NumPy Tutorials
NumPy provides a comprehensive curriculum for numerical computing in Python. Our tutorials cover array operations, mathematical functions, and data processing techniques, suitable for both beginners and experienced data scientists. Through hands - on labs and real - world examples, you'll gain practical experience in efficient numerical computations. Our scientific Python playground allows you to experiment with NumPy functions in real - time.
Introduction to NumPy Universal Functions
In this lab, we will explore the basics of NumPy Universal Functions (ufuncs). Ufuncs are functions that operate on ndarrays in an element-by-element fashion, supporting array broadcasting, type casting, and other standard features. We will learn about the different methods of ufuncs, broadcasting rules, type casting rules, and how to override ufunc behavior.
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Structured Arrays in NumPy
In this lab, we will learn about structured arrays in NumPy. Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields. They are useful for working with structured data, such as tabular data, where each field represents a different attribute of the data.
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Fundamentals of NumPy Array Manipulation
In this lab, you will learn the basics of working with NumPy arrays. NumPy is a powerful library for numerical computing in Python. It provides efficient data structures and functions for performing mathematical operations on arrays.
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NumPy Broadcasting for Efficient Computation
Broadcasting is a powerful feature in NumPy that allows arrays with different shapes to be used in arithmetic operations. It provides a way to vectorize array operations and improve computational efficiency. This lab will guide you through the basics of broadcasting in NumPy.
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Understanding NumPy Data Types
This lab will provide a step-by-step guide to understanding the different data types available in NumPy, and how to modify an array's data type. NumPy supports a wide range of numerical types, including booleans, integers, floating point numbers, and complex numbers. Understanding these data types is important for performing various numerical computations and data analysis tasks using NumPy.
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Introduction to Indexing in NumPy
In this lab, we will explore the basics of indexing in NumPy. Indexing allows us to access and manipulate specific elements or subsets of elements in an array. Understanding how to use indexing effectively is crucial for working with arrays in NumPy.
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Fundamental NumPy Array Creation Techniques
This lab provides a step-by-step guide on how to create arrays using NumPy, a fundamental library for array containers in Python. You will learn different methods for array creation, including converting Python sequences, using intrinsic NumPy array creation functions, replicating and joining existing arrays, reading arrays from disk, creating arrays from raw bytes, and using special library functions.
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Numpy Transpose Function
In this lab, we will learn how to use the numpy.transpose() function in Python NumPy library. We will learn how this function is used to permute or reverse the axes of an array. By the end of the lab, you will be able to use numpy.transpose() to modify arrays with ease.
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NumPy Trunc Function
In this lab, we will learn about the NumPy trunc() function in the Python language. We will understand its syntax, parameters, and how to use this function to return the truncated value of input array elements. We will also see some examples to have a better understanding.
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NumPy Swapcase Function
In this lab, we will cover the step-by-step process of using the Swapcase() function of the char module in the Numpy library. This tutorial is for those who want to manipulate strings in Numpy and change the cases of the characters in their string.
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NumPy Zeros Function
In this lab, you will learn about the numpy.matlib.zeros() function of the NumPy library. This function is used to create and return a matrix filled with zeros of a given shape and data type using the numpy.matlib matrix library.
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NumPy STD Function
In this lab, we will cover the numpy.std() function of the NumPy library. We will understand what standard deviation means and how to use numpy.std() to calculate the standard deviation of an array.
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NumPy Upper Function
In this lab, you will learn how to use the upper() function in the char module of the NumPy library. This function is used to convert all lowercase characters of a string to uppercase. If there are no lowercase characters in the given string, it returns the original string. We will cover the syntax required to use this function, its returned values, and provide examples of its use.
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Sorting NumPy Arrays with Algorithms
Sorting is a process where elements of an array are arranged in an ordered sequence based on the given criteria. In the NumPy library, there are various functions available that perform sorting operations based on different sorting algorithms such as quicksort, heapsort, and mergesort. In this lab, we will learn how to sort ndarrays in NumPy using different sorting algorithms.
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NumPy Rand Function
In this tutorial, we will learn about the numpy.rand() mathematical function of the Numpy library, which is used to generate a matrix with random values in the range [0, 1). We will go over the syntax, parameters, return values, and provide an example of the function in use.
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Numpy PTP Function
In this lab, you will learn about the numpy.ptp() function in Python. The 'ptp' stands for 'peak to peak.' This function is used to return a range of values along an axis. The range can be calculated using range = maximum_value - minimum_value.
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NumPy Replace Function
Numpy is one of the most powerful scientific computing libraries in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.
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Numpy Split Function
In this lab, we will cover the split() function of the char module in the Numpy library. The split() function is used to split an input string into a list of strings based on a specified separator.
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