Table Exploration
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                          June 2018

Feel free to add your tips and tricks, anything useful/semi-useful will have a home here while being periodically updated and improved by users. 

 

Table of Strong Collections

data_corr = data.corr()
# Set the threshold to select only highly correlated attributes
threshold = 0.5
# List of pairs along with correlation above threshold
corr_list = []
#Search for the highly correlated pairs
for i in range(0,size): #for 'size' features
    for j in range(i+1,size): #avoid repetition
        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):
            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index
#Sort to show higher ones first            
s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))
#Print correlations and column names
for v,i,j in s_corr_list:
    print ("%s and %s = %.2f" % (cols[i],cols[j],v))

Search for Highly Correlated Pairs

#Search for the highly correlated pairs
for i in range(0,size): #for 'size' features
    for j in range(i+1,size): #avoid repetition
        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):
            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index

#Sort to show higher ones first            
s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))

Correlation with Target

# Correlation amongst top feautres and target
corr = df.corr()
abb = corr["target"].sort_values(ascending=False)[::5].index.values.tolist()
corr = corr[corr.index.isin(abb)]
corr = corr[abb]
corr.ix[:,0].abs().sort_values(ascending=False)
corr.shape

Classification Report