!pip install pandas-datareader

	import pandas_datareader as pdr
	import matplotlib.pyplot as plt
	import numpy as np
	plt.style.use('ggplot')
	%matplotlib inline

	df_microsoft = pdr.get_data_yahoo('MSFT')

	plt.figure(figsize=(16,6))
	plt.plot(df_microsoft.loc['2017','Close'])

	les_max = df_microsoft.High.resample('W').agg(['max', 'mean'])
	les_min = df_microsoft.High.resample('W').agg(['min'])

	plt.figure(figsize=(16,6))
	les_max['2018']['mean'].plot(c='green')
	plt.fill_between(
    		les_max['2018'].index,
    		les_max['2018']['max'],
    		les_min['2018']['min'],
    		alpha=0.3
	)

	price_max_from_2017_to_2022 = df_microsoft.High.agg(['max'])
	price_min_from_2017_to_2022 = df_microsoft.Low.agg(['min'])

	index_max = df_microsoft[df_microsoft['High'] == price_max_from_2017_to_2022['max']].index
	index_min = df_microsoft[df_microsoft['Low'] == price_min_from_2017_to_2022['min']].index
	plt.figure(figsize=(16, 8))
	plt.plot(df_microsoft.index, df_microsoft.High, c='g')
	plt.scatter(np.array([index_max]), price_max_from_2017_to_2022['max'], lw=13, c="b", label=f'Le {index_max[0].strftime("%d/%m%Y")}')
	plt.scatter(np.array([index_min]), price_min_from_2017_to_2022['min'], lw=13, c="r" , label=f'Le {index_min[0].strftime("%d/%m/%Y")}')
	plt.legend()

	def periode_fr(periode):
    		p = ''
    		if periode == 'Q':
        		p = 'Trimestre'
    		elif periode == 'M':
        		p = 'Mois'
    		elif periode == 'D':
        		p = 'Jour'
    		elif periode == 'W':
        		p = 'Semaine'
    		return p

	def graphiphique_perso(df_microsoft, periode, annee, col, fun_agg, c='g'):
    		data = df_microsoft.resample(periode).agg(['max', 'mean', 'min']);
    		plt.figure(figsize=(16, 5))
    		plt.plot(data[annee][col][fun_agg], label=f'{fun_agg}', c=c)
    		plt.fill_between(
        		data[annee][col].index, 
        		data[annee][col]['min'], 
        		data[annee][col]['max'],
        		alpha=0.3)
    		plt.title(f'L\'analyse de la colonne {col} par {periode_fr(periode)}')
    		plt.legend();