All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Load the world_happiness.csv and store its data in a df variable.
Create your histogram and, when ready, save it to a happiness_hist variable and validate it with the following button.
Your code should look like:
happiness_hist = df["Happiness score"].plot(.......
Important! we'll interactively check that your variables
happiness_histcontain the correct information
Create your horizontal bar chart and, when ready, save it to a happiness_gap_chart variable and validate it with the following button.
Your code should look like:
happiness_gap_chart = pivot_df.plot(.......)
Important! we'll interactively check that your variable
happiness_gap_chartcontain the correct information
Create your horizontal bar chart and, when ready, save it to a generosity_gap_chart variable and validate it with the following button.
Your code should look like:
generosity_gap_chart = generosity_pivot_df.plot(.......)
Important! we'll interactively check that your variable
generosity_gap_chartcontain the correct information
Create your pie chart and, when ready, save it to a GDP_vs_health_pie variable and validate it with the following button.
Your code should look like:
GDP_vs_health_pie = GDP_vs_health_percent.plot(.......)
Important! we'll interactively check that your variables
GDP_vs_health_piecontain the correct information
Create your pie chart and, when ready, save it to a factors_pie variable and validate it with the following button.
Your code should look like:
factors_pie = factors_percent.plot(.......)
Important! we'll interactively check that your variables
factors_piecontain the correct information
Create your horizontal stacked bar chart and, when ready, save it to a top_10_factors_contribution_chart variable and validate it with the following button.
Your code should look like:
top_10_factors_contribution_chart = top_10_factors.plot(.......)
Important! we'll interactively check that your variables
top_10_factorsandtop_10_factors_contribution_chartcontain the correct information
