Creating Stunning Charts with Datawrapper and Python

Figure 1: Datawrapper Charts Catalogue

Key Features of Datawrapper

• Easy-to-use interface

Datawrapper’s intuitive design makes it accessible to users of all skill levels. Even those without a strong background in data visualization can create professional-looking charts and maps.

• Diverse chart types

From simple bar charts and line graphs to more complex maps and scatter plots, Datawrapper offers a variety of chart types to suit different data sets and storytelling needs.

• Customization

Users can customize their visualizations to match their brand or personal style. This includes options for changing colors, fonts, and layouts.

• Interactivity

Datawrapper allows users to create interactive visualizations that respond to user input. For example, users can hover over data points to see more detailed information or filter data based on specific criteria.

• Embedding and sharing

Once a visualization is created, it can be easily embedded into websites, blogs, or social media posts. Users can also share their visualizations directly with others.

• Collaboration

Datawrapper supports collaboration, allowing multiple users to work on the same visualization simultaneously. This is particularly useful for teams or organizations that need to create data visualizations together.

Use Cases for Datawrapper

• Journalism

Datawrapper is a popular tool among journalists who want to present complex data in a clear and visually engaging way. It can be used to create interactive charts, maps, and infographics that enhance storytelling.

• Research

Researchers can use Datawrapper to visualize their findings and make them more accessible to a wider audience. By presenting data in a visual format, researchers can communicate their ideas more effectively and increase the impact of their work.

• Business

Businesses can use Datawrapper to create dashboards, reports, and presentations that help them make data-driven decisions. By visualizing key metrics and trends, businesses can gain valuable insights into their performance.

• Education

Teachers and students can use Datawrapper to create interactive visualizations that help them understand and learn from data. It can be used to teach data analysis, statistics, and other subjects.

Visualization Types

Collection of available Datawrapper chart types and their IDs

Table 1: Datawrapper Chart Types

Chart	TypeID
Bar Chart	d3-bars
Split Bars	d3-bars-split
Stacked Bars	d3-bars-stacked
Bullet Bars	d3-bars-bullet
Dot Plot	d3-dot-plot
Range Plot	d3-range-plot
Arrow Plot	d3-arrow-plot
Column Chart	column-chart
Grouped Column Chart	grouped-column-chart
Stacked Column Chart	stacked-column-chart
Area Chart	d3-area
Line Chart	d3-lines
Multiple Lines Chart	multiple-lines
Pie Chart	d3-pies
Donut Chart	d3-donuts
Multiple Pies	d3-multiple-pies
Multiple Donuts	d3-multiple-donuts
Scatter Plot	d3-scatter-plot
Election Donut	election-donut-chart
Table	tables
Choropleth Map	d3-maps-choropleth
Symbol Map	d3-maps-symbols
Locator Map	locator-map

More information

Environment settings

# Import libraries
from datawrapper import Datawrapper
import numpy as np
import pandas as pd
import polars as pl
import json
import warnings
warnings.filterwarnings('ignore')

# Token gotten from datawrapper
filename = 'credentials.json'
# read json file
with open(filename) as f:
    keys = json.load(f)
# read credentials
token = keys['datawrapper_api']

# Access datawrapper api
dw = Datawrapper(access_token = token)

Datawrapper account

# Check your account details
dw.get_my_account()

Dataset

# Read dataset courtesy of Sergio Sanchez
df = pd.read_csv(
    'https://raw.githubusercontent.com/chekos/datasets/master/data/datawrapper_example.csv',
    sep=";",
)

# Select columns
df.columns = ['country','%pop in the capital','%pop in urban areas','%pop in rural areas']

# Show dataframe
df

	country	%pop in the capital	%pop in urban areas	%pop in rural areas
0	Iceland (Reykjavík)	56.02	38.0	6.0
1	Argentina (Buenos Aires)	34.95	56.6	8.4
2	Japan (Tokyo)	29.52	63.5	7.0
3	UK (London)	22.70	59.6	17.7
4	Denmark (Copenhagen)	22.16	65.3	12.5
5	France (Paris)	16.77	62.5	20.7
6	Russia (Moscow)	8.39	65.5	26.1
7	Niger (Niamey)	5.53	12.9	81.5
8	Germany (Berlin)	4.35	70.7	24.9
9	India (Delhi)	1.93	30.4	67.6
10	USA (Washington, D.C.)	1.54	79.9	18.6
11	China (Beijing)	1.40	53.0	45.6

Create stackbar chart

# Create Datawrapper bar chart
pop = dw.create_chart(
    title='Where do people live?', chart_type='d3-bars-stacked', data=df
)

Update chart description

dw.update_description(
    pop['id'],
    source_name = 'UN Population Division',
    source_url = 'https://population.un.org/wup/',
    byline = 'Jesus L. Monroy<br>Economist & Data Scientist<br><br>',
    intro = 'Population percentage living in the capital by Country'
)

Publish chart

dw.publish_chart(chart_id = pop['id'])

Customize metadata

dw.update_chart(
    chart_id = pop['id'],
    metadata = {
        'visualize': {
            'sharing': {'enabled': True},
            'thick': True,
            'custom-colors': {
                '%pop in rural areas': '#dadada',
                '%pop in urban areas': '#1d81a2',
                '%pop in the capital': '#15607a',
               },
           },
        'publish': {
            'blocks': {'get-the-data': False},
           },
    }
)

Republish chart

dw.publish_chart(pop['id'])

Display interactive chart

Figure 2: Datawrapper Interactive Chart

NoteNote

You can hover over the bars on the above chart to get dynamic results.

Conclusions

To start using Datawrapper, simply create a free account on their website. Once you’re logged in, you can begin creating your first visualization.

Datawrapper offers a variety of tutorials and resources to help you get started and make the most of the platform.

By leveraging the power of Datawrapper, you can create compelling data visualizations that help you tell your story more effectively.

Whether you’re a seasoned data analyst or just starting out, Datawrapper is a valuable tool that can help you bring your data to life.

References

• Datawrapper (2024) Barcharts in Datawrapper Academy
• Khanchandani E. (2020) How to use Datawrapper for journalists in Interhacktives
• Sanchez, S. (2023) A lightweight Python wrapper for the Datawrapper API in Datawrapper API

Contact

Jesus LM
Economist & Data Scientist

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