*This is part 1 of the numpy tutorial covering all the core aspects of performing data manipulation and analysis with numpy’s ndarrays. Numpy is the most basic and a powerful package for scientific computing and data manipulation in python. *

Numpy Tutorial Part 1: Introduction to Arrays. Photo by Bryce Canyon.

#### Also Read:

Numpy Tutorial – Gentle Introduction [Part 1] [This Article]

Numpy – Vital Functions for Data Analysis [Part 2]

## Contents

- Introduction to numpy
- How to create a numpy array?
- How to inspect the size and shape of a numpy array?
- How to extract specific items from an array?

4.1 How to reverse the rows and the whole array?

4.2 How to represent missing values and infinite?

4.3 How to compute mean, min, max on the ndarray? - How to create a new array from an existing array?
- Reshaping and Flattening Multidimensional arrays

6.1 What is the difference between flatten() and ravel()? - How to create sequences, repetitions, and random numbers?

7.1 How to create repeating sequences?

7.2 How to generate random numbers?

7.3 How to get the unique items and the counts?

## 1. Introduction to Numpy

Numpy is the most basic and a powerful package for working with data in python.

If you are going to work on data analysis or machine learning projects, then having a solid understanding of numpy is nearly mandatory.

Because other packages for data analysis (like pandas) is built on top of numpy and the scikit-learn package which is used to build machine learning applications works heavily with numpy as well.

So what does numpy provide?

At the core, numpy provides the excellent ndarray objects, short for n-dimensional arrays.

In a ‘ndarray’ object, aka ‘array’, you can store multiple items of the same data type. It is the facilities around the array object that makes numpy so convenient for performing math and data manipulations.

You might wonder, *‘I can store numbers and other objects in a python list itself and do all sorts of computations and manipulations through list comprehensions, for-loops etc. What do I need a numpy array for?’*

Well, there are very significant advantages of using numpy arrays overs lists.

To understand this, let’s first see how to create a numpy array.

## 2. How to create a numpy array?

There are multiple ways to create a numpy array, most of which will be covered as you read this. However one of the most common ways is to create one from a list or a list like an object by passing it to the `np.array`

function.

`# Create an 1d array from a list`

import numpy as np list1 = [0,1,2,3,4] arr1d = np.array(list1)`# Print the array and its type`

print(type(arr1d)) arr1d`#> class 'numpy.ndarray'`

`#> array([0, 1, 2, 3, 4])`

The key difference between an array and a list is, arrays are designed to handle vectorized operations while a python list is not.

That means, if you apply a function it is performed on every item in the array, rather than on the whole array object.

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Let’s suppose you want to add the number 2 to every item in the list. The intuitive way to do it is something like this:

`list1 + 2 ``# error`

That was not possible with a list. But you can do that on a ndarray.

`# Add 2 to each element of arr1d`

arr1d + 2`#> array([2, 3, 4, 5, 6])`

Another characteristic is that, once a numpy array is created, you cannot increase its size. To do so, you will have to create a new array. But such a behavior of extending the size is natural in a list.

Nevertheless, there are so many more advantages. Let’s find out.

So, that’s about 1d array. You can also pass a list of lists to create a matrix like a 2d array.

`# Create a 2d array from a list of lists`

list2 = [[0,1,2], [3,4,5], [6,7,8]] arr2d = np.array(list2) arr2d`#> array([[0, 1, 2], #> [3, 4, 5], #> [6, 7, 8]])`

You may also specify the datatype by setting the dtype argument. Some of the most commonly used numpy dtypes are: `'float'`

, `'int'`

, `'bool'`

, `'str'`

and `'object'`

.

To control the memory allocations you may choose to use one of ‘float32’, ‘float64’, ‘int8’, ‘int16’ or ‘int32’.

`# Create a float 2d array`

arr2d_f = np.array(list2, dtype='float') arr2d_f`#> array([[ 0., 1., 2.], #> [ 3., 4., 5.], #> [ 6., 7., 8.]])`

The decimal point after each number is indicative of the float datatype. You can also convert it to a different datatype using the `astype`

method.

`# Convert to 'int' datatype`

arr2d_f.astype('int')`#> array([[0, 1, 2], #> [3, 4, 5], #> [6, 7, 8]])`

`# Convert to int then to str datatype`

arr2d_f.astype('int').astype('str')`#> array([['0', '1', '2'], #> ['3', '4', '5'], #> ['6', '7', '8']], #> dtype='U21')`

A numpy array must have all items to be of the same data type, unlike lists. This is another significant difference.

However, if you are uncertain about what datatype your array will hold or if you want to hold characters and numbers in the same array, you can set the `dtype`

as `'object'`

.

`# Create a boolean array`

arr2d_b = np.array([1, 0, 10], dtype='bool') arr2d_b`#> array([ True, False, True], dtype=bool)`

`# Create an object array to hold numbers as well as strings`

arr1d_obj = np.array([1, 'a'], dtype='object') arr1d_obj`#> array([1, 'a'], dtype=object)`

Finally, you can always convert an array back to a python list using `tolist()`

.

`# Convert an array back to a list`

arr1d_obj.tolist()`#> [1, 'a']`

To summarise, the main differences with python lists are:

- Arrays support vectorised operations, while lists don’t.
- Once an array is created, you cannot change its size. You will have to create a new array or overwrite the existing one.
- Every array has one and only one dtype. All items in it should be of that dtype.
- An equivalent numpy array occupies much less space than a python list of lists.

## 3. How to inspect the size and shape of a numpy array?

Every array has some properties I want to understand in order to know about the array.

Let’s consider the array, arr2d. Since it was created from a list of lists, it has 2 dimensions that can be shown as rows and columns, like in a matrix.

Had I created one from a list of list of lists, it would have 3 dimensions, as in a cube. And so on.

Let’s suppose you were handed a numpy vector that you didn’t create yourself. What are the things you would want to explore in order to know about that array?

Well, I want to know:

- If it is a 1D or a 2D array or more. (use
`ndim`

) - How many items are present in each dimension (use
`shape`

) - What is its datatype (use
`dtype`

) - What is the total number of items in it (use
`size`

) - Samples of first few items in the array (through indexing)

`# Create a 2d array with 3 rows and 4 columns`

list2 = [[1, 2, 3, 4],[3, 4, 5, 6], [5, 6, 7, 8]] arr2 = np.array(list2, dtype='float') arr2`#> array([[ 1., 2., 3., 4.], #> [ 3., 4., 5., 6.], #> [ 5., 6., 7., 8.]])`

`# shape`

print('Shape: ', arr2.shape)`# dtype`

print('Datatype: ', arr2.dtype)`# size`

print('Size: ', arr2.size)`# ndim`

print('Num Dimensions: ', arr2.ndim)`#> Shape: (3, 4) #> Datatype: float64 #> Size: 12 #> Num Dimensions: 2`

## 4. How to extract specific items from an array?

```
arr2
``````
#> array([[ 1., 2., 3., 4.],
#> [ 3., 4., 5., 6.],
#> [ 5., 6., 7., 8.]])
```

You can extract specific portions on an array using indexing starting with 0, something similar to how you would do with python lists.

But unlike lists, numpy arrays can optionally accept as many parameters in the square brackets as there is number of dimensions.

`# Extract the first 2 rows and columns`

arr2[:2, :2] list2[:2, :2] # error`#> array([[ 1., 2.], #> [ 3., 4.]])`

Additionally, numpy arrays support boolean indexing.

A boolean index array is of the same shape as the array-to-be-filtered and it contains only True and False values. The values corresponding to True positions are retained in the output.

`# Get the boolean output by applying the condition to each element.`

b = arr2 > 4 b`#> array([[False, False, False, False], #> [False, False, True, True], #> [ True, True, True, True]], dtype=bool)`

```
arr2[b]
````#> array([ 5., 6., 5., 6., 7., 8.])`

## 4.1 How to reverse the rows and the whole array?

Reversing an array works like how you would do with lists, but you need to do for all the axes (dimensions) if you want a complete reversal.

`# Reverse only the row positions`

arr2[::-1, ]`#> array([[ 5., 6., 7., 8.], #> [ 3., 4., 5., 6.], #> [ 1., 2., 3., 4.]])`

`# Reverse the row and column positions`

arr2[::-1, ::-1]`#> array([[ 8., 7., 6., 5.], #> [ 6., 5., 4., 3.], #> [ 4., 3., 2., 1.]])`

## 4.2 How to represent missing values and infinite?

Missing values can be represented using `np.nan`

object, while np.inf represents infinite. Let’s place some in arr2d.

`# Insert a nan and an inf`

arr2[1,1] = np.nan # not a number arr2[1,2] = np.inf # infinite arr2`#> array([[ 1., 2., 3., 4.], #> [ 3., nan, inf, 6.], #> [ 5., 6., 7., 8.]])`

`# Replace nan and inf with -1. Don't use arr2 == np.nan`

missing_bool = np.isnan(arr2) | np.isinf(arr2) arr2[missing_bool] = -1 arr2`#> array([[ 1., 2., 3., 4.], #> [ 3., -1., -1., 6.], #> [ 5., 6., 7., 8.]])`

## 4.3 How to compute mean, min, max on the ndarray?

The ndarray has the respective methods to compute this for the whole array.

`# mean, max and min`

print("Mean value is: ", arr2.mean()) print("Max value is: ", arr2.max()) print("Min value is: ", arr2.min())`#> Mean value is: 3.58333333333 #> Max value is: 8.0 #> Min value is: -1.0`

However, if you want to compute the minimum values row wise or column wise, use the np.amin version instead.

`# Row wise and column wise min`

print("Column wise minimum: ", np.amin(arr2, axis=0)) print("Row wise minimum: ", np.amin(arr2, axis=1))`#> Column wise minimum: [ 1. -1. -1. 4.] #> Row wise minimum: [ 1. -1. 5.]`

Computing the minimum row-wise is fine. But what if you want to do some other computation/function row-wise? It can be done using the `np.apply_over_axis`

which you will see in the upcoming topic.

`# Cumulative Sum`

np.cumsum(arr2)`#> array([ 1., 3., 6., 10., 13., 12., 11., 17., 22., 28., 35., 43.])`

## 5. How to create a new array from an existing array?

If you just assign a portion of an array to another array, the new array you just created actually refers to the parent array in memory.

That means, if you make any changes to the new array, it will reflect in the parent array as well.

So to avoid disturbing the parent array, you need to make a copy of it using copy(). All numpy arrays come with the copy() method.

`# Assign portion of arr2 to arr2a. Doesn't really create a new array.`

arr2a = arr2[:2,:2] arr2a[:1, :1] = 100`# 100 will reflect in arr2`

arr2`#> array([[ 100., 2., 3., 4.], #> [ 3., -1., -1., 6.], #> [ 5., 6., 7., 8.]])`

`# Copy portion of arr2 to arr2b`

arr2b = arr2[:2, :2].copy() arr2b[:1, :1] = 101`# 101 will not reflect in arr2`

arr2`#> array([[ 100., 2., 3., 4.], #> [ 3., -1., -1., 6.], #> [ 5., 6., 7., 8.]])`

## 6. Reshaping and Flattening Multidimensional arrays

Reshaping is changing the arrangement of items so that shape of the array changes while maintaining the same number of dimensions.

Flattening, however, will convert a multi-dimensional array to a flat 1d array. And not any other shape.

First, let’s reshape the `arr2`

array from 3×4 to 4×3 shape.

`# Reshape a 3x4 array to 4x3 array`

arr2.reshape(4, 3)`#> array([[ 100., 2., 3.], #> [ 4., 3., -1.], #> [ -1., 6., 5.], #> [ 6., 7., 8.]])`

## 6.1 What is the difference between flatten() and ravel()?

There are 2 popular ways to implement flattening. That is using the `flatten()`

method and the other using the `ravel()`

method.

The difference between ravel and flatten is, the new array created using ravel is actually a reference to the parent array. So, any changes to the new array will affect the parent as well. But is memory efficient since it does not create a copy.

`# Flatten it to a 1d array`

arr2.flatten()`#> array([ 100., 2., 3., 4., 3., -1., -1., 6., 5., 6., 7., 8.])`

`# Changing the flattened array does not change parent`

b1 = arr2.flatten() b1[0] = 100`# changing b1 does not affect arr2`

arr2`#> array([[ 100., 2., 3., 4.], #> [ 3., -1., -1., 6.], #> [ 5., 6., 7., 8.]])`

`# Changing the raveled array changes the parent also.`

b2 = arr2.ravel() b2[0] = 101`# changing b2 changes arr2 also`

arr2`#> array([[ 101., 2., 3., 4.], #> [ 3., -1., -1., 6.], #> [ 5., 6., 7., 8.]])`

## 7. How to create sequences, repetitions and random numbers using numpy?

The `np.arange`

function comes handy to create customised number sequences as `ndarray`

.

`# Lower limit is 0 be default`

print(np.arange(5))`# 0 to 9`

print(np.arange(0, 10))`# 0 to 9 with step of 2`

print(np.arange(0, 10, 2))`# 10 to 1, decreasing order`

print(np.arange(10, 0, -1))`#> [0 1 2 3 4] #> [0 1 2 3 4 5 6 7 8 9] #> [0 2 4 6 8] #> [10 9 8 7 6 5 4 3 2 1]`

You can set the starting and end positions using np.arange. But if you are focussed on the number of items in the array you will have to manually calculate the appropriate step value.

Say, you want to create an array of exactly 10 numbers between 1 and 50, Can you compute what would be the step value?

Well, I am going to use the `np.linspace`

instead.

`# Start at 1 and end at 50`

np.linspace(start=1, stop=50, num=10, dtype=int)`#> array([ 1, 6, 11, 17, 22, 28, 33, 39, 44, 50])`

Notice since I explicitly forced the dtype to be int, the numbers are not equally spaced because of the rounding.

Similar to np.linspace, there is also np.logspace which rises in a logarithmic scale. In np.logspace, the given start value is actually base^start and ends with base^stop, with a default based value of 10.

`# Limit the number of digits after the decimal to 2`

np.set_printoptions(precision=2)`# Start at 10^1 and end at 10^50`

np.logspace(start=1, stop=50, num=10, base=10) #> array([ 1.00e+01, 2.78e+06, 7.74e+11, 2.15e+17, 5.99e+22, #> 1.67e+28, 4.64e+33, 1.29e+39, 3.59e+44, 1.00e+50])

The `np.zeros`

and `np.ones`

functions lets you create arrays of desired shape where all the items are either 0’s or 1’s.

```
np.zeros([2,2])
``````
#> array([[ 0., 0.],
#> [ 0., 0.]])
```

```
np.ones([2,2])
``````
#> array([[ 1., 1.],
#> [ 1., 1.]])
```

## 7.1 How to create repeating sequences?

`np.tile`

will repeat a whole list or array n times. Whereas, `np.repeat`

repeats each item n times.

a = [1,2,3]`# Repeat whole of 'a' two times`

print('Tile: ', np.tile(a, 2))`# Repeat each element of 'a' two times`

print('Repeat: ', np.repeat(a, 2))`#> Tile: [1 2 3 1 2 3] #> Repeat: [1 1 2 2 3 3]`

## 7.2 How to generate random numbers?

The `random`

module provides nice functions to generate random numbers (and also statistical distributions) of any given shape.

`# Random numbers between [0,1) of shape 2,2`

print(np.random.rand(2,2))`# Normal distribution with mean=0 and variance=1 of shape 2,2`

print(np.random.randn(2,2))`# Random integers between [0, 10) of shape 2,2`

print(np.random.randint(0, 10, size=[2,2]))`# One random number between [0,1)`

print(np.random.random())`# Random numbers between [0,1) of shape 2,2`

print(np.random.random(size=[2,2]))`# Pick 10 items from a given list, with equal probability`

print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10))`# Pick 10 items from a given list with a predefined probability 'p'`

print(np.random.choice(['a', 'e', 'i', 'o', 'u'], size=10, p=[0.3, .1, 0.1, 0.4, 0.1]))`# picks more o's`

`#> [[ 0.84 0.7 ] #> [ 0.52 0.8 ]] #> [[-0.06 -1.55] #> [ 0.47 -0.04]] #> [[4 0] #> [8 7]] #> 0.08737272424956832 #> [[ 0.45 0.78] #> [ 0.03 0.74]] #> ['i' 'a' 'e' 'e' 'a' 'u' 'o' 'e' 'i' 'u'] #> ['o' 'a' 'e' 'a' 'a' 'o' 'o' 'o' 'a' 'o']`

Now, everytime you run any of the above functions, you get a different set of random numbers.

If you want to repeat the same set of random numbers every time, you need to set the seed or the random state. The see can be any value. The only requirement is you must set the seed to the same value every time you want to generate the same set of random numbers.

Once `np.random.RandomState`

is created, all the functions of the `np.random`

module becomes available to the created randomstate object.

`# Create the random state`

rn = np.random.RandomState(100)`# Create random numbers between [0,1) of shape 2,2`

print(rn.rand(2,2))`#> [[ 0.54 0.28] #> [ 0.42 0.84]]`

`# Set the random seed`

np.random.seed(100)`# Create random numbers between [0,1) of shape 2,2`

print(np.random.rand(2,2))`#> [[ 0.54 0.28] #> [ 0.42 0.84]]`

## 7.3 How to get the unique items and the counts?

The `np.unique`

method can be used to get the unique items. If you want the repetition counts of each item, set the return_counts parameter to True.

`# Create random integers of size 10 between [0,10)`

np.random.seed(100) arr_rand = np.random.randint(0, 10, size=10) print(arr_rand)`#> [8 8 3 7 7 0 4 2 5 2]`

`# Get the unique items and their counts`

uniqs, counts = np.unique(arr_rand, return_counts=True) print("Unique items : ", uniqs) print("Counts : ", counts)`#> Unique items : [0 2 3 4 5 7 8] #> Counts : [1 2 1 1 1 2 2]`

## 8.0 Conclusion

This completes the part 1 of the numpy series. The next one is advanced numpy for data analysis, where I will explicit on the functionalities that is an essential toolkit of data analysis.

**Recommended Course**: If you liked this article, you will enjoy the exhaustive NumPy Course. It covers NumPy from first principles, the recommended way to learn NumPy to for a strong foundation for programming for ML / AI: Numpy for Data Science.