Convert scikit-learn confusion matrix to pandas DataFrame

cm2df.py

from sklearn.metrics import confusion_matrix
import pandas as pd

def cm2df(cm, labels):
    df = pd.DataFrame()
    # rows
    for i, row_label in enumerate(labels):
        rowdata={}
        # columns
        for j, col_label in enumerate(labels): 
            rowdata[col_label]=cm[i,j]
        df = df.append(pd.DataFrame.from_dict({row_label:rowdata}, orient='index'))
    return df[labels]

## to use, first generate confusion matrix:
#cm = confusion_matrix(expected, predicted)
## then convert to pandas DataFrame:
#cm_as_df=cm2df(cm,dataset.target_names)
## and output:
#cm_as_df

def precision_recall_fscore_support_metrics2df(prfs, labels):
    df = pd.DataFrame()
    for p,r,f,s,label in zip(prfs[0], prfs[1], prfs[2], prfs[3], dataset.target_names):
        rowdata={}
        rowdata['precision']=p
        rowdata['recall']=r
        rowdata['f1-score']=f
        rowdata['support']=s
        df = df.append(pd.DataFrame.from_dict({label:rowdata}, orient='index'))   
    return df[['precision','recall','f1-score','support']]

Please show me how you write and constitute the zip function and the call of function "precision_recall_fscore_support_metrics2df" with such parameters as the call of function "cm2df" ?

Thanks in advance