data-journalism

name	data-journalism
description	Data journalism workflows for analysis, visualization, and storytelling. Use when analyzing datasets, creating charts and maps, cleaning messy data, calculating statistics or building data-driven stories. Essential for reporters, newsrooms and researchers working with quantitative information.

Data journalism methodology

Systematic approaches for finding, analyzing and presenting data in journalism.

Story structure for data journalism

Data journalism framework

The framework for data journalism was established by Philip Meyer, a journalist for Knight-Ridder, Harvard Nieman Fellow and professor at UNC-Chapel Hill. In his book <i>The New Precision Journalism</i>, which outlines his ideas, Meyer encourages journalists to treat journalism "as if it were a science" by adopting the scientific method:
- Making observation(s) / formulating a questiom
- Researching the question / Collect, store and retrieve data
- Formulate a hypothesis
- Test the hypothesis, using both qualitative (interviews, documents etc.) and quantitative (data analysis etc.) methods
- Analyze the results and reduce them to the most important findings
- Present them to the audience

This process should be thought of as iterative, rather than sequential.

## The data story arc

### 1. The hook (nut graf)
- What's the key finding(s)?
- Why should readers care?
- What's the human impact?

### 2. The evidence
- Show the data
- Explain the methodology
- Acknowledge limitations

### 3. The context
- How does this compare to past?
- How does this compare to elsewhere?
- What's the trend?

### 4. The human element
- Individual examples that illustrate the data
- Expert interpretation
- Affected voices

### 5. The implications
- What does this mean going forward?
- What questions remain?
- What actions could result?

### 6. The methodology box
- Where did data come from?
- How was it analyzed?
- What are the limitations?
- How can readers explore further?

Methodology documentation template

## How we did this analysis

### Data sources
[List all data sources with links and access dates]

### Time period
[Specify exactly what time period is covered]

### Definitions
[Define key terms and how you operationalized them]

### Analysis steps
1. [First step of analysis]
2. [Second step]
3. [Continue...]

### Limitations
- [Limitation 1]
- [Limitation 2]

### What we excluded and why
- [Excluded category]: [Reason]

### Verification
[How findings were verified/checked]

### Code and data availability
[Link to GitHub repo if sharing code/data]

### Contact
[How readers can reach you with questions]

Data acquisition

Public data sources

## Federal data sources

### General
- Data.gov - Federal open data portal
- Census Bureau (census.gov) - Demographics, economic data
- BLS (bls.gov) - Employment, inflation, wages
- BEA (bea.gov) - GDP, economic accounts
- Federal Reserve (federalreserve.gov) - Financial data
- SEC EDGAR - Corporate filings

### Specific domains
- EPA (epa.gov/data) - Environmental data
- FDA (fda.gov/data) - Drug approvals, recalls, adverse events
- CDC WONDER - Health statistics
- NHTSA - Vehicle safety data
- DOT - Transportation statistics
- FEC - Campaign finance
- USASpending.gov - Federal contracts and grants

### State and local
- State open data portals (search: "[state] open data")
- Socrata-powered sites (many cities/states)
- OpenStreets, municipal GIS portals
- State comptroller/auditor reports

Data request strategies

## Getting data that isn't public

### Public records request (ie. FOIA) for datasets
- Request databases, not just documents
- Ask for data dictionary/schema
- Request in native format (CSV, SQL dump)
- Specify field-level needs

### Building your own dataset
- Scraping public information
- Crowdsourcing from readers
- Systematic document review
- Surveys (with proper methodology)

### Commercial data sources (for newsrooms)
- LexisNexis
- Refinitiv
- Bloomberg
- Industry-specific databases

Data cleaning and preparation

Common data problems

from typing import Any

import pandas as pd
import numpy as np
from rapidfuzz import fuzz
from itertools import combinations

# Inflation adjustment
import cpi
import wbdata

def standardize_name(name: Any) -> str | None:
    """Standardize name format to 'First Last'."""
    if pd.isna(name):
        return None
    name = str(name).strip().upper()
    # Handle "LAST, FIRST" format
    if ',' in name:
        parts = name.split(',')
        name = f"{parts[1].strip()} {parts[0].strip()}"
    return name

def parse_date(date_str: Any) -> pd.Timestamp | None:
    """Parse dates in various formats."""
    if pd.isna(date_str):
        return None

    formats = [
        '%m/%d/%Y', '%Y-%m-%d', '%B %d, %Y',
        '%d-%b-%y', '%m-%d-%Y', '%Y/%m/%d'
    ]

     fmt  formats:
        :
             pd.to_datetime(date_str, =fmt)
        :
            

    
    :
         pd.to_datetime(date_str)
    :
         


 () -> pd.DataFrame:
     
     thresh  data_clean.isna().() >= thresh:
         df.dropna(subset=[required_col]).reset_index(drop=).copy()
    
     per_thresh  (data_clean.isna().() / (data_clean) * ) >= per_thresh:
         df.dropna(subset=[required_col]).reset_index(drop=).copy()
    
    :
         df


 ()
    
     thresh  df.duplicated().() >= thresh:
         df.drop_duplicates().reset_index(drop=).copy()
    :
         df


 () -> pd.DataFrame:
    
    
    names = df[name_col].dropna().unique()
    
    
    dup_names: [] = {
        name
         name1, name2  combinations(names, )
         fuzz.ratio((name1).lower(), (name2).lower()) >= threshold
         name  (name1, name2)
    }
    
    df[] = df[name_col].isin(dup_names)
     df


 () -> pd.Series:
    
     method == :
        Q1 = series.quantile()
        Q3 = series.quantile()
        IQR = Q3 - Q1
        lower = Q1 - threshold * IQR
        upper = Q3 + threshold * IQR
         (series < lower) | (series > upper)
     method == :
        z_scores = np.((series - series.mean()) / series.std())
         z_scores > threshold




data_clean = (pd

            
            
            .read_csv()

            
            
            .assign(
                    amount =  x: pd.to_numeric(x[], errors=),
                    
                    
                    status =  x: pd.to_Categorical(x[])) 
            
            .assign(
                    
                    
                    
                    name_clean =  x: standaridize_name(x[]),
                    
                    
                    
                    
                    parse_date =  x: parse_date(x[]),
                    
                    
                    
                    amount_outlier =  x: flag_outliers(x[]),
                    
                    )
            
            
            
            .pipe(find_similar_names, name_col=, threshold=)

            
            
            
            .pipe(handle_missing, thresh=, per_thresh=)

            
            .pipe(handle_duplicates, thresh=)
            
            .reset_index(drop=)
            .copy())

Data validation checklist

## Pre-analysis data validation

### Structural checks
- [ ] Row count matches expected
- [ ] Column count and names correct
- [ ] Data types appropriate
- [ ] No unexpected null columns

### Content checks
- [ ] Date ranges make sense
- [ ] Numeric values within expected bounds
- [ ] Categorical values match expected options
- [ ] Geographic data resolves correctly
- [ ] IDs are unique where expected

### Consistency checks
- [ ] Totals add up to expected values
- [ ] Cross-tabulations balance
- [ ] Related fields are consistent
- [ ] Time series is continuous

### Source verification
- [ ] Can trace back to original source
- [ ] Methodology documented
- [ ] Known limitations noted
- [ ] Update frequency understood

Statistical analysis for journalism

Basic statistics with context

# Essential statistics for any dataset
def describe_for_journalism(df: pd.DataFrame, col: str) -> pd.DataFrame:
    """Generate journalist-friendly statistics."""
    stats = df[col].describe(percentiles=[0.25, 0.5, 0.75, 0.9, 0.99])
    
    # Add skewness to the describe() output
    stats['skewness'] = df[col].skew()
    
    return stats.to_frame(name=col)

# Example interpretation
stats = describe_for_journalism(salaries, 'salary')

print(f"""
ANALYSIS
---------------
We analyzed {stats['count']:,} salary records.

The median salary is ${stats['median']:,.0f}, meaning half of workers
earn more and half earn less.

The average salary is ${stats['mean']:,.0f}, which is
{'higher' if stats['mean'] > stats['median'] else 'lower'} than the median,
indicating the distribution is {'right-skewed (pulled up by high earners)'
if stats['skewness'] > 0 else 'left-skewed'}.

The top 10% of earners make at least ${stats['90th_percentile']:,.0f}.
The top 1% make at least ${stats['99th_percentile']:,.0f}.
""")

Comparisons and context

# Calculate change metrics for a column
def calculate_change(df: pd.DataFrame, col: str, periods: int = 1) -> pd.DataFrame:
    """Add change metrics to a DataFrame using built-in pandas methods.
    
    Args:
        df: Input DataFrame
        col: Column to calculate changes for
        periods: Number of rows to look back (1=previous row, 12=year-over-year for monthly)
    """
    return df.assign(
        absolute_change=df[col].diff(periods),
        percent_change=df[col].pct_change(periods) * 100,
        direction=np.sign(df[col].diff(periods)).map({1: 'increased', -1: 'decreased', 0: 'unchanged'})
    )

# Usage:
# changes = data_clean.pipe(calculate_change, 'revenue', periods=12) # Year-over-year for monthly data

# Per capita calculations (essential for fair comparisons)
def per_capita(value: float, population: float, multiplier: int = 100000) -> float:
    """Calculate per capita rate."""
    return (value / population) * multiplier  # Per 100,000 is standard

# Example: Crime rates
city_a = {'crimes': 5000, 'population': 100000}
city_b = {'crimes': 8000, 'population': 500000}

rate_a = per_capita(city_a['crimes'], city_a['population'])
rate_b = per_capita(city_b[], city_b[])

()
()



 () ->  | pd.Series:
    
     country == :
        
         (from_year, pd.Series):
             pd.Series([cpi.inflate(amt, yr, to=to_year) 
                             amt, yr  (amount, from_year)], index=amount.index)
         cpi.inflate(amount, from_year, to=to_year)
    :
        
        cpi_data = wbdata.get_dataframe(
            {: }, 
            country=country
        )[].to_dict()
        
        from_cpi = pd.Series(from_year).(cpi_data)  (from_year, pd.Series)  cpi_data[from_year]
        to_cpi = cpi_data[to_year]
         amount * (to_cpi / from_cpi)

Correlation vs causation

## Reporting correlations responsibly

### What you CAN say
- "X and Y are correlated"
- "As X increases, Y tends to increase"
- "Areas with higher X also tend to have higher Y"
- "X is associated with Y"

### What you CANNOT say (without more evidence)
- "X causes Y"
- "X leads to Y"
- "Y happens because of X"

### Questions to ask before implying causation
1. Is there a plausible mechanism?
2. Does the timing make sense (cause before effect)?
3. Is there a dose-response relationship?
4. Has the finding been replicated?
5. Have confounding variables been controlled?
6. Are there alternative explanations?

### Red flags for spurious correlations
- Extremely high correlation (r > 0.95) with unrelated things
- No logical connection between variables
- Third variable could explain both
- Small sample size with high variance

Data visualization

Chart selection guide

## Choosing the right chart

### Comparison
- **Bar chart**: Compare categories
- **Grouped bar**: Compare categories across groups
- **Bullet chart**: Actual vs target

### Change over time
- **Line chart**: Trends over time
- **Area chart**: Cumulative totals over time
- **Slope chart**: Change between two points

### Distribution
- **Histogram**: Distribution of one variable
- **Box plot**: Compare distributions across groups
- **Violin plot**: Detailed distribution shape

### Relationship
- **Scatter plot**: Relationship between two variables
- **Bubble chart**: Three variables (x, y, size)
- **Connected scatter**: Change in relationship over time

### Composition
- **Pie chart**: Parts of a whole (almost never use, max 5 slices, prefer donut charts)
- **Donut chart**: Parts of a whole
- **Stacked bar**: Parts of whole across categories
- **Treemap**: Hierarchical composition

### Geographic
- **Choropleth**: Values by region (use normalized data!)
- **Dot map**: Individual locations
- **Proportional symbol**: Magnitude at locations

Exploratory interactive visualizations with Plotly Express

import plotly.express as px

# Set default template for all charts
px.defaults.template = 'simple_white'

def create_bar_chart(
    data: pd.DataFrame, 
    title: str, 
    source: str,
    desc: str  = '', 
    x_val: str, 
    y_val: str,
    x_lab: str | None,
    y_lab: str | None
) -> px.bar:
    """Create a bar chart."""
    
    fig = px.bar(
        data, 
        x=x_val, 
        y=y_val,
        text=desc,
        title=title,
        labels={'category': (x_lab if x_lab else x_val), 'value': (y_lab if y_lab else y_val)}
    )
    
    return fig

# Example
fig = create_bar_chart(
    data,
    title='Annual Widget Production',
    source='Department of Widgets, 2024',
    desc='The widget department increased its production dramatically starting in 2014.',
    x_val='year',
    y_val='widgets_prod',
    x_lab='Year',
    y_label='Units produced'
)

fig.show()  # Interactive display

Publication-ready automated data visualizations with Datawrapper

import pandas as pd
import datawrapper as dw

# Authentication: Set DATAWRAPPER_ACCESS_TOKEN environment variable, 
# or read from file and pass to create()
with open('datawrapper_api_key.txt', 'r') as f:
    api_key = f.read().strip()

# read in your data
data = pd.read_csv('../data/raw/data.csv')

# Create a bar chart using the new OOP API
chart = dw.BarChart(
    title='My Bar Chart Title',
    intro='Subtitle or description text',
    data=data,

    # Formatting options
    value_label_format=dw.NumberFormat.ONE_DECIMAL,
    show_value_labels=True,
    value_label_alignment='left',
    sort_bars=True,  # sort by value
    reverse_order=False,

    # Source attribution
    source_name='Your Data Source',
    source_url='https://example.com',
    byline='Your Name',

    # Optional: custom base color
    base_color='#1d81a2'
)

# Create and publish (uses DATAWRAPPER_ACCESS_TOKEN env var, or pass token)
chart.create(access_token=api_key)
chart.publish()

# Get chart URL and embed code
print(f"Chart ID: {chart.chart_id}")
print(f"Chart URL: https://datawrapper.dwcdn.net/{chart.chart_id}")
iframe_code = chart.get_iframe_code(responsive=True)

# Update existing chart with new data (for live-updating charts)
existing_chart = dw.get_chart('YOUR_CHART_ID')  # retrieve by ID
existing_chart.data = new_df  
existing_chart.title =   
existing_chart.update()  
existing_chart.publish()  


chart.export(filepath=, width=, height=)


chart

Avoiding misleading visualizations

## Chart integrity checklist

### Axes
- [ ] Y-axis starts at zero (for bar charts)
- [ ] Axis labels are clear
- [ ] Scale is appropriate (not truncated to exaggerate)
- [ ] Both axes labeled with units

### Data representation
- [ ] All data points visible
- [ ] Colors are distinguishable (including colorblind)
- [ ] Proportions are accurate
- [ ] 3D effects not distorting perception

### Context
- [ ] Title describes what's shown, not conclusion
- [ ] Time period clearly stated
- [ ] Source cited
- [ ] Sample size/methodology noted if relevant
- [ ] Uncertainty shown where appropriate

### Honesty
- [ ] Cherry-picking dates avoided
- [ ] Outliers explained, not hidden
- [ ] Dual axes justified (usually avoid)
- [ ] Annotations don't mislead

Working with geospatial data

Geocoding data

U.S. Census Geocoder

Best for: U.S. addresses only. Returns Census geography (tract, block, FIPS codes) along with coordinates—essential for joining with Census demographic data.

Pros: Completely free with no API key required. Returns Census geographies (state/county FIPS, tract, block) that let you join with ACS/decennial Census data. Good match rates for standard U.S. addresses.

Cons: Limited to 10,000 addresses per batch. U.S. addresses only. Slower than commercial alternatives. Lower match rates for non-standard addresses (PO boxes, rural routes, new construction).

Use when: You need to geocode nicely formatted U.S. addresses or you don't have budget for a paid service.

# pip install censusbatchgeocoder
import censusbatchgeocoder
import pandas as pd

# DataFrame must have columns: id, address, city, state, zipcode
# (state and zipcode are optional but improve match rates)

def census_geocode(
    df: pd.DataFrame,
    id_col: str = 'id',
    address_col: str = 'address',
    city_col: str = 'city',
    state_col: str = 'state',
    zipcode_col: str = 'zipcode',
    chunk_size: int = 9999
) -> pd.DataFrame:
    """
    Geocode a DataFrame using the U.S. Census batch geocoder.
    Automatically handles datasets larger than 10,000 rows by chunking.
    
    Returns DataFrame with: latitude, longitude, state_fips, county_fips, 
    tract, block, is_match, is_exact, returned_address, geocoded_address
    """
    # Rename columns to expected format
    col_map = {id_col: 'id', address_col: 'address', city_col: 'city'}
    if state_col and state_col in df.columns:
        col_map[state_col] = 'state'
    if zipcode_col and zipcode_col in df.columns:
        col_map[zipcode_col] = 'zipcode'
    
    renamed_df = df.rename(columns=col_map)
    records = renamed_df.to_dict('records')
    
    # Small dataset: geocode directly
    if len(records) <= chunk_size:
        results = censusbatchgeocoder.geocode(records)
        return pd.DataFrame(results)
    
    
    all_results = []
     i  (, (records), chunk_size):
        chunk = records[i:i + chunk_size]
        ()
        
        :
            results = censusbatchgeocoder.geocode(chunk)
            all_results.extend(results)
         Exception  e:
            ()
             record  chunk:
                all_results.append({**record, : , : , : })
    
     pd.DataFrame(all_results)


geocoded = (pd
              .read_csv()
              .assign(= x: x.index)
              .pipe(census_geocode, 
                    id_col=, 
                    address_col=, 
                    city_col=.
                    state_col=,
                    zipcode_col=))

Google Maps Geocoder

Best for: International addresses, high match rates, and messy/non-standard address formats.

Pros: Excellent match rates even for poorly formatted addresses. Works worldwide. Fast and reliable. Returns rich metadata (place types, address components, place IDs).

Cons: Costs money ($5 per 1,000 requests after free tier). Requires API key and billing account. Does not return Census geography—you'd need to do a separate spatial join.

Use when: You need to geocode international addresses, have messy address data that the Census geocoder can't match, or need the highest possible match rate and have budget for it.

import googlemaps
from typing import Optional

def geocode_address_google(address: str, api_key: str) -> Optional[dict]:
    """
    Geocode address using Google Maps API.
    Requires API key with Geocoding API enabled.
    """
    gmaps = googlemaps.Client(key=api_key)
    result = gmaps.geocode(address)
    
    if result:
        location = result[0]['geometry']['location']
        return {
            'formatted_address': result[0]['formatted_address'],
            'lat': location['lat'],
            'lon': location['lng'],
            'place_id': result[0]['place_id']
        }
    return None

# Batch geocode a DataFrame
def batch_geocode(df: pd.DataFrame, address_col: str, api_key: str) -> pd.DataFrame:
    gmaps = googlemaps.Client(key=api_key)
    
    results = []
    for address in df[address_col]:
        try:
            result = gmaps.geocode(address)
            if result:
                loc = result[0]['geometry']['location']
                results.append({'lat': loc['lat'], 'lon': loc[]})
            :
                results.append({: , : })
         Exception:
            results.append({: , : })
    
     pd.concat([df, pd.DataFrame(results)], axis=)

Geopandas

import geopandas as gpd
import pandas as pd
from shapely.geometry import Point

# Read data from various formats
gdf = gpd.read_file('data.geojson')                    # GeoJSON
gdf = gpd.read_file('data.shp')                         # Shapefile
gdf = gpd.read_file('https://example.com/data.geojson') # From URL
gdf = gpd.read_parquet('data.parquet')                  # GeoParquet (fast!)

# Transform DataFrame with lat/lon to GeoDataFrame
df = pd.read_csv('locations.csv')
geometry = [Point(xy) for xy in zip(df['longitude'], df['latitude'])]
gdf = gpd.GeoDataFrame(df, geometry=geometry)

# Set CRS (Coordinate Reference System)
# EPSG:4326 = WGS84 (standard latitude, longitude)
gdf = gdf.set_crs('EPSG:4326')

# Transform to different CRS (for area/distance calculations, use projected CRS)
gdf_projected = gdf.to_crs('EPSG:3857')  # Web Mercator, for distance in meters

# Basic spatial operations

#Find the area of a shape
gdf['area'] = gdf_projected.geometry.area 

#Find the center of a shape
gdf['centroid'] = gdf.geometry.centroid

#Draw a 1km boundary around a point
gdf['buffer_1km'] = gdf_projected.geometry.buffer(1000) #when set to CRS 3857

# Spatial join: find points within polygons
points = gpd.read_file('points.geojson')
polygons = gpd.read_file('boundaries.geojson')
joined = gpd.sjoin(points, polygons, predicate=)


dissolved = gdf.dissolve(by=, aggfunc=)


gdf.to_parquet()          
gdf.to_file(, driver=) 

Geo-Visualization with .explore(), lonboard and Datawrapper

.explore()

Best for: Quick exploration and prototyping during data analysis.

Pros: Built into GeoPandas—method is available on any GeoDataFrame. Great for exploratory data analysis—checking that your data looks right, exploring spatial patterns, and iterating quickly on map designs.

Cons: Becomes slow with large datasets (>100k features). Limited customization compared to dedicated mapping libraries. Requires extra dependencies to be installed.

Use when: You're in the middle of analysis and want to quickly visualize your GeoDataFrame without switching tools.

Required dependencies:

pip install folium mapclassify matplotlib

• folium - Required for .explore() to work at all (renders the interactive map)
• mapclassify - Required when using scheme= parameter for classification (e.g., 'naturalbreaks', 'quantiles', 'equalinterval')
• matplotlib - Required for colormap (cmap=) support

import geopandas as gpd
# folium, mapclassify, and matplotlib must be installed but don't need to be imported
# geopandas imports them automatically when you call .explore()

# Basic interactive map (uses folium under the hood)
gdf.explore()

# Choropleth map with customization
# (requires mapclassify for scheme parameter)
gdf.explore(
    column='population',           # Column for color scale
    cmap='YlOrRd',                 # Matplotlib colormap
    scheme='naturalbreaks',        # Classification scheme (needs mapclassify)
    k=5,                           # Number of bins
    legend=True,
    tooltip=['name', 'population'],  # Columns to show on hover
    popup=True,                    # Show all columns on click
    tiles='CartoDB positron',      # Background tiles
    style_kwds={'color': 'black', 'weight': 0.5}  # Border style
)

lonboard

Best for: Large datasets and high-performance visualization in Jupyter notebooks.

Pros: GPU-accelerated rendering via deck.gl can handle millions of points smoothly. Excellent interactivity—pan, zoom, and hover work fluidly even with massive datasets. Native support for GeoArrow format for efficient data transfer.

Cons: Requires separate installation (pip install lonboard). Styling options are more technical (RGBA arrays, deck.gl conventions).

Use when: You have large point datasets (crime incidents, sensor readings, business locations) or need smooth interactivity with 100k+ features.

import geopandas as gpd
from lonboard import viz, Map, ScatterplotLayer, PolygonLayer

# Quick visualization (auto-detects geometry type)
viz(gdf)

# Custom ScatterplotLayer for points
layer = ScatterplotLayer.from_geopandas(
    gdf,
    get_radius=100,
    get_fill_color=[255, 0, 0, 200],  # RGBA
    pickable=True
)
m = Map(layer)
m

# PolygonLayer with color based on column
from lonboard.colormap import apply_continuous_cmap
import matplotlib.pyplot as plt

colors = apply_continuous_cmap(gdf['value'], plt.cm.viridis)
layer = PolygonLayer.from_geopandas(
    gdf,
    get_fill_color=colors,
    get_line_color=[0, 0, 0, 100],
    pickable=True
)
Map(layer)

Datawrapper

Best for: Publication-ready choropleth and proportional symbol maps for articles and reports.

Pros: Beautiful, professional defaults out of the box. Generates embeddable, responsive iframes that work in any CMS. Readers can interact (hover, click) without running any code. Accessible and mobile-friendly. Easy to update data programmatically for updating data.

Cons: Requires a Datawrapper account (free tier available). Limited to Datawrapper's supported boundary files—you can't bring arbitrary geometries. Less flexibility for custom visualizations.

Use when: You need a polished map for publication. Ideal for choropleth maps showing statistics by region (unemployment by state, COVID cases by county, election results by district). Your audience will view the map in a browser, not a notebook.

Unlike .explore() or lonboard, you don't pass raw geometry—instead you match your data to Datawrapper's built-in boundary files using standard codes (FIPS, ISO, etc.).

import datawrapper as dw
import pandas as pd

# Read API key
with open('datawrapper_api_key.txt', 'r') as f:
    api_key = f.read().strip()

# Prepare data with location codes that match Datawrapper's boundaries
# For US states: use 2-letter abbreviations or FIPS codes
# For countries: use ISO 3166-1 alpha-2 codes
df = pd.DataFrame({
    'state': ['AL', 'AK', 'AZ', 'AR', 'CA'],  # State abbreviations
    'unemployment_rate': [4.9, 3.2, 7.1, 4.2, 5.8]
})

# Create a choropleth map
chart = dw.ChoroplethMap(
    title='Unemployment Rate by State',
    intro='Percentage of labor force unemployed, 2024',
    data=df,

    # Map configuration
    basemap='us-states',           # Built-in US states boundaries
    basemap_key='state',           # Column in your data with location codes
    value_column='unemployment_rate',

    # Styling
    color_palette='YlOrRd',        # Color scheme
    legend_title='Unemployment %',

    # Attribution
    source_name='Bureau of Labor Statistics',
    source_url='https://www.bls.gov/',
    byline='Your Name'
)

# Create and publish
chart.create(access_token=api_key)
chart.publish()


iframe = chart.get_iframe_code(responsive=)
()


new_df = pd.DataFrame({...})  
existing_chart = dw.get_chart()
existing_chart.data = new_df
existing_chart.update()
existing_chart.publish()

Available Datawrapper basemaps include:

• us-states, us-counties, us-congressional-districts
• world, europe, africa, asia
• Country-specific maps (e.g., germany-states, uk-constituencies)

Learning resources

• NICAR (Investigative Reporters & Editors)
• Knight Center for Journalism in the Americas
• Data Journalism Handbook (datajournalism.com)
• Flowing Data (flowingdata.com)
• The Pudding (pudding.cool) - examples
• Sigma Awards (https://www.sigmaawards.org/) - examples