I'm trying to plot data using the Seaborn library where:
x-axis - movie release year
y-axis - movie rating (0-10, discrete)
I'm using a scatterplot at the moment. My data is in a Pandas dataframe.
Obviously because the rating data I have is discrete integers, a lot of them stack on top of each other. How can I make the size of each dot scale with the frequency of appearance in the dataset?
For instance, if the number of 6/10 ratings in 2008 is higher than any other rating/year combination, I want that dot size (or something else in the plot) to indicate this.
Is there a different plot I should use for something like this instead?
1 Answer
Is there a different plot I should use for something like this instead?
I suggest visualizing this as a heatmap of a rating-year crosstab:
years = range(df['Release Year'].min(), df['Release Year'].max() + 1) cross = pd.crosstab(df['IMDB Rating'], df['Release Year']).reindex(columns=years, fill_value=0) fig, ax = plt.subplots(figsize=(30, 5)) sns.heatmap(cross, cbar_kws=dict(label='Count'), ax=ax) ax.invert_yaxis() 
But if you still prefer a scatterplot bubble chart, set the size param via groupby.size:
counts = df.groupby(['Release Year', 'IMDB Rating']).size().reset_index(name='Count') fig, ax = plt.subplots(figsize=(30, 5)) sns.scatterplot(data=counts, x='Release Year', y='IMDB Rating', size='Count', ax=ax) ax.grid(axis='y') sns.despine(left=True, bottom=True) 
Data for reference:
url = ' df = pd.read_json(url)[['Title', 'Release Date', 'IMDB Rating']] df['IMDB Rating'] = df['IMDB Rating'].round().astype('Int8') df['Release Year'] = pd.to_datetime(df['Release Date']).dt.year df = df.loc[df['Release Year'] <= 2010] 
