scikit-learn: Decision Tree split looks wrong

Description

When looking for an optimal split on a decision tree, DecisionTreeClassifier gives a seemingly wrong answer

Steps/Code to Reproduce

raw dataset used : https://github.com/juan-barragan/titanic/blob/master/titanic_ds.csv

import pandas as pd
import seaborn
import matplotlib.pyplot as plt
from sklearn import tree
import graphviz
import numpy as np

def gini_by_age(df, t):
    df['age_group'] = df['age'].apply(lambda row : 0 if row <= t else 1)
    kids = df[df['age_group'] == 0]
    kids0 = kids[kids['survived'] == 0]
    kids1 = kids[kids['survived'] == 1]
    adults = df[df['age_group'] == 1]
    adults0 = adults[adults['survived'] == 0]
    adults1 = adults[adults['survived'] == 1] 
    gk = 1 - (len(kids0)**2 + len(kids1)**2)/float(len(kids))**2
    ga = 1 - (len(adults0)**2 + len(adults1)**2)/float(len(adults))**2
    return gk + ga

def plot_gini_by_age(df):    
    ages = range(6,25)
    y = [gini_by_age(df, a) for a in ages]
    plt.plot(ages, y)
    plt.show()

def use_tree(df):
    X = np.array(df['age']).reshape((len(df['age']),1))
    y = df['survived']
    clf = tree.DecisionTreeClassifier(max_depth=1).fit(X,y)    
    dot_data = tree.export_graphviz(clf, out_file=None)
    graph = graphviz.Source(dot_data)
    graph.render("age")

titanic_df = pd.read_csv("titanic_ds.csv")
ages_cov = titanic_df[['age', 'survived']].dropna()
plot_gini_by_age(ages_cov)
use_tree(ages_cov)
print gini_by_age(ages_cov, 5)
print gini_by_age(ages_cov, 6)
print gini_by_age(ages_cov, 8.5)
print gini_by_age(ages_cov, 15)

Expected Results

gini split at around 5

Actual Results

Got 8.5 instead 0.925844132419 0.93508349655 0.937732003001 0.963875889772

Versions

Linux-4.4.0-21-generic-x86_64-with-LinuxMint-18-sarah (‘Python’, ‘2.7.12 (default, Nov 20 2017, 18:23:56) \n[GCC 5.4.0 20160609]’) (‘NumPy’, ‘1.11.2’) (‘SciPy’, ‘0.18.1’) (‘Scikit-Learn’, ‘0.18’)