Validation curve unlike SKLearn sample
This is exactly your code just with digits data:

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.svm import SVC
from sklearn.learning_curve import validation_curve
import psutil
from sklearn.tree import DecisionTreeClassifier

np.random.seed(0)

# X, y = prepareDataframeX.values, prepareDataframeY.values.ravel()


digits = load_digits()
X, y = digits.data, digits.target

indices = np.arange(y.shape[0])
np.random.shuffle(indices)
X, y = X[indices], y[indices]

param_range = np.arange(1, 41, 2)
train_scores, test_scores = validation_curve(
    DecisionTreeClassifier(class_weight='balanced'), X, y, param_name="max_depth", cv=10, 
    param_range=param_range,n_jobs=psutil.cpu_count(),
    scoring="accuracy")

train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.figure(figsize=(10,10))
plt.title("Validation Curve with DecisionTree")
plt.xlabel("max_depth")
plt.ylabel("Score")
plt.ylim(0.0, 1.1)
#plt.fill_between(param_range, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.2, color="r")
plt.plot(param_range, train_scores_mean, label="Training score",
             color="r")
#plt.fill_between(param_range, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.2, color="g")
plt.plot(param_range, test_scores_mean, label="Cross-validation score",
             color="g")

plt.legend(loc="best")
plt.xticks(param_range)
plt.savefig('DSSE.jpg')
plt.show()


And this is the result:



So whatever the problem is, it's in your data that you didn't explain how you created it. Either double check it or post it here so your question can be answered. I assume somewhere in data preparation you made X and Y identical by mistake.