Incorrect multi-variate anomaly detection - Isolation Forest Python
One way to improve the efficiency of the prediction is to convert the dataframe type to float32. I got better result while converting the data type to float32.
df = pd.DataFrame(np.array([[45,1411.27],[47754,1596200.68],[105894,3750304.55],[372953,14368324.86],[389915,14899302.85]]),columns=['ROW_CNT','TOT_SALE'],dtype=np.float32)
def outlier_func(df):
    model = IsolationForest(behaviour='new',n_estimators=1000,  max_samples='auto', 
    contamination='auto', max_features=1.0)  
    model.fit(df[['ROW_CNT', 'TOT_SALE']])
    df['scores'] = model.decision_function(df[['ROW_CNT', 'TOT_SALE']])
    df['anomaly'] = model.predict(df[['ROW_CNT', 'TOT_SALE']])
    anomaly = df.loc[df['anomaly'] == -1]
    anomaly_index = list(anomaly.index)
    return anomaly          

outlier_func(df)