#Matriz de covarianza, correlaciones, gráfica de dependencia líneal y número de condición
cov_df = df_num_norm.cov()
var_global = sum(np.diag(cov_df))
det = np.linalg.det(cov_df)
corr_df = df_num_norm.corr()
sns.heatmap(corr_df, center=0, cmap='Blues_r')
cond_cov = np.linalg.cond(cov_df)
# In[]
#Identificación de outliers y Eliminación del 10%
#a=[]
a_rob = []
media_num_norm = np.array(df_num_norm.mean())
mediana_num_norm = np.array(df_num_norm.median())
inv_cov = np.linalg.inv(np.array(cov_df))
for i in range(len(df_num_norm.index)):
#b = distance.mahalanobis(np.array(df_num_norm.iloc[i,:]),media_num_norm,inv_cov)
b_rob = distance.mahalanobis(np.array(df_num_norm.iloc[i, :]),
mediana_num_norm, inv_cov)
#a.append(b)
a_rob.append(b_rob)
#df_num_norm['mahal_normal'] = a
df_num_norm['mahal_rob'] = a_rob
#df_v2['mahal_normal'] = a
df_v2['mahal_rob'] = a_rob
#a = pd.DataFrame(a)
