Space Missions Analysis

Space Missions Analysis

Python data science project from Anglea Yu's course 100 Days of Code: Python on Udemy. This is a professional portfolio project to showcase what I learned from the 100 day challenge.

Project Details

• Details
• Link to Demo
• Tools Used
• What I learned

Details

This analysis digs into the Space Missions Launches dataset from Kaggle. Loads data in, cleans as necessary for NaN or null values, and explores the data via a series of questions.

Link to Demo

Kaggle Notebook

Tools Used

• Python
• Excel
• numpy
• pandas
• plotly
• matplotlib
• seaborn

What I learned

Below are some code snippets I'm proud of from this project:

Answering the question: How has the dominance of launches changed over time between the different players?

line_chart = px.line(
    launches_by_year_by_org,
    x=launches_by_year_by_org["year"],
    y=launches_by_year_by_org["Date"],
    color=launches_by_year_by_org["Organisation"],
    hover_name=launches_by_year_by_org["Organisation"]
)

line_chart.update_layout(
    xaxis_title="Year",
    yaxis_title="Number of Launches"
)

line_chart.show()

Charting the number of launches per year

plt.figure(figsize=(8,4), dpi=200)

with sns.axes_style("darkgrid"):
    ax = sns.scatterplot(
        data=num_launches_per_year,
        x=num_launches_per_year.index,
        y=num_launches_per_year.values,
        color="dodgerblue"
    )

    ax.set(
        xlabel="Year",
        ylabel="Number of Launches"
    )