Cara menggunakan vertical array python

Welcome to NumPy!

NumPy (Numerical Python) is an open source Python library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems. NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development. The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.

The NumPy library contains multidimensional array and matrix data structures (you’ll find more information about this in later sections). It provides ndarray, a homogeneous n-dimensional array object, with methods to efficiently operate on it. NumPy can be used to perform a wide variety of mathematical operations on arrays. It adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices and it supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices.

Learn more about !

Installing NumPy

To install NumPy, we strongly recommend using a scientific Python distribution. If you’re looking for the full instructions for installing NumPy on your operating system, see Installing NumPy.

If you already have Python, you can install NumPy with:

conda install numpy

or

pip install numpy

If you don’t have Python yet, you might want to consider using Anaconda. It’s the easiest way to get started. The good thing about getting this distribution is the fact that you don’t need to worry too much about separately installing NumPy or any of the major packages that you’ll be using for your data analyses, like pandas, Scikit-Learn, etc.

How to import NumPy

To access NumPy and its functions import it in your Python code like this:

import numpy as np

We shorten the imported name to

pip install numpy

Reading the example code

If you aren’t already comfortable with reading tutorials that contain a lot of code, you might not know how to interpret a code block that looks like this:

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

If you aren’t familiar with this style, it’s very easy to understand. If you see

pip install numpy

pip install numpy

pip install numpy

pip install numpy

What’s the difference between a Python list and a NumPy array?

NumPy gives you an enormous range of fast and efficient ways of creating arrays and manipulating numerical data inside them. While a Python list can contain different data types within a single list, all of the elements in a NumPy array should be homogeneous. The mathematical operations that are meant to be performed on arrays would be extremely inefficient if the arrays weren’t homogeneous.

Why use NumPy?

NumPy arrays are faster and more compact than Python lists. An array consumes less memory and is convenient to use. NumPy uses much less memory to store data and it provides a mechanism of specifying the data types. This allows the code to be optimized even further.

What is an array?

An array is a central data structure of the NumPy library. An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element. It has a grid of elements that can be indexed in . The elements are all of the same type, referred to as the array

pip install numpy

An array can be indexed by a tuple of nonnegative integers, by booleans, by another array, or by integers. The

pip install numpy

pip install numpy

One way we can initialize NumPy arrays is from Python lists, using nested lists for two- or higher-dimensional data.

For example:

>>> a = np.array([1, 2, 3, 4, 5, 6])

or:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

We can access the elements in the array using square brackets. When you’re accessing elements, remember that indexing in NumPy starts at 0. That means that if you want to access the first element in your array, you’ll be accessing element “0”.

>>> print(a[0])
[1 2 3 4]

More information about arrays

This section covers

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

You might occasionally hear an array referred to as a “ndarray,” which is shorthand for “N-dimensional array.” An N-dimensional array is simply an array with any number of dimensions. You might also hear 1-D, or one-dimensional array, 2-D, or two-dimensional array, and so on. The NumPy

pip install numpy

What are the attributes of an array?

An array is usually a fixed-size container of items of the same type and size. The number of dimensions and items in an array is defined by its shape. The shape of an array is a tuple of non-negative integers that specify the sizes of each dimension.

In NumPy, dimensions are called axes. This means that if you have a 2D array that looks like this:

[[0., 0., 0.],
 [1., 1., 1.]]

Your array has 2 axes. The first axis has a length of 2 and the second axis has a length of 3.

Just like in other Python container objects, the contents of an array can be accessed and modified by indexing or slicing the array. Unlike the typical container objects, different arrays can share the same data, so changes made on one array might be visible in another.

Array attributes reflect information intrinsic to the array itself. If you need to get, or even set, properties of an array without creating a new array, you can often access an array through its attributes.

and learn about .

How to create a basic array

This section covers

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

To create a NumPy array, you can use the function

pip install numpy

All you need to do to create a simple array is pass a list to it. If you choose to, you can also specify the type of data in your list. .

>>> import numpy as np
>>> a = np.array([1, 2, 3])

You can visualize your array this way:

Be aware that these visualizations are meant to simplify ideas and give you a basic understanding of NumPy concepts and mechanics. Arrays and array operations are much more complicated than are captured here!

Besides creating an array from a sequence of elements, you can easily create an array filled with

pip install numpy

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

Or an array filled with

pip install numpy

pip install numpy

Or even an empty array! The function

pip install numpy

pip install numpy

pip install numpy

pip install numpy

You can create an array with a range of elements:

pip install numpy

And even an array that contains a range of evenly spaced intervals. To do this, you will specify the first number, last number, and the step size.

pip install numpy

You can also use

pip install numpy

pip install numpy

Specifying your data type

While the default data type is floating point (

pip install numpy

pip install numpy

pip install numpy

Adding, removing, and sorting elements

This section covers

pip install numpy

pip install numpy

Sorting an element is simple with

pip install numpy

If you start with this array:

pip install numpy

You can quickly sort the numbers in ascending order with:

pip install numpy

In addition to sort, which returns a sorted copy of an array, you can use:

• , which is an indirect sort along a specified axis,

• , which is an indirect stable sort on multiple keys,

• , which will find elements in a sorted array, and

• , which is a partial sort.

To read more about sorting an array, see: .

If you start with these arrays:

pip install numpy

You can concatenate them with

pip install numpy

pip install numpy

Or, if you start with these arrays:

import numpy as np

You can concatenate them with:

import numpy as np

In order to remove elements from an array, it’s simple to use indexing to select the elements that you want to keep.

To read more about concatenate, see: .

How do you know the shape and size of an array?

This section covers

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

For example, if you create this array:

import numpy as np

To find the number of dimensions of the array, run:

import numpy as np

To find the total number of elements in the array, run:

import numpy as np

And to find the shape of your array, run:

import numpy as np

Can you reshape an array?

This section covers

pip install numpy

Yes!

Using

pip install numpy

If you start with this array:

import numpy as np

You can use

pip install numpy

import numpy as np

With

pip install numpy

import numpy as np

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

pip install numpy

If you want to learn more about C and Fortran order, you can . Essentially, C and Fortran orders have to do with how indices correspond to the order the array is stored in memory. In Fortran, when moving through the elements of a two-dimensional array as it is stored in memory, the first index is the most rapidly varying index. As the first index moves to the next row as it changes, the matrix is stored one column at a time. This is why Fortran is thought of as a Column-major language. In C on the other hand, the last index changes the most rapidly. The matrix is stored by rows, making it a Row-major language. What you do for C or Fortran depends on whether it’s more important to preserve the indexing convention or not reorder the data.

.

How to convert a 1D array into a 2D array (how to add a new axis to an array)

This section covers

pip install numpy

pip install numpy

You can use

pip install numpy

pip install numpy

Using

pip install numpy

For example, if you start with this array:

import numpy as np

You can use

pip install numpy

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can explicitly convert a 1D array with either a row vector or a column vector using

pip install numpy

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

Or, for a column vector, you can insert an axis along the second dimension:

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can also expand an array by inserting a new axis at a specified position with

pip install numpy

For example, if you start with this array:

import numpy as np

You can use

pip install numpy

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can add an axis at index position 0 with:

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

Find more information about and

import numpy as np

Indexing and slicing

You can index and slice NumPy arrays in the same ways you can slice Python lists.

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can visualize it this way:

You may want to take a section of your array or specific array elements to use in further analysis or additional operations. To do that, you’ll need to subset, slice, and/or index your arrays.

If you want to select values from your array that fulfill certain conditions, it’s straightforward with NumPy.

For example, if you start with this array:

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can easily print all of the values in the array that are less than 5.

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can also select, for example, numbers that are equal to or greater than 5, and use that condition to index an array.

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You can select elements that are divisible by 2:

>>> a = np.array([1, 2, 3, 4, 5, 6])

Or you can select elements that satisfy two conditions using the

import numpy as np

import numpy as np

>>> a = np.array([1, 2, 3, 4, 5, 6])

You can also make use of the logical operators & and | in order to return boolean values that specify whether or not the values in an array fulfill a certain condition. This can be useful with arrays that contain names or other categorical values.

>>> a = np.array([1, 2, 3, 4, 5, 6])

You can also use

import numpy as np

Starting with this array:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

You can use

import numpy as np

>>> a = np.array([1, 2, 3, 4, 5, 6])

In this example, a tuple of arrays was returned: one for each dimension. The first array represents the row indices where these values are found, and the second array represents the column indices where the values are found.

If you want to generate a list of coordinates where the elements exist, you can zip the arrays, iterate over the list of coordinates, and print them. For example:

>>> a = np.array([1, 2, 3, 4, 5, 6])

You can also use

import numpy as np

>>> a = np.array([1, 2, 3, 4, 5, 6])

If the element you’re looking for doesn’t exist in the array, then the returned array of indices will be empty. For example:

>>> a = np.array([1, 2, 3, 4, 5, 6])

Learn more about and .

Read more about using the nonzero function at: .

How to create an array from existing data

This section covers

import numpy as np

import numpy as np

import numpy as np

import numpy as np

import numpy as np

import numpy as np

You can easily create a new array from a section of an existing array.

Let’s say you have this array:

>>> a = np.array([1, 2, 3, 4, 5, 6])

You can create a new array from a section of your array any time by specifying where you want to slice your array.

>>> a = np.array([1, 2, 3, 4, 5, 6])

Here, you grabbed a section of your array from index position 3 through index position 8.

You can also stack two existing arrays, both vertically and horizontally. Let’s say you have two arrays,

import numpy as np

import numpy as np

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

You can stack them vertically with

import numpy as np

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

Or stack them horizontally with

import numpy as np

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

You can split an array into several smaller arrays using

import numpy as np

Let’s say you have this array:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

If you wanted to split this array into three equally shaped arrays, you would run:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

If you wanted to split your array after the third and fourth column, you’d run:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

.

You can use the

import numpy as np

Views are an important NumPy concept! NumPy functions, as well as operations like indexing and slicing, will return views whenever possible. This saves memory and is faster (no copy of the data has to be made). However it’s important to be aware of this - modifying data in a view also modifies the original array!

Let’s say you create this array:

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

Now we create an array

import numpy as np

pip install numpy

import numpy as np

pip install numpy

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

Using the

import numpy as np

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

.

Basic array operations

This section covers addition, subtraction, multiplication, division, and more

Once you’ve created your arrays, you can start to work with them. Let’s say, for example, that you’ve created two arrays, one called “data” and one called “ones”

You can add the arrays together with the plus sign.

>>> a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

You can, of course, do more than just addition!

>>> print(a[0])
[1 2 3 4]

Basic operations are simple with NumPy. If you want to find the sum of the elements in an array, you’d use

import numpy as np

>>> print(a[0])
[1 2 3 4]

To add the rows or the columns in a 2D array, you would specify the axis.

If you start with this array:

>>> print(a[0])
[1 2 3 4]

You can sum over the axis of rows with:

>>> print(a[0])
[1 2 3 4]

You can sum over the axis of columns with:

>>> print(a[0])
[1 2 3 4]

.

Broadcasting

There are times when you might want to carry out an operation between an array and a single number (also called an operation between a vector and a scalar) or between arrays of two different sizes. For example, your array (we’ll call it “data”) might contain information about distance in miles but you want to convert the information to kilometers. You can perform this operation with:

>>> print(a[0])
[1 2 3 4]

NumPy understands that the multiplication should happen with each cell. That concept is called broadcasting. Broadcasting is a mechanism that allows NumPy to perform operations on arrays of different shapes. The dimensions of your array must be compatible, for example, when the dimensions of both arrays are equal or when one of them is 1. If the dimensions are not compatible, you will get a

import numpy as np

.

More useful array operations

This section covers maximum, minimum, sum, mean, product, standard deviation, and more

NumPy also performs aggregation functions. In addition to

import numpy as np

import numpy as np

import numpy as np

import numpy as np

import numpy as np

import numpy as np

>>> print(a[0])
[1 2 3 4]

Let’s start with this array, called “a”

>>> print(a[0])
[1 2 3 4]

It’s very common to want to aggregate along a row or column. By default, every NumPy aggregation function will return the aggregate of the entire array. To find the sum or the minimum of the elements in your array, run:

>>> print(a[0])
[1 2 3 4]

Or:

>>> print(a[0])
[1 2 3 4]

You can specify on which axis you want the aggregation function to be computed. For example, you can find the minimum value within each column by specifying

import numpy as np

[[0., 0., 0.],
 [1., 1., 1.]]

The four values listed above correspond to the number of columns in your array. With a four-column array, you will get four values as your result.

Read more about .

Creating matrices

You can pass Python lists of lists to create a 2-D array (or “matrix”) to represent them in NumPy.

[[0., 0., 0.],
 [1., 1., 1.]]

Indexing and slicing operations are useful when you’re manipulating matrices:

[[0., 0., 0.],
 [1., 1., 1.]]

You can aggregate matrices the same way you aggregated vectors:

[[0., 0., 0.],
 [1., 1., 1.]]

You can aggregate all the values in a matrix and you can aggregate them across columns or rows using the

import numpy as np

[[0., 0., 0.],
 [1., 1., 1.]]

Once you’ve created your matrices, you can add and multiply them using arithmetic operators if you have two matrices that are the same size.

[[0., 0., 0.],
 [1., 1., 1.]]

You can do these arithmetic operations on matrices of different sizes, but only if one matrix has only one column or one row. In this case, NumPy will use its broadcast rules for the operation.

[[0., 0., 0.],
 [1., 1., 1.]]

Be aware that when NumPy prints N-dimensional arrays, the last axis is looped over the fastest while the first axis is the slowest. For instance:

[[0., 0., 0.],
 [1., 1., 1.]]

There are often instances where we want NumPy to initialize the values of an array. NumPy offers functions like

import numpy as np

import numpy as np

import numpy as np

[[0., 0., 0.],
 [1., 1., 1.]]

You can also use

import numpy as np

import numpy as np

import numpy as np

[[0., 0., 0.],
 [1., 1., 1.]]

Read more about creating arrays, filled with

pip install numpy

pip install numpy

Generating random numbers

The use of random number generation is an important part of the configuration and evaluation of many numerical and machine learning algorithms. Whether you need to randomly initialize weights in an artificial neural network, split data into random sets, or randomly shuffle your dataset, being able to generate random numbers (actually, repeatable pseudo-random numbers) is essential.

With

import numpy as np

import numpy as np

You can generate a 2 x 4 array of random integers between 0 and 4 with:

>>> import numpy as np
>>> a = np.array([1, 2, 3])

.

How to get unique items and counts

This section covers

import numpy as np

You can find the unique elements in an array easily with

import numpy as np

For example, if you start with this array:

>>> import numpy as np
>>> a = np.array([1, 2, 3])

you can use

import numpy as np

>>> import numpy as np
>>> a = np.array([1, 2, 3])

To get the indices of unique values in a NumPy array (an array of first index positions of unique values in the array), just pass the

import numpy as np

import numpy as np

>>> import numpy as np
>>> a = np.array([1, 2, 3])

You can pass the

import numpy as np

import numpy as np

>>> import numpy as np
>>> a = np.array([1, 2, 3])

This also works with 2D arrays! If you start with this array:

>>> import numpy as np
>>> a = np.array([1, 2, 3])

You can find unique values with:

>>> import numpy as np
>>> a = np.array([1, 2, 3])

If the axis argument isn’t passed, your 2D array will be flattened.

If you want to get the unique rows or columns, make sure to pass the

import numpy as np

import numpy as np

import numpy as np

>>> import numpy as np
>>> a = np.array([1, 2, 3])

To get the unique rows, index position, and occurrence count, you can use:

>>> import numpy as np
>>> a = np.array([1, 2, 3])

To learn more about finding the unique elements in an array, see .

Transposing and reshaping a matrix

This section covers

pip install numpy

import numpy as np

import numpy as np

It’s common to need to transpose your matrices. NumPy arrays have the property

import numpy as np

You may also need to switch the dimensions of a matrix. This can happen when, for example, you have a model that expects a certain input shape that is different from your dataset. This is where the

import numpy as np

>>> import numpy as np
>>> a = np.array([1, 2, 3])

You can also use

import numpy as np

If you start with this array:

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

You can transpose your array with

import numpy as np

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

You can also use

import numpy as np

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

To learn more about transposing and reshaping arrays, see and .

How to reverse an array

This section covers

import numpy as np

NumPy’s

import numpy as np

import numpy as np

Reversing a 1D array

If you begin with a 1D array like this one:

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

You can reverse it with:

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

If you want to print your reversed array, you can run:

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

Reversing a 2D array

A 2D array works much the same way.

If you start with this array:

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

You can reverse the content in all of the rows and all of the columns with:

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

You can easily reverse only the rows with:

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

Or reverse only the columns with:

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

You can also reverse the contents of only one column or row. For example, you can reverse the contents of the row at index position 1 (the second row):

pip install numpy

You can also reverse the column at index position 1 (the second column):

pip install numpy

Read more about reversing arrays at .

Reshaping and flattening multidimensional arrays

This section covers

import numpy as np

import numpy as np

There are two popular ways to flatten an array:

import numpy as np

import numpy as np

import numpy as np

import numpy as np

If you start with this array:

pip install numpy

You can use

import numpy as np

pip install numpy

When you use

import numpy as np

For example:

pip install numpy

But when you use

import numpy as np

For example:

pip install numpy

Read more about

import numpy as np

import numpy as np

How to access the docstring for more information

This section covers

import numpy as np

import numpy as np

import numpy as np

When it comes to the data science ecosystem, Python and NumPy are built with the user in mind. One of the best examples of this is the built-in access to documentation. Every object contains the reference to a string, which is known as the docstring. In most cases, this docstring contains a quick and concise summary of the object and how to use it. Python has a built-in

import numpy as np

import numpy as np

For example:

pip install numpy

Because access to additional information is so useful, IPython uses the

import numpy as np

For example:

pip install numpy

You can even use this notation for object methods and objects themselves.

Let’s say you create this array:

>>> a = np.array([1, 2, 3, 4, 5, 6])

Then you can obtain a lot of useful information (first details about

pip install numpy

pip install numpy

pip install numpy

pip install numpy

This also works for functions and other objects that you create. Just remember to include a docstring with your function using a string literal (

import numpy as np

import numpy as np

For example, if you create this function:

pip install numpy

You can obtain information about the function:

pip install numpy

You can reach another level of information by reading the source code of the object you’re interested in. Using a double question mark (

import numpy as np

For example:

pip install numpy

If the object in question is compiled in a language other than Python, using

import numpy as np

import numpy as np

pip install numpy

and :

pip install numpy

have the same output because they were compiled in a programming language other than Python.

Working with mathematical formulas

The ease of implementing mathematical formulas that work on arrays is one of the things that make NumPy so widely used in the scientific Python community.

For example, this is the mean square error formula (a central formula used in supervised machine learning models that deal with regression):

Implementing this formula is simple and straightforward in NumPy:

What makes this work so well is that

import numpy as np

import numpy as np

You can visualize it this way:

In this example, both the predictions and labels vectors contain three values, meaning

import numpy as np

How to save and load NumPy objects

This section covers

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

You will, at some point, want to save your arrays to disk and load them back without having to re-run the code. Fortunately, there are several ways to save and load objects with NumPy. The ndarray objects can be saved to and loaded from the disk files with

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

The .npy and .npz files store data, shape, dtype, and other information required to reconstruct the ndarray in a way that allows the array to be correctly retrieved, even when the file is on another machine with different architecture.

If you want to store a single ndarray object, store it as a .npy file using

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

It’s easy to save and load and array with

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.array([1, 2, 3, 4, 5, 6])

You can save it as “filename.npy” with:

pip install numpy

You can use

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

pip install numpy

If you want to check your array, you can run:

pip install numpy

You can save a NumPy array as a plain text file like a .csv or .txt file with

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

For example, if you create this array:

pip install numpy

You can easily save it as a .csv file with the name “new_file.csv” like this:

pip install numpy

You can quickly and easily load your saved text file using

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

pip install numpy

The

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

With , you can specify headers, footers, comments, and more.

Learn more about .

Importing and exporting a CSV

It’s simple to read in a CSV that contains existing information. The best and easiest way to do this is to use Pandas.

pip install numpy

It’s simple to use Pandas in order to export your array as well. If you are new to NumPy, you may want to create a Pandas dataframe from the values in your array and then write the data frame to a CSV file with Pandas.

If you created this array “a”

pip install numpy

You could create a Pandas dataframe

pip install numpy

You can easily save your dataframe with:

pip install numpy

And read your CSV with:

pip install numpy

You can also save your array with the NumPy

>>> a = np.arange(6)
>>> a2 = a[np.newaxis, :]
>>> a2.shape
(1, 6)

pip install numpy

If you’re using the command line, you can read your saved CSV any time with a command such as:

pip install numpy

Or you can open the file any time with a text editor!

If you’re interested in learning more about Pandas, take a look at the official Pandas documentation. Learn how to install Pandas with the official Pandas installation information.

Plotting arrays with Matplotlib

If you need to generate a plot for your values, it’s very simple with Matplotlib.

For example, you may have an array like this one:

pip install numpy

If you already have Matplotlib installed, you can import it with:

pip install numpy

All you need to do to plot your values is run:

pip install numpy

For example, you can plot a 1D array like this:

pip install numpy

With Matplotlib, you have access to an enormous number of visualization options.

pip install numpy

To read more about Matplotlib and what it can do, take a look at the official documentation. For directions regarding installing Matplotlib, see the official installation section.