Python sklearn - average classification reports
It maybe a little bit complicated, since I convert the reports to pandas.DataFrame for calculation. But I think it's worth it, because it works well with two or more report as well. Try below:

import pandas as pd
import numpy as np
from functools import reduce
def report_average(*args):
    report_list = list()
    for report in args:
        splited = [' '.join(x.split()) for x in report.split('\n\n')]
        header = [x for x in splited[0].split(' ')]
        data = np.array(splited[1].split(' ')).reshape(-1, len(header) + 1)
        data = np.delete(data, 0, 1).astype(float)
        avg_total = np.array([x for x in splited[2].split(' ')][3:]).astype(float).reshape(-1, len(header))
        df = pd.DataFrame(np.concatenate((data, avg_total)), columns=header)
        report_list.append(df)
    res = reduce(lambda x, y: x.add(y, fill_value=0), report_list) / len(report_list)
    return res.rename(index={res.index[-1]: 'avg / total'})


output:

report_average  = report_average(report_1, report_2)
print(report_average)
             precision  recall  f1-score  support
0                 0.75    0.75      0.75      2.0
1                 0.75    0.75      0.75      2.0
avg / total       0.75    0.75      0.75      4.0

report_3 = report_2
report_average  = report_average(report_1, report_2,report_3)
print(report_average)
             precision    recall  f1-score  support
0             0.666667  0.666667  0.666667      2.0
1             0.666667  0.666667  0.666667      2.0
avg / total   0.666667  0.666667  0.666667      4.0