Pair plots for feature relationships in Data Analysis Python - Mini Project: Build & Apply

Pair plots for feature relationships

📖 Scenario: You are a data analyst working with a small dataset of flower measurements. You want to explore how different features relate to each other visually.

🎯 Goal: Create a pair plot to visualize relationships between the features in the dataset.

📋 What You'll Learn

Create a dictionary with flower data

Convert the dictionary to a pandas DataFrame

Set a variable to select specific features

Use seaborn to create a pair plot for the selected features

Print the DataFrame to see the data

💡 Why This Matters

🌍 Real World

Pair plots help data scientists quickly see how features relate to each other, which is useful in exploring new datasets.

💼 Career

Understanding feature relationships is key in data analysis and machine learning tasks, helping to select important variables and detect patterns.

1

Create the flower data dictionary

Create a dictionary called flower_data with these exact entries: 'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0], 'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6], 'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4], 'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2], and 'species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa'].

Data Analysis Python

# Create the flower_data dictionary with the given keys and values
# Your code here

Hint

Use curly braces to create the dictionary. Each key should map to a list of values.

2

Convert dictionary to DataFrame and select features

Import pandas as pd. Create a DataFrame called df from flower_data. Then create a list called features with these exact strings: 'sepal_length', 'sepal_width', 'petal_length', and 'petal_width'.

Data Analysis Python

flower_data = {
    'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0],
    'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6],
    'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4],
    'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2],
    'species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa']
}
# Import pandas as pd
# Create DataFrame df from flower_data
# Create list features with 'sepal_length', 'sepal_width', 'petal_length', 'petal_width'
# Your code here

Hint

Use pd.DataFrame() to convert the dictionary. Create the list exactly as shown.

3

Create the pair plot using seaborn

Import seaborn as sns. Use sns.pairplot() to create a pair plot from df using the columns in features. Store the result in a variable called pair_plot.

Data Analysis Python

import pandas as pd

flower_data = {
    'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0],
    'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6],
    'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4],
    'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2],
    'species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa']
}

df = pd.DataFrame(flower_data)
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']
# Import seaborn as sns
# Create pair plot from df using features
# Your code here

Hint

Use sns.pairplot() and pass df[features] to select columns.

4

Print the DataFrame to see the data

Write a print statement to display the DataFrame df.

Data Analysis Python

import pandas as pd
import seaborn as sns

flower_data = {
    'sepal_length': [5.1, 4.9, 4.7, 4.6, 5.0],
    'sepal_width': [3.5, 3.0, 3.2, 3.1, 3.6],
    'petal_length': [1.4, 1.4, 1.3, 1.5, 1.4],
    'petal_width': [0.2, 0.2, 0.2, 0.2, 0.2],
    'species': ['setosa', 'setosa', 'setosa', 'setosa', 'setosa']
}

df = pd.DataFrame(flower_data)
features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']

pair_plot = sns.pairplot(df[features])
# Print the DataFrame df
# Your code here

Hint

Use print(df) to display the DataFrame.