Building a popularity histogram

You've calculated quantiles and binned streams - now visualize the full popularity distribution to complete your analysis. The popularity column from the Spotify dataset ranges from 0 to 100. Build histogram counts with custom bin edges, then plot the distribution with Plotly to see where most albums cluster.

polars is loaded as pl and plotly.express as px. The DataFrame spotify is available.

Deze oefening maakt deel uit van de cursus

Data Transformation with Polars

Bekijk cursus

Oefeninstructies

Build histogram counts from popularity with bins at 0, 25, 50, 75, and 100.
Create a bar chart with category on the x-axis and count on the y-axis.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

# Build histogram counts with custom bins
hist = spotify["popularity"].____(bins=[0, 25, 50, ____, ____])

# Create a bar chart of the distribution
fig = px.____(hist, x="____", y="____")
fig.show()

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