SENCE 2023 Examples - 3. Semester¶

Common libraries and parameters¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd import matplotlib.pyplot as plt import seaborn as sns

In [2]:

# Don't show too many rows in Pandas Dataframes
pd.options.display.max_rows = 7

# Don't show too many rows in Pandas Dataframes pd.options.display.max_rows = 7

In [3]:

# Larger plots
plt.rcParams['figure.figsize'] = [16, 8]

# Larger plots plt.rcParams['figure.figsize'] = [16, 8]

Bubble map¶

https://www.python-graph-gallery.com/bubble-map/

In [4]:

# "pip install folium" might be needed first : https://pypi.org/project/folium/
import folium

# "pip install folium" might be needed first : https://pypi.org/project/folium/ import folium

In [5]:

# Make a data frame with dots to show on the map.
# All the values are the same, in order to check if the projection distorts the circles
data = pd.DataFrame({
   'lon':[-58, 2, 145, 30.32, -4.03, -73.57, 36.82, -38.5],
   'lat':[-34, 49, -38, 59.93, 5.33, 45.52, -1.29, -12.97],
   'name':['Buenos Aires', 'Paris', 'Melbourne', 'St Petersbourg', 'Abidjan', 'Montreal', 'Nairobi', 'Salvador'],
   'value': [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
})

data

# Make a data frame with dots to show on the map. # All the values are the same, in order to check if the projection distorts the circles data = pd.DataFrame({ 'lon':[-58, 2, 145, 30.32, -4.03, -73.57, 36.82, -38.5], 'lat':[-34, 49, -38, 59.93, 5.33, 45.52, -1.29, -12.97], 'name':['Buenos Aires', 'Paris', 'Melbourne', 'St Petersbourg', 'Abidjan', 'Montreal', 'Nairobi', 'Salvador'], 'value': [50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0] }) data

Out[5]:

	lon	lat	name	value
0	-58.00	-34.00	Buenos Aires	50.0
1	2.00	49.00	Paris	50.0
2	145.00	-38.00	Melbourne	50.0
...	...	...	...	...
5	-73.57	45.52	Montreal	50.0
6	36.82	-1.29	Nairobi	50.0
7	-38.50	-12.97	Salvador	50.0

8 rows × 4 columns

Circles are distorted by Mercator projection¶

see https://en.wikipedia.org/wiki/Tissot%27s_indicatrix for more information

In [6]:

# Make an empty map
m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2)

# add marker one by one on the map
for city in data.itertuples():
    folium.Circle(
        location=[city.lat, city.lon],
        popup=city.name,
        radius=city.value * 20000.0,
        color='crimson',
        fill=True,
        fill_color='crimson'
    ).add_to(m)

m.get_root().html.add_child(folium.Element("<h3 align='center'>Map with distorted circles</h3>"))

# Show the map
m

# Make an empty map m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2) # add marker one by one on the map for city in data.itertuples(): folium.Circle( location=[city.lat, city.lon], popup=city.name, radius=city.value * 20000.0, color='crimson', fill=True, fill_color='crimson' ).add_to(m) m.get_root().html.add_child(folium.Element("

Map with distorted circles

")) # Show the map m

Out[6]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Avoiding deformation¶

In [7]:

import math
m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2)

# add marker one by one on the map, and account for Mercator deformation
for city in data.itertuples():
    local_deformation = math.cos(city.lat * math.pi / 180)
    folium.Circle(
        location=[city.lat, city.lon],
        popup='%s (%.1f)' % (city.name, city.value),
        radius=city.value * 20000.0 * local_deformation,
        color='crimson',
        fill=True,
        fill_color='crimson'
    ).add_to(m)

m.get_root().html.add_child(folium.Element("<h3 align='center'>Map with circles of correct size</h3>"))

m.save('output/bubble_map.html')

m

import math m = folium.Map(location=[20,0], tiles="OpenStreetMap", zoom_start=2) # add marker one by one on the map, and account for Mercator deformation for city in data.itertuples(): local_deformation = math.cos(city.lat * math.pi / 180) folium.Circle( location=[city.lat, city.lon], popup='%s (%.1f)' % (city.name, city.value), radius=city.value * 20000.0 * local_deformation, color='crimson', fill=True, fill_color='crimson' ).add_to(m) m.get_root().html.add_child(folium.Element("

Map with circles of correct size

")) m.save('output/bubble_map.html') m

Out[7]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Heatmap¶

https://www.python-graph-gallery.com/heatmap/

Basic example¶

In [8]:

# initialize columns
data = {
    'A': [0, 1, 2, 3, 4, 5, 6],
    'B': [1, 2, 3, 4, 5, 6, 7],
    'C': [2, 3, 4, 5, 6, 7, 8],
    'D': [3, 4, 5, 6, 7, 8, 9],
    'E': [4, 5, 6, 7, 8, 9, 10],
    'F': [5, 6, 7, 8, 9, 10, 11]
}
df = pd.DataFrame(data)

# initialize columns data = { 'A': [0, 1, 2, 3, 4, 5, 6], 'B': [1, 2, 3, 4, 5, 6, 7], 'C': [2, 3, 4, 5, 6, 7, 8], 'D': [3, 4, 5, 6, 7, 8, 9], 'E': [4, 5, 6, 7, 8, 9, 10], 'F': [5, 6, 7, 8, 9, 10, 11] } df = pd.DataFrame(data)

In [9]:

df

df

Out[9]:

	A	B	C	D	E	F
0	0	1	2	3	4	5
1	1	2	3	4	5	6
2	2	3	4	5	6	7
3	3	4	5	6	7	8
4	4	5	6	7	8	9
5	5	6	7	8	9	10
6	6	7	8	9	10	11

In [10]:

colors = 'viridis' # See https://matplotlib.org/stable/gallery/color/colormap_reference.html
sns.heatmap(df, cmap=colors)
plt.title("Heatmap from pandas dataframe, with '%s' colormap." % colors)
plt.show()

colors = 'viridis' # See https://matplotlib.org/stable/gallery/color/colormap_reference.html sns.heatmap(df, cmap=colors) plt.title("Heatmap from pandas dataframe, with '%s' colormap." % colors) plt.show()

Heatmap from timeseries¶

In [11]:

# Parse a whole year of weather data
weather_df = pd.read_csv('output/SchPark01.csv',
            sep = ';',
            na_values = ' ',
            names = ['date', 'time', 'ghi', 'ta'],
            parse_dates = [[0, 1]],
            index_col = 'date_time'
           )
weather_df

# Parse a whole year of weather data weather_df = pd.read_csv('output/SchPark01.csv', sep = ';', na_values = ' ', names = ['date', 'time', 'ghi', 'ta'], parse_dates = [[0, 1]], index_col = 'date_time' ) weather_df

Out[11]:

	ghi	ta
date_time
2011-01-01 00:00:00	0.0	-0.6
2011-01-01 00:15:00	0.0	-0.4
2011-01-01 00:30:00	0.0	-0.5
...	...	...
2011-12-31 23:15:00	0.0	8.4
2011-12-31 23:30:00	0.0	8.5
2011-12-31 23:45:00	0.0	8.1

35040 rows × 2 columns

In [12]:

# Temperatures(day_of_year, time)
temperatures = pd.pivot_table(weather_df, values='ta', index=weather_df.index.time, columns=weather_df.index.dayofyear)
temperatures

# Temperatures(day_of_year, time) temperatures = pd.pivot_table(weather_df, values='ta', index=weather_df.index.time, columns=weather_df.index.dayofyear) temperatures

Out[12]:

date_time	1	2	3	4	5	6	7	8	9	10	...	356	357	358	359	360	361	362	363	364	365
00:00:00	-0.6	1.0	0.7	-3.3	-6.7	-2.0	8.4	9.6	10.9	3.9	...	2.9	6.9	7.2	4.9	6.0	7.9	5.3	3.5	4.7	4.6
00:15:00	-0.4	1.0	0.5	-3.7	-7.9	-2.5	8.4	9.4	10.7	3.8	...	2.8	7.0	7.1	4.9	6.0	8.2	5.2	3.6	4.3	4.6
00:30:00	-0.5	1.0	0.5	-3.0	-7.4	-2.1	8.4	9.2	10.8	3.6	...	2.8	7.0	7.1	4.8	6.0	8.2	4.9	4.4	5.2	4.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
23:15:00	1.0	0.6	-3.5	-6.9	-2.2	8.4	9.0	10.5	4.2	3.7	...	6.7	7.3	5.0	6.0	8.3	5.5	5.0	4.4	4.1	8.4
23:30:00	1.0	0.5	-3.5	-7.3	-2.2	8.4	10.2	10.7	4.2	3.5	...	6.7	7.3	5.2	6.1	8.5	5.4	4.4	4.5	4.2	8.5
23:45:00	1.0	0.7	-3.3	-6.9	-2.5	8.6	10.4	10.9	4.1	3.5	...	6.9	7.2	4.9	6.1	8.1	5.3	3.9	5.1	4.6	8.1

96 rows × 357 columns

In [13]:

sns.heatmap(temperatures, annot=False)
plt.title('Temperatures in Scharnhauser Park, 2011')
plt.show()

sns.heatmap(temperatures, annot=False) plt.title('Temperatures in Scharnhauser Park, 2011') plt.show()

Correlogram¶

https://www.python-graph-gallery.com/correlogram/

In [14]:

# What are the available datasets?
', '.join(sns.get_dataset_names())

# What are the available datasets? ', '.join(sns.get_dataset_names())

Out[14]:

'anagrams, anscombe, attention, brain_networks, car_crashes, diamonds, dots, dowjones, exercise, flights, fmri, geyser, glue, healthexp, iris, mpg, penguins, planets, seaice, taxis, tips, titanic'

In [15]:

penguins_df = sns.load_dataset('penguins')
penguins_df

penguins_df = sns.load_dataset('penguins') penguins_df

Out[15]:

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
...	...	...	...	...	...	...	...
341	Gentoo	Biscoe	50.4	15.7	222.0	5750.0	Male
342	Gentoo	Biscoe	45.2	14.8	212.0	5200.0	Female
343	Gentoo	Biscoe	49.9	16.1	213.0	5400.0	Male

344 rows × 7 columns

In [16]:

# Basic correlogram
sns.pairplot(penguins_df, hue='species')
plt.show()

# Basic correlogram sns.pairplot(penguins_df, hue='species') plt.show()