Visualisierung

Visualisierung#

Ziel#

Wir visualisieren die Daten im EM-DAT Datensatz

Datenpipeline

# Install a pip package im Jupyter Notebook
!pip3 install pandas

Requirement already satisfied: pandas in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (2.1.3)

Requirement already satisfied: numpy<2,>=1.23.2 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from pandas) (1.26.2)
Requirement already satisfied: python-dateutil>=2.8.2 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from pandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from pandas) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from pandas) (2023.3)
Requirement already satisfied: six>=1.5 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)

import pandas as pd
data = pd.read_csv('../../data/clean_emdat.csv', index_col=0)

data

	Year	Country	Disaster Subroup	Disaster Type	Disaster Subtype	Total Events	Total Affected	Total Deaths	Total Damage (USD, original)	CPI
1	2000	Afghanistan	Climatological	Drought	Drought	1	2580000.0	37.0	50000.0	58.840648
2	2000	Algeria	Hydrological	Flood	Flash flood	2	105.0	37.0	NaN	58.840648
3	2000	Algeria	Hydrological	Flood	Flood (General)	2	100.0	7.0	NaN	58.840648
4	2000	Algeria	Meteorological	Storm	Storm (General)	1	10.0	4.0	NaN	58.840648
5	2000	Angola	Hydrological	Flood	Flood (General)	3	9011.0	15.0	NaN	58.840648
...	...	...	...	...	...	...	...	...	...	...
5834	2023	Viet Nam	Meteorological	Storm	Tropical cyclone	1	3.0	1.0	NaN	NaN
5835	2023	Yemen	Hydrological	Flood	Flood (General)	1	169035.0	39.0	NaN	NaN
5836	2023	Zambia	Hydrological	Flood	Flash flood	1	154608.0	0.0	NaN	NaN
5837	2023	Zambia	Hydrological	Flood	Flood (General)	1	22000.0	0.0	NaN	NaN
5838	2023	Zimbabwe	Meteorological	Storm	Tropical cyclone	1	2.0	2.0	NaN	NaN

5838 rows × 10 columns

data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 5838 entries, 1 to 5838
Data columns (total 10 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Year                          5838 non-null   int64  
 1   Country                       5838 non-null   object 
 2   Disaster Subroup              5838 non-null   object 
 3   Disaster Type                 5838 non-null   object 
 4   Disaster Subtype              5838 non-null   object 
 5   Total Events                  5838 non-null   int64  
 6   Total Affected                5838 non-null   float64
 7   Total Deaths                  5838 non-null   float64
 8   Total Damage (USD, original)  2032 non-null   float64
 9   CPI                           5648 non-null   float64
dtypes: float64(4), int64(2), object(4)
memory usage: 501.7+ KB

Das Dashboard…#

Recherchefragen#

Wie viele Todesopfer gibt es in einem Land?
Wie stark ist ein Land von Naturkatastrophen betroffen?
Welchen Anteil haben die unterschiedlichen Naturkatastrophentypen daran?
Wie hat sich die Anzahl der Naturkatastrophen über die Jahre hin entwickelt?

Wie viele Todesopfer gibt es in einem Land?#

Wie viele Todesopfer gibt es insgesamt?#

data["Total Deaths"].sum()

1416055.0

Wie viele Todesopfer gibt es insgesamt in Deutschland?#

data['Country'] == 'Germany'

     False
     False
     False
     False
     False
        ...  
  False
  False
  False
  False
  False
Name: Country, Length: 5838, dtype: bool

data[data['Country'] == 'Germany']

	Year	Country	Disaster Subroup	Disaster Type	Disaster Subtype	Total Events	Total Affected	Total Deaths	Total Damage (USD, original)	CPI
370	2001	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	6.0	6.0	3.000000e+08	60.503579
636	2002	Germany	Hydrological	Flood	Flood (General)	1	330108.0	27.0	1.160000e+10	61.463188
637	2002	Germany	Meteorological	Storm	Extra-tropical storm	1	11.0	11.0	1.800000e+09	61.463188
638	2002	Germany	Meteorological	Storm	Storm (General)	2	19.0	11.0	2.500000e+08	61.463188
929	2003	Germany	Meteorological	Extreme temperature	Heat wave	1	9355.0	9355.0	1.650000e+09	62.858460
930	2003	Germany	Meteorological	Storm	Extra-tropical storm	1	5.0	5.0	3.000000e+08	62.858460
931	2003	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	10.0	10.0	NaN	62.858460
1163	2004	Germany	Geophysical	Earthquake	Ground movement	1	150.0	0.0	1.200000e+07	64.541329
1164	2004	Germany	Meteorological	Storm	Storm (General)	1	2.0	2.0	1.300000e+08	64.541329
1434	2005	Germany	Hydrological	Flood	Riverine flood	2	450.0	1.0	2.200000e+08	66.731058
1435	2005	Germany	Meteorological	Extreme temperature	Cold wave	1	165.0	1.0	3.000000e+08	66.731058
1436	2005	Germany	Meteorological	Storm	Extra-tropical storm	1	2.0	2.0	2.700000e+08	66.731058
1694	2006	Germany	Hydrological	Flood	Riverine flood	1	1000.0	0.0	NaN	68.883759
1695	2006	Germany	Meteorological	Extreme temperature	Heat wave	1	2.0	2.0	NaN	68.883759
1696	2006	Germany	Meteorological	Extreme temperature	Severe winter conditions	1	10.0	10.0	NaN	68.883759
1697	2006	Germany	Meteorological	Storm	Hail	1	100.0	1.0	NaN	68.883759
1698	2006	Germany	Meteorological	Storm	Storm (General)	2	200.0	10.0	NaN	68.883759
1948	2007	Germany	Hydrological	Flood	Riverine flood	1	1.0	1.0	NaN	70.848793
1949	2007	Germany	Meteorological	Storm	Blizzard/Winter storm	1	7.0	7.0	NaN	70.848793
1950	2007	Germany	Meteorological	Storm	Extra-tropical storm	1	130.0	11.0	5.500000e+09	70.848793
2205	2008	Germany	Meteorological	Storm	Extra-tropical storm	1	5.0	5.0	1.200000e+09	73.568746
2206	2008	Germany	Meteorological	Storm	Severe weather	1	3.0	3.0	1.500000e+09	73.568746
2435	2009	Germany	Hydrological	Flood	Riverine flood	1	0.0	0.0	2.000000e+07	73.307177
2436	2009	Germany	Meteorological	Extreme temperature	Cold wave	2	15.0	15.0	NaN	73.307177
2437	2009	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	1.0	1.0	5.000000e+07	73.307177
2668	2010	Germany	Hydrological	Flood	Flash flood	1	3.0	3.0	NaN	74.509444
2669	2010	Germany	Meteorological	Extreme temperature	Cold wave	1	1.0	1.0	NaN	74.509444
2670	2010	Germany	Meteorological	Storm	Blizzard/Winter storm	1	0.0	0.0	NaN	74.509444
2671	2010	Germany	Meteorological	Storm	Extra-tropical storm	1	4.0	4.0	1.000000e+09	74.509444
2907	2011	Germany	Hydrological	Flood	Riverine flood	1	4.0	4.0	NaN	76.861595
3128	2012	Germany	Meteorological	Extreme temperature	Cold wave	2	6.0	6.0	NaN	78.452114
3334	2013	Germany	Hydrological	Flood	Riverine flood	1	6350.0	4.0	1.290000e+10	79.601309
3335	2013	Germany	Meteorological	Storm	Extra-tropical storm	2	2.0	7.0	NaN	79.601309
3336	2013	Germany	Meteorological	Storm	Hail	1	0.0	0.0	4.800000e+09	79.601309
3528	2014	Germany	Meteorological	Storm	Lightning/Thunderstorms	2	1.0	8.0	4.000000e+08	80.892624
3987	2016	Germany	Hydrological	Flood	Flood (General)	1	7.0	7.0	2.000000e+09	82.010331
4219	2017	Germany	Hydrological	Flood	Riverine flood	1	600.0	0.0	NaN	83.757255
4220	2017	Germany	Meteorological	Storm	Hail	1	2.0	2.0	7.400000e+08	83.757255
4221	2017	Germany	Meteorological	Storm	Severe weather	1	24.0	3.0	1.590000e+08	83.757255
4448	2018	Germany	Meteorological	Extreme temperature	Heat wave	1	0.0	0.0	NaN	85.803026
4449	2018	Germany	Meteorological	Storm	Extra-tropical storm	1	12.0	5.0	5.884750e+08	85.803026
4694	2019	Germany	Meteorological	Extreme temperature	Heat wave	2	4.0	4.0	NaN	87.358007
4695	2019	Germany	Meteorological	Storm	Blizzard/Winter storm	1	1.0	1.0	NaN	87.358007
4934	2020	Germany	Meteorological	Storm	Extra-tropical storm	1	33.0	0.0	NaN	88.435641
5194	2021	Germany	Hydrological	Flood	Flood (General)	1	1000.0	197.0	4.000000e+10	92.590193
5195	2021	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	600.0	0.0	NaN	92.590193
5196	2021	Germany	Meteorological	Storm	Storm (General)	1	4.0	1.0	NaN	92.590193
5461	2022	Germany	Meteorological	Extreme temperature	Heat wave	1	8173.0	8173.0	NaN	100.000000
5462	2022	Germany	Meteorological	Storm	Extra-tropical storm	3	2.0	7.0	1.023156e+09	100.000000
5707	2023	Germany	Meteorological	Storm	Severe weather	1	1.0	1.0	NaN	NaN

data[data['Country'] == 'Germany']['Total Deaths'].sum()

17935.0

data_germany = data[data['Country'] == 'Germany']
data_germany['Total Deaths'].sum()

17935.0

Wie viele Todesopfer gab es insgesamt in Indien?#

data_land = data[data['Country'] == 'India']
data_land['Total Deaths'].sum()

87391.0

Allgemeine Funktion die Gesamtzahl der Todesopfer eines Landes ausgibt#

def get_total_deaths(data, country):
    data_land = data[data['Country'] == country]
    return data_land['Total Deaths'].sum()

get_total_deaths(data, 'Japan')

23066.0

get_total_deaths(data, 'Sweden')

56.0

get_total_deaths(data, 'South Africa')

1311.0

Dashboard Teil II#

Welchen Anteil haben die unterschiedlichen Naturkatastrophentypen in Deutschland?#

germany_data = data[data['Country'] == 'Germany']
germany_data['Disaster Type'].value_counts(normalize=True)

Disaster Type
Storm                  0.56
Flood                  0.22
Extreme temperature    0.20
Earthquake             0.02
Name: proportion, dtype: float64

Visualisierung von DataFrames#

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Matplotlib makes easy things easy and hard things possible.

Install matplotlib:

!pip3 install --upgrade pip
!pip3 install --upgrade Pillow
!pip3 install matplotlib

import matplotlib.pyplot as plt

Requirement already satisfied: pip in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (23.3.1)

Requirement already satisfied: Pillow in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (10.1.0)

Requirement already satisfied: matplotlib in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (3.8.2)
Requirement already satisfied: contourpy>=1.0.1 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (1.2.0)
Requirement already satisfied: cycler>=0.10 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (4.46.0)
Requirement already satisfied: kiwisolver>=1.3.1 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (1.4.5)

Requirement already satisfied: numpy<2,>=1.21 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (1.26.2)
Requirement already satisfied: packaging>=20.0 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (23.2)
Requirement already satisfied: pillow>=8 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (10.1.0)
Requirement already satisfied: pyparsing>=2.3.1 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (3.1.1)
Requirement already satisfied: python-dateutil>=2.7 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from matplotlib) (2.8.2)

Requirement already satisfied: six>=1.5 in /home/natalie/Dokumente/Datenjournalismus in Python/Code/.venv/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)

germany_data = data[data['Country'] == 'Germany']
germany_data['Disaster Type'].value_counts()

Disaster Type
Storm                  28
Flood                  11
Extreme temperature    10
Earthquake              1
Name: count, dtype: int64

germany_data['Disaster Type'].value_counts().plot(kind='bar')

<Axes: xlabel='Disaster Type'>

../_images/5fbdbe4f6c003d85dc01181609dccc822facd1f63194c4dd766585ee0efcfc71.png

germany_data['Disaster Type'].value_counts().plot(kind='pie')

<Axes: ylabel='count'>

../_images/90b9861ce6542f1404236a538e6798a7aad48cdca6694dac299e190c820dfc20.png

Recherchefragen#

Welche Naturkatastrophen fordern die meisten Todesopfer?#

.groupby() gruppiert einen DataFrame nach den Werten einer oder mehreren Spalten.

Die Spalten nach denen man Gruppieren möchte werden als Argument übergeben. Danach folgt die gewünschte Berechnung auf dieser Gruppe. Das Ergebnis wird als DataFrame zurückgegeben.

data

	Year	Country	Disaster Subroup	Disaster Type	Disaster Subtype	Total Events	Total Affected	Total Deaths	Total Damage (USD, original)	CPI
1	2000	Afghanistan	Climatological	Drought	Drought	1	2580000.0	37.0	50000.0	58.840648
2	2000	Algeria	Hydrological	Flood	Flash flood	2	105.0	37.0	NaN	58.840648
3	2000	Algeria	Hydrological	Flood	Flood (General)	2	100.0	7.0	NaN	58.840648
4	2000	Algeria	Meteorological	Storm	Storm (General)	1	10.0	4.0	NaN	58.840648
5	2000	Angola	Hydrological	Flood	Flood (General)	3	9011.0	15.0	NaN	58.840648
...	...	...	...	...	...	...	...	...	...	...
5834	2023	Viet Nam	Meteorological	Storm	Tropical cyclone	1	3.0	1.0	NaN	NaN
5835	2023	Yemen	Hydrological	Flood	Flood (General)	1	169035.0	39.0	NaN	NaN
5836	2023	Zambia	Hydrological	Flood	Flash flood	1	154608.0	0.0	NaN	NaN
5837	2023	Zambia	Hydrological	Flood	Flood (General)	1	22000.0	0.0	NaN	NaN
5838	2023	Zimbabwe	Meteorological	Storm	Tropical cyclone	1	2.0	2.0	NaN	NaN

5838 rows × 10 columns

data['Total Deaths'].sum()

1416055.0

data.groupby('Disaster Type')['Total Deaths'].sum()

Disaster Type
Animal incident                    12.0
Drought                         23913.0
Earthquake                     785228.0
Extreme temperature            235687.0
Flood                          128317.0
Glacial lake outburst flood       439.0
Impact                              0.0
Infestation                         0.0
Mass movement (dry)               382.0
Mass movement (wet)             19413.0
Storm                          218907.0
Volcanic activity                1711.0
Wildfire                         2046.0
Name: Total Deaths, dtype: float64

data.groupby('Disaster Type').sum()

	Year	Country	Disaster Subroup	Disaster Subtype	Total Events	Total Affected	Total Deaths	Total Damage (USD, original)	CPI
Disaster Type
Animal incident	2014	Niger	Biological	Animal incident	1	5.000000e+00	12.0	0.000000e+00	80.892624
Drought	772205	AfghanistanArmeniaAzerbaijanBolivia (Plurinati...	ClimatologicalClimatologicalClimatologicalClim...	DroughtDroughtDroughtDroughtDroughtDroughtDrou...	396	1.651828e+09	23913.0	1.751722e+11	28990.398302
Earthquake	778193	AzerbaijanBangladeshChinaColombiaGreeceIceland...	GeophysicalGeophysicalGeophysicalGeophysicalGe...	Ground movementGround movementGround movementG...	652	1.398904e+08	785228.0	6.647881e+11	27980.773490
Extreme temperature	896633	ArgentinaBangladeshBelarusBolivia (Plurination...	MeteorologicalMeteorologicalMeteorologicalMete...	Cold waveCold waveCold waveCold waveCold waveC...	487	9.774977e+07	235687.0	5.086219e+10	33081.773472
Flood	4826898	AlgeriaAlgeriaAngolaAngolaArgentinaArgentinaAu...	HydrologicalHydrologicalHydrologicalHydrologic...	Flash floodFlood (General)Flood (General)River...	3968	1.782933e+09	128317.0	7.282075e+11	176689.363211
Glacial lake outburst flood	6066	IndiaItalyIndia	ClimatologicalClimatologicalClimatological	Glacial lake outburst floodGlacial lake outbur...	4	8.843500e+04	439.0	2.100000e+08	192.590193
Impact	2013	Russian Federation	Extra-terrestrial	Collision	1	3.014910e+05	0.0	3.300000e+07	79.601309
Infestation	58273	AustraliaPeruChinaRussian FederationAustraliaB...	BiologicalBiologicalBiologicalBiologicalBiolog...	Locust infestationLocust infestationLocust inf...	29	2.800000e+06	0.0	1.200000e+08	2112.085926
Mass movement (dry)	26120	PhilippinesEcuadorChinaChinaColombiaEgyptPapua...	GeophysicalGeophysicalGeophysicalGeophysicalGe...	Landslide (dry)Landslide (dry)Landslide (dry)L...	13	4.424000e+03	382.0	8.000000e+06	956.344621
Mass movement (wet)	665755	AngolaAustriaAzerbaijanBosnia and HerzegovinaB...	HydrologicalHydrologicalHydrologicalHydrologic...	Landslide (wet)Avalanche (wet)Avalanche (wet)L...	443	5.124087e+06	19413.0	5.113121e+09	23928.007940
Storm	3037392	AlgeriaArgentinaArgentinaAustraliaAustriaBangl...	MeteorologicalMeteorologicalMeteorologicalMete...	Storm (General)Blizzard/Winter stormLightning/...	2482	8.144219e+08	218907.0	1.595769e+12	110203.802317
Volcanic activity	209182	GuatemalaJapanMexicoPhilippinesDemocratic Repu...	GeophysicalGeophysicalGeophysicalGeophysicalGe...	Ash fallAsh fallAsh fallAsh fallAsh fallAsh fa...	123	6.429001e+06	1711.0	3.465433e+09	7814.313176
Wildfire	460576	ArgentinaAustraliaBulgariaCroatiaCyprusEthiopi...	ClimatologicalClimatologicalClimatologicalClim...	Forest fireLand fire (Brush, Bush, Pasture)For...	294	1.470967e+07	2046.0	1.085216e+11	16594.190195

.groupby() kann auch auf mehrere Spalten gleichzeitig angewendet werden

data.groupby(['Disaster Type', 'Disaster Subtype'])['Total Deaths'].sum()

Disaster Type                Disaster Subtype                
Animal incident              Animal incident                         12.0
Drought                      Drought                              23913.0
Earthquake                   Ground movement                     532498.0
                             Tsunami                             252730.0
Extreme temperature          Cold wave                            11323.0
                             Heat wave                           220684.0
                             Severe winter conditions              3680.0
Flood                        Coastal flood                          606.0
                             Flash flood                          24663.0
                             Flood (General)                      33094.0
                             Riverine flood                       69954.0
Glacial lake outburst flood  Glacial lake outburst flood            439.0
Impact                       Collision                                0.0
Infestation                  Locust infestation                       0.0
                             Worms infestation                        0.0
Mass movement (dry)          Avalanche (dry)                         16.0
                             Landslide (dry)                        247.0
                             Rockfall (dry)                         119.0
Mass movement (wet)          Avalanche (wet)                       1856.0
                             Landslide (wet)                      14504.0
                             Mudslide                              2638.0
                             Rockfall (wet)                         128.0
                             Sudden Subsidence (wet)                287.0
Storm                        Blizzard/Winter storm                 2779.0
                             Derecho                                 24.0
                             Extra-tropical storm                   363.0
                             Hail                                   302.0
                             Lightning/Thunderstorms               2061.0
                             Sand/Dust storm                        316.0
                             Severe weather                        1256.0
                             Storm (General)                      15590.0
                             Storm surge                             52.0
                             Tornado                               2085.0
                             Tropical cyclone                    194079.0
Volcanic activity            Ash fall                               630.0
                             Lava flow                               33.0
                             Pyroclastic flow                       532.0
                             Volcanic activity (General)            516.0
Wildfire                     Forest fire                           1080.0
                             Land fire (Brush, Bush, Pasture)       487.0
                             Wildfire (General)                     479.0
Name: Total Deaths, dtype: float64

Visualisierung#

data.groupby('Disaster Type')['Total Deaths'].sum().plot(kind='pie')

<Axes: ylabel='Total Deaths'>

../_images/2e88f51cf703a3e005b4056a135f182bfad0e693fe55ea50e2c8c28e589ef6b1.png

Welche Naturkatastrophen fordern die meisten Todesopfer in Deutschland?#

germany_data = data[data['Country'] == 'Germany']
germany_data.groupby('Disaster Type')['Total Deaths'].sum().plot(kind='pie')

<Axes: ylabel='Total Deaths'>

../_images/2f30fbfc631328a35042bb44826a412fabdd3ea45bfd38704c917ea05008c376.png

Generelle Funktion#

country = 'India'
country_data = data[data['Country'] == country]
country_data.groupby('Disaster Type')['Total Deaths'].sum().plot(kind='pie')

<Axes: ylabel='Total Deaths'>

../_images/2f724652f3a3e72f341dd5042687904c9e7dbf85616efd9999c7061373e0e61a.png

def plot_death_by_disastertype(data, country):
    country_data = data[data['Country'] == country]
    country_data.groupby('Disaster Type')['Total Deaths'].sum().plot(kind='pie')

plot_death_by_disastertype(data, 'Egypt')

../_images/92c2be512502ca8da83615b81a23f70c7b953185878021f435f50b7202c645c2.png

Dashboard Teil III#

Wie hat sich die Anzahl der Naturkatastrophen über die Jahre hin entwickelt?#

Berechne für jedes Jahr die Summe aus allen Events

data.groupby('Year')['Total Events'].sum()

Year
  412
  377
  421
  360
  362
  430
  402
  412
  353
  345
  394
  333
  347
  331
  322
  387
  324
  344
  318
  400
  400
  433
  417
  269
Name: Total Events, dtype: int64

data['Total Events'].sum()

data.groupby('Year').min()

	Country	Disaster Subroup	Disaster Type	Disaster Subtype	Total Events	Total Affected	Total Deaths	Total Damage (USD, original)	CPI
Year
2000	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	35000.0	58.840648
2001	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	10000.0	60.503579
2002	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	30000.0	61.463188
2003	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	50000.0	62.858460
2004	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	173000.0	64.541329
2005	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	20000.0	66.731058
2006	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	500000.0	68.883759
2007	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	9000.0	70.848793
2008	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	29000.0	73.568746
2009	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	5000.0	73.307177
2010	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	78000.0	74.509444
2011	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	123000.0	76.861595
2012	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	760000.0	78.452114
2013	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	102000.0	79.601309
2014	Afghanistan	Biological	Animal incident	Animal incident	1	0.0	0.0	791000.0	80.892624
2015	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	2000.0	80.988579
2016	Afghanistan	Climatological	Drought	Avalanche (wet)	1	0.0	0.0	50000.0	82.010331
2017	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	1655000.0	83.757255
2018	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	36000.0	85.803026
2019	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	4000000.0	87.358007
2020	Afghanistan	Biological	Drought	Ash fall	1	0.0	0.0	1500000.0	88.435641
2021	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	120000.0	92.590193
2022	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	0.0	100.000000
2023	Afghanistan	Climatological	Drought	Ash fall	1	0.0	0.0	1000000.0	NaN

data[['Total Events', 'Year']].groupby('Year')['Total Events'].sum()

Year
  412
  377
  421
  360
  362
  430
  402
  412
  353
  345
  394
  333
  347
  331
  322
  387
  324
  344
  318
  400
  400
  433
  417
  269
Name: Total Events, dtype: int64

yearly_events = data.groupby('Year')['Total Events'].sum()
yearly_events.plot(kind='line', x='Year', y='Total Events', title='Anzahl an Naturkatastrophen pro Jahr')

<Axes: title={'center': 'Anzahl an Naturkatastrophen pro Jahr'}, xlabel='Year'>

../_images/0e43fafc034adc92234657ac61cfc22a60c666ab102d93a3da2b0c64185c6d35.png

Plotte für Deutschland die Entwicklung der Anzahl an Naturkatastrophen über die Jahre hinweg

yearly_events =germany_data.groupby('Year')['Total Events'].sum()
yearly_events.plot(kind='line', x='Year', y='Total Events', title='Anzahl an Naturkatastrophen pro Jahr')

<Axes: title={'center': 'Anzahl an Naturkatastrophen pro Jahr'}, xlabel='Year'>

../_images/9adc34ec5273748d24db7c081c426b68b7ff78a00bd6eba2a78de68ac928f568.png

Generelle Funktion

def plot_evolution(data, country):
    land_data = data[data['Country'] == county]
    yearly_events = land_data.groupby('Year')['Total Events'].sum()
    yearly_events.plot(kind='line', x='Year', y='Total Events', title='Anzahl an Naturkatastrophen pro Jahr')

Visualisierung

Contents

Visualisierung#

Ziel#

Das Dashboard…#

Recherchefragen#

Wie viele Todesopfer gibt es in einem Land?#

Wie viele Todesopfer gibt es insgesamt?#

Wie viele Todesopfer gibt es insgesamt in Deutschland?#

Wie viele Todesopfer gab es insgesamt in Indien?#

Allgemeine Funktion die Gesamtzahl der Todesopfer eines Landes ausgibt#

Dashboard Teil II#

Welchen Anteil haben die unterschiedlichen Naturkatastrophentypen in Deutschland?#

Visualisierung von DataFrames#

Recherchefragen#

Welche Naturkatastrophen fordern die meisten Todesopfer?#

Visualisierung#

Welche Naturkatastrophen fordern die meisten Todesopfer in Deutschland?#

Generelle Funktion#

Dashboard Teil III#

Wie hat sich die Anzahl der Naturkatastrophen über die Jahre hin entwickelt?#