sig_num_columns.py

def sig_num_columns(X_train, y_train, p_thres=0.05):
    """Which numerical features held in columns within the training data set are significantly correlated with 
    the target. Returns a dataframe with the column name and its p value. pvalue set to 0.05 for 
    95% confidence level enter a new p_thres if you want to change it. Only returns the significant columns
    only pass numerical columns to the function! Other column types will return a shape error1"""
    from scipy.stats import linregress
    global sig_num
    sig_num = {}
    for col in X_train:
        slope, intercept, rvalue, pvalue, stderr = linregress(X_train[col], y_train)
        if pvalue <= p_thres:
            sig_num[col] = pvalue
    sig_num = pd.DataFrame.from_dict(sig_num, orient='index')
    sig_num = sig_num[0].sort_values(ascending=True)
    return sig_num