Notebook-miner tests

This Notebook is meant to test the notebook-miner initial library (currently base_loader), making sure that we are able to import and use it as expected.



In [1]:

    
from base_loader import base_loader

Test.ipynb is only a few cells, and will be good for initial testing of the loader. Let's load it in, and check which version of python it is



In [6]:

    
notebook_loaded = base_loader('example_notebooks/ML-Exercise2.ipynb')



In [7]:

    
notebook_loaded.get_kernelspec()









    Out[7]:





{'display_name': 'Python 2', 'language': 'python', 'name': 'python2'}



In [8]:

    
print (notebook_loaded.get_all_python())









    




#Cell 1


# coding: utf-8

# In[ ]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')



#Cell 2


# coding: utf-8

# In[ ]:

import os
path = os.getcwd() + '\data\ex2data1.txt'
data = pd.read_csv(path, header=None, names=['Exam 1', 'Exam 2', 'Admitted'])
data.head()



#Cell 3


# coding: utf-8

# In[ ]:

positive = data[data['Admitted'].isin([1])]
negative = data[data['Admitted'].isin([0])]

fig, ax = plt.subplots(figsize=(12,8))
ax.scatter(positive['Exam 1'], positive['Exam 2'], s=50, c='b', marker='o', label='Admitted')
ax.scatter(negative['Exam 1'], negative['Exam 2'], s=50, c='r', marker='x', label='Not Admitted')
ax.legend()
ax.set_xlabel('Exam 1 Score')
ax.set_ylabel('Exam 2 Score')



#Cell 4


# coding: utf-8

# In[ ]:

def sigmoid(z):
    return 1 / (1 + np.exp(-z))



#Cell 5


# coding: utf-8

# In[ ]:

nums = np.arange(-10, 10, step=1)

fig, ax = plt.subplots(figsize=(12,8))
ax.plot(nums, sigmoid(nums), 'r')



#Cell 6


# coding: utf-8

# In[ ]:

def cost(theta, X, y):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    first = np.multiply(-y, np.log(sigmoid(X * theta.T)))
    second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))
    return np.sum(first - second) / (len(X))



#Cell 7


# coding: utf-8

# In[ ]:

# add a ones column - this makes the matrix multiplication work out easier
data.insert(0, 'Ones', 1)

# set X (training data) and y (target variable)
cols = data.shape[1]
X = data.iloc[:,0:cols-1]
y = data.iloc[:,cols-1:cols]

# convert to numpy arrays and initalize the parameter array theta
X = np.array(X.values)
y = np.array(y.values)
theta = np.zeros(3)



#Cell 8


# coding: utf-8

# In[ ]:

X.shape, theta.shape, y.shape



#Cell 9


# coding: utf-8

# In[ ]:

cost(theta, X, y)



#Cell 10


# coding: utf-8

# In[ ]:

def gradient(theta, X, y):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    
    parameters = int(theta.ravel().shape[1])
    grad = np.zeros(parameters)
    
    error = sigmoid(X * theta.T) - y
    
    for i in range(parameters):
        term = np.multiply(error, X[:,i])
        grad[i] = np.sum(term) / len(X)
    
    return grad



#Cell 11


# coding: utf-8

# In[ ]:

gradient(theta, X, y)



#Cell 12


# coding: utf-8

# In[ ]:

import scipy.optimize as opt
result = opt.fmin_tnc(func=cost, x0=theta, fprime=gradient, args=(X, y))
result



#Cell 13


# coding: utf-8

# In[ ]:

cost(result[0], X, y)



#Cell 14


# coding: utf-8

# In[ ]:

def predict(theta, X):
    probability = sigmoid(X * theta.T)
    return [1 if x >= 0.5 else 0 for x in probability]



#Cell 15


# coding: utf-8

# In[ ]:

theta_min = np.matrix(result[0])
predictions = predict(theta_min, X)
correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]
accuracy = (sum(map(int, correct)) % len(correct))
print 'accuracy = {0}%'.format(accuracy)



#Cell 16


# coding: utf-8

# In[ ]:

path = os.getcwd() + '\data\ex2data2.txt'
data2 = pd.read_csv(path, header=None, names=['Test 1', 'Test 2', 'Accepted'])
data2.head()



#Cell 17


# coding: utf-8

# In[ ]:

positive = data2[data2['Accepted'].isin([1])]
negative = data2[data2['Accepted'].isin([0])]

fig, ax = plt.subplots(figsize=(12,8))
ax.scatter(positive['Test 1'], positive['Test 2'], s=50, c='b', marker='o', label='Accepted')
ax.scatter(negative['Test 1'], negative['Test 2'], s=50, c='r', marker='x', label='Rejected')
ax.legend()
ax.set_xlabel('Test 1 Score')
ax.set_ylabel('Test 2 Score')



#Cell 18


# coding: utf-8

# In[ ]:

degree = 5
x1 = data2['Test 1']
x2 = data2['Test 2']

data2.insert(3, 'Ones', 1)

for i in range(1, degree):
    for j in range(0, i):
        data2['F' + str(i) + str(j)] = np.power(x1, i-j) * np.power(x2, j)

data2.drop('Test 1', axis=1, inplace=True)
data2.drop('Test 2', axis=1, inplace=True)

data2.head()



#Cell 19


# coding: utf-8

# In[ ]:

def costReg(theta, X, y, learningRate):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    first = np.multiply(-y, np.log(sigmoid(X * theta.T)))
    second = np.multiply((1 - y), np.log(1 - sigmoid(X * theta.T)))
    reg = (learningRate / 2 * len(X)) * np.sum(np.power(theta[:,1:theta.shape[1]], 2))
    return np.sum(first - second) / (len(X)) + reg



#Cell 20


# coding: utf-8

# In[ ]:

def gradientReg(theta, X, y, learningRate):
    theta = np.matrix(theta)
    X = np.matrix(X)
    y = np.matrix(y)
    
    parameters = int(theta.ravel().shape[1])
    grad = np.zeros(parameters)
    
    error = sigmoid(X * theta.T) - y
    
    for i in range(parameters):
        term = np.multiply(error, X[:,i])
        
        if (i == 0):
            grad[i] = np.sum(term) / len(X)
        else:
            grad[i] = (np.sum(term) / len(X)) + ((learningRate / len(X)) * theta[:,i])
    
    return grad



#Cell 21


# coding: utf-8

# In[ ]:

# set X and y (remember from above that we moved the label to column 0)
cols = data2.shape[1]
X2 = data2.iloc[:,1:cols]
y2 = data2.iloc[:,0:1]

# convert to numpy arrays and initalize the parameter array theta
X2 = np.array(X2.values)
y2 = np.array(y2.values)
theta2 = np.zeros(11)



#Cell 22


# coding: utf-8

# In[ ]:

learningRate = 1



#Cell 23


# coding: utf-8

# In[ ]:

costReg(theta2, X2, y2, learningRate)



#Cell 24


# coding: utf-8

# In[ ]:

gradientReg(theta2, X2, y2, learningRate)



#Cell 25


# coding: utf-8

# In[ ]:

result2 = opt.fmin_tnc(func=costReg, x0=theta2, fprime=gradientReg, args=(X2, y2, learningRate))
result2



#Cell 26


# coding: utf-8

# In[ ]:

theta_min = np.matrix(result2[0])
predictions = predict(theta_min, X2)
correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y2)]
accuracy = (sum(map(int, correct)) % len(correct))
print 'accuracy = {0}%'.format(accuracy)



#Cell 27


# coding: utf-8

# In[ ]:

from sklearn import linear_model
model = linear_model.LogisticRegression(penalty='l2', C=1.0)
model.fit(X2, y2.ravel())



#Cell 28


# coding: utf-8

# In[ ]:

model.score(X2, y2)

Now that we know what we're dealing with, let's gather statistics on this notebook



In [9]:

    
summary_string = ''
summary_string += "Number of cells in notebook is: " + str(notebook_loaded.get_number_cells()) + "\n"
summary_string += "Number of python cells in notebook is: " + str(notebook_loaded.get_number_python_cells()) + "\n"
summary_string += "Number of markdown cells in notebook is: " + str(notebook_loaded.get_number_markdown_cells()) + "\n"
print (summary_string)









    



Number of cells in notebook is: 63
Number of python cells in notebook is: 28
Number of markdown cells in notebook is: 35

Now say we want to see the output of all the python cells. First, let's check how many cells were actually executed, then let's look through them to see the outputs



In [10]:

    
for i, cell_ind in enumerate(notebook_loaded.get_execution_order()):
    print ("Executed cell number ",i)
    #print (notebook_loaded.get_cell_by_execution_order(i))
    print ("Output of this cell ")
    print (notebook_loaded.get_cell_outputs(cell_ind))









    



Executed cell number  0
Output of this cell 
[]
Executed cell number  1
Output of this cell 
[{'data': {'text/html': '<div style="max-height:1000px;max-width:1500px;overflow:auto;">\n<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>Exam 1</th>\n      <th>Exam 2</th>\n      <th>Admitted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 34.623660</td>\n      <td> 78.024693</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 30.286711</td>\n      <td> 43.894998</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 35.847409</td>\n      <td> 72.902198</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 60.182599</td>\n      <td> 86.308552</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 79.032736</td>\n      <td> 75.344376</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n</div>', 'text/plain': '      Exam 1     Exam 2  Admitted\n0  34.623660  78.024693         0\n1  30.286711  43.894998         0\n2  35.847409  72.902198         0\n3  60.182599  86.308552         1\n4  79.032736  75.344376         1'}, 'execution_count': 2, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  2
Output of this cell 
[{'data': {'text/plain': '<matplotlib.text.Text at 0xd17d7b8>'}, 'execution_count': 3, 'metadata': {}, 'output_type': 'execute_result'}, {'data': {'image/png': 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KoQrb60E48Kupd+jBBjwSll4S5K+Ue5jCNvgqbK8H4cCvpsFEDzYADFMp9jCF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\nAluSJEnq0f8ButxExp+t9tQAAAAASUVORK5CYII=\n', 'text/plain': '<matplotlib.figure.Figure at 0xd120da0>'}, 'metadata': {}, 'output_type': 'display_data'}]
Executed cell number  3
Output of this cell 
[]
Executed cell number  4
Output of this cell 
[{'data': {'text/plain': '[<matplotlib.lines.Line2D at 0xd120048>]'}, 'execution_count': 5, 'metadata': {}, 'output_type': 'execute_result'}, {'data': {'image/png': 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X2HTTtqsB6BgjEwBMzznnJIceKgwDI08gBhhVVpcASGJkAmA0PfBAsvvuyT33JLNnt10N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\nnhJtkQmYAAAAAElFTkSuQmCC\n', 'text/plain': '<matplotlib.figure.Figure at 0xd120128>'}, 'metadata': {}, 'output_type': 'display_data'}]
Executed cell number  5
Output of this cell 
[]
Executed cell number  6
Output of this cell 
[]
Executed cell number  7
Output of this cell 
[{'data': {'text/plain': '((100L, 3L), (3L,), (100L, 1L))'}, 'execution_count': 8, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  8
Output of this cell 
[{'data': {'text/plain': '0.69314718055994529'}, 'execution_count': 9, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  9
Output of this cell 
[]
Executed cell number  10
Output of this cell 
[{'data': {'text/plain': 'array([ -0.1       , -12.00921659, -11.26284221])'}, 'execution_count': 11, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  11
Output of this cell 
[{'data': {'text/plain': '(array([-25.87355624,   0.21193682,   0.20722586]), 51, 1)'}, 'execution_count': 12, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  12
Output of this cell 
[{'data': {'text/plain': '0.20357134412164668'}, 'execution_count': 13, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  13
Output of this cell 
[]
Executed cell number  14
Output of this cell 
[{'name': 'stdout', 'output_type': 'stream', 'text': 'accuracy = 89%\n'}]
Executed cell number  15
Output of this cell 
[{'data': {'text/html': '<div style="max-height:1000px;max-width:1500px;overflow:auto;">\n<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>Test 1</th>\n      <th>Test 2</th>\n      <th>Accepted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 0.051267</td>\n      <td> 0.69956</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.092742</td>\n      <td> 0.68494</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.213710</td>\n      <td> 0.69225</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.375000</td>\n      <td> 0.50219</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-0.513250</td>\n      <td> 0.46564</td>\n      <td> 1</td>\n    </tr>\n  </tbody>\n</table>\n</div>', 'text/plain': '     Test 1   Test 2  Accepted\n0  0.051267  0.69956         1\n1 -0.092742  0.68494         1\n2 -0.213710  0.69225         1\n3 -0.375000  0.50219         1\n4 -0.513250  0.46564         1'}, 'execution_count': 16, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  16
Output of this cell 
[{'data': {'text/plain': '<matplotlib.text.Text at 0x17856dd8>'}, 'execution_count': 17, 'metadata': {}, 'output_type': 'execute_result'}, {'data': {'image/png': 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JYAzMZDAx5ZpNJBKJUGwjbxU7kwxrKACBRMGNUGMmAyCcKnYmmfQ0pOkiNl3c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\nJUmSpA79A109nxJVGE9DAAAAAElFTkSuQmCC\n', 'text/plain': '<matplotlib.figure.Figure at 0xd120dd8>'}, 'metadata': {}, 'output_type': 'display_data'}]
Executed cell number  17
Output of this cell 
[{'data': {'text/html': '<div style="max-height:1000px;max-width:1500px;overflow:auto;">\n<table border="1" class="dataframe">\n  <thead>\n    <tr style="text-align: right;">\n      <th></th>\n      <th>Accepted</th>\n      <th>Ones</th>\n      <th>F10</th>\n      <th>F20</th>\n      <th>F21</th>\n      <th>F30</th>\n      <th>F31</th>\n      <th>F32</th>\n      <th>F40</th>\n      <th>F41</th>\n      <th>F42</th>\n      <th>F43</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td> 0.051267</td>\n      <td> 0.002628</td>\n      <td> 0.035864</td>\n      <td> 0.000135</td>\n      <td> 0.001839</td>\n      <td> 0.025089</td>\n      <td> 0.000007</td>\n      <td> 0.000094</td>\n      <td> 0.001286</td>\n      <td> 0.017551</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td>-0.092742</td>\n      <td> 0.008601</td>\n      <td>-0.063523</td>\n      <td>-0.000798</td>\n      <td> 0.005891</td>\n      <td>-0.043509</td>\n      <td> 0.000074</td>\n      <td>-0.000546</td>\n      <td> 0.004035</td>\n      <td>-0.029801</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td>-0.213710</td>\n      <td> 0.045672</td>\n      <td>-0.147941</td>\n      <td>-0.009761</td>\n      <td> 0.031616</td>\n      <td>-0.102412</td>\n      <td> 0.002086</td>\n      <td>-0.006757</td>\n      <td> 0.021886</td>\n      <td>-0.070895</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td>-0.375000</td>\n      <td> 0.140625</td>\n      <td>-0.188321</td>\n      <td>-0.052734</td>\n      <td> 0.070620</td>\n      <td>-0.094573</td>\n      <td> 0.019775</td>\n      <td>-0.026483</td>\n      <td> 0.035465</td>\n      <td>-0.047494</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 1</td>\n      <td> 1</td>\n      <td>-0.513250</td>\n      <td> 0.263426</td>\n      <td>-0.238990</td>\n      <td>-0.135203</td>\n      <td> 0.122661</td>\n      <td>-0.111283</td>\n      <td> 0.069393</td>\n      <td>-0.062956</td>\n      <td> 0.057116</td>\n      <td>-0.051818</td>\n    </tr>\n  </tbody>\n</table>\n</div>', 'text/plain': '   Accepted  Ones       F10       F20       F21       F30       F31       F32  \\\n0         1     1  0.051267  0.002628  0.035864  0.000135  0.001839  0.025089   \n1         1     1 -0.092742  0.008601 -0.063523 -0.000798  0.005891 -0.043509   \n2         1     1 -0.213710  0.045672 -0.147941 -0.009761  0.031616 -0.102412   \n3         1     1 -0.375000  0.140625 -0.188321 -0.052734  0.070620 -0.094573   \n4         1     1 -0.513250  0.263426 -0.238990 -0.135203  0.122661 -0.111283   \n\n        F40       F41       F42       F43  \n0  0.000007  0.000094  0.001286  0.017551  \n1  0.000074 -0.000546  0.004035 -0.029801  \n2  0.002086 -0.006757  0.021886 -0.070895  \n3  0.019775 -0.026483  0.035465 -0.047494  \n4  0.069393 -0.062956  0.057116 -0.051818  '}, 'execution_count': 18, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  18
Output of this cell 
[]
Executed cell number  19
Output of this cell 
[]
Executed cell number  20
Output of this cell 
[]
Executed cell number  21
Output of this cell 
[]
Executed cell number  22
Output of this cell 
[{'data': {'text/plain': '0.6931471805599454'}, 'execution_count': 23, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  23
Output of this cell 
[{'data': {'text/plain': 'array([ 0.00847458,  0.01878809,  0.05034464,  0.01150133,  0.01835599,\n        0.00732393,  0.00819244,  0.03934862,  0.00223924,  0.01286005,\n        0.00309594])'}, 'execution_count': 24, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  24
Output of this cell 
[{'data': {'text/plain': '(array([  0.35872309,  -3.22200653,  18.97106363,  -4.25297831,\n         18.23053189,  20.36386672,   8.94114455, -43.77439015,\n        -17.93440473, -50.75071857,  -2.84162964]), 110, 1)'}, 'execution_count': 25, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  25
Output of this cell 
[{'name': 'stdout', 'output_type': 'stream', 'text': 'accuracy = 91%\n'}]
Executed cell number  26
Output of this cell 
[{'data': {'text/plain': "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)"}, 'execution_count': 27, 'metadata': {}, 'output_type': 'execute_result'}]
Executed cell number  27
Output of this cell 
[{'data': {'text/plain': '0.66101694915254239'}, 'execution_count': 28, 'metadata': {}, 'output_type': 'execute_result'}]

Summary Statistics add on



In [11]:

    
from summary_statistics import summary_statistics
ss = summary_statistics(notebook_loaded)
print (ss.full_statistics())









    



Number of cells in notebook is: 63
Number of markdown cells in notebook is: 35
Number of python cells in notebook is: 28
Number of executed cells in notebook is: 28
Number of lines of code is: 344
Number of words of code is: 791
Number of characters of code is: 5799
Largest code cell has 543 characters
Smallest code cell has 46 characters

NBConvert Speed test

As you can see below, nbconvert might not be the best bet -- if we can just replace magics with their comments, we should be better off



In [12]:

    
%%time
for i in range(100): notebook_loaded.get_cell_and_convert(0)









    



---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-12-515226276121> in <module>()
----> 1 get_ipython().run_cell_magic('time', '', 'for i in range(100): notebook_loaded.get_cell_and_convert(0)')

/anaconda/envs/snap-general/lib/python3.6/site-packages/IPython/core/interactiveshell.py in run_cell_magic(self, magic_name, line, cell)
   2113             magic_arg_s = self.var_expand(line, stack_depth)
   2114             with self.builtin_trap:
-> 2115                 result = fn(magic_arg_s, cell)
   2116             return result
   2117 

<decorator-gen-59> in time(self, line, cell, local_ns)

/anaconda/envs/snap-general/lib/python3.6/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
    186     # but it's overkill for just that one bit of state.
    187     def magic_deco(arg):
--> 188         call = lambda f, *a, **k: f(*a, **k)
    189 
    190         if callable(arg):

/anaconda/envs/snap-general/lib/python3.6/site-packages/IPython/core/magics/execution.py in time(self, line, cell, local_ns)
   1183         else:
   1184             st = clock2()
-> 1185             exec(code, glob, local_ns)
   1186             end = clock2()
   1187             out = None

<timed exec> in <module>()

AttributeError: 'base_loader' object has no attribute 'get_cell_and_convert'



In [9]:

    
%%time
for i in range(100): notebook_loaded.get_raw_cell_converted(0)









    



CPU times: user 19.9 s, sys: 553 ms, total: 20.4 s
Wall time: 21.9 s



In [10]:

    
%%time
for i in range(100): notebook_loaded.get_raw_cell(0)









    



CPU times: user 216 µs, sys: 0 ns, total: 216 µs
Wall time: 223 µs

Getting an array of notebooks



In [12]:

    
import os

notebook_array = []
for nb_file in os.listdir("example_notebooks"):
    if nb_file.endswith(".ipynb"):
        nb_file = os.path.join("example_notebooks" , nb_file)
        notebook_array.append(base_loader(nb_file))



In [13]:

    
for i, notebook_loaded in enumerate(notebook_array):
    print ("Notebook",i+1,"/",len(notebook_array))
    print ("Name:",notebook_loaded.get_filename())
    print ( summary_statistics(notebook_loaded).full_statistics() )









    



Notebook 1 / 13
Name: example_notebooks/02_Linear_Regression.ipynb
Number of cells in notebook is: 21
Number of markdown cells in notebook is: 14
Number of python cells in notebook is: 7
Number of executed cells in notebook is: 7
Number of lines of code is: 269
Number of words of code is: 1103
Number of characters of code is: 7934
Largest code cell has 1644 characters
Smallest code cell has 30 characters

Notebook 2 / 13
Name: example_notebooks/DEAP.ipynb
Number of cells in notebook is: 20
Number of markdown cells in notebook is: 6
Number of python cells in notebook is: 14
Number of executed cells in notebook is: 14
Number of lines of code is: 259
Number of words of code is: 666
Number of characters of code is: 6066
Largest code cell has 1473 characters
Smallest code cell has 107 characters

Notebook 3 / 13
Name: example_notebooks/Intro.ipynb
Number of cells in notebook is: 80
Number of markdown cells in notebook is: 15
Number of python cells in notebook is: 65
Number of executed cells in notebook is: 65
Number of lines of code is: 638
Number of words of code is: 912
Number of characters of code is: 5244
Largest code cell has 334 characters
Smallest code cell has 34 characters

Notebook 4 / 13
Name: example_notebooks/ML-Exercise1.ipynb
Number of cells in notebook is: 54
Number of markdown cells in notebook is: 29
Number of python cells in notebook is: 25
Number of executed cells in notebook is: 25
Number of lines of code is: 278
Number of words of code is: 497
Number of characters of code is: 3852
Largest code cell has 530 characters
Smallest code cell has 35 characters

Notebook 5 / 13
Name: example_notebooks/ML-Exercise2.ipynb
Number of cells in notebook is: 63
Number of markdown cells in notebook is: 35
Number of python cells in notebook is: 28
Number of executed cells in notebook is: 28
Number of lines of code is: 344
Number of words of code is: 791
Number of characters of code is: 5799
Largest code cell has 543 characters
Smallest code cell has 46 characters

Notebook 6 / 13
Name: example_notebooks/ML-Exercise3.ipynb
Number of cells in notebook is: 25
Number of markdown cells in notebook is: 13
Number of python cells in notebook is: 12
Number of executed cells in notebook is: 12
Number of lines of code is: 201
Number of words of code is: 550
Number of characters of code is: 4077
Largest code cell has 865 characters
Smallest code cell has 50 characters

Notebook 7 / 13
Name: example_notebooks/ML-Exercise4.ipynb
Number of cells in notebook is: 37
Number of markdown cells in notebook is: 17
Number of python cells in notebook is: 20
Number of executed cells in notebook is: 20
Number of lines of code is: 366
Number of words of code is: 1155
Number of characters of code is: 8759
Largest code cell has 2000 characters
Smallest code cell has 49 characters

Notebook 8 / 13
Name: example_notebooks/ML-Exercise6.ipynb
Number of cells in notebook is: 34
Number of markdown cells in notebook is: 16
Number of python cells in notebook is: 18
Number of executed cells in notebook is: 18
Number of lines of code is: 229
Number of words of code is: 444
Number of characters of code is: 3885
Largest code cell has 659 characters
Smallest code cell has 82 characters

Notebook 9 / 13
Name: example_notebooks/ML-Exercise7.ipynb
Number of cells in notebook is: 54
Number of markdown cells in notebook is: 23
Number of python cells in notebook is: 31
Number of executed cells in notebook is: 31
Number of lines of code is: 354
Number of words of code is: 635
Number of characters of code is: 4666
Largest code cell has 426 characters
Smallest code cell has 46 characters

Notebook 10 / 13
Name: example_notebooks/ML-Exercise8.ipynb
Number of cells in notebook is: 67
Number of markdown cells in notebook is: 30
Number of python cells in notebook is: 37
Number of executed cells in notebook is: 37
Number of lines of code is: 514
Number of words of code is: 1183
Number of characters of code is: 9304
Largest code cell has 1285 characters
Smallest code cell has 42 characters

Notebook 11 / 13
Name: example_notebooks/NumPy.ipynb
Number of cells in notebook is: 60
Number of markdown cells in notebook is: 4
Number of python cells in notebook is: 56
Number of executed cells in notebook is: 56
Number of lines of code is: 488
Number of words of code is: 635
Number of characters of code is: 3315
Largest code cell has 142 characters
Smallest code cell has 31 characters

Notebook 12 / 13
Name: example_notebooks/SciPy.ipynb
Number of cells in notebook is: 24
Number of markdown cells in notebook is: 5
Number of python cells in notebook is: 19
Number of executed cells in notebook is: 19
Number of lines of code is: 227
Number of words of code is: 475
Number of characters of code is: 2933
Largest code cell has 814 characters
Smallest code cell has 35 characters

Notebook 13 / 13
Name: example_notebooks/Test.ipynb
Number of cells in notebook is: 4
Number of markdown cells in notebook is: 1
Number of python cells in notebook is: 3
Number of executed cells in notebook is: 2
Number of lines of code is: 30
Number of words of code is: 44
Number of characters of code is: 271
Largest code cell has 176 characters
Smallest code cell has 35 characters



In [ ]: