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python_scripts/get_dwd_precipitation/Parse Niederschlag.ipynb
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%% Cell type:markdown id:d52465eb tags: %% Cell type:markdown id:d52465eb tags:
   
# Niederschlag from DWD # Niederschlag from DWD
   
## Klimadaten ## Klimadaten
   
[stundenwerte_RR_05705_hist.zip](https://www.dwd.de/DE/leistungen/_config/leistungsteckbriefPublication.zip?view=nasPublication&nn=16102&imageFilePath=675349470684952943382999480164406894835507349402593906628786280354581421008571928705011438849092751258299962952312473511744392814375916459681379337607540679777573246835730326122056459559982440454207144420355774317891799102452512290249737221071767178890331035105331501334382788925&download=true) [stundenwerte_RR_05705_hist.zip](https://www.dwd.de/DE/leistungen/_config/leistungsteckbriefPublication.zip?view=nasPublication&nn=16102&imageFilePath=675349470684952943382999480164406894835507349402593906628786280354581421008571928705011438849092751258299962952312473511744392814375916459681379337607540679777573246835730326122056459559982440454207144420355774317891799102452512290249737221071767178890331035105331501334382788925&download=true)
   
from [Klimadaten Deutschland - Stundenwerte (Archiv)](https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivstunden.html) "Würzburg, Niederschlag, Historisch" from [Klimadaten Deutschland - Stundenwerte (Archiv)](https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivstunden.html) "Würzburg, Niederschlag, Historisch"
   
## Climate data center ## Climate data center
   
https://cdc.dwd.de/portal/ https://cdc.dwd.de/portal/
   
%% Cell type:code id:316a929e tags: %% Cell type:code id:1cfa0188 tags:
   
``` python ``` python
import pandas as pd import pandas as pd
``` ```
   
%% Cell type:raw id:daf94ab6 tags: %% Cell type:raw id:c566f571 tags:
   
# Old version # Old version
   
from datetime import datetime from datetime import datetime
dateparse = lambda x: datetime.strptime(x, '%Y%m%d%H') dateparse = lambda x: datetime.strptime(x, '%Y%m%d%H')
   
df = pd.read_csv('produkt_rr_stunde_19950901_20211231_05705.txt', df = pd.read_csv('produkt_rr_stunde_19950901_20211231_05705.txt',
sep=';', sep=';',
skipinitialspace=True, skipinitialspace=True,
usecols = [1, 3], usecols = [1, 3],
names = ['datetime', 'precipitation'], names = ['datetime', 'precipitation'],
skiprows = 1, skiprows = 1,
parse_dates = ['datetime'], parse_dates = ['datetime'],
date_parser=dateparse, date_parser=dateparse,
index_col = 'datetime' index_col = 'datetime'
) )
df df
   
%% Cell type:code id:f16392a3 tags: %% Cell type:code id:fef5e23e tags:
   
``` python ``` python
df = pd.read_csv('data_OBS_DEU_PT1H_RR_5705.csv', df = pd.read_csv('data_OBS_DEU_PT1H_RR_5705.csv',
index_col=False, index_col=False,
parse_dates=['datetime'], parse_dates=['datetime'],
usecols = [2,3], usecols = [2,3],
names=['datetime', 'precipitation'], names=['datetime', 'precipitation'],
skiprows=1 skiprows=1
) )
df = df.set_index('datetime') df = df.set_index('datetime')
df df
``` ```
   
%% Output %% Output
   
precipitation precipitation
datetime datetime
1995-09-01 00:00:00 0.0 1995-09-01 00:00:00 0.0
1995-09-01 01:00:00 0.2 1995-09-01 01:00:00 0.2
1995-09-01 02:00:00 0.2 1995-09-01 02:00:00 0.2
1995-09-01 03:00:00 0.1 1995-09-01 03:00:00 0.1
1995-09-01 04:00:00 0.0 1995-09-01 04:00:00 0.0
... ... ... ...
2023-10-27 04:00:00 0.0 2023-10-27 04:00:00 0.0
2023-10-27 05:00:00 0.0 2023-10-27 05:00:00 0.0
2023-10-27 06:00:00 0.0 2023-10-27 06:00:00 0.0
2023-10-27 07:00:00 0.0 2023-10-27 07:00:00 0.0
2023-10-27 08:00:00 0.2 2023-10-27 08:00:00 0.2
[245850 rows x 1 columns] [245850 rows x 1 columns]
   
%% Cell type:code id:52f73e31 tags: %% Cell type:code id:52f73e31 tags:
   
``` python ``` python
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (20, 10) plt.rcParams["figure.figsize"] = (20, 10)
``` ```
   
%% Cell type:code id:672300ca tags: %% Cell type:code id:672300ca tags:
   
``` python ``` python
df2 = df[df.precipitation >= 0] df2 = df[df.precipitation >= 0]
``` ```
   
%% Cell type:code id:0fdf4a6f tags: %% Cell type:code id:0fdf4a6f tags:
   
``` python ``` python
df2.resample('d').sum().plot(title='Niederschlag in Würzburg [mm / Tag]'); df2.resample('d').sum().plot(title='Niederschlag in Würzburg [mm / Tag]');
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:9a68733c tags: %% Cell type:code id:9a68733c tags:
   
``` python ``` python
df2.resample('M').sum().plot(title='Niederschlag in Würzburg [mm / Monat]', ylim=(0, None)); df2.resample('M').sum().plot(title='Niederschlag in Würzburg [mm / Monat]', ylim=(0, None));
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:929bacb6 tags: %% Cell type:code id:929bacb6 tags:
   
``` python ``` python
yearly_values = df2[df2.index.year >= 1996].resample('Y').sum() yearly_values = df2[df2.index.year >= 1996].resample('Y').sum()
``` ```
   
%% Cell type:code id:c4dd7535 tags: %% Cell type:code id:c4dd7535 tags:
   
``` python ``` python
yearly_values.describe() df2[(df2.index.year >= 2010) & (df2.index.year < 2023)].resample('Y').sum().describe()
``` ```
   
%% Output %% Output
   
precipitation precipitation
count 28.000000 count 13.000000
mean 563.071429 mean 559.800000
std 96.674589 std 93.541524
min 410.500000 min 432.100000
25% 512.550000 25% 493.200000
50% 548.900000 50% 537.900000
75% 614.400000 75% 628.900000
max 807.100000 max 744.400000
   
%% Cell type:code id:0a1cc458 tags: %% Cell type:code id:0a1cc458 tags:
   
``` python ``` python
yearly_values.plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None)); yearly_values.plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
plt.savefig('wuerzburg_yearly_precipitation.png', facecolor='w', bbox_inches='tight')
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:c74631eb tags: %% Cell type:code id:c74631eb tags:
   
``` python ``` python
yearly_values.boxplot() yearly_values.boxplot()
plt.title("Niederschlag in Würzburg, zwischen 1996 & 2022 [mm / Jahr]") plt.title("Niederschlag in Würzburg, zwischen 1996 & 2022 [mm / Jahr]")
plt.savefig('wurzburg_yearly_precipitations_1996_2022.png', facecolor='w', bbox_inches='tight') plt.savefig('wuerzburg_yearly_precipitations_1996_2022.png', facecolor='w', bbox_inches='tight')
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:faf431eb tags: %% Cell type:code id:faf431eb tags:
   
``` python ``` python
df3 = df2[df2.index.year == 2022] df3 = df2[df2.index.year == 2022]
``` ```
   
%% Cell type:code id:2509790f tags: %% Cell type:code id:2509790f tags:
   
``` python ``` python
rain2022 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear) rain2022 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear)
rain2022 rain2022
``` ```
   
%% Output %% Output
   
datetime 1 2 3 4 5 6 7 8 9 10 ... 356 357 \ datetime 1 2 3 4 5 6 7 8 9 10 ... 356 357 \
00:00:00 0.0 0.0 0.0 0.6 0.8 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.2 00:00:00 0.0 0.0 0.0 0.6 0.8 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.2
01:00:00 0.0 0.0 0.0 1.1 0.4 0.0 0.0 0.0 0.3 0.0 ... 0.0 0.6 01:00:00 0.0 0.0 0.0 1.1 0.4 0.0 0.0 0.0 0.3 0.0 ... 0.0 0.6
02:00:00 0.0 0.0 2.4 1.1 0.0 0.0 0.0 0.0 0.3 0.0 ... 0.1 0.2 02:00:00 0.0 0.0 2.4 1.1 0.0 0.0 0.0 0.0 0.3 0.0 ... 0.1 0.2
03:00:00 0.0 0.0 0.1 0.6 0.6 0.0 0.0 0.3 0.4 0.0 ... 0.4 0.3 03:00:00 0.0 0.0 0.1 0.6 0.6 0.0 0.0 0.3 0.4 0.0 ... 0.4 0.3
04:00:00 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.2 0.8 0.0 ... 0.0 0.2 04:00:00 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.2 0.8 0.0 ... 0.0 0.2
05:00:00 0.0 0.0 0.0 1.6 0.0 0.0 0.0 1.0 0.9 0.0 ... 0.0 0.0 05:00:00 0.0 0.0 0.0 1.6 0.0 0.0 0.0 1.0 0.9 0.0 ... 0.0 0.0
06:00:00 0.0 0.0 0.2 2.5 0.0 0.0 0.0 0.4 0.1 0.0 ... 0.1 0.1 06:00:00 0.0 0.0 0.2 2.5 0.0 0.0 0.0 0.4 0.1 0.0 ... 0.1 0.1
07:00:00 0.0 0.0 1.2 1.6 0.0 0.0 0.0 0.1 0.2 0.0 ... 1.4 0.0 07:00:00 0.0 0.0 1.2 1.6 0.0 0.0 0.0 0.1 0.2 0.0 ... 1.4 0.0
08:00:00 0.0 0.0 2.8 1.1 0.0 0.0 0.0 0.0 0.8 0.0 ... 0.3 0.0 08:00:00 0.0 0.0 2.8 1.1 0.0 0.0 0.0 0.0 0.8 0.0 ... 0.3 0.0
09:00:00 0.0 0.0 0.1 1.2 0.0 0.1 0.0 0.0 0.2 0.0 ... 1.1 0.0 09:00:00 0.0 0.0 0.1 1.2 0.0 0.1 0.0 0.0 0.2 0.0 ... 1.1 0.0
10:00:00 0.0 0.0 0.1 1.2 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 10:00:00 0.0 0.0 0.1 1.2 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0
11:00:00 0.0 0.0 0.9 1.5 0.0 0.0 0.1 0.0 0.1 0.0 ... 0.0 0.0 11:00:00 0.0 0.0 0.9 1.5 0.0 0.0 0.1 0.0 0.1 0.0 ... 0.0 0.0
12:00:00 0.0 0.0 0.0 3.2 0.0 0.0 0.2 0.0 0.0 0.0 ... 0.1 0.1 12:00:00 0.0 0.0 0.0 3.2 0.0 0.0 0.2 0.0 0.0 0.0 ... 0.1 0.1
13:00:00 0.0 0.0 0.0 1.9 0.0 0.0 0.1 0.0 0.0 0.0 ... 0.0 0.5 13:00:00 0.0 0.0 0.0 1.9 0.0 0.0 0.1 0.0 0.0 0.0 ... 0.0 0.5
14:00:00 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.5 14:00:00 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.5
15:00:00 0.0 0.0 0.2 0.6 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.4 2.3 15:00:00 0.0 0.0 0.2 0.6 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.4 2.3
16:00:00 0.0 0.0 0.1 0.8 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.3 2.3 16:00:00 0.0 0.0 0.1 0.8 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.3 2.3
17:00:00 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.1 17:00:00 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.1
18:00:00 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.4 18:00:00 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.4
19:00:00 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.8 19:00:00 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.2 1.8
20:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.6 20:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.6
21:00:00 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.1 1.5 21:00:00 0.0 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.1 1.5
22:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.7 0.1 22:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.7 0.1
23:00:00 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.3 0.0 23:00:00 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.3 0.0
datetime 358 359 360 361 362 363 364 365 datetime 358 359 360 361 362 363 364 365
00:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 00:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
01:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 01:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
02:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 02:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
03:00:00 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 03:00:00 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
04:00:00 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 04:00:00 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0
05:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 05:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
06:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 06:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
07:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 07:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
08:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 08:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
09:00:00 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 09:00:00 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0
10:00:00 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 10:00:00 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0
11:00:00 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 11:00:00 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0
12:00:00 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12:00:00 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 13:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14:00:00 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 14:00:00 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15:00:00 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.0 15:00:00 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.0
16:00:00 0.0 0.0 0.0 0.0 0.0 0.7 0.1 0.0 16:00:00 0.0 0.0 0.0 0.0 0.0 0.7 0.1 0.0
17:00:00 0.0 0.0 0.0 0.0 0.0 1.4 1.1 0.0 17:00:00 0.0 0.0 0.0 0.0 0.0 1.4 1.1 0.0
18:00:00 0.0 0.0 2.2 0.0 0.0 0.0 0.1 0.0 18:00:00 0.0 0.0 2.2 0.0 0.0 0.0 0.1 0.0
19:00:00 0.0 0.0 1.0 0.0 0.0 0.0 0.1 0.0 19:00:00 0.0 0.0 1.0 0.0 0.0 0.0 0.1 0.0
20:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 21:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
22:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 22:00:00 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0
23:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 23:00:00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[24 rows x 365 columns] [24 rows x 365 columns]
   
%% Cell type:code id:42ddd82d tags: %% Cell type:code id:42ddd82d tags:
   
``` python ``` python
import seaborn as sns import seaborn as sns
``` ```
   
%% Cell type:code id:f1b17ef2 tags: %% Cell type:code id:f1b17ef2 tags:
   
``` python ``` python
sns.heatmap(rain2022, annot=False, cmap='viridis', vmax=5) sns.heatmap(rain2022, annot=False, cmap='viridis', vmax=5)
plt.title('mm/h Regen in Würzburg, 2022') plt.title('mm/h Regen in Würzburg, 2022')
plt.show() plt.show()
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:29be9114 tags: %% Cell type:code id:29be9114 tags:
   
``` python ``` python
df4 = df2[(df2.index.year >= 2010)].resample('M').sum() df4 = df2[(df2.index.year >= 2010)].resample('M').sum()
df4 df4
``` ```
   
%% Output %% Output
   
precipitation precipitation
datetime datetime
2010-01-31 35.9 2010-01-31 35.9
2010-02-28 28.5 2010-02-28 28.5
2010-03-31 29.7 2010-03-31 29.7
2010-04-30 23.7 2010-04-30 23.7
2010-05-31 68.4 2010-05-31 68.4
... ... ... ...
2023-06-30 21.3 2023-06-30 21.3
2023-07-31 54.2 2023-07-31 54.2
2023-08-31 69.9 2023-08-31 69.9
2023-09-30 18.6 2023-09-30 18.6
2023-10-31 52.7 2023-10-31 52.7
[166 rows x 1 columns] [166 rows x 1 columns]
   
%% Cell type:code id:283cd890 tags: %% Cell type:code id:283cd890 tags:
   
``` python ``` python
import calendar import calendar
df4['Year'] = df4.index.year df4['Year'] = df4.index.year
df4['Month'] = df4.index.month df4['Month'] = df4.index.month
df4['Month'] = df4['Month'].apply(lambda x: calendar.month_abbr[x]) df4['Month'] = df4['Month'].apply(lambda x: calendar.month_abbr[x])
df4 df4
``` ```
   
%% Output %% Output
   
precipitation Year Month precipitation Year Month
datetime datetime
2010-01-31 35.9 2010 Jan 2010-01-31 35.9 2010 Jan
2010-02-28 28.5 2010 Feb 2010-02-28 28.5 2010 Feb
2010-03-31 29.7 2010 Mar 2010-03-31 29.7 2010 Mar
2010-04-30 23.7 2010 Apr 2010-04-30 23.7 2010 Apr
2010-05-31 68.4 2010 May 2010-05-31 68.4 2010 May
... ... ... ... ... ... ... ...
2023-06-30 21.3 2023 Jun 2023-06-30 21.3 2023 Jun
2023-07-31 54.2 2023 Jul 2023-07-31 54.2 2023 Jul
2023-08-31 69.9 2023 Aug 2023-08-31 69.9 2023 Aug
2023-09-30 18.6 2023 Sep 2023-09-30 18.6 2023 Sep
2023-10-31 52.7 2023 Oct 2023-10-31 52.7 2023 Oct
[166 rows x 3 columns] [166 rows x 3 columns]
   
%% Cell type:code id:28a0fd61 tags: %% Cell type:code id:28a0fd61 tags:
   
``` python ``` python
df4 = df4.set_index(['Year', 'Month']) df4 = df4.set_index(['Year', 'Month'])
df4 df4
``` ```
   
%% Output %% Output
   
precipitation precipitation
Year Month Year Month
2010 Jan 35.9 2010 Jan 35.9
Feb 28.5 Feb 28.5
Mar 29.7 Mar 29.7
Apr 23.7 Apr 23.7
May 68.4 May 68.4
... ... ... ...
2023 Jun 21.3 2023 Jun 21.3
Jul 54.2 Jul 54.2
Aug 69.9 Aug 69.9
Sep 18.6 Sep 18.6
Oct 52.7 Oct 52.7
[166 rows x 1 columns] [166 rows x 1 columns]
   
%% Cell type:code id:8e3bd7d1 tags: %% Cell type:code id:8e3bd7d1 tags:
   
``` python ``` python
df_precipitation = df4.reset_index().pivot_table(columns='Year',index='Month',values='precipitation', sort=False) df_precipitation = df4.reset_index().pivot_table(columns='Year',index='Month',values='precipitation', sort=False)
df_precipitation df_precipitation
``` ```
   
%% Output %% Output
   
Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 \ Year 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 \
Month Month
Jan 35.9 38.6 66.6 29.8 31.1 53.3 62.2 14.7 56.1 37.2 23.2 Jan 35.9 38.6 66.6 29.8 31.1 53.3 62.2 14.7 56.1 37.2 23.2
Feb 28.5 31.4 12.2 35.1 39.6 8.4 60.1 21.2 12.4 10.6 106.5 Feb 28.5 31.4 12.2 35.1 39.6 8.4 60.1 21.2 12.4 10.6 106.5
Mar 29.7 6.2 5.7 26.1 7.7 43.3 36.5 41.6 46.0 44.8 27.0 Mar 29.7 6.2 5.7 26.1 7.7 43.3 36.5 41.6 46.0 44.8 27.0
Apr 23.7 14.2 11.8 42.1 40.7 17.3 49.6 20.9 34.5 27.8 10.9 Apr 23.7 14.2 11.8 42.1 40.7 17.3 49.6 20.9 34.5 27.8 10.9
May 68.4 6.6 34.7 99.7 73.7 22.4 66.9 118.4 48.4 72.0 41.2 May 68.4 6.6 34.7 99.7 73.7 22.4 66.9 118.4 48.4 72.0 41.2
Jun 21.5 110.0 67.2 44.7 16.7 39.3 53.4 49.3 27.3 44.6 75.6 Jun 21.5 110.0 67.2 44.7 16.7 39.3 53.4 49.3 27.3 44.6 75.6
Jul 75.2 94.6 66.8 36.3 77.5 26.7 47.5 85.0 54.4 25.3 15.5 Jul 75.2 94.6 66.8 36.3 77.5 26.7 47.5 85.0 54.4 25.3 15.5
Aug 185.1 43.4 28.0 108.9 95.1 67.6 30.6 61.1 22.5 39.2 61.9 Aug 185.1 43.4 28.0 108.9 95.1 67.6 30.6 61.1 22.5 39.2 61.9
Sep 55.3 28.3 44.8 88.9 31.2 26.2 32.5 68.3 24.1 31.2 31.9 Sep 55.3 28.3 44.8 88.9 31.2 26.2 32.5 68.3 24.1 31.2 31.9
Oct 27.6 36.1 41.9 56.5 39.2 28.5 38.1 36.1 8.8 65.9 41.6 Oct 27.6 36.1 41.9 56.5 39.2 28.5 38.1 36.1 8.8 65.9 41.6
Nov 87.0 0.6 79.7 60.2 35.3 87.8 66.0 62.4 11.4 37.2 9.6 Nov 87.0 0.6 79.7 60.2 35.3 87.8 66.0 62.4 11.4 37.2 9.6
Dec 106.5 109.5 78.5 30.2 41.3 27.6 7.4 49.9 86.2 54.2 48.3 Dec 106.5 109.5 78.5 30.2 41.3 27.6 7.4 49.9 86.2 54.2 48.3
Year 2021 2022 2023 Year 2021 2022 2023
Month Month
Jan 54.6 50.7 41.8 Jan 54.6 50.7 41.8
Feb 48.6 51.0 20.5 Feb 48.6 51.0 20.5
Mar 35.8 18.4 72.5 Mar 35.8 18.4 72.5
Apr 17.6 87.8 49.3 Apr 17.6 87.8 49.3
May 69.8 28.9 21.9 May 69.8 28.9 21.9
Jun 100.9 23.2 21.3 Jun 100.9 23.2 21.3
Jul 138.7 17.5 54.2 Jul 138.7 17.5 54.2
Aug 82.0 40.2 69.9 Aug 82.0 40.2 69.9
Sep 6.0 109.1 18.6 Sep 6.0 109.1 18.6
Oct 42.5 43.9 52.7 Oct 42.5 43.9 52.7
Nov 26.3 43.7 NaN Nov 26.3 43.7 NaN
Dec 60.5 46.9 NaN Dec 60.5 46.9 NaN
   
%% Cell type:code id:fa12c1b9 tags: %% Cell type:code id:fa12c1b9 tags:
   
``` python ``` python
ax=sns.heatmap(df_precipitation, cmap='viridis_r', annot=True, fmt=".0f") ax=sns.heatmap(df_precipitation, cmap='viridis_r', annot=True, fmt=".0f")
ax.invert_yaxis() ax.invert_yaxis()
plt.title("Precipitation in Würzburg [mm / month]"); plt.title("Precipitation in Würzburg [mm / month]");
plt.savefig('wurzburg_monthly_precipitations.png', facecolor='w', bbox_inches='tight') plt.savefig('wuerzburg_monthly_precipitation.png', facecolor='w', bbox_inches='tight')
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:470cf56e tags: %% Cell type:code id:470cf56e tags:
   
``` python ``` python
df_precipitation.sum().plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None)); df_precipitation.sum().plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:40727a96 tags: %% Cell type:code id:40727a96 tags:
   
``` python ``` python
df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\n1966 -> 2022\n[mm / Monat]', df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\n1966 -> 2022\n[mm / Monat]',
ylim=(0, None)); ylim=(0, None));
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:0a195120 tags: %% Cell type:code id:0a195120 tags:
   
``` python ``` python
df_precipitation.transpose().plot.box(showfliers=False, ylim=(0, None)) df_precipitation.transpose().plot.box(showfliers=False, ylim=(0, None))
duration = "2010 -> 2022" duration = "2010 -> 2022"
plt.title(f"Variation of monthly precipitation in Würzburg, {duration}") plt.title(f"Variation of monthly precipitation in Würzburg, {duration}")
plt.ylabel("[mm / month]"); plt.ylabel("[mm / month]");
plt.savefig('monthly_precipitation_variation_wuerzburg.png', facecolor="w", bbox_inches = 'tight') plt.savefig('wuerzburg_monthly_precipitation_variation.png', facecolor="w", bbox_inches = 'tight')
``` ```
   
%% Output %% Output
   
   
%% Cell type:code id:e5d682d5 tags: %% Cell type:code id:e5d682d5 tags:
   
``` python ``` python
# TODO: Compare 1995 -> 2021 with 2015 -> 2021 # TODO: Compare 1995 -> 2021 with 2015 -> 2021
# TODO: Get duration from min max index # TODO: Get duration from min max index
# TODO: Ridgeline for each month? # TODO: Ridgeline for each month?
# TODO: apply to Feuerbach # TODO: apply to Feuerbach
# TODO: Push and document # TODO: Push and document
# TODO: Show on map # TODO: Show on map
``` ```
   
%% Cell type:code id:803223b4 tags: %% Cell type:code id:803223b4 tags:
   
``` python ``` python
``` ```
......
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