If you want to make a career in big data, you need to learn NumPy. Read on to get started with one of Python's most popular libraries.

I will walk you through the basics of NumPy. If you want to do machine learning then knowledge of NumPy is necessary. It one of the most widely used Python libraries. It is the most useful library if you are dealing with numbers in Python. NumPy guarantees great execution speed compared to standard Python libraries. It comes with a great number of built-in functions.

Advantages of using NumPy with Python:

- Array-oriented computing.
- Efficiently implemented multi-dimensional arrays.
- Designed for scientific computation.

First, let’s talk about its installation. NumPy is not part of the basic Python installation. We need to install it after the installation of Python in our system. We can do it by the pip using command, `pip install NumPy`

, or by installing Conda.

We are done with the installation and now we can jump right into NumPy. First, let’s start with the most important object in NumPy, the ndarray or multi-dimensional array. A multi-dimensional array is an array of arrays. In multi-dimensional arrays, this array, `[1,2,3]`

, is a one-dimensional array because it contains only one row. The below is array is a two-dimensional array, as it contains multiple rows as well as multiple columns.

[[1 2 3][4 5 6]

[7 8 9]]

Let’s do some coding now. Here I am using Jupyter Notebook to run my code; you can use any IDE available and best suited to you.

We start with `import NumPy`

.

In the following code, I am renaming the package to `np`

for convenience sake.

import numpy as np

Now, in order to create an array in NumPy, we use its array function as shown below:

array = np.array([1,2,3])print(array)

Output: [1 2 3]

This an example of a one-dimensional array.

Another way to create an array in NumPy is by using the `zeros`

function.

zeros = np.zeros(3)print(zeros)

Output: [0. 0. 0.]

If you look closely at the output, the generated array contains three zeros, but the type of the value is a float and, by default, NumPy creates the array of float values.

type(zeros[0])Output: numpy.float64

Going back to the first example inside NumPy’s `array`

function, we pass a list so we can also pass the `list`

variable inside the `array`

function and the output will be the same.

my_list = [1,2,3]array = np.array(my_list)

print(array)

Output: [1 2 3]

Now, let’s look into how to create a two-dimensional array using NumPy. Instead of passing the list now we have to pass a list of tuples or list of lists as mentioned below.

two_dim_array = np.array([(1,2,3), (4,5,6), (7,8,9)])print(two_dim_array)

Output:

[[1 2 3]

[4 5 6]

[7 8 9]]

Note that the number of columns should be equal, otherwise NumPy will create an array of a list.

arr = np.array([[1,2,3], [4,6], [7,8,9]])print(arr)

Output: [list([1, 2, 3]) list([4, 6]) list([7, 8, 9])]

Now, to create an array of a range, which is very good for making plots, we use the `linspace`

function.

range_array = np.linspace(0, 10, 4)print(range_array)

Output: [ 0. 3.33333333 6.66666667 10. ]

Here, the first argument is the starting point and next is the endpoint and the last argument defines how many elements you want in your array.

Now, to create random arrays we can use the `random`

function. Here, I’ve created an array of random integers, and, therefore, used `randint`

where first I specified the maximum value and then the size of my array.

random_array = np.random.randint(15, size=10)print(random_array)

Output: [ 7 11 8 2 6 4 9 6 10 9]

Now we know the basics of how to create arrays in NumPy. Now let’s look into some of its basic operations. First, we will start by finding the size and shape of an array. Size will give the number of elements in an array whereas shape will give us the shape of an array.

For a one dimensional array, the shape would be `(n, )`

, where `n`

is the number of elements in your array.

For a two dimensional array, the shape would be `(n,m)`

, where `n`

is the number of rows and `m`

is the number of columns in your array

print(array.size)Output: 3

print(array.shape)

Output: (3,)

print(multi_dim_array.size)

Output: 9

print(multi_dim_array.shape)

Output: (3, 3)

If we want to change the shape of an array we can easily do it with the `reshape`

function. It will look like something like this:

two_dim_array = np.array([(1,2,3,4), (5,6,7,8)])two_dim_array = two_dim_array.reshape(4,2)

print(two_dim_array)

Output:

[[1 2]

[3 4]

[5 6]

[7 8]]

We need to make sure that the rows and columns can be interchangeable. For example, here, we can change rows and columns from (2,4) to (4,2) but can not change them to (4,3) because, for that, we’d need 12 elements and we have only 8. Doing so will give an error as shown below.

ValueError: cannot reshape array of size 8 into shape (4,3)

To check the dimensions of our array. we can use the `ndim`

function.

print(two_dim_array.ndim)Output: 2

Now, to get values from an array, a process known as slicing can be done in various ways. For example, `array[1]`

will fetch the second element of my array, but if we want a range we can use `array[0:1]`

, which will give us the first two elements. For the last value of the array, we can use `array[-1]`

, which is similar to the standard method of getting elements from a list in Python.

Now to find the sum all we have to use is the `sum()`

, function but if we want to find the sum of the axis we can pass an argument for the axis.

print(two_dim_array.sum(axis=0))Output: [ 6 8 10 12]

print(two_dim_array.sum(axis=1))

Output: [10 26]

Now to add two arrays all we have to use if + operator. For example:

print(two_dim_array + two_dim_array)Output:

[[ 2 4 6 8]

[10 12 14 16]]

Similarly, we can use other operands as well, like multiple, subtract, and divide.

We have many other operations present in NumPy like `sqrt`

, which will give us the square root of every element, and `std`

, which is used to find the standard deviation. To explore more about these operations visit the NumPy’s documentation.

And that’s it for the introduction of NumPy.

☞ Learn Programming with Python Step by Step

☞ MySQL Databases With Python Tutorial

☞ Creating Web Sites using Python and Flask

☞ Complete Python: Go from zero to hero in Python

☞ An A-Z of useful Python tricks

☞ A Complete Machine Learning Project Walk-Through in Python

☞ Learning Python: From Zero to Hero

☞ MongoDB with Python Crash Course - Tutorial for Beginners

☞ Introduction to PyTorch and Machine Learning

*Originally published by Prabhat Kashyap at **https://dzone.com*

Python GUI Programming Projects using Tkinter and Python 3

Description

Learn Hands-On Python Programming By Creating Projects, GUIs and Graphics

Python is a dynamic modern object -oriented programming language

It is easy to learn and can be used to do a lot of things both big and small

Python is what is referred to as a high level language

Python is used in the industry for things like embedded software, web development, desktop applications, and even mobile apps!

SQL-Lite allows your applications to become even more powerful by storing, retrieving, and filtering through large data sets easily

If you want to learn to code, Python GUIs are the best way to start!

I designed this programming course to be easily understood by absolute beginners and young people. We start with basic Python programming concepts. Reinforce the same by developing Project and GUIs.

Why Python?

The Python coding language integrates well with other platforms – and runs on virtually all modern devices. If you’re new to coding, you can easily learn the basics in this fast and powerful coding environment. If you have experience with other computer languages, you’ll find Python simple and straightforward. This OSI-approved open-source language allows free use and distribution – even commercial distribution.

When and how do I start a career as a Python programmer?

In an independent third party survey, it has been revealed that the Python programming language is currently the most popular language for data scientists worldwide. This claim is substantiated by the Institute of Electrical and Electronic Engineers, which tracks programming languages by popularity. According to them, Python is the second most popular programming language this year for development on the web after Java.

Python Job Profiles

Software Engineer

Research Analyst

Data Analyst

Data Scientist

Software Developer

Python Salary

The median total pay for Python jobs in California, United States is $74,410, for a professional with one year of experience

Below are graphs depicting average Python salary by city

The first chart depicts average salary for a Python professional with one year of experience and the second chart depicts the average salaries by years of experience

Who Uses Python?

This course gives you a solid set of skills in one of today’s top programming languages. Today’s biggest companies (and smartest startups) use Python, including Google, Facebook, Instagram, Amazon, IBM, and NASA. Python is increasingly being used for scientific computations and data analysis

Take this course today and learn the skills you need to rub shoulders with today’s tech industry giants. Have fun, create and control intriguing and interactive Python GUIs, and enjoy a bright future! Best of Luck

Who is the target audience?

Anyone who wants to learn to code

For Complete Programming Beginners

For People New to Python

This course was designed for students with little to no programming experience

People interested in building Projects

Anyone looking to start with Python GUI development

Basic knowledge

Access to a computer

Download Python (FREE)

Should have an interest in programming

Interest in learning Python programming

Install Python 3.6 on your computer

What will you learn

Build Python Graphical User Interfaces(GUI) with Tkinter

Be able to use the in-built Python modules for their own projects

Use programming fundamentals to build a calculator

Use advanced Python concepts to code

Build Your GUI in Python programming

Use programming fundamentals to build a Project

Signup Login & Registration Programs

Quizzes

Assignments

Job Interview Preparation Questions

& Much More

Guide to Python Programming Language

Description

The course will lead you from beginning level to advance in Python Programming Language. You do not need any prior knowledge on Python or any programming language or even programming to join the course and become an expert on the topic.

The course is begin continuously developing by adding lectures regularly.

Please see the Promo and free sample video to get to know more.

Hope you will enjoy it.

Basic knowledge

An Enthusiast Mind

A Computer

Basic Knowledge To Use Computer

Internet Connection

What will you learn

Will Be Expert On Python Programming Language

Build Application On Python Programming Language

Understanding neural networks using Python and Numpy by coding

If you are a **junior data scientist** who sort of understands how neural nets work, or a **machine learning enthusiast** who only knows a little about **deep learning**, this is the article that you cannot miss. Here is **how you can build a neural net from scratch using NumPy** in ** 9 steps **— from data pre-processing to back-propagation — a must-do practice.

Basic understanding of **machine learning**, **artificial neural network**, **Python syntax**, and programming logic is preferred (but not necessary as you can learn on the go).

*Codes are available on **Github**.*

Step one. Import NumPy. Seriously.

import numpy as np np.random.seed(42) # for reproducibility2. Data Generation

**Deep learning** is data-hungry. Although there are many clean datasets available online, we will generate our own for simplicity — for inputs **a** and **b**, we have outputs **a+b**, **a-b**, and **|a-b|**. 10,000 datum points are generated.

X_num_row, X_num_col = [2, 10000] # Row is no. of feature, col is no. of datum points X_raw = np.random.rand(X_num_row,X_num_col) * 100 y_raw = np.concatenate(([(X_raw[0,:] + X_raw[1,:])], [(X_raw[0,:] - X_raw[1,:])], np.abs([(X_raw[0,:] - X_raw[1,:])]))) # for input a and b, output is a+b; a-b and |a-b| y_num_row, y_num_col = y_raw.shape3. Train-test Splitting

Our dataset is split into training (70%) and testing (30%) set. Only training set is leveraged for tuning neural networks. Testing set is used only for performance evaluation when the training is complete.

train_ratio = 0.7 num_train_datum = int(train_ratio*X_num_col) X_raw_train = X_raw[:,0:num_train_datum] X_raw_test = X_raw[:,num_train_datum:] y_raw_train = y_raw[:,0:num_train_datum] y_raw_test = y_raw[:,num_train_datum:]4. Data Standardization

Data in the training set is standardized so that the distribution for each standardized feature is zero-mean and unit-variance. The scalers generated from the abovementioned procedure can then be applied to the testing set.

class scaler: def __init__(self, mean, std): self.mean = mean self.std = stddef get_scaler(row):

mean = np.mean(row)

std = np.std(row)

return scaler(mean, std)def standardize(data, scaler):

return (data - scaler.mean) / scaler.stddef unstandardize(data, scaler):

Construct scalers from training set

return (data * scaler.std) + scaler.meanX_scalers = [get_scaler(X_raw_train[row,:]) for row in range(X_num_row)]

X_train = np.array([standardize(X_raw_train[row,:], X_scalers[row]) for row in range(X_num_row)])y_scalers = [get_scaler(y_raw_train[row,:]) for row in range(y_num_row)]

Apply those scalers to testing set

y_train = np.array([standardize(y_raw_train[row,:], y_scalers[row]) for row in range(y_num_row)])X_test = np.array([standardize(X_raw_test[row,:], X_scalers[row]) for row in range(X_num_row)])

Check if data has been standardized

y_test = np.array([standardize(y_raw_test[row,:], y_scalers[row]) for row in range(y_num_row)])print([X_train[row,:].mean() for row in range(X_num_row)]) # should be close to zero

print([X_train[row,:].std() for row in range(X_num_row)]) # should be close to oneprint([y_train[row,:].mean() for row in range(y_num_row)]) # should be close to zero

print([y_train[row,:].std() for row in range(y_num_row)]) # should be close to one

The scaler therefore does not contain any information from our testing set. We do not want our neural net to gain any information regarding testing set before network tuning.

We have now completed the data pre-processing procedures in ** 4 steps**.

Photo by freestocks.org on Unsplash

We objectify a ‘layer’ using class in Python. Every layer (except the input layer) has a weight matrix **W**, a bias vector ** b**, and an activation function. Each layer is appended to a list called

class layer:

definit(self, layer_index, is_output, input_dim, output_dim, activation):

self.layer_index = layer_index # zero indicates input layer

self.is_output = is_output # true indicates output layer, false otherwise

self.input_dim = input_dim

self.output_dim = output_dim

self.activation = activationChange layers_dim to configure your own neural net!`# the multiplication constant is sorta arbitrary if layer_index != 0: self.W = np.random.randn(output_dim, input_dim) * np.sqrt(2/input_dim) self.b = np.random.randn(output_dim, 1) * np.sqrt(2/input_dim)`

layers_dim = [X_num_row, 4, 4, y_num_row] # input layer --- hidden layers --- output layers

Construct the net layer by layer

neural_net = []for layer_index in range(len(layers_dim)):

Simple check on overfitting

if layer_index == 0: # if input layer

neural_net.append(layer(layer_index, False, 0, layers_dim[layer_index], 'irrelevant'))

elif layer_index+1 == len(layers_dim): # if output layer

neural_net.append(layer(layer_index, True, layers_dim[layer_index-1], layers_dim[layer_index], activation='linear'))

else:

neural_net.append(layer(layer_index, False, layers_dim[layer_index-1], layers_dim[layer_index], activation='relu'))pred_n_param = sum([(layers_dim[layer_index]+1)*layers_dim[layer_index+1] for layer_index in range(len(layers_dim)-1)])

act_n_param = sum([neural_net[layer_index].W.size + neural_net[layer_index].b.size for layer_index in range(1,len(layers_dim))])

print(f'Predicted number of hyperparameters: {pred_n_param}')

print(f'Actual number of hyperparameters: {act_n_param}')

print(f'Number of data: {X_num_col}')if act_n_param >= X_num_col:

raise Exception('It will overfit.')

Finally, we do a sanity check on the number of hyperparameters using the following formula, and by counting. The number of datums available should exceed the number of hyperparameters, otherwise it will definitely overfit.

N^l is number of hyperparameters at l-th layer, L is number of layers (excluding input layer)

6. Forward PropagationWe define a function for forward propagation given a certain set of weights and biases. The connection between layers is defined in matrix form as:

σ is element-wise activation function, superscript T means transpose of a matrix

Activation functions are defined one by one. ReLU is implemented as ** a → max(a,0)**, whereas sigmoid function should return

def activation(input_, act_func):

if act_func == 'relu':

return np.maximum(input_, np.zeros(input_.shape))

elif act_func == 'linear':

return input_

else:

raise Exception('Activation function is not defined.')def forward_prop(input_vec, layers_dim=layers_dim, neural_net=neural_net):

neural_net[0].A = input_vec # Define A in input layer for for-loop convenience

for layer_index in range(1,len(layers_dim)): # W,b,Z,A are undefined in input layer

neural_net[layer_index].Z = np.add(np.dot(neural_net[layer_index].W, neural_net[layer_index-1].A), neural_net[layer_index].b)

neural_net[layer_index].A = activation(neural_net[layer_index].Z, neural_net[layer_index].activation)

return neural_net[layer_index].A

Photo by Holger Link on Unsplash

This is the most tricky part where many of us simply do not understand. Once we have defined a loss metric *e* for evaluating performance, we would like to know how the loss metric change when we perturb each weight or bias.

We want to know how sensitive each weight and bias is with respect to the loss metric.

This is represented by partial derivatives **∂e/∂W** (denoted dW in code) and **∂e/∂b** (denoted db in code) respectively, and can be calculated analytically.

⊙ represents element-wise multiplication

These back-propagation equations assume only one datum *y* is compared. The gradient update process would be very noisy as the performance of each iteration is subject to one datum point only. Multiple datums can be used to reduce the noise where **∂W(y 1, y2, …) **would be the mean of

def get_loss(y, y_hat, metric='mse'):

if metric == 'mse':

individual_loss = 0.5 * (y_hat - y) ** 2

return np.mean([np.linalg.norm(individual_loss[:,col], 2) for col in range(individual_loss.shape[1])])

else:

raise Exception('Loss metric is not defined.')def get_dZ_from_loss(y, y_hat, metric):

if metric == 'mse':

return y_hat - y

else:

raise Exception('Loss metric is not defined.')def get_dactivation(A, act_func):

if act_func == 'relu':

return np.maximum(np.sign(A), np.zeros(A.shape)) # 1 if backward input >0, 0 otherwise; then diaganolize

elif act_func == 'linear':

return np.ones(A.shape)

else:

raise Exception('Activation function is not defined.')def backward_prop(y, y_hat, metric='mse', layers_dim=layers_dim, neural_net=neural_net, num_train_datum=num_train_datum):

for layer_index in range(len(layers_dim)-1,0,-1):

if layer_index+1 == len(layers_dim): # if output layer

dZ = get_dZ_from_loss(y, y_hat, metric)

else:

dZ = np.multiply(np.dot(neural_net[layer_index+1].W.T, dZ),

get_dactivation(neural_net[layer_index].A, neural_net[layer_index].activation))

dW = np.dot(dZ, neural_net[layer_index-1].A.T) / num_train_datum

db = np.sum(dZ, axis=1, keepdims=True) / num_train_datum`neural_net[layer_index].dW = dW neural_net[layer_index].db = db`

We now have every building block for training a neural network.

Once we know the sensitivities of weights and biases, we try to ** minimize** (hence the minus sign) the loss metric iteratively by gradient descent using the following update rule:

W = W - learning_rate * ∂W

b = b - learning_rate * ∂b

Photo by Rostyslav Savchyn on Unsplash

learning_rate = 0.01

max_epoch = 1000000for epoch in range(1,max_epoch+1):

y_hat_train = forward_prop(X_train) # update y_hat

backward_prop(y_train, y_hat_train) # update (dW,db)`for layer_index in range(1,len(layers_dim)): # update (W,b) neural_net[layer_index].W = neural_net[layer_index].W - learning_rate * neural_net[layer_index].dW neural_net[layer_index].b = neural_net[layer_index].b - learning_rate * neural_net[layer_index].db if epoch % 100000 == 0: print(f'{get_loss(y_train, y_hat_train):.4f}')`

Training loss should be going down as it iterates

9. TestingThe model generalizes well if the testing loss is not much higher than the training loss. We also make some test cases to see how the model performs.

# test lossprint(get_loss(y_test, forward_prop(X_test)))

def predict(X_raw_any):

X_any = np.array([standardize(X_raw_any[row,:], X_scalers[row]) for row in range(X_num_row)])

y_hat = forward_prop(X_any)

y_hat_any = np.array([unstandardize(y_hat[row,:], y_scalers[row]) for row in range(y_num_row)])

return y_hat_anypredict(np.array([[30,70],[70,30],[3,5],[888,122]]).T)

This is how you can build a neural net from scratch using NumPy in ** 9 steps**.

My implementation by no means is the most efficient way to build and train a neural net. There is so much room for improvement but that is a story for another day. Codes are available on Github. Happy coding!

**Thanks for reading** ❤

If you liked this post, share it with all of your programming buddies!

Follow us on **Facebook** | **Twitter**

☞ The Data Science Course 2019: Complete Data Science Bootcamp

☞ Machine Learning A-Z™: Hands-On Python & R In Data Science

☞ Tableau 10 A-Z: Hands-On Tableau Training For Data Science!

☞ R Programming A-Z™: R For Data Science With Real Exercises!

☞ Machine Learning, Data Science and Deep Learning with Python