Danish weather

Import some plotting libraries and set some defaults:

%config InlineBackend.figure_formats = ['svg'] 
from IPython.display import display, Markdown
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
sns.set_style("whitegrid")
import random
import numpy as np
import pandas as pd

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	1.747650	1.115449
1	1	1.452317	1.134909
2	2	1.783481	1.239925
3	3	1.837637	1.460844
4	4	1.308335	1.890218
...	...	...	...
145	145	1.801730	1.499466
146	146	1.434560	1.981628
147	147	1.394330	1.913791
148	148	1.122685	1.730421
149	149	1.478548	1.712684

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	1.747650
1	1	wind	1.452317
2	2	wind	1.783481
3	3	wind	1.837637
4	4	wind	1.308335
...	...	...	...
295	145	precipitation	1.499466
296	146	precipitation	1.981628
297	147	precipitation	1.913791
298	148	precipitation	1.730421
299	149	precipitation	1.712684

300 rows × 3 columns

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

Figure 1.1: Danish weather: This is the weather forcast for your project

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