Additional Resources and Tutorials

• 1: Tutorials
• 1.1: Python Tutorial 1: Basic Operations and Plotting
• 1.2: Python Tutorial 2: Making Maps
• 2: Additional Resources
• 3: FAQ

1 - Tutorials

1.1 - Python Tutorial 1: Basic Operations and Plotting

This tutorial shows basic examples on how to load, handle, and plot the EM-DAT data using the pandas Python data analysis package and the matplotlib charting library.

Note: The Jupyter Notebook version of this tutorial is available on the EM-DAT Python Tutorials GitHub Repository.

Import Modules

Let us import the necessary modules and print their versions. For this tutorial, we used pandas v.2.1.1 and matplotlib v.3.8.3. If your package versions are different, you may have to adapt this tutorial by checking the corresponding package documentation.

import pandas as pd #data analysis package
import matplotlib as mpl
import matplotlib.pyplot as plt #plotting library
for i in [pd, mpl]:
    print(i.__name__, i.__version__)

pandas 2.1.1
matplotlib 3.8.3

Load EM-DAT

To load EM-DAT:

• Download the EM-DAT data at https://public.emdat.be/ (registration is required, see the EM-DAT Documentation page on Data Accessibility);
• Use the pd.read_excel method to load and parse the data into a pd.DataFrame object;
• Check if the data has been successfully parsed with the pd.DataFrame.info method.

Notes:

1. You may need to install the openpyxl package or another engine to make it possible to read the data.
2. Another option is to export the .xlsx file into a .csv, and use the pd.read_csv method;
3. If not in the same folder as the Python code, replace the filename with the relative path or the full path, e.g., E:/MyDATa/public_emdat_2024-01-08.xlsx

#!pip install openpyxl
df = pd.read_excel('public_emdat_2024-01-08.xlsx') # <-- modify file name or path
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15560 entries, 0 to 15559
Data columns (total 46 columns):
 #   Column                                     Non-Null Count  Dtype  
---  ------                                     --------------  -----  
 0   DisNo.                                     15560 non-null  object 
 1   Historic                                   15560 non-null  object 
 2   Classification Key                         15560 non-null  object 
 3   Disaster Group                             15560 non-null  object 
 4   Disaster Subgroup                          15560 non-null  object 
 5   Disaster Type                              15560 non-null  object 
 6   Disaster Subtype                           15560 non-null  object 
 7   External IDs                               2371 non-null   object 
 8   Event Name                                 4904 non-null   object 
 9   ISO                                        15560 non-null  object 
 10  Country                                    15560 non-null  object 
 11  Subregion                                  15560 non-null  object 
 12  Region                                     15560 non-null  object 
 13  Location                                   14932 non-null  object 
 14  Origin                                     3864 non-null   object 
 15  Associated Types                           3192 non-null   object 
 16  OFDA Response                              15560 non-null  object 
 17  Appeal                                     15560 non-null  object 
 18  Declaration                                15560 non-null  object 
 19  AID Contribution ('000 US$)                490 non-null    float64
 20  Magnitude                                  3356 non-null   float64
 21  Magnitude Scale                            9723 non-null   object 
 22  Latitude                                   1809 non-null   float64
 23  Longitude                                  1809 non-null   float64
 24  River Basin                                1197 non-null   object 
 25  Start Year                                 15560 non-null  int64  
 26  Start Month                                15491 non-null  float64
 27  Start Day                                  14068 non-null  float64
 28  End Year                                   15560 non-null  int64  
 29  End Month                                  15401 non-null  float64
 30  End Day                                    14132 non-null  float64
 31  Total Deaths                               12485 non-null  float64
 32  No. Injured                                5694 non-null   float64
 33  No. Affected                               7046 non-null   float64
 34  No. Homeless                               1312 non-null   float64
 35  Total Affected                             11508 non-null  float64
 36  Reconstruction Costs ('000 US$)            33 non-null     float64
 37  Reconstruction Costs, Adjusted ('000 US$)  29 non-null     float64
 38  Insured Damage ('000 US$)                  691 non-null    float64
 39  Insured Damage, Adjusted ('000 US$)        683 non-null    float64
 40  Total Damage ('000 US$)                    3070 non-null   float64
 41  Total Damage, Adjusted ('000 US$)          3020 non-null   float64
 42  CPI                                        15056 non-null  float64
 43  Admin Units                                8336 non-null   object 
 44  Entry Date                                 15560 non-null  object 
 45  Last Update                                15560 non-null  object 
dtypes: float64(20), int64(2), object(24)
memory usage: 5.5+ MB

Example 1: Japan Earthquake Data

Filtering

Let us focus on the EM-DAT earthquakes in Japan from the years 2000 to 2003 and create a suitable filter utilizing the EM-DAT columns Disaster Type, ISO and Start Year.

For simplicity, let’s retain only the columns Start Year, Magnitude, and Total Deaths and display the first five entries using the pd.DataFrame.head method.

Note: For further details about the columns, we refer to the EM-DAT Documentation page EM-DAT Public Table.

eq_jpn = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['ISO'] == 'JPN') &
    (df['Start Year'] < 2024)
][['Start Year', 'Magnitude', 'Total Deaths', 'Total Affected']]
eq_jpn.head(5)

Grouping

Let us group the data to calculate the number of earthquake events by year and plot the results.

• Use the groupby method to group based on one or more columns in a DataFrame, e.g., Start Year;
• Use the size method as an aggregation method (or count).
• Plot the results using the pd.DataFrame.plot method.

Note: The count method provides the total number of non-missing values, while size gives the total number of elements (including missing values). Since the field Start Year is always defined, both methods should return the same results.

eq_jpn.groupby(['Start Year']).size().plot(kind='bar', ylabel='Count')

<Axes: xlabel='Start Year', ylabel='Count'>

Customize Chart

The pandas library relies on the matplotlib package to draw charts. To have more flexibility on the rendered chart, let us create the figure using the imported plt submodule.

# Group earthquake data by 'Start Year' and count occurrences
eq_cnt = eq_jpn.groupby(['Start Year']).size()

# Initialize plot with specified figure size
fig, ax = plt.subplots(figsize=(7, 2))

# Plot number of earthquakes per year
ax.bar(eq_cnt.index, eq_cnt)

# Set axis labels and title
ax.set_xlabel('Year')
ax.set_ylabel('N° of Earthquakes')
ax.set_yticks([0, 1, 2, 3])  # Define y-axis tick marks
ax.set_title('EM-DAT Earthquakes in Japan (2000-2023)')

Text(0.5, 1.0, 'EM-DAT Earthquake in Japan (2000-2023)')

Example 2: Comparing Regions

Let us compare earthquake death toll by continents. As before, we filter the original dataframe df according to our specific needs, including the Region column.

eq_all = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
][['Start Year', 'Magnitude', 'Region', 'Total Deaths', 'Total Affected']]
eq_all.head(5)

In this case,

• Use the groupby method to group based on the Region column;
• Use the sum method for the Total Deaths field as aggregation method;
• Plot the results easilly using the pd.DataFrame.plot method.

eq_sum = eq_all.groupby(['Region'])['Total Deaths'].sum()
eq_sum

Region
Africa        5863.0
Americas    229069.0
Asia        548766.0
Europe         783.0
Oceania        641.0
Name: Total Deaths, dtype: float64

Finally, let us make an horizontal bar chart of it using matplotlib. In particular,

• use the ax.ticklabel_format method to set the x axis label as scientific (in thousands of deaths);
• use the ax.invert_yaxis to display the regions in alphabetical order from top to bottom.

fig, ax = plt.subplots(figsize=(4,3))
ax.barh(eq_sum.index, eq_sum)
ax.set_xlabel('Total Earthquake Deaths')
ax.ticklabel_format(style='sci',scilimits=(3,3),axis='x')
ax.invert_yaxis()
ax.set_title('EM-DAT Earthquake Deaths by Regions')

Text(0.5, 1.0, 'EM-DAT Earthquake Deaths by Regions')

Example 3: Multiple Grouping

At last, let us report the earthquake time series by continents. To avoid the creation of a ['Region', 'Start Year'] multiindex for future processing, we set the argument as_index to False. As such, Region and Start Year remain columns.

eq_reg_ts = eq_all.groupby(
    ['Region', 'Start Year'], as_index=False
)['Total Deaths'].sum()
eq_reg_ts

97 rows × 3 columns

Next, we apply the pivot method to restructure the table in a way it could be plot easilly.

eq_pivot_ts = eq_reg_ts.pivot(
    index='Start Year', columns='Region', values='Total Deaths'
)
eq_pivot_ts.head()

ax = eq_pivot_ts.plot(kind='bar', width=1, figsize=(6,3))
ax.set_ylabel('Total Deaths')
ax.set_title('EM-DAT Earthquake Deaths by Regions')

Text(0.5, 1.0, 'EM-DAT Earthquake Deaths by Regions')

In order to be able to visualize the data in more details, let us make a subplot instead by setting the subplot argument to True within the plot method.

ax = eq_pivot_ts.plot(kind='bar', subplots=True, legend=False, figsize=(6,6))
plt.tight_layout() # <-- adjust plot layout

We have just covered the most common manipulations applied to a pandas DataFrame containing the EM-DAT data. To delve further into your analyses, we encourage you to continue your learning of pandas and matplotlib with the many resources available online, starting with the official documentation.

If you are interested in learning the basics of making maps based on EM-DAT data, you can also follow the second EM-DAT Python Tutorial.

1.2 - Python Tutorial 2: Making Maps

If you have followed the first EM-DAT Python Tutorial 1 or are already familiar with pandas and matplotlib, this second tutorial will show you basic examples on how to make maps with the EM-DAT data using the geopandas Python package.

Note: The Jupyter Notebook version of this tutorial is available on the EM-DAT Python Tutorials GitHub Repository.

Import Modules

Let us import the necessary modules and print their versions. For this tutorial, we used pandas v.2.1.1, geopandas v.0.14.3, and matplotlib v.3.8.3. If your package versions are different, you may have to adapt this tutorial by checking the corresponding package documentation.

import pandas as pd #data analysis package
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt #plotting library
for i in [pd, gpd, mpl]:
    print(i.__name__, i.__version__)

pandas 2.2.1
geopandas 0.14.3
matplotlib 3.8.3

Creating a World Map

To create a world map, we need the EM-DAT data and a shapefile containing the country geometries.

EM-DAT: We download and load the EM-DAT data using pandas.

Country Shapefile: We download a country shapefile from Natural Earth Data. For a world map, we download the low resolution 1:110m Adimin 0 - Countries (last accessed: March 10, 2024) and unzip it.

Load and Filter EM-DAT

Let us load EM-DAT and filter it to make a global map of Earthquake disasters between 2000 and 2023. We calculate the number of unique identifiers (DisNo.) per country (ISO) We refer to the standard ISO column instead of the Country column of the EM-DAT Public Table to be able to make a join with the country shapefile.

df = pd.read_excel('public_emdat_2024-01-08.xlsx')
earthquake_counts = df[
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
].groupby('ISO')["DisNo."].count().reset_index(name='EarthquakeCount')
earthquake_counts

91 rows × 2 columns

Load the Country Shapefile

We use the gpd.read_file method to load the country shapefile and parse it into a geodataframe. A geodataframe is similar to a pandas dataframe, extept that has a geometry column.

We provide the filename argument, which is either a file name if located in the same directory than the running script, or a relative or absolute path, if not. In our case the shapefile with the .shp extension is located in the ne_110m_admin_0_countries folder.

Since the geodataframe contains 169 columns, we only keep the two column that we are interrested in, i.e., ISO_A3 and geometry.

gdf = gpd.read_file ('ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp') # <-- Change path if necessary
gdf = gdf[['ISO_A3', 'geometry']]
gdf

Cannot find header.dxf (GDAL_DATA is not defined)

177 rows × 2 columns

Important Notice: Above, some geometries do not have a ISO code, such as the one at row 174. Below, you will see that some ISO in EM-DAT are not matched with a geometries. Beyond this basic tutorial, we advice to carefully evaluate these correspondance and non-correspondance between ISO codes and to read the EM-DAT Documentation about ISO codes.

Join the Two Datasets

Let us merge the two dataset with an outer join, using the merge method. We prefer an outer join to keep the geometries of countries for which EM-DAT has no records.

earthquake_counts_with_geom = gdf.merge(
    earthquake_counts, left_on='ISO_A3', right_on='ISO', how='outer')
earthquake_counts_with_geom

190 rows × 4 columns

Make the Map

To make the map, we use the geopandas built-in API, through the plot method built on the top of matplotlib. Below, we show an hybrid plotting approach and first create an empty figure fig and ax object with matplotlib before passing the ax object as an argument within the plot method. This approach gives more control to users familiar with matplotlib to further customize the chart.

fig, ax = plt.subplots(figsize=(8,3))
earthquake_counts_with_geom.plot(
    column='EarthquakeCount',
    ax=ax,
    cmap='Reds',
    vmin=1,
    legend=True,
    legend_kwds={"label": "Nb of EM-DAT Earthquake"},
    missing_kwds= dict(color = "lightgrey",)
)
_ = ax.set_xlabel('Lon.')
_ = ax.set_ylabel('Lat.')

Creating a Map at Admin Level 1

We can create a more detailed map using the Admin Units column in the EM-DAT Public Table. This column contains the identifiers of administrative units of level 1or 2 as defined by the Global Administrative Unit Layer (GAUL) for country impacted by non-biological natural hazards.

Similarly to the country map, we need to download a file containing GAUL geometries. The file corresponds to the last version of GAUL published in 2015. In this tutorial, we will focus on Japanese earthquake occurrence in EM-DAT.

Note: the file size is above 1.3Go and requires a performant computer to process in Python. Using a Geographical Information Software (GIS) for the preprocessing is another option.

Load the Admin Units Geopackage

The file is a geopackage .gpkg that contains multiple layers. Let us first describe these layers with the fiona package, which is a geopandas dependency.

import fiona
print(fiona.__name__, fiona.__version__)
for layername in fiona.listlayers('gaul2014_2015.gpkg'):
    with fiona.open('gaul2014_2015.gpkg', layer=layername) as src:
        print(layername, len(src))

fiona 1.9.5
level2 38258
level1 3422
level0 277

• The level0 layer contains the country geometries defined in GAUL.
• Here, we make a map at the level1.
• Still, we need to load the administrative level2 because the Admin Units column may refer to Admin 2 levels without mentionning the corresponding Admin 1 level.
• Given the high size the admin 2 layer, we filter the data about Japan and overwrite our geodataframe variable to save memory.

gaul_adm2 = gpd.read_file ('gaul2014_2015.gpkg', layer='level2') 
gaul_adm2 = gaul_adm2[gaul_adm2['ADM0_NAME'] == 'Japan']
gaul_adm2.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Index: 3348 entries, 23205 to 26552
Data columns (total 13 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   ADM2_CODE   3348 non-null   int64   
 1   ADM2_NAME   3348 non-null   object  
 2   STR2_YEAR   3348 non-null   int64   
 3   EXP2_YEAR   3348 non-null   int64   
 4   ADM1_CODE   3348 non-null   int64   
 5   ADM1_NAME   3348 non-null   object  
 6   STATUS      3348 non-null   object  
 7   DISP_AREA   3348 non-null   object  
 8   ADM0_CODE   3348 non-null   int64   
 9   ADM0_NAME   3348 non-null   object  
 10  Shape_Leng  3348 non-null   float64 
 11  Shape_Area  3348 non-null   float64 
 12  geometry    3348 non-null   geometry
dtypes: float64(2), geometry(1), int64(5), object(5)
memory usage: 366.2+ KB

The Admin 2 geodataframe has 12 columns describing the 3348 level 2 administrative units in Japan.

Filter EM-DAT Data

df_jpn = df[
    (df['ISO'] == 'JPN') &
    (df['Disaster Type'] == 'Earthquake') &
    (df['Start Year'] < 2024)
][['DisNo.', 'Admin Units']]
df_jpn

Note: The last two events were not geolocated at a higher administrative levels.

Convert Admin 2 units to Admin 1 units

We create a python function, json_to_amdmin1, to extract the administrative level 1 codes from the Admin Units column of EM-DAT, based on the ADM1_CODE and ADM2_CODE of the Japan geodataframe.

import json

def json_to_admin1(json_str, gdf):
    """
    Convert a JSON string to a set of administrative level 1 codes.

    Parameters
    ----------
    json_str
        A JSON string representing administrative areas, or None.
    gdf
        A GeoDataFrame containing administrative codes and their corresponding 
        levels.

    Returns
    -------
    A set of administrative level 1 (ADM1) codes extracted from the input JSON.

    Raises
    ------
    ValueError
        If the administrative code is missing from the input data or ADM2_CODE 
        not found in the provided GeoDataFrame.

    """
    adm_list = json.loads(json_str) if isinstance(json_str, str) else None
    adm1_list = []
    if adm_list is not None:
        for entry in adm_list:
            if 'adm1_code' in entry.keys():
                adm1_code = entry['adm1_code']
            elif 'adm2_code' in entry.keys():
                gdf_sel = gdf[gdf['ADM2_CODE'] == entry['adm2_code']]
                if not gdf_sel.empty:
                    adm1_code = gdf_sel.iloc[0]['ADM1_CODE']
                else:
                    raise ValueError(
                        'ADM2_CODE not found in the provided GeoDataFrame.'
                    )
            else:
                raise ValueError(
                    'Administrative code is missing from the provided data.'
                )
            adm1_list.append(adm1_code)
    return set(adm1_list)

We apply the function to all elements of the Admin Units column.

df_jpn.loc[:, 'Admin_1'] = df_jpn['Admin Units'].apply(
    lambda x: json_to_admin1(x, gaul_adm2))

df_jpn[['Admin Units', 'Admin_1']] 

Count Earthquakes per Admin 1 Units

The can be done applying the explode method on the new Admin_1 column. The method will add rows based on the number of Admin 1 we have in each set inside the Admin_1 column. Then the counting can be performed using the former groupby approach.

count_per_adm1 = df_jpn.explode('Admin_1').groupby(
    'Admin_1')['DisNo.'].count().rename('EQ Count')
count_per_adm1.head()

Admin_1
1651    1
1652    4
1653    4
1654    1
1655    1
Name: EQ Count, dtype: int64

Recreate the Admin 1 Layer

Since the Japan geodataframe contains the admin2 geometries, we could load the Admin 1 layer or simply dissolve the geometries based on the ADM1_CODE column. The geopandas package is equipped with the dissolve method.

gdf_jpn_adm1 = gaul_adm2.dissolve(by='ADM1_CODE') 
gdf_jpn_adm1.plot()

<Axes: >

Join the Two Datasets

Again, we can use the merge method to join the datasets together, here, based on their index.

gdf_jpn_adm1_merged = gdf_jpn_adm1.merge(count_per_adm1, 
                                         left_index=True, 
                                         right_index=True, 
                                         how='outer')

Make the Map

fig, ax = plt.subplots()

gdf_jpn_adm1_merged.plot(
    column='EQ Count', cmap='YlOrRd',
    linewidth=0.8, ax=ax,edgecolor='0.8',
    legend=True, 
    legend_kwds={'label': "Earthquake Count in EM-DAT"}
)
_ = ax.set_xlabel('Longitude')
_ = ax.set_ylabel('Latitude')

We have just covered the basics on how to join the EM-DAT pandas DataFrame with a geopandas GeoDataFrame to make maps. To delve further into your analyses, we encourage you to continue your learning of geopandas, matplotlib, or, in particular, cartopy for more advanced map customization, with the many resources available online.

2 - Additional Resources

Unless stated otherwise, these resources were neither developed nor are they maintained by the EM-DAT team. Please contact the corresponding authors with any questions.

EM-DAT Archive

The EM-DAT Archive, hosted on the University of Louvain (UCLouvain) Dataverse and maintained by the EM-DAT team, preserves official versions of the EM-DAT public data to support open and reproducible research. Each version includes the EM-DAT Public Table, its column description, and a PDF copy of this documentation, and is released under a CC BY-NC-ND 4.0 license (see also Data Accessibility).

Version 1.1 contains the same data files as version 1.0. When using an archived version, please cite the corresponding reference:

• Version 2.0: Delforge, D., Below, R., Wathelet, V., Alonso, A., & Speybroeck, N. (2026). The 2024 Emergency Events Database Archive: The year in review and historical data updates [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.20071134
• Versions 1.0 and 1.1: Delforge, D., Wathelet, V., Below, R., Lanfredi Sofia, C., Tonnelier, M., van Loenhout, J. A. F., & Speybroeck, N. (2025). EM-DAT: The Emergency Events Database. International Journal of Disaster Risk Reduction, 124, Article 105509. https://doi.org/10.1016/j.ijdrr.2025.105509

Software

• EM-VIEW: A Streamlit Dashboard to explore and analyze the EM-DAT Public Table. Also available as a GitHub repo for customization. Learn more from its initial announcement.
• EM-TEST: An open-source testing framework, built on the pandas and pandera Python packages, to validate EM-DAT public data files and check the compatibility between versions. Developed by the EM-DAT team and archived on Zenodo.

Augmented EM-DAT Data

• Geocoded Disasters (GDIS): an augmented dataset with geocoding, based on the GADM data, for 9,924 disasters between 1960 and 2018 (Rosvold & Buhaug, 2021).
• Flood Disasters (FLODIS): an augmented dataset for flood disasters (2000–2018), based on Tellman et al. (2021) and IDMC data, that links EM-DAT to hazard footprints, exposed and displaced population data (Mester et al., 2023).
• Geo-Disasters: geocoding of climate-related events in EM-DAT (Teber et al., 2026).
• Disaster Storylines and Knowledge Graphs: storylines and knowledge graphs for over 3,000 EM-DAT disasters (2014–2024), extracted from global news of the Europe Media Monitor using large language models and retrieval-augmented generation (Ronco et al., 2026).

Other Disaster Occurrence and Loss Datasets

• Wikimpacts 1.0: a global climate impact database built by extracting information from Wikipedia with large language models, covering 2,726 events from 1034 to 2024 at the event, national, and sub-national levels (Li et al., 2026).
• ROUGE: the Red Cross Operations Unified Global Emergency database, documenting socio-economic impacts of natural hazards at national and sub-national levels, extracted with large language models from operational reports of the International Federation of Red Cross and Red Crescent Societies (IFRC), with broad coverage of the Global South (Hasbini et al., 2026).
• HANZE v2.1: the Historical Analysis of Natural HaZards in Europe database, covering 2,521 riverine, pluvial, coastal, and compound flood events in 42 European countries from 1870 to 2020, with fatalities, affected persons, inundated areas, and economic losses (Paprotny et al., 2024).
• Risk Data Hub – Disaster Losses: a curated European dataset of historical disaster losses since 1980, harmonized from multiple sources by the Joint Research Centre (JRC) of the European Commission, with unique event identifiers at the national and European levels. The data can be explored through the Risk Data Hub dashboards and accessed through the Risk Data Hub API under a CC BY 4.0 license.

Other Useful Resources

• Worldwide disaster loss and damage databases: a systematic review comparing available disaster loss databases (Mazhin et al., 2021).
• Natural Disasters: a comprehensive global overview of natural hazards and disasters on Our World in Data (Ritchie et al., 2022).
• A note on missing values and imputations summarizing insights from Jones et al. (2022) and Jones et al. (2023).

3 - FAQ

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General

Finding and Interpreting Records

Access and Conditions of Use

Tools and Visualization

Contributing and Support