Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lernen Challenge 3: Relational Plots | Seaborn
Data Science Interview Challenge

book
Challenge 3: Relational Plots

Understanding relationships between variables is essential in data analysis. A robust way to visualize these relationships is through relational plots. Seaborn, with its intricate API, provides an array of tools to showcase how variables interact with one another.

Relational plots in Seaborn can:

  • Identify patterns, correlations, and outliers among two variables.

  • Present the relationship between multiple variables across complex datasets.

  • Delineate data over time or other common variables using hue semantics.

By delving into Seaborn's relational plots, analysts can derive insights into multivariate relationships and how they evolve across parameters.

Aufgabe

Swipe to start coding

Using Seaborn, visualize the relationships in a dataset:

  1. Create a line plot to track changes in a variable over time or sequential order.
  2. Display the relationship between two numeric variables with a scatter plot and differentiate data using color semantics.

Lösung

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = sns.load_dataset('flights')
months = data['month']
passengers = data['passengers']
year = data['year']

# 1. Tracking changes using a line plot
sns.lineplot(x=months, y=passengers)
plt.title('Monthly Passenger Counts Over Time')
plt.show()

# 2. Scatter plot with color semantics
sns.scatterplot(x=year, y=passengers, hue=months)
plt.title('Yearly Passenger Count Differentiated by Month')
plt.show()

War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 5. Kapitel 3
single

single

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
data = sns.load_dataset('flights')
months = data['month']
passengers = data['passengers']
year = data['year']

# 1. Tracking changes using a line plot
sns.___(x=months, y=passengers)
plt.title('Monthly Passenger Counts Over Time')
plt.show()

# 2. Scatter plot with color semantics
sns.___(x=year, y=passengers, ___=___)
plt.title('Yearly Passenger Count Differentiated by Month')
plt.show()

Fragen Sie AI

expand

Fragen Sie AI

ChatGPT

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

some-alt