https://www.youtube.com/watch?v=dO7o0ejtc08

https://www.youtube.com/results?search_query=t%C3%A2che+7+graphique+interactifs+lin%C3%A9aire+et+surfaciques

https://www.youtube.com/watch?v=Oht6cf-Acl0&list=TLPQMDMwNzIwMjKHiJuDXvRjjg&index=2

Tâche 1: Importation des librairies et les donnée

# Importation des librairies / fonctions

import plotly
import plotly.offline as py
import plotly.graph_objs as go
import pandas as pd
import numpy as np
from plotly.figure_factory import create_table
import plotly.express as px

gapminder = px.data.gapminder()

# gapninder est bel et bien une DataFrame pandas
type(gapminder)

pandas.core.frame.DataFrame

Tâche 2: Diagramme à barres

gapminder.describe()

	year	lifeExp	pop	gdpPercap	iso_num
count	1704.00000	1704.000000	1.704000e+03	1704.000000	1704.000000
mean	1979.50000	59.474439	2.960121e+07	7215.327081	425.880282
std	17.26533	12.917107	1.061579e+08	9857.454543	248.305709
min	1952.00000	23.599000	6.001100e+04	241.165876	4.000000
25%	1965.75000	48.198000	2.793664e+06	1202.060309	208.000000
50%	1979.50000	60.712500	7.023596e+06	3531.846988	410.000000
75%	1993.25000	70.845500	1.958522e+07	9325.462346	638.000000
max	2007.00000	82.603000	1.318683e+09	113523.132900	894.000000

gapminder.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1704 entries, 0 to 1703
Data columns (total 8 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   country    1704 non-null   object 
 1   continent  1704 non-null   object 
 2   year       1704 non-null   int64  
 3   lifeExp    1704 non-null   float64
 4   pop        1704 non-null   int64  
 5   gdpPercap  1704 non-null   float64
 6   iso_alpha  1704 non-null   object 
 7   iso_num    1704 non-null   int64  
dtypes: float64(2), int64(3), object(3)
memory usage: 106.6+ KB

# Evolution de la population du Canada

gapminder['country'].unique()

array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',
       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',
       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',
       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',
       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',
       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',
       'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba', 'Czech Republic',
       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',
       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',
       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',
       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',
       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',
       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',
       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',
       'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Mauritania',
       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Morocco',
       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',
       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman',
       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',
       'Portugal', 'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',
       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',
       'Sierra Leone', 'Singapore', 'Slovak Republic', 'Slovenia',
       'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'Sudan',
       'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',
       'Tanzania', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Tunisia',
       'Turkey', 'Uganda', 'United Kingdom', 'United States', 'Uruguay',
       'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',
       'Zambia', 'Zimbabwe'], dtype=object)

data_canada = px.data.gapminder().query("country == 'Canada'")
data_canada

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
240	Canada	Americas	1952	68.750	14785584	11367.16112	CAN	124
241	Canada	Americas	1957	69.960	17010154	12489.95006	CAN	124
242	Canada	Americas	1962	71.300	18985849	13462.48555	CAN	124
243	Canada	Americas	1967	72.130	20819767	16076.58803	CAN	124
244	Canada	Americas	1972	72.880	22284500	18970.57086	CAN	124
245	Canada	Americas	1977	74.210	23796400	22090.88306	CAN	124
246	Canada	Americas	1982	75.760	25201900	22898.79214	CAN	124
247	Canada	Americas	1987	76.860	26549700	26626.51503	CAN	124
248	Canada	Americas	1992	77.950	28523502	26342.88426	CAN	124
249	Canada	Americas	1997	78.610	30305843	28954.92589	CAN	124
250	Canada	Americas	2002	79.770	31902268	33328.96507	CAN	124
251	Canada	Americas	2007	80.653	33390141	36319.23501	CAN	124

fig = px.bar(data_canada, x = 'year', y = 'pop', title= "Evolution de la population du canada")
fig.show()

# Autres arguments de px.bar()
fig = px.bar(data_canada, x = 'year', y = 'pop', 
             hover_data = ['lifeExp', 'gdpPercap'],
             color = 'lifeExp',
             title= "Evolution de la population du canada")
fig.show()

data_rdc = px.data.gapminder().query("country == 'Congo, Dem. Rep.'")
data_rdc

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
324	Congo, Dem. Rep.	Africa	1952	39.143	14100005	780.542326	COD	180
325	Congo, Dem. Rep.	Africa	1957	40.652	15577932	905.860230	COD	180
326	Congo, Dem. Rep.	Africa	1962	42.122	17486434	896.314634	COD	180
327	Congo, Dem. Rep.	Africa	1967	44.056	19941073	861.593242	COD	180
328	Congo, Dem. Rep.	Africa	1972	45.989	23007669	904.896068	COD	180
329	Congo, Dem. Rep.	Africa	1977	47.804	26480870	795.757282	COD	180
330	Congo, Dem. Rep.	Africa	1982	47.784	30646495	673.747818	COD	180
331	Congo, Dem. Rep.	Africa	1987	47.412	35481645	672.774812	COD	180
332	Congo, Dem. Rep.	Africa	1992	45.548	41672143	457.719181	COD	180
333	Congo, Dem. Rep.	Africa	1997	42.587	47798986	312.188423	COD	180
334	Congo, Dem. Rep.	Africa	2002	44.966	55379852	241.165876	COD	180
335	Congo, Dem. Rep.	Africa	2007	46.462	64606759	277.551859	COD	180

# Autres arguments de px.bar()
fig1 = px.bar(data_rdc, x = 'year', y = 'pop', 
             hover_data = ['lifeExp', 'gdpPercap'],
             color = 'lifeExp',
             title= "Evolution de la population de la RDC jusqu'à 2007")
fig1.show()

Tâche 3: Nuage de points

# DataFrame de l'année 2007

gapminder2007 = gapminder.query("year == 2007")
gapminder2007

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
11	Afghanistan	Asia	2007	43.828	31889923	974.580338	AFG	4
23	Albania	Europe	2007	76.423	3600523	5937.029526	ALB	8
35	Algeria	Africa	2007	72.301	33333216	6223.367465	DZA	12
47	Angola	Africa	2007	42.731	12420476	4797.231267	AGO	24
59	Argentina	Americas	2007	75.320	40301927	12779.379640	ARG	32
...	...	...	...	...	...	...	...	...
1655	Vietnam	Asia	2007	74.249	85262356	2441.576404	VNM	704
1667	West Bank and Gaza	Asia	2007	73.422	4018332	3025.349798	PSE	275
1679	Yemen, Rep.	Asia	2007	62.698	22211743	2280.769906	YEM	887
1691	Zambia	Africa	2007	42.384	11746035	1271.211593	ZMB	894
1703	Zimbabwe	Africa	2007	43.487	12311143	469.709298	ZWE	716

142 rows × 8 columns

# Expérance de vie en fonction du produit intérieur brut
px.scatter(gapminder2007, x="gdpPercap", y="lifeExp", 
           title="Expérance de vie en fonction du produit intérieur brut pour 2007")

gapminder['continent'].unique()

array(['Asia', 'Europe', 'Africa', 'Americas', 'Oceania'], dtype=object)

# Colorier les points par une variable catégorielle
px.scatter(gapminder2007, x="gdpPercap", y="lifeExp", 
           color='continent',
           title="Expérance de vie en fonction du produit intérieur brut pour 2007")

Tâche 4: Graphique interactifs à bulles (Bulle Charts)

px.scatter(gapminder2007, x="gdpPercap", y="lifeExp", 
           color='continent',
           size='pop',
           title="Expérance de vie en fonction du produit intérieur brut pour 2007")

# Ajout de la variable 'country'
px.scatter(gapminder2007, x="gdpPercap", y="lifeExp", 
           color='continent',
           size='pop', 
           hover_name='country',
           title="Expérance de vie en fonction du produit intérieur brut pour 2007")

Tâche 5: Création d'animations interactives

# Animation
px.scatter(gapminder, x="gdpPercap", y="lifeExp", 
           color='continent',
           size='pop', 
           hover_name='country',
           animation_frame="year",
           animation_group="country",
           title="Expérance de vie en fonction du produit intérieur brut")

# Mettre l'axe x en log
px.scatter(gapminder, 
           x="gdpPercap", 
           y="lifeExp", 
           log_x=True,
           color='continent',
           size='pop', 
           hover_name='country',
           animation_frame="year",
           animation_group="country",
           title="Expérance de vie en fonction du produit intérieur brut")
#fig.show()

# facet_col
px.scatter(gapminder, 
           x="gdpPercap", 
           y="lifeExp", 
           log_x=True,
           facet_col="continent",
           color='continent',
           size='pop', 
           hover_name='country',
           animation_frame="year",
           animation_group="country",
           title="Expérance de vie en fonction du produit intérieur brut")

Tâche 6: Représentation des données géographiques

gapminder.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

fig2 = px.line_geo(gapminder2007, locations= 'iso_alpha', color='continent', projection='orthographic')
fig2.show()

Vous pouvez consulter la documentation de la fonction px.cloropleth()

px.choropleth(gapminder, locations="iso_alpha",
              color="lifeExp",
              hover_name="country",
              animation_frame="year",
              color_continuous_scale=px.colors.sequential.Plasma,
              projection="natural earth")

# Projection en 2D

px.choropleth(gapminder, locations="iso_alpha",
              color="lifeExp",
              hover_name="country",
              animation_frame="year")

Tâche 7 Graphique interactifs linéaire et surfaciques

px.line(data_canada, x = "year", y = "lifeExp", hover_name= "pop")

px.line(gapminder, x = "year", y = "lifeExp", hover_name= "country",
        color = "continent", line_group = "country",
        line_shape = "spline", render_mode = "svg")

px.area(gapminder, x="year", y="pop", color="continent", line_group="country")