Printing data in Tabular Form in Python

Question

I'm trying to look for accuarcy/PRecision/Reacll etc... So i used this code and it works verry well for me but actually i want to change the output form as tabular My output:

    Column 2 acc: 1.0
    Column 2 p: 1.0
    Column 2 r: 1.0
    Column 1 acc: 1.0
    Column 1 p: 1.0
    Column 1 r: 1.0
    Column 3 acc: 1.0
    Column 3 p: 1.0
    Column 3 r: 1.0

The output that i want:

+----------+-----------+-------+---------+
|  Feature | Precision |Recall | Accuracy|
+----------+-----------+-------+---------+
|    1     |    1.0   |  1.0   |  1.0    |
|    2     |    1.0   |  1.0   |  1.0    |
|    3     |    1.0   |  1.0   |  1.0    |
+----------+----------+--------+---------+

My code:

def calc_acc(original, predect1):
    common_columns = list(set(original.columns).intersection(predect1.columns))

    avg_a = 0.0
    avg_p = 0.0
    avg_r = 0.0
    for c in common_columns:
        c_acc = accuracy_score(original[c], predect1[c])
        p = precision_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c]))
        r = recall_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c]))
        print(f'Column {c} acc: {c_acc}')
        print(f'Column {c} p: {p}')
        print(f'Column {c} r: {r}')
        avg_a += c_acc/len(common_columns)
        avg_p += p/len(common_columns)
        avg_r += r/len(common_columns)

NB: c is the column

Answer 1

You can use pandas dataframe.

import pandas as pd

df = pd.DataFrame({
    "Feature": col,
    "Precision": prec,
    "Recall": rec,
    "Accuracy": acc
})

print(df)

Note: col, prec, rec, acc --> list datatypes. Create these list from your for loop and then just convert them into a dataframe, that's it.

Answer 2

use this code to draw PrettyTable:

from prettytable import PrettyTable
pt = PrettyTable()
pt.field_names = ['Feature','Precision','Recall','Accuracy']
pt.add_row([c,p,r,c_acc])

finally your desire code and output like this:

from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score, recall_score
from prettytable import PrettyTable
 

def calc_acc(original, predect1):
    common_columns = list(set(original.columns).intersection(predect1.columns))

    avg_a = 0.0
    avg_p = 0.0
    avg_r = 0.0
    
    pt = PrettyTable()
    pt.field_names = ['Feature','Precision','Recall','Accuracy']

    for c in common_columns:
        c_acc = accuracy_score(original[c], predect1[c])
        p = precision_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c]))
        r = recall_score(original[c], predect1[c], average='macro', labels=np.unique(predect1[c]))

        pt.add_row([c,p,r,c_acc])
        
        avg_a += c_acc/len(common_columns)
        avg_p += p/len(common_columns)
        avg_r += r/len(common_columns)
        
    print(pt)
        
pre = [[1, 1, 3], [2, 3, 4]]
pre = pd.DataFrame(pre, columns= ['1', '2', '3'])

calc_acc(pre, pre)

output: