Datenanalyse Teil III

Datenanalyse Teil III#

Natalie Widmann#

Wintersemester 2023 / 2024

Universität Leipzig

Ziel#

Wir visualisieren die Daten im EM-DAT Datensatz und bauen ein Länderdashboard damit.

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
pd.set_option('display.float_format', '{:.2f}'.format)

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)
1	2000	Afghanistan	Climatological	Drought	Drought	1	2580000.00	37.00	50000.00
2	2000	Algeria	Hydrological	Flood	Flash flood	2	105.00	37.00	1639896721.42
3	2000	Algeria	Hydrological	Flood	Flood (General)	2	100.00	7.00	1639896721.42
4	2000	Algeria	Meteorological	Storm	Storm (General)	1	10.00	4.00	1639896721.42
5	2000	Angola	Hydrological	Flood	Flood (General)	3	9011.00	15.00	1639896721.42
...	...	...	...	...	...	...	...	...	...
5834	2023	Viet Nam	Meteorological	Storm	Tropical cyclone	1	3.00	1.00	1639896721.42
5835	2023	Yemen	Hydrological	Flood	Flood (General)	1	169035.00	39.00	1639896721.42
5836	2023	Zambia	Hydrological	Flood	Flash flood	1	154608.00	0.00	1639896721.42
5837	2023	Zambia	Hydrological	Flood	Flood (General)	1	22000.00	0.00	1639896721.42
5838	2023	Zimbabwe	Meteorological	Storm	Tropical cyclone	1	2.00	2.00	1639896721.42

5838 rows × 9 columns

data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 5838 entries, 1 to 5838
Data columns (total 9 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)  5838 non-null   float64
dtypes: float64(3), int64(2), object(4)
memory usage: 456.1+ 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)
370	2001	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	6.00	6.00	300000000.00
636	2002	Germany	Hydrological	Flood	Flood (General)	1	330108.00	27.00	11600000000.00
637	2002	Germany	Meteorological	Storm	Extra-tropical storm	1	11.00	11.00	1800000000.00
638	2002	Germany	Meteorological	Storm	Storm (General)	2	19.00	11.00	250000000.00
929	2003	Germany	Meteorological	Extreme temperature	Heat wave	1	9355.00	9355.00	1650000000.00
930	2003	Germany	Meteorological	Storm	Extra-tropical storm	1	5.00	5.00	300000000.00
931	2003	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	10.00	10.00	1639896721.42
1163	2004	Germany	Geophysical	Earthquake	Ground movement	1	150.00	0.00	12000000.00
1164	2004	Germany	Meteorological	Storm	Storm (General)	1	2.00	2.00	130000000.00
1434	2005	Germany	Hydrological	Flood	Riverine flood	2	450.00	1.00	220000000.00
1435	2005	Germany	Meteorological	Extreme temperature	Cold wave	1	165.00	1.00	300000000.00
1436	2005	Germany	Meteorological	Storm	Extra-tropical storm	1	2.00	2.00	270000000.00
1694	2006	Germany	Hydrological	Flood	Riverine flood	1	1000.00	0.00	1639896721.42
1695	2006	Germany	Meteorological	Extreme temperature	Heat wave	1	2.00	2.00	1639896721.42
1696	2006	Germany	Meteorological	Extreme temperature	Severe winter conditions	1	10.00	10.00	1639896721.42
1697	2006	Germany	Meteorological	Storm	Hail	1	100.00	1.00	1639896721.42
1698	2006	Germany	Meteorological	Storm	Storm (General)	2	200.00	10.00	1639896721.42
1948	2007	Germany	Hydrological	Flood	Riverine flood	1	1.00	1.00	1639896721.42
1949	2007	Germany	Meteorological	Storm	Blizzard/Winter storm	1	7.00	7.00	1639896721.42
1950	2007	Germany	Meteorological	Storm	Extra-tropical storm	1	130.00	11.00	5500000000.00
2205	2008	Germany	Meteorological	Storm	Extra-tropical storm	1	5.00	5.00	1200000000.00
2206	2008	Germany	Meteorological	Storm	Severe weather	1	3.00	3.00	1500000000.00
2435	2009	Germany	Hydrological	Flood	Riverine flood	1	0.00	0.00	20000000.00
2436	2009	Germany	Meteorological	Extreme temperature	Cold wave	2	15.00	15.00	1639896721.42
2437	2009	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	1.00	1.00	50000000.00
2668	2010	Germany	Hydrological	Flood	Flash flood	1	3.00	3.00	1639896721.42
2669	2010	Germany	Meteorological	Extreme temperature	Cold wave	1	1.00	1.00	1639896721.42
2670	2010	Germany	Meteorological	Storm	Blizzard/Winter storm	1	0.00	0.00	1639896721.42
2671	2010	Germany	Meteorological	Storm	Extra-tropical storm	1	4.00	4.00	1000000000.00
2907	2011	Germany	Hydrological	Flood	Riverine flood	1	4.00	4.00	1639896721.42
3128	2012	Germany	Meteorological	Extreme temperature	Cold wave	2	6.00	6.00	1639896721.42
3334	2013	Germany	Hydrological	Flood	Riverine flood	1	6350.00	4.00	12900000000.00
3335	2013	Germany	Meteorological	Storm	Extra-tropical storm	2	2.00	7.00	1639896721.42
3336	2013	Germany	Meteorological	Storm	Hail	1	0.00	0.00	4800000000.00
3528	2014	Germany	Meteorological	Storm	Lightning/Thunderstorms	2	1.00	8.00	400000000.00
3987	2016	Germany	Hydrological	Flood	Flood (General)	1	7.00	7.00	2000000000.00
4219	2017	Germany	Hydrological	Flood	Riverine flood	1	600.00	0.00	1639896721.42
4220	2017	Germany	Meteorological	Storm	Hail	1	2.00	2.00	740000000.00
4221	2017	Germany	Meteorological	Storm	Severe weather	1	24.00	3.00	159000000.00
4448	2018	Germany	Meteorological	Extreme temperature	Heat wave	1	0.00	0.00	1639896721.42
4449	2018	Germany	Meteorological	Storm	Extra-tropical storm	1	12.00	5.00	588475000.00
4694	2019	Germany	Meteorological	Extreme temperature	Heat wave	2	4.00	4.00	1639896721.42
4695	2019	Germany	Meteorological	Storm	Blizzard/Winter storm	1	1.00	1.00	1639896721.42
4934	2020	Germany	Meteorological	Storm	Extra-tropical storm	1	33.00	0.00	1639896721.42
5194	2021	Germany	Hydrological	Flood	Flood (General)	1	1000.00	197.00	40000000000.00
5195	2021	Germany	Meteorological	Storm	Lightning/Thunderstorms	1	600.00	0.00	1639896721.42
5196	2021	Germany	Meteorological	Storm	Storm (General)	1	4.00	1.00	1639896721.42
5461	2022	Germany	Meteorological	Extreme temperature	Heat wave	1	8173.00	8173.00	1639896721.42
5462	2022	Germany	Meteorological	Storm	Extra-tropical storm	3	2.00	7.00	1023156000.00
5707	2023	Germany	Meteorological	Storm	Severe weather	1	1.00	1.00	1639896721.42

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

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)
1	2000	Afghanistan	Climatological	Drought	Drought	1	2580000.00	37.00	50000.00
2	2000	Algeria	Hydrological	Flood	Flash flood	2	105.00	37.00	1639896721.42
3	2000	Algeria	Hydrological	Flood	Flood (General)	2	100.00	7.00	1639896721.42
4	2000	Algeria	Meteorological	Storm	Storm (General)	1	10.00	4.00	1639896721.42
5	2000	Angola	Hydrological	Flood	Flood (General)	3	9011.00	15.00	1639896721.42
...	...	...	...	...	...	...	...	...	...
5834	2023	Viet Nam	Meteorological	Storm	Tropical cyclone	1	3.00	1.00	1639896721.42
5835	2023	Yemen	Hydrological	Flood	Flood (General)	1	169035.00	39.00	1639896721.42
5836	2023	Zambia	Hydrological	Flood	Flash flood	1	154608.00	0.00	1639896721.42
5837	2023	Zambia	Hydrological	Flood	Flood (General)	1	22000.00	0.00	1639896721.42
5838	2023	Zimbabwe	Meteorological	Storm	Tropical cyclone	1	2.00	2.00	1639896721.42

5838 rows × 9 columns

data['Total Deaths'].sum()

1416055.0

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

Disaster Type
Animal incident                   12.00
Drought                        23913.00
Earthquake                    785228.00
Extreme temperature           235687.00
Flood                         128317.00
Glacial lake outburst flood      439.00
Impact                             0.00
Infestation                        0.00
Mass movement (dry)              382.00
Mass movement (wet)            19413.00
Storm                         218907.00
Volcanic activity               1711.00
Wildfire                        2046.00
Name: Total Deaths, dtype: float64

.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                        12.00
Drought                      Drought                             23913.00
Earthquake                   Ground movement                    532498.00
                             Tsunami                            252730.00
Extreme temperature          Cold wave                           11323.00
                             Heat wave                          220684.00
                             Severe winter conditions             3680.00
Flood                        Coastal flood                         606.00
                             Flash flood                         24663.00
                             Flood (General)                     33094.00
                             Riverine flood                      69954.00
Glacial lake outburst flood  Glacial lake outburst flood           439.00
Impact                       Collision                               0.00
Infestation                  Locust infestation                      0.00
                             Worms infestation                       0.00
Mass movement (dry)          Avalanche (dry)                        16.00
                             Landslide (dry)                       247.00
                             Rockfall (dry)                        119.00
Mass movement (wet)          Avalanche (wet)                      1856.00
                             Landslide (wet)                     14504.00
                             Mudslide                             2638.00
                             Rockfall (wet)                        128.00
                             Sudden Subsidence (wet)               287.00
Storm                        Blizzard/Winter storm                2779.00
                             Derecho                                24.00
                             Extra-tropical storm                  363.00
                             Hail                                  302.00
                             Lightning/Thunderstorms              2061.00
                             Sand/Dust storm                       316.00
                             Severe weather                       1256.00
                             Storm (General)                     15590.00
                             Storm surge                            52.00
                             Tornado                              2085.00
                             Tropical cyclone                   194079.00
Volcanic activity            Ash fall                              630.00
                             Lava flow                              33.00
                             Pyroclastic flow                      532.00
                             Volcanic activity (General)           516.00
Wildfire                     Forest fire                          1080.00
                             Land fire (Brush, Bush, Pasture)      487.00
                             Wildfire (General)                    479.00
Name: Total Deaths, dtype: float64

data.groupby('Disaster Type')

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f335898ba50>

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)
Year
2000	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	35000.00
2001	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	10000.00
2002	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	30000.00
2003	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	50000.00
2004	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	173000.00
2005	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	20000.00
2006	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	500000.00
2007	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	9000.00
2008	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	29000.00
2009	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	5000.00
2010	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	78000.00
2011	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	123000.00
2012	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	760000.00
2013	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	102000.00
2014	Afghanistan	Biological	Animal incident	Animal incident	1	0.00	0.00	791000.00
2015	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	2000.00
2016	Afghanistan	Climatological	Drought	Avalanche (wet)	1	0.00	0.00	50000.00
2017	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	1655000.00
2018	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	36000.00
2019	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	4000000.00
2020	Afghanistan	Biological	Drought	Ash fall	1	0.00	0.00	1500000.00
2021	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	120000.00
2022	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	0.00
2023	Afghanistan	Climatological	Drought	Ash fall	1	0.00	0.00	1000000.00

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')

Länderdashboard#

def plot_pie(country_data):
    country_data.groupby('Disaster Type')['Total Deaths'].sum().plot(kind='pie', title='Anteil an getöten Menschen nach Naturkatastrophentyp')
    plt.show()

def plot_time_evolution(country_data):
    yearly_events = country_data.groupby('Year')['Total Events'].sum()
    yearly_events.plot(kind='line', x='Year', y='Total Events', title='Anzahl an Naturkatastrophen pro Jahr')
    plt.show()

def death_overview(country_data):
    total_deaths = country_data["Total Deaths"].sum()
    print(f'Getötete Menschen seit 1900: {total_deaths:,.0f}')

def compute_anteil(country_total, world_total):
    anteil = round(country_total / (world_total / 100.0), 2)
    print(f'{anteil}% aller Menschen die weltweit von Naturkatastrophen betroffen sind leben hier.')

def analyze(data, country):
    print(f'Naturkatastrophen in {country.upper()} \n')
    country_data = data[data['Country'] == country]
    
    # compute_anteil(country_data['Total Affected'].sum(), data['Total Affected'].sum())
    text = death_overview(country_data)
    plot_pie(country_data)
    plot_time_evolution(country_data)

analyze(data, 'Bangladesh')

Naturkatastrophen in BANGLADESH 

Getötete Menschen seit 1900: 11,564

../_images/578c74e45897c3eaf4e6de397cb6ab69201e6bf2bd88c1335a2ac0ef818350ec.png

../_images/77443f3456e3ef001cfc59cc248aa357b46ce41f718fce736475abc0000963a0.png

Überblick über die Welt#

Wie können wir die selbstdefinierte Funktion analyze() anpassen, so dass auch eine Gesamtstatistik, die alle Länder der Welt enthält abgefragt werden kann?

def analyze(data, country):
    print(f'Naturkatastrophen in {country.upper()} \n')
    country_data = data[data['Country'] == country]
    if country == 'world':
        country_data = data   
    compute_anteil(country_data['Total Affected'].sum(), data['Total Affected'].sum())
    death_overview(country_data)
    plot_pie(country_data)
    plot_time_evolution(country_data)

analyze(data, 'world')

Naturkatastrophen in WORLD 

100.0% aller Menschen die weltweit von Naturkatastrophen betroffen sind leben hier.
Getötete Menschen seit 1900: 1,416,055

../_images/41758785f385586922fa440f636e1693be8a2246eca5990b88c82469606d8e08.png

Datenanalyse Teil III

Contents

Datenanalyse Teil III#

Natalie Widmann#

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?#

Länderdashboard#

Überblick über die Welt#