# Get the loading values for each PC of Xworkdf_std
pc_loading_values = pca_std.components_
# Create a dataframe of the loading values and feature names for each PC
pc_loading_values_df = pd.DataFrame(columns=['PC', 'feature', 'loading_value'])
for i in range(len(pc_loading_values))꞉
    loading_values_df = pd.DataFrame({'feature'꞉ Xworkdf_std.columns, 'loading_value'꞉ p
    loading_values_df['PC'] = i+1
    pc_loading_values_df = pc_loading_values_df.append(loading_values_df)
# Sort the dataframe by loading value for each PC
pc_loading_values_df.sort_values(by='loading_value', ascending=False, inplace=True)
# Print the top positively and negatively correlated features for each PC
for i in range(1, len(pc_loading_values)+1)꞉
    print("\n")
    print("*******************************************************")
    print("*****Top positively correlated features for PC%d꞉ *****" % i)
    print(pc_loading_values_df[pc_loading_values_df.PC == i].head(5))
    print("*****Top negatively correlated features for PC%d꞉ *****" % I)
print(pc_loading_values_df[pc_loading_values_df.PC == i].tail(5))
    print("*******************************************************")
*******************************************************
*****Top positively correlated features for PC1꞉ *****
   PC                feature  loading_value
0   1             Power (hp)       0.256465
13  1  Fuel system_injection       0.237628
4   1    Fuel capacity (lts)       0.231231
2   1              Bore (mm)       0.229115
1   1            Torque (Nm)       0.201063
*****Top negatively correlated features for PC1꞉ *****
   PC                          feature  loading_value
35  1         Front brakes_single disc      ‑0.157724
66  1        Engine stroke_ two‑stroke      ‑0.157918
95  1   Fuel control_Not Given/Unknown      ‑0.159546
11  1          Fuel system_carburettor      ‑0.170060
54  1  Engine cylinder_Single cylinder      ‑0.237255
*******************************************************
*******************************************************
*****Top positively correlated features for PC2꞉ *****
    PC                             feature  loading_value
38   2  Front suspension_not given/unknown       0.304755
14   2       Fuel system_not given/unknown       0.257800
27   2              Front brakes_dual disc       0.206781
128  2                     Rear tire_other       0.202512
122  2                    Front tire_other       0.177771
*****Top negatively correlated features for PC2꞉ *****
    PC                          feature  loading_value
10   2    