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Python Introduction to Data Visualization with Matplotlib Chart Toppers Scatter Plot

Posted by Pranshu Mittal
Pranshu Mittal
4,276 Points

Could someone explain what is the value stored at i[4] ?

The groupby method's use has not been exxplained properly. the major doubt is in the lambda expression used.

2 Answers

Steven Parker
Steven Parker
243,318 Points
Steven Parker
Steven Parker
243,318 Points

The lambda funciton selects the fifth item (index 4) of "i", and "i" represents an item from "irises". The "irises" data was loaded from the "iris_data" CSV file, and the documentation for that file says the fifth attribute is the species.

This is consistent with the name given to the key by the loop, which is "species".

Nancy Melucci
PLUS
Nancy Melucci
Courses Plus Student 36,461 Points
Nancy Melucci
Nancy Melucci
Courses Plus Student 36,461 Points

This video is (I guess) about 8 years old. I've been a member of Treehouse for over a decade and have watched it become more like a ghosttown as staff has been cut and video/code content is no longer maintained. It was more personable and helpful (and current) before they cut staff massively. Anyway, this code is obsolete. I can't make this scatterplot becaue both Google Colab and my local Anacond installation hang. This viz is easier to make using a dataframe and the iris data IS included in scikit. If anyone is out there, here is my solution, which is virtually identical:

#Load the Iris dataset and create a DataFrame
iris = load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df['species'] = iris.target

# Create the plot
fig, ax = plt.subplots(figsize=(8, 6))

# Define colors and names for each species and plot
colors = {0: 'red', 1: 'green', 2: 'blue'}
species_names = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}
for species_id, color in colors.items():
    species_data = df[df['species'] == species_id]
    ax.scatter(
        species_data['sepal length (cm)'],
        species_data['sepal width (cm)'],
        c=color,
        label=species_names[species_id],
        alpha=0.5
    )

# Add labels, title, and legend
ax.set_xlabel('Sepal Length (cm)', fontsize=14)
ax.set_ylabel('Sepal Width (cm)', fontsize=14)
ax.set_title('Iris Data: Sepal Length vs Sepal Width', fontsize=16)
ax.legend(title='Species')
ax.grid(True)

# Display the plot
plt.show()