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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Step1:  numpy.testing
Provides useful assertion methods for values that are numerically close and for numpy arrays.
Step2:  engarde decorators
A new library that lets you practice defensive program--specifically with pandas DataFrame objects. It provides a set of decorators that check the return value of any function that returns a DataFrame and confirms that it conforms to the rules.
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Python Code:
from __future__ import print_function
import os
import numpy as np
import pandas as pd
PROJ_ROOT = os.path.abspath(os.path.join(os.pardir, os.pardir))
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            <a href="http://www.drivendata.org">
                <img src="https://s3.amazonaws.com/drivendata.org/kif-example/img/dd.png" />
            </a>
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Data Science is Software
Developer #lifehacks for the Jupyter Data Scientist
Section 4:  Don't let other people break your toys
Motivation
"Many machine learning algorithms have a curious property: they are robust against bugs. Since they’re designed to deal with noisy data, they can often deal pretty well with noise caused by math mistakes as well. If you make a math mistake in your implementation, the algorithm might still make sensible-looking predictions. This is bad news, not good news. It means bugs are subtle and hard to detect. Your algorithm might work well in some situations, such as small toy datasets you use for validation, and completely fail in other situations — high dimensions, large numbers of training examples, noisy observations, etc." — Roger Gross, "Testing MCMC code, part 1: unit tests", Harvard Intelligent Probabilistic Systems group
End of explanation
data = np.random.normal(0.0, 1.0, 1000000)
assert np.mean(data) == 0.0
np.testing.assert_almost_equal(np.mean(data), 0.0, decimal=2)
a = np.random.normal(0, 0.0001, 10000)
b = np.random.normal(0, 0.0001, 10000)
np.testing.assert_array_equal(a, b)
np.testing.assert_array_almost_equal(a, b, decimal=3)
Explanation: numpy.testing
Provides useful assertion methods for values that are numerically close and for numpy arrays.
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import engarde.decorators as ed
test_data = pd.DataFrame({'a': np.random.normal(0, 1, 100),
                          'b': np.random.normal(0, 1, 100)})
@ed.none_missing()
def process(dataframe):
    dataframe.loc[10, 'a'] = 1.0
    return dataframe
process(test_data).head()
Explanation: engarde decorators
A new library that lets you practice defensive program--specifically with pandas DataFrame objects. It provides a set of decorators that check the return value of any function that returns a DataFrame and confirms that it conforms to the rules.
End of explanation
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