This app demonstrates the flexibilty Streamlit offers by making your plot code dynamic and controlled via widgets!
In the plot code below, the variables x_lim and y_lim are set and adjusted via two number_input Streamlit widgets.
Plotting code:
fig, ax = plt.subplots()
genre_list = column_multiselect
if genre_list:
data = data.query("Genre in @genre_list").reset_index()
lib.scatterplot(
data=data, x='NA_Sales', y='Global_Sales', hue='Genre',
style='Genre', palette='bright', s=100
)
x_lim = min(x_lim, data.index.max())
y_lim = min(y_lim, data.index.max())
plt.title('NA Sales vs. Global Sales by Genre')
plt.xlabel('NA Sales')
plt.ylabel('Global Sales')
plt.legend(title='Genre', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.grid(True)
X_zoom = (data['NA_Sales'].sort_values(ascending=False)
.reset_index()['NA_Sales']
.loc[
data['NA_Sales']
.idxmax() + x_lim - 1
]
)
y_zoom = (data['Global_Sales'].sort_values(ascending=False)
.reset_index()['Global_Sales']
.loc[
data['NA_Sales']
.idxmax() + y_lim - 1
]
)
# Adjust the idxmax values to focus on a specific range
plt.xlim(0, X_zoom + 1)
plt.ylim(0, y_zoom + 1)
