Travaux Pratiques: Chimie et Numérique

1. Version de numpy

import numpy

print(numpy.__version__)


import numpy as np

print(np.__version__)


2. Tableau

import numpy as np

a = np.array([1, 2, 3, 4])

print(a)
print(type(a))


import numpy as np

a = np.array([1, 2, 3, 4])

print(a.shape)


3. Tableau à deux dimensions

import numpy as np

a = np.array([[1, 2, 3, 4], [5,6,7,8]])

print(a.shape)


4. Tableau à trois dimensions

import numpy as np

a = np.array([[[1, 2], [5,6]], [[1, 2], [5,6]]])

print(a.shape)
print(a.ndim)


import numpy as np

a = np.array([[[1, 2], [5,6]], [[1, 2], [5,6]]])

print(a.dtype)


import numpy as np

a = np.array([[[1.1, 2.2], [5.3,6.4]], [[1.4, 2.5], [5.5,6.4]]])

print(a.dtype)


import numpy as np

a = np.array([[[1.1, 2.2], [5.3,6.4]], [[1.4, 2.5], [5.5,6.4]]])

print(a.ndim)


import numpy as np

a = np.array([[[1.1, 2.2], [5.3,6.4]], [[1.4, 2.5], [5.5,6.4]]])

print(a.itemsize)


5. Opérations

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.array([[4, 5], [6, 7]])

c = a + b
print(c)

c = a - b
print(c)

c = a * b
print(c)

d = np.dot(a, b)
print(d)


6. Forme

import numpy as np

a = np.array([1,2,3,4,5,6,7,8])
print(a)
print(a.shape)

b = a.reshape(4,2)
print(b)
print(b.shape)

b = a.reshape(2,4)
print(b)
print(b.shape)

b = a.reshape(2,2,2)
print(b)
print(b.shape)


1. Graphique avec matplotlib

import matplotlib.pyplot as plt
import numpy as np

a = np.array([1,2,3,4,5,6,7,8])
b = np.array([1,2,3,4,5,6,7,8])
plt.plot(a,b)


import matplotlib.pyplot as plt
import numpy as np

a = np.array([1,2,3,4,5,6,7,8])
b = np.array([1,4,9,16,25,36,49,64])
plt.plot(a,b)


2. Lecture de fichiers CSV et tracer les données.

Copier ce fichier CSV dans votre répertoire. Le fichier contient la population entre 1901 et 2016. Tracer un graphique.

import numpy as np
dataset = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")
print(dataset)


Exécutez le code ci-dessous et comparez la différence.

import numpy as np

year, population = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8", unpack= True)

print(year)


import numpy as np
import matplotlib.pyplot as plt

year, population = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8", unpack= True)

plt.plot(year, population)
plt.show()


3. Diagramme en barres

import numpy as np
import matplotlib.pyplot as plt

year, population = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8", unpack= True)

plt.bar(year, population)
plt.show()


import matplotlib.pyplot as plot

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
y = [0, 1, 0, 0, 1, 0, 1, 1, 1, 0]
plot.plot(x, y, linewidth=3, drawstyle="steps", color="#00363a")
plot.show()


import numpy as np
import matplotlib.pyplot as plt

year, population = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8", unpack= True)

plt.plot(year, population, linewidth=3, drawstyle="steps", color="#00363a")
plt.show()


1. Manipulation des données

import numpy as np

dataset = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

print(dataset[:]['year'])
print(dataset[:]['population'])


import numpy as np

dataset = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

print(dataset[0:10]['year'])


2. Analyse des données

import numpy as np

dataset = np.loadtxt("population.csv", dtype={'names': ('year', 'population'), 'formats': ('i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

population = dataset[:]['population']
print(population.max())
print(population.min())
print(population.mean())
print(population.std())
print(population.sum())


SELECT DISTINCT ?countryLabel ?year ?population
WHERE {
 ?country wdt:P31 wd:Q6256; #Country
   p:P1082 ?populationStatement;
   rdfs:label ?countryLabel. #Label
 ?populationStatement ps:P1082 ?population; #population
 pq:P585 ?date. #period in time
 FILTER(lang(?countryLabel)="en") #Label in English
 BIND(YEAR(?date) as ?year)
 FILTER(?year>2000)
}
ORDER by ?countryLabel ?year


Voici une requête SPARQL. Exécutez cette requête sur Wikidata et téléchargez les résultats en format CSV en nommant le fichier countrypopulation.csv.

import numpy as np

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")
print(dataset)


3. pandas

import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe)


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.dtypes)
print(dataframe.columns)
print(dataframe.index)


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.head())
print(dataframe.tail())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.head(1))
print(dataframe.tail(1))


4. Sélection de données

import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

print(dataframe['year'].value_counts())
print(dataframe['countryLabel'].value_counts())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.loc[dataframe['year']==2017])


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.loc[dataframe['countryLabel'] =="Sweden"])


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
swedenpopulation = dataframe.loc[dataframe['countryLabel'] =="Sweden"]
swedenpopulation2017 = swedenpopulation.loc[swedenpopulation['year'] == 2017]
print(swedenpopulation2017)


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
print(dataframe.loc[(dataframe['year']==2017) & (dataframe['countryLabel'] =="Sweden")])


5. Tri de données

import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
population = dataframe.sort_values(by='population')
print(population.head(1))
print(population.tail(1))


6. Analyse de données

import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
'formats': ('<U100', 'i4', 'i')},
skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])
dataframe2015 = dataframe.loc[dataframe['year']==2015]
print(dataframe2015)
print(dataframe2015.describe(include=[np.int32])['population'])


1. Groupement de résultat

import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel']).groups
print(groups.keys())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel'])
print(groups.first())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel'])
print(groups.last())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel'])
print(groups['population'].max())
print(groups['population'].min())


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel'], as_index=False)
print(groups.agg({'population' : max}))


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['countryLabel'], as_index=False)
print(groups.agg({'population' : min}))


import numpy as np
import pandas as pd

dataset = np.loadtxt("countrypopulation.csv", dtype={'names': ('countryLabel', 'year', 'population'),
  'formats': ('<U100', 'i4', 'i')},
  skiprows=1, delimiter=",", encoding="UTF-8")

dataframe = pd.DataFrame(dataset, columns=['countryLabel', 'year', 'population'])

groups = dataframe.groupby(['year'], as_index=False)
print(groups.agg({'population' : max}))


Question 1: Exécutez le requête SPARQL ci-dessus sur Wikidata et cette fois téléchargez les résultats en format JSON en nommant le fichier countrypopulation.json. Codez en Python en utilisant pandas afin d'avoir les résultats suivants:

• Calculez la population maximum, minimum et moyenne de chaque pays en 2015
• Calculez la population moyenne, maximum et minimum de chaque pays
• Calculez la population maximum, minimum et moyenne de chaque année pour les pays dont les noms commencent par 'A'
• Tracer un graphique montrant la population de tous les pays en 2015
• Tracer un graphique montrant la population de tous les pays

Indices

• Python Json
• pandas.DataFrame.astype
• matplotlib.pyplot.subplot