Lære Challenge: Client Industry Breakdown | Analyzing and Visualizing Freelance Data

To better understand which industries most of your freelance projects come from, you can use Python and the seaborn library to create a visually appealing bar plot. This kind of analysis helps you spot trends, identify your strongest client sectors, and potentially target new clients more effectively. Your task is to create a script that displays the number of projects you have completed for each client industry, using hardcoded data.

Start by preparing your data as two lists: one for the industry names and another for the corresponding project counts. Next, you will use seaborn to generate a bar plot. Make sure to customize the colors and add clear axis labels and a descriptive title so that your chart is both informative and attractive.


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import seaborn as sns
import matplotlib.pyplot as plt

# Hardcoded data for industries and project counts
industries = ["Tech", "Healthcare", "Finance", "Education", "Retail", "Media"]
project_counts = [15, 7, 12, 9, 5, 8]

# Set a custom color palette
custom_palette = sns.color_palette("pastel")

# Create the bar plot
sns.barplot(x=industries, y=project_counts, palette=custom_palette)

# Add labels and title
plt.xlabel("Client Industry")
plt.ylabel("Number of Projects")
plt.title("Freelance Projects by Client Industry")

# Show the plot
plt.tight_layout()
plt.show()

This script uses the seaborn library's barplot function to visualize your freelance work distribution by industry. The custom_palette variable makes the plot more visually appealing by applying soft, pastel colors. The axis labels and title ensure that anyone viewing the chart can quickly understand what the data represents. By running this script, you will gain a clear, colorful overview of which industries are most prominent in your freelance portfolio.

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Create a Python script that visualizes the number of freelance projects you have completed for each client industry using the seaborn library. Follow these steps:

Use the provided hardcoded lists defined_industries and defined_project_counts for your data.
Set a custom color palette using seaborn.
Generate a bar plot showing the number of projects for each industry.
Add clear axis labels and a descriptive title to your plot.
Ensure the plot is both visually appealing and informative by customizing the colors and layout.
Display the plot so it is visible when the script runs.

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Sektion 2. Kapitel 5

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Suggested prompts:

Can you explain how to interpret the resulting bar plot?

What other types of visualizations could I use for this data?

How can I update the data if I get more projects in the future?

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import seaborn as sns
import matplotlib.pyplot as plt

# Hardcoded data for industries and project counts
industries = ["Tech", "Healthcare", "Finance", "Education", "Retail", "Media"]
project_counts = [15, 7, 12, 9, 5, 8]

# Set a custom color palette
custom_palette = sns.color_palette("pastel")

# Create the bar plot
sns.barplot(x=industries, y=project_counts, palette=custom_palette)

# Add labels and title
plt.xlabel("Client Industry")
plt.ylabel("Number of Projects")
plt.title("Freelance Projects by Client Industry")

# Show the plot
plt.tight_layout()
plt.show()