EX Exercises

✨ Simple Visualization Practice Tasks

These tasks are designed to help you practice simple data visualization in Python using Matplotlib. You will use a CSV file as a data source, load it into a DataFrame, and create different plots. The total estimated time for all tasks is about 20 minutes.

🔥 Task 1: Plot a single product line

  1. Download the provided CSV file (data.csv) or load it directly read_csv(url) from the URL: https://geomoer.github.io/moer-base-python/assets/tests/unit08/data.csv using pandas.
  2. Create a line plot that shows the revenue of Product A over the years(X-Axis).
  3. Add a descriptive title to your plot (e.g., “Revenue of Product A over Time”).
  4. Label the x-axis as “Year” and the y-axis as “Revenue in USD”.
  5. Use a solid line style (e.g., linestyle=’-‘) so that the line appears as “—-“.
  6. Add different markers to your line using marker(see the table on https://geomoer.github.io/moer-base-python/unit08/unit08-04_creating.html.

📘 Task 2: Exploring the Titanic Dataset

In this exercise, you will load the Titanic dataset and perform a basic first exploration using Pandas. This will help you understand the structure of the data before creating diagrams with Matplotlib.

🔹 Load and Inspect the Dataset

  1. Load the Titanic CSV file into a Pandas DataFrame 
    import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv")
  2. Display the first rows of the dataset 
    df.head()
  3. Show basic information about the dataset**
    • Number of rows and columns
    • Column names
    • Data types
    • Missing values 
      df.info()
  4. Generate basic descriptive statistics 
    df.describe()
  5. Count how many passengers are in each category, for example:
    • Number of males and females
    • Number of passengers in each passenger class 
      df['Sex'].value_counts()
df['Pclass'].value_counts()

  6. Create Basic Visualizations

    • A bar chart showing the number of male vs. female passengers Use the categories from df['Sex'].value_counts().index as the x-axis labels. Use the counts from df['Sex'].value_counts().values as the bar heights. 
       categories = df['Pclass'].value_counts().index
    • A bar chart showing the number of passengers in each passenger class (Pclass) Use the categories from df['Pclass'].value_counts().index as the x-axis labels. Use the counts from df['Pclass'].value_counts().values as the bar heights.

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