How To NumPy

NumPy is essential for Data Science.

NumPy is a powerful library for scientific computing in Python. It provides tools for working with arrays and matrices of numerical data and performing mathematical operations on them.

If you are new to Python, read about why you should learn Python:
https://medium.com/@nmh.workspace/5-reasons-why-python-is-a-must-have-for-all-data-scientists-8862698b1171

In this tutorial, we’ll introduce some of the key features of NumPy and show you how to use them.

To use NumPy, you’ll first need to install it.

The easiest way to do this is using pip, the Python package manager. Open a terminal and enter the following command:

This will install NumPy and all its dependencies.

Once NumPy is installed, you can import it into your Python code using the import statement.

By convention, NumPy is usually imported with the alias np, like this:

This allows you to use the shortened name np when referring to NumPy functions and objects in your code. This is generally the alias used universally by Data Scientists.

One of the main features of NumPy is its ndarray (n-dimensional array) object, which is used to store and manipulate large arrays of homogeneous data (i.e., data of the same type, such as integers or floating point values).

You can create a NumPy array using the array function, which takes a sequence or iterable (such as a list or tuple) as input and returns a new NumPy array containing the data. For example:

You can also create a multidimensional array by providing a nested sequence or iterable. For example:

NumPy arrays have several important properties, such as the shape, dtype, and ndim attributes.

The shape attribute gives the size of the array along each dimension, the dtype attribute gives the data type of the array elements, and the ndim attribute gives the number of dimensions of the array.

For example:

You can also create an array of a specific shape and data type using the zeros, ones, and empty functions. These functions return a new array filled with zeros, ones, or uninitialized (random) values, respectively. For example:

You can access individual elements of a NumPy array using square brackets and indices. For example:

You can also slice a NumPy array to extract a subarray. For example:

You can change the shape of a NumPy array using the reshape method. For example:

You can transpose a NumPy array using the T attribute or the transpose function. This swaps the rows and columns of the array. For example:

You can perform element-wise arithmetic operations on NumPy arrays using the standard arithmetic operators (+, -, *, /, etc.). For example:

NumPy provides a range of functions for performing linear algebra operations, such as matrix multiplication, vector dot products, and singular value decomposition. For example:

NumPy provides a range of functions for aggregating (summarizing) the values in an array. For example:

You can also specify the axis argument to perform the aggregation along a specific dimension of the array. For example:

You can use a Boolean mask to select specific elements of a NumPy array based on a condition. A mask is an array of the same shape as the original array, with True values indicating the elements to keep and False values indicating the elements to discard. For example:

If you are interested in more NumPy usages or have any questions about NumPy, feel free to leave a comment and I will answer within the same day. You can also reach out to discuss about Data Science career or chat about life in general.

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