
Pandas Selecting Data
In this lab, you will learn the fundamental techniques for selecting and subsetting data from Pandas DataFrames, including selecting columns, rows, and specific slices of data.
Pandas

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.
NumPyPython

Pandas Reading External Data
In this lab, you will learn the fundamentals of reading external data into a Pandas DataFrame. You will use the powerful `read_csv` function and its key parameters to handle various real-world CSV file formats.
Pandas

Matplotlib Subplots Creation
In this lab, you will learn how to create and customize multiple subplots in a single figure using Matplotlib, a powerful plotting library in Python. You will practice creating subplots, plotting data on them, and adjusting layouts.
Matplotlib

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.
NumPyPython

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.
NumPyPython

Pandas Sorting Data
In this lab, you will learn the essential techniques for sorting data in a Pandas DataFrame. You'll explore sorting by single and multiple columns, controlling the sort order, and managing the DataFrame's index after sorting operations.
Pandas

NumPy Indexing on ndarrays
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.
NumPyPython

Pandas Grouping and Aggregating
In this lab, you will learn the fundamentals of data grouping and aggregation using the Pandas library. You'll practice using groupby() to create groups and apply various aggregation functions.
Pandas

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.
Pandas

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.
Pandas

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.
NumPyPython

Scikit-learn Cross-Validation
In this lab, you will learn how to perform cross-validation using scikit-learn to evaluate the performance of a machine learning model more robustly.
scikit-learn

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.
Pandas

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.
Pandas

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.
NumPyPython

Pandas Introduction and Setup
In this lab, you will get started with Pandas, a powerful data analysis library in Python. You will learn how to verify its installation, import it, create a basic Series, access its elements, and inspect its properties.
Pandas

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.
NumPyPython