Data Cleaning : Complete Case Analysis

[@derrickntsieri71]

trés fructieux ce cours

[@preferebrigesmounzeomoundo8352]

Cours très intéressant, s'il vous plait pouvez vous mettre lien de la source de donnée sinon le dataset que vous avez utilisé?

[@fredwilliamson8700]

import numpy as np import pandas as pd d = { "matricule": ["Cr7","Luka10", "Ney10","Toni8", "Kylian7", "Kostas9"], "nom" : ["Ronaldo", "Modric", "Neymar", "Kross", "Mbappe", "Mitroglou"], "age": [37, 36, 30, 32, 23, np.nan], "taille": [1.87, 1.72, 1.75, 1.83, 1.78, 1.88], "masse":[85, 66, 68, 76, 73, 72], "salaire": [26520000, 10500000, 36800000, 10400000, 22000000, 1500000], "pays": ["Potugal", "Croatie", "Brezil","Allemagne","France", "Grèce"], "club": ["Manchester United", "Real Madrid", "Paris Saint-Germain", "Real Madrid", "Paris Saint-Germain", "Marseille"] } df = pd.DataFrame(d) df