Danish weather

Import some plotting libraries and set some defaults:

import sys
import numpy as np
import pandas as pd
from IPython.display import display, Markdown
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
sns.set_style("whitegrid")

import random
random_seed = 5

sys.path.append('..')
from global_params import load_params

Tip:
Producing figures in svg format (scalable vector graphics) makes for sharp plots on webpages. However, if you make plots with thousands of observations you should set this to 'png' instead:

%config InlineBackend.figure_formats = ['svg']

Tip:
Some values apply globally to your analysis. E.g., sample sizes, cutoffs, names, rates, etc. Keeping those in a a yml file like weather_params.yml and loading them in each notebook avoids the risk of manually adding/updating them in each notebook where they are used. You can use the load_globals function imported above from global_params.py to produce an object holding all the global values:

params = load_params('../weather_params.yml')
params

{'nr_project_days': 150}

params.nr_project_days

Here is some computation:

nr_project_days = 150
df = pd.DataFrame({'day': list(range(nr_project_days)), 
              'wind': [random.random()+1 for i in range(nr_project_days)], 
              'precipitation': [random.random()+1 for i in range(nr_project_days)]})
df

	day	wind	precipitation
0	0	1.013950	1.900108
1	1	1.433512	1.612078
2	2	1.465184	1.844704
3	3	1.438446	1.120334
4	4	1.052238	1.491236
...	...	...	...
145	145	1.071599	1.038249
146	146	1.884493	1.853783
147	147	1.743312	1.075927
148	148	1.794445	1.605397
149	149	1.266453	1.681632

150 rows × 3 columns

Weather data

Weather data was collected… blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah

long_format = df.melt(id_vars=['day'], value_vars=['wind', 'precipitation'], var_name='weather', value_name='value')
long_format

	day	weather	value
0	0	wind	1.013950
1	1	wind	1.433512
2	2	wind	1.465184
3	3	wind	1.438446
4	4	wind	1.052238
...	...	...	...
295	145	precipitation	1.038249
296	146	precipitation	1.853783
297	147	precipitation	1.075927
298	148	precipitation	1.605397
299	149	precipitation	1.681632

300 rows × 3 columns

sns.lineplot(data=long_format, x='day', y='value', hue='weather')
plt.ylim(bottom=0) ;

Figure 2.1: Danish weather: This is the weather forcast for your project

From this plot, it seems Danish weather is quite unpredictable.