Python Tutorials
Our Python tutorials provide a comprehensive curriculum for learning this versatile programming language. They cover Python basics, advanced concepts, and popular libraries, suitable for both beginners and experienced developers. Through hands - on labs and real - world code examples, you'll gain practical Python programming experience. Our interactive Python playground allows you to test different Python features and see immediate results.
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Online scikit-learn Playground
LabEx provides an Online Sklearn Playground, an online environment that allows you to quickly set up a Python environment with scikit-learn pre-installed.
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Online R Playground
LabEx provides an Online R Playground, an online environment that allows you to quickly set up an R development environment for learning and experimentation.
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Online Python Interpreter and Playground
LabEx provides an Online Python Interpreter and Playground, an online environment that allows you to quickly set up a Python programming environment for learning and experimentation.
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Online Pandas Playground
LabEx provides an Online Pandas Playground, an online environment that allows you to quickly set up a Python environment with Pandas pre-installed.
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Online NumPy Playground
LabEx provides an Online NumPy Playground, an online environment that allows you to quickly set up a Python environment with NumPy pre-installed for numerical computing.
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Online Matplotlib Playground
LabEx provides an Online Matplotlib Playground, an online environment that allows you to quickly set up a Python environment for data visualization.
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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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NumPy Array Creation
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, and reading arrays from disk.
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NumPy Copies and Views
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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Pandas Filtering Data
In this lab, you will learn the fundamental techniques for filtering data in Pandas DataFrames, including boolean indexing, combining conditions, using isin, and handling missing values.
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Perform IP Subnetting and Binary Conversion in the Linux Terminal
In this lab, you will master IP subnetting and binary conversion in the Linux terminal. Using Python, you'll convert IP addresses between dotted-decimal and binary, translate CIDR masks, identify network/host portions, and calculate usable hosts and subnets for a given CIDR block.
PythonLinux
Pandas Descriptive Statistics
In this lab, you will learn how to compute various descriptive statistics for a Pandas DataFrame, including mean, median, min/max, and more.
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NumPy Broadcasting
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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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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NumPy IO Genfromtxt
In this lab, we will learn how to import data using the numpy.genfromtxt function. This function allows us to read tabular data from various sources and convert it into NumPy arrays. We will explore different options for defining the input, splitting the lines into columns, choosing columns, setting the data type, and tweaking the conversion.
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Pandas Basic Data Cleaning
In this lab, you will learn the fundamental techniques for cleaning data using the Pandas library, including handling missing values, removing duplicates, and correcting data types.
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NumPy Structured Arrays
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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Pandas Creating DataFrames
In this lab, you will learn the fundamental ways to create Pandas DataFrames, including from dictionaries, and how to customize their columns and indexes.
Python
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