How to append to numpy array

How does one add rows to a numpy array?

I have an array A:

I wish to add rows to this array from another array X if the first element of each row in X meets a specific condition.

Numpy arrays do not have a method ‘append’ like that of lists, or so it seems.

If A and X were lists I would merely do:

Is there a numpythonic way to do the equivalent?

10 Answers 10

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You can do this:

EDIT after OP’s comment:

add to A all rows from X where the first element :

As this question is been 7 years before, in the latest version which I am using is numpy version 1.13, and python3, I am doing the same thing with adding a row to a matrix, remember to put a double bracket to the second argument, otherwise, it will raise dimension error.

In here I am adding on matrix A

same usage in np.r_

Just to someone’s intersted, if you would like to add a column,

array = np.c_[A,np.zeros(#A’s row size)]

following what we did before on matrix A, adding a column to it

If you want to prepend, you can just flip the order of the arguments, i.e.:

If no calculations are necessary after every row, it’s much quicker to add rows in python, then convert to numpy. Here are timing tests using python 3.6 vs. numpy 1.14, adding 100 rows, one at a time:

So, the simple solution to the original question, from seven years ago, is to use vstack() to add a new row after converting the row to a numpy array. But a more realistic solution should consider vstack’s poor performance under those circumstances. If you don’t need to run data analysis on the array after every addition, it is better to buffer the new rows to a python list of rows (a list of lists, really), and add them as a group to the numpy array using vstack() before doing any data analysis.

How to use the Numpy append function

This tutorial will show you how to use the NumPy append function (sometimes called np.append).

Here, I’ll explain what the function does. I’ll explain the syntax (piece by piece), and I’ll show you some step-by-step examples so you can see exactly how np.append works.

Numpy append appends values to an existing numpy array

The NumPy append function enables you to append new values to an existing NumPy array.

Other tutorials here at Sharp Sight have shown you ways to create a NumPy array. You can create one from a list using the np.array function. You can use the zeros function to create a NumPy array with all zeros. You can use the NumPy arange function to create NumPy arrays as sequences of regularly spaced values. All of those methodologies enable you to create a new NumPy array.

But often times, you’ll have an existing array and you need to add new elements. To do that, none of those functions will do. You need a new tool.

Enter the np.append function.

The syntax of numpy append

Let’s take a look at the syntax of the np.append function.

Much like the other functions from NumPy, the syntax is fairly straightforward and easy to understand. Let’s break it down.

Once you call the function itself – like all NumPy functions – there are a set of parameters that enable you to precisely control the behavior of the append function.

Let’s take a look at the parameters of NumPy append.

arr
The arr parameter specifies the base array to which you will append the new values. Said differently, it’s the array that you’re going to modify by appending new values.

values
The values parameter specifies the values that you want to append to the base array (i.e., the values you will append to the array specified in the arr parameter).

The values that you specify here can be presented as a list of literal values (i.e. [1, 2, 3] ) or you can specify a ndarray object by providing the name of the NumPy array.

axis (optional)
The axis parameter specifies the axis upon which you will append the new values to the original array.

Now at this point, you might be asking … “what the hell is an axis?”

I’ll be honest. Axes in the NumPy system are one of the hardest things for most beginners to understand. It’s not that hard once they are explained, but array axes are not intuitive (at least, they aren’t intuitive the way they’ve been implemented in NumPy).

Axes are best explained with examples, so further down in this tutorial, I’ll show you exactly what the array axes are and how to think of them with respect to this syntax.

Examples: how to use numpy append

Now that we’ve examined the syntax at a high level, let’s take a look at some simple examples.

(By the way, when you’re learning any new syntax, the best way to master it is by studying and practicing simple examples.)

Append values to a 1-dimensional array

First, we’ll work with a very simple example.

First, let’s just create a simple, 1-dimensional array filled with ones.

Now, let’s append 3 new values to the end of this array. To do this, we’ll use the NumPy append function.

Which produces the following output:

Visually though, we can think of this operation as follows:

The np.append function is basically taking the new values ( [6, 7, 8] ) and attaching them to the end of the original array.

It’s pretty straight forward.

Append values to a 2-d array, WITHOUT an axis

Now, let’s append values to a 2-dimensional array.

There are a couple ways to do this. Importantly, you can append new values as a new row, or a new column, so to speak. Additionally, you can append new values without specifying whether it should be a row or column. That’s actually the simplest to do, so we’ll look at that first.

First, let’s create a simple 2 dimensional array:

This code creates a simple 2 by 2 array filled with ones that looks like this:

Now that we have a base array to work with, let’s append two values. We’re going to use np.append to append two new values to this array. However, we are not going to specify where to add them. That is, we are not going to use the axis parameter to specify whether we will add the values as a new row or a new column.

Remember that the array base_array_2x2 is 2 dimensional. Also, notice that we did not use the axis parameter here to specify exactly where to add these new vales.

How will NumPy append handle this?

If you have a multi-dimensional array and you do not specify an axis with the axis parameter, np.append will flatten the original array first. That is, it will transform the array from a multi-dimensional array to a 1-dimensional array.

Once the array is flattened out, it will simply append the new values to the end.

This is often not what people want when they try to append new values to a multi-dimensional NumPy array, so you need to be careful.

If you want the base-array to maintain its original shape, you need to use the axis parameter of np.append to specify exactly where and how to attach the new values.

Let’s take a look at some examples of how to do that.

How to append new values as a new row

As we just saw, appending new values to a NumPy array gets a little more complicated when you’re working with multi-dimensional arrays. In particular, things get more complicated when you want to add new values specifically as new rows or columns.

If you want to append new values as a row (or a column), then you have to use the axis parameter of the NumPy append function. There are also a couple other details that you need to be mindful of, otherwise you’ll get an error.

Let’s take a look at an example, so you can see what I mean.

Here, we’re going to append two new values as a new row of data. That is, we’re going to append the values to the bottom of a 2-d NumPy array.

First, let’s create our 2-d NumPy array:

This is a very simple 2-dimensional array that contains all 1’s:

Next, we’re going to add two new values to the bottom of the array:

I’m going to be honest. Array axes are one of the more challenging and un-intuitive things in NumPy. I’ll probably write a blog post to explain them at some point in the future.

There’s also something else that you need to pay attention to.

Critically, when you use the axis parameter to append new values to an existing NumPy array, the new values must have the right dimensions. So if your original array is a 2-dimensional array, the new values that you’re appending must also be structured as a 2-d array.

If the new values are not structured properly, you’ll get an error. For example:

This code produces the following error:

Why? WTF is going on here?

Essentially, when you’re appending values like this, need to watch the number of brackets. The number of brackets that will dictate the number of dimensions of your new values … and np.append expects the new values to have the same dimensions of the original array.

How to append new values as a new column

Now let’s append new values as a new column.

This works in a way that’s very similar to our prior example of adding values as a new row. Having said that, make sure you’ve read the prior example before trying this … the principle are the same, so you need to understand what was written in the prior example.

Ok. Here, we’re going to create a simple 2-d array containing all 1’s.

Next, we’re going to create another NumPy array that has 2 rows and 1 column. To do this, we’re going to create the array with the np.array function, and then reshape the array using np.reshape.

We need this new array to be shaped in a particular way, because when we append new values to a multi-dimensional array, the array dimensions must match. (If you don’t understand this, please review the previous section.) In this case, it’s easier to create an array with the right dimensions by using np.array along with the reshape method.

Ok, now that we have two arrays with the right dimensions, we will append the new array to the base array using the np.append function:

Once again, I’ll point out that the axis parameter can be a little confusing, especially for beginners. I recommend that you just memorize which is which.

Numpy is important so make sure to master np.append

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Append Values to a Numpy Array

You can use the numpy append() function to append values to a numpy array. In this tutorial, we’ll look at the syntax and usage of the numpy append() function through some examples.

Numpy append() function

It is used to append values at the end of an array. Note that it does not modify the original array. Rather, the values are appended to a copy of the original array and the resulting array is returned. The following is its syntax:

Parameters:

Returns:

A copy of the original array (a numpy ndarray object) with the values appended along the given axis.

Examples

Let’s look at some of the use-cases of the append() function through examples –

1. Append values to a 1D array

In the above example, note that we didn’t provide an axis. The append() function thus flattened the array and value to be appended and returned the resulting array.

2. Append a new row to a 2D array

*Some lines in the above error message are skipped to focus on main reason for the error.

Here, the array [[7,8,9]] has shape (1, 3) which is compatible to be appended as a row to the original (2, 3) array.

3. Append a new column to a 2D array

Keep in mind

You know that numpy arrays are objects of the numpy ndarray class but it’s important to keep in mind that append() is a function of the numpy class and not the numpy ndarray class. Thus, you cannot directly call the append() function from a numpy array like this –

If you want to use the append() function, you’ll have call it through numpy directly. Like this –

For more on the numpy append() function, refer to its official documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

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numpy.append() – Python

In this article, we will discuss how to append elements at the end on a Numpy Array in python using numpy.append()

Overview of numpy.append()

Python’s Numpy module provides a function to append elements to the end of a Numpy Array.

Arguments:

Returns:

It doesn’t modify the original array in parameter arr. It creates a copy of this array and appends the elements from values parameter, to the end of this new copied array. So, basically it returns a copy of numpy array provided with values appended to it.

Let’s understand by examples :

Append elements at the end of 1D numpy array

Let’s create a Numpy array i.e.

Append a single element to the Numpy array

Contents of the new Numpy Array returned :

Now let’s see how append multiple elements to a Numpy array.

Append elements from a list to the Numpy array

Contents of the new Numpy Array returned :

Flatten 2D Numpy Array and add items to it

Let’s create a 2D numpy array i.e.

Now append 1D list to this 2D Numpy array.

As axis parameter is not provided in call to append(), so both the arrays will be flattened first and then values will appended. Therefore, contents of the new flattened Numpy Array returned are,

Add a Numpy Array to another array row wise

If we provide axis parameter in append() call then both the arrays should be of same shape. Let’s create two 2D numpy arrays,

Now let’s append the rows from a numpy array to the end of another numpy array by passing axis as 0 i.e.

Contents of 2D array matrixArr2 will be appended to the contents of matrixArr1 as rows in new array. Contents of the returned array are,

Add a NumPy Array to another array – Column Wise

In the above example if instead of passing axis as 0 we pass axis=1 then contents of 2D array matrixArr2 will be appended to the contents of matrixArr1 as columns in new array i.e.

Contents of the new Numpy Array returned are,

Error while appending elements in Numpy array of different shapes

If you are providing axis parameter in numpy.append() then both the arrays should be of same shape along the given axis, otherwise it will raise Error. For example,

Let’s try to append a 1D array to 2D array with axis = 1 i.e.

It will give following error,

We are trying to row wise append 1D array to a 2D array of shape 2X3, shapes are not compatible therefore, it gave the error. We should make the shape NX3 where N can be anything greater than 1.

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How to Append NumPy Arrays Examples

Table of Contents

numpy.append() is used to append two or multiple arrays at the end of the specified NumPy array. The NumPy append() function is a built-in function in the NumPy package of python. This function returns a new array after appending the array to the specified array by keeping the original array unchanged.

1. Quick Examples of How to Append NumPy Arrays

If you are in a hurry, below are some quick examples of how to append Python NumPy arrays.

2. Syntax of NumPy Array append()

Following is the syntax of the NumPy array append() function. This function internally using NumPy concatenate() function.

2.1 Parameters of append()

This method allows three parameters :

2.2 Return Value of append()

The return value of this function is the NumPy array which is the copy of the array with the appended passed values to the axis.

2.3 Create a NumPy Array

In order to work with an example, first, let’s create a NumPy array using numpy.array() method. Before going to create the NumPy array we have to import NumPy module.

3. Usage of NumPy Array append()

You can use numpy.append() function to add an element in a NumPy array. You can pass the NumPy array and multiple values as arguments to the append() function. It doesn’t modify the existing array but returns a copy of the passed array with given values added.

You can observe, that from the above code, I have imported NumPy with the alias name np. I have created an array arr using the np.array() function, and also I have taken another array. Then I applied the NumPy array append() function for both two arrays. After that, I assigned a value of the append() function to the app_arr variable. Finally, we have got a flattened array.

3.1 Append NumPy Arrays Along with axis = 0

append() function is also used to append a 2-D NumPy array with some values and array along with axis=0 value. For example,

As in the above program, the append() function is used to append 2-Darray with 2-D array(vertically). The appended arrays we can see in the output, the append is done vertically as axis value is 0.

Not using axis=0 on the above example results in the below output.

3.2 Append Arrays Along with axis = 1

Let’s see another example of appending 2-D Numpy arrays by using axis=1 value. For example,

As in the above program, the append() function is used to append 2-Darray with 2-D array(horizontally). The appended arrays we can see in the output, the append is done horizontally as axis value is 1.