: 41
Hobby: Eat, sleep, play, WATCH
Classes completed
392
Quizzes submitted
340
Projects submitted
369
Activities
Restaurant Bill Explorer
In this activity you will load a real restaurant tipping dataset and use five types of Seaborn charts to explore the data — bar plots, count plots, box plots, strip plots, swarm plots, joint plots, a pair plot, point plots, and an lmplot. All charts are created from the same built-in dataset with no CSV file needed.
World Animals Explorer
You will build a World Animals Explorer using Seaborn’s built-in penguins dataset. They will clean and inspect the data, create histograms, KDE plots, scatter plots, and heatmaps, and use these visualisations to explore penguin body measurements, species differences, and relationships between variables.
My Score Tracker
You will build a Python programme that draws two charts of quiz scores across a school week — Monday to Friday. Working through six parts, you will import matplotlib, plot the raw data, add a title and axis labels, turn on gridlines, fix the y-axis range, style the line with a colour, dot markers, and a dashed line, and then draw the same data as a bar chart. By the end you will have two complete, polished charts — a line graph and a bar chart — showing everything you learned in one programme.
My Score Tracker
You will build a Python programme that draws two charts of quiz scores across a school week — Monday to Friday. Working through six parts, you will import matplotlib, plot the raw data, add a title and axis labels, turn on gridlines, fix the y-axis range, style the line with a colour, dot markers, and a dashed line, and then draw the same data as a bar chart. By the end you will have two complete, polished charts — a line graph and a bar chart — showing everything you learned in one programme.
Gaming Leaderboard Analyser
A Python program called game-stats-analyser.py that loads and explores a gaming leaderboard dataset. PART 1 creates a Pandas Series of top player scores with custom player-name labels. PART 2 builds a DataFrame of five players with columns Player, Level, Score, and Wins. PART 3 accesses individual rows using df.loc[]. PART 4 loads leaderboard.csv, then calls head(), tail(), and info() to inspect it. PART 5 cleans the CSV data using dropna() and fillna().
Projects
Student Marks Analyser
In this assignment, you will build a Student Marks Analyser using Pandas. You will create a labelled Series, build a DataFrame table, save and read a CSV file, view rows, inspect data information, and clean missing values. You will also calculate total and average marks to understand how Pandas helps you work with data quickly.