Section 1.1: Creating an Animated Scatter Plot

A scatter plot visually represents the relationship between two variables using dots on a Cartesian plane. With Plotly, creating a scatter plot is straightforward, and we can add extra dimensions by varying the size and color of the dots.

For our animation, each country will be represented by a dot. We'll aggregate suicide counts across all age groups by year and gender:

df_agg = df.groupby(['year', 'country', 'sex']).agg({'suicides_no':'sum', 'population':'first', 'gdp_per_capita ($)': 'first'}).reset_index()

Now, with the aggregated data, we can generate an animated scatter plot:

import plotly.express as px

px.scatter(df_agg, 'gdp_per_capita ($)', 'suicides_no', size='population', color='sex', animation_frame='year', hover_name='country',

category_orders={'year': ['1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']},

title='Global Suicide Rates by GDP per Capita, Population, and Gender')

The first three arguments specify the data frame, X-axis, and Y-axis. The size of the dots is determined by the population, while the color represents gender. The animation_frame parameter is key for creating the animation. Hovering over the dots reveals additional information about each country.

From this animation, we can observe that male suicide rates generally exceed those of females, with fluctuations over the years. You might also want to experiment by plotting the suicide rate (suicides per 100k population) instead of total numbers.

Section 2.1: Animated Bar Plot

If you're interested in analyzing trends among different age groups, we can aggregate our data further to visualize it using an animated bar plot:

df_agg1 = df.groupby(['year', 'country', 'age']).agg({'suicides_no':'sum', 'population':'first', 'gdp_per_capita ($)': 'first'}).reset_index()

We can create the animated bar plot with:

px.bar(df_agg1, 'age', 'suicides_no', color='age', animation_frame='year', hover_name='country',

category_orders={'year': ['1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016'],

'age': ['5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+ years']},

range_y=[0, 70000], title='Total Suicides Worldwide by Age Group per Year')

This graph effectively illustrates suicide trends across age groups. Notably, the age range of 35–54 has consistently experienced the highest suicide rates. The overall trend indicates a rise in suicides across all age categories, with a notable drop in 2016, likely due to incomplete data for that year.

The second video, "Easy Animated Plots with Python and Plotly," provides additional techniques for creating dynamic plots, enhancing your data visualization skills.

Section 2.2: Animated Map

Next, let’s visualize the same data on a global map to compare suicide rates across countries. We'll aggregate the data again and employ Plotly's choropleth function:

df_agg_country = df.groupby(['year', 'country']).agg({'suicides_no':'sum', 'population':'first', 'gdp_per_capita ($)': 'first'}).reset_index()

px.choropleth(df_agg_country, locations='country', color="suicides_no",

hover_name="country", locationmode='country names',

animation_frame="year",

color_continuous_scale='viridis',

height=600, title='Global Suicide Rates by Country Over Time')

This map succinctly captures suicide statistics across various countries. For instance, the United States had the highest suicide rates in 1985, with Japan following closely. Over the years, Russia emerged as a leading country for suicide rates, illustrating shifts in global patterns.

Section 2.3: Animated Line Plot

To observe how suicide rates have evolved over time in a specific country, we can create an animated line plot. Although Plotly Express doesn't directly support this type of plot, we can utilize the underlying graph objects to achieve it.

We'll first create a data frame for a specific country, such as the United Kingdom, and aggregate the suicide counts:

uk = df[df['country'] == 'United Kingdom']

uk.sort_values(by=['year', 'age'], inplace=True)

uk_agg = uk.groupby(['year']).agg({'suicides_no':'sum'}).reset_index()

The code for generating the animated line plot is as follows:

import plotly.graph_objects as go

frame_list = []

year_list = [1985]

count_list = [0]

for ind, df in uk_agg.iterrows():

year_list.append(df["year"])

count_list.append(df["suicides_no"])

frame_list.append(go.Frame(data=[go.Scatter(x=year_list, y=count_list, mode="lines")]))

fig1 = go.Figure(

data=[go.Scatter(x=[1985, 1985], y=[0, 0])],

layout=go.Layout(

xaxis=dict(range=[1985, 2015], autorange=False),

yaxis=dict(range=[0, 5500], autorange=False),

title="Suicide Counts in the UK",

updatemenus=[dict(

type="buttons",

buttons=[dict(label="Play",

method="animate",

args=[None])])]),

frames=frame_list)

fig1.show()

In this approach, we manually construct frames for each year, allowing us to create an intuitive visualization of suicide trends in the UK.

This concludes our exploration of animated visualizations using Plotly. I hope this guide has provided you with valuable insights into enhancing your data presentations. Thank you for reading, and keep learning!