10. Making Area Charts#

An area chart is a type of graph that visually represents quantitative data over time or across categories, using filled areas below a line. It’s similar to a line chart, but the area between the line and the x-axis is shaded, emphasizing the magnitude of the data points. This shaded area helps to highlight the volume or cumulative totals of the dataset. Area charts are particularly useful for displaying trends, comparing multiple data series, or showing how a part contributes to a whole over time. They can also be stacked to display multiple datasets, making it easier to compare several categories simultaneously.

🚀 When to use them:

Area charts are useful when you want to show how different segments contribute to a total and how this total changes over time. For example, they are commonly used in financial reports to track revenue sources or in scientific studies to display changes in variables like temperature or population.

⚠️ Be aware:

However, area charts are not always the best choice, especially when precise comparisons between data points are needed. The stacked areas can become hard to differentiate, making it difficult to discern small differences between data series. Additionally, when too many layers or data series are included, the chart can become cluttered, reducing clarity. This can lead to visual distortion, especially when overlapping areas obscure smaller trends or patterns.

Getting ready#

import pandas as pd

data = pd.read_csv('data/data_recipe10.csv')

data.columns = ['Entity', 'Code', 'Year', 'Renewable', 'Nuclear', 'Fossil']

data = data[data.Entity == 'World'].reset_index(drop=True)

data1 = data.melt(id_vars=['Year', 'Entity', 'Code'])

data1.head(3)
Year Entity Code variable value
0 1985 World OWID_WRL Renewable 2058.0222
1 1986 World OWID_WRL Renewable 2091.6704
2 1987 World OWID_WRL Renewable 2124.9146 
data2 = data
data2['year'] = pd.to_datetime(data2['Year'], format='%Y')

data2.head(3)
Entity Code Year Renewable Nuclear Fossil year
0 World OWID_WRL 1985 2058.0222 1488.9216 6285.4736 1985-01-01
1 World OWID_WRL 1986 2091.6704 1594.7357 6439.9746 1986-01-01
2 World OWID_WRL 1987 2124.9146 1734.7332 6757.7617 1987-01-01

How to do it#

  1. Import the plotly.express module as px

import plotly.express as px

df = data1

  1. Make a minimal area chart by using the function area and passing the inputs

  • x

  • y

  • color

fig = px.area(df, x='Year', y='value', color='variable')
fig.show()

  1. Add a title to your chart by passing a string as the input title into the function bar; and customise the size of the figure by using the inputs height and width. Both have to be integers and correspond to the size of the figure in pixels.

fig = px.area(df, x='Year', y='value', color='variable',
              height = 500, width = 800,
             title = 'Electricity production from fossil fuels, nuclear and renewables, World')
fig.show()

  1. Customise the colors used in the scatter by using the input color_discrete_sequence

fig = px.area(df, x='Year', y='value', color='variable',
              height = 500, width = 800,
              color_discrete_sequence= px.colors.qualitative.Prism,
              title = 'Electricity production from fossil fuels, nuclear and renewables, World')
fig.show()

fig = px.area(df, x='Year', y='value', color='variable',
              height = 500, width = 800,
              color_discrete_sequence=['rgb(120,0,100)', 'rgb(30,0,100)','rgb(0,0,0)'],
              title = 'Electricity production from fossil fuels, nuclear and renewables, World')
fig.show()

There is more…#

import numpy as np

# new data set for animation
start=10
stop = len(data2) 
step = 1
df_animation = pd.DataFrame() # container for df with new data set
for i in np.arange(start, stop, step):
    dfa = data2.head(i).copy()
    frame = dfa['Year'].values[-1]
    dfa['frame']=frame
    df_animation = pd.concat([df_animation, dfa])

df_animation.head(3)
Entity Code Year Renewable Nuclear Fossil year frame
0 World OWID_WRL 1985 2058.0222 1488.9216 6285.4736 1985-01-01 1994
1 World OWID_WRL 1986 2091.6704 1594.7357 6439.9746 1986-01-01 1994
2 World OWID_WRL 1987 2124.9146 1734.7332 6757.7617 1987-01-01 1994 
fig = px.area(df_animation, x="year", y=[ 'Renewable'],
              height = 500, width = 800,
              animation_frame="frame",
              color_discrete_sequence=['rgb(120,0,100)', 'rgb(30,0,100)','rgb(0,0,0)'],
              title = 'Electricity production from fossil fuels, nuclear and renewables, World')

fig.layout.updatemenus[0].buttons[0]['args'][1]['frame']['redraw'] = True
fig.show()

fig = px.area(df_animation, x="year", y=[ 'Renewable', 'Nuclear', 'Fossil'],
              height = 500, width = 800,
              animation_frame="frame",
              color_discrete_sequence=['rgb(120,0,100)', 'rgb(30,0,100)','rgb(0,0,0)'],
              title = 'Electricity production from fossil fuels, nuclear and renewables, World')

fig.layout.updatemenus[0].buttons[0]['args'][1]['frame']['redraw'] = True
fig.show()

Current Animation Limitations and Caveats Animations are designed to work well when each row of input is present across all animation frames, and when categorical values mapped to symbol, color and facet are constant across frames. Animations may be misleading or inconsistent if these constraints are not met. Although Plotly Express supports animation for many chart and map types, smooth inter-frame transitions are today only possible for scatter and bar Plotly Express will not automatically compute the union of all x/y/color ranges, so these must be specified manually to avoid scale jumps across frames

fig = px.line(df_animation, x="year", y=[ 'Renewable', 'Nuclear', 'Fossil',  ],
              height = 500, width = 800,
              animation_frame="frame",
              title = 'Electricity production from fossil fuels, nuclear and renewables, World')

fig.layout.updatemenus[0].buttons[0]['args'][1]['frame']['redraw'] = True
fig.show()