Create new column and assign conditional value to pandas dataframe

I have a dataframe:

 sepallength sepalwidth petallength petalwidth        class   cluster
0         5.1        3.5         1.4        0.2  Iris-setosa  cluster1
1         4.9          3         1.4        0.2  Iris-setosa  cluster1
2         4.7        3.2         1.3        0.2  Iris-setosa  cluster1
3         4.6        3.1         1.5        0.2  Iris-setosa  cluster1
4           5        3.6         1.4        0.2  Iris-setosa  cluster1
5         5.4        3.9         1.7        0.4  Iris-setosa  cluster1
6         4.6        3.4         1.4        0.3  Iris-setosa  cluster1
7           5        3.4         1.5        0.2  Iris-setosa  cluster1
8         4.4        2.9         1.4        0.2  Iris-setosa  cluster1
9         4.9        3.1         1.5        0.1  Iris-setosa  cluster1

      

        
        
        
      

    

and a dictionary:

{'cluster2': 'Iris-virginica', 'cluster0': 'Iris-versicolor', 'cluster1': 'Iris-setosa'}

      

        
        
        
      

    

I need to add another column and fill it with values ​​from this dictionary df ['cluster'] == key

I tried using np.where

def countTruth(df):
    # dictionary mapping cluster to most frequent class

    clustersClass = df.groupby(['cluster'])['class'].agg(lambda x:x.value_counts().index[0]).to_dict()
    for eachKey in clustersClass:
        newv = clustersClass[eachKey]
        print df
        df['new'] = np.where(df['cluster']==eachKey , newv) 

      

        
        
        
      

    

a message is displayed stating that either x or y must be specified

my ultimate goal is to count true positives, true negatives, FP and FN based on cluster and class label. it is a step aside.