xgboost error: Booster' object has no attribute 'handle'

I am try to use the xgboost to model my data. A small part of the data is attached as an excel file (text.xlsx). The data contain 10 samples (Each line represents a sample) which have 17 features (column A to column Q). These features are discrete classification variable which are represented by alphabetic letters. The column R represents the labels (classifications) of the samples.

I use the following python 2 code to use the xgboost:

# !/usr/bin/python
# -*- encoding:utf-8 -*-

import xgboost as xgb
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split   # cross_validation
from sklearn.metrics import accuracy_score

def translate(x):
    if x == 'A':
        x = 1
    elif x == 'C':
        x = 2
    elif x == 'D':
        x = 3
    elif x == 'E':
        x = 4
    elif x == 'F':
        x = 5
    elif x == 'G':
        x = 6
    elif x == 'H':
        x = 7
    elif x == 'I':
        x = 8
    elif x == 'K':
        x = 9
    elif x == 'L':
        x = 10
    elif x == 'M':
        x = 11
    elif x == 'N':
        x = 12
    elif x == 'P':
        x = 13
    elif x == 'Q':
        x = 14
    elif x == 'R':
        x = 15
    elif x == 'S':
        x = 16
    elif x == "T":
        x = 17
    elif x == 'V':
        x = 18
    elif x == 'W':
        x = 19
    elif x == 'Y':
        x = 20
    return x

def g_h(y_hat, y):
    p = 1.0 / (1.0 + np.exp(-y_hat))    
    g = p - y.get_label()
    h = p * (1.0-p)
    return g, h


def error_rate(y_hat, y):
    return 'error', float(sum(y.get_label() != (y_hat > 0.5))) / len(y_hat)

def plot_variable_importance(x, y):
    tree = DecisionTreeClassifier( random_state = 99 )
    tree.fit(x, y)
    plot_model_var_imp(tree, x, y)

def plot_model_var_imp(model, x, y):
    imp = pd.DataFrame(
        model.feature_importances_  ,
        columns = ['Importance'] ,
        index = x.columns
    )

if __name__ == "__main__":
    data = pd.read_excel('test.xlsx',header = None)
    x, y = data[range(17)], data[17]
    x = pd.get_dummies(x)
    x = x.applymap(translate)
    for i in range(0,10):
         for j in range(0,17):
             x.iloc[i][j] = translate(x.iloc[i][j])
    print "x = \n", x
    print "y = \n", y
    x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, test_size=0.7)
    # print "x_test = \n", x_test
    # print "x_train = \n", x_train
    # plot_variable_importance(x_train, y_train)

    data_train = xgb.DMatrix(x_train, label=y_train)
    data_test = xgb.DMatrix(x_test, label=y_test)
    print data_train
    watch_list = [(x_test, 'eval'), (x_train, 'train')]
    param = {'max_depth': 4, 'eta': 0.3, 'silent': 1, 'objective': 'binary:logistic'}
    bst = xgb.train(param, x_train, num_boost_round=6, evals=watch_list)
    y_hat = bst.predict(x_test)
    print "y_hat = \n", y_hat

Running this code gives the follow error message:

*****TypeError: invalid cache item: DataFrame Exception AttributeError: "'Booster' object has no attribute 'handle'" in > ignored*****

Could someone tell me how should I revise to code to make it work?