Crash course: Introduction to Pandas and NumPy

Name: Crash course: Introduction to Pandas and NumPy
Rating: 4.4 (9 reviews)

Bottom-up introduction to Python data science frameworks: NumPy and Pandas

Created byMaxime Vandegar

Last updated 4/2023

English

English [Auto],

What you'll learn

NumPy arrays
Operations on NumPy arrays
Image processing with NumPy
Vectorial operations
The framework Pandas
Pandas DataFrame
Manipulating Pandas DataFrame
Arithmetic operations and statistical computations

Course content

3 sections • 27 lectures • 3h 34m total length

Introduction6:35
Learn how numpy enables fast numerical computations on n-dimensional arrays, with a pandas-backed workflow for data science and machine learning, and explore interoperability with PyTorch and TensorFlow.
Applications2:52
Explore the wide range of numpy applications in data science, machine learning, and scientific computing, from data processing to feeding pipelines and interoperability with PyTorch and TensorFlow.
NumPy Arrays9:27
Learn to use numpy arrays in python by importing numpy as np, converting lists to arrays, indexing, slicing, and applying functions like min, max, mean, and understanding dtype and shape.
Matrices7:08
Transform Python list of lists into a numpy matrix, index by row and column, and inspect shape and dtype. Multiply matrices with dot or the @ operator.
Tensors3:32
Explore how tensors extend numpy arrays to higher dimensions, build a 3d array from multiple matrices, and index into multi-dimensional data like images with width, height, and channels.
Array creation routines8:47
Learn how to create numpy arrays with zeros, ones, and empty, specify shapes as tuples (e.g., 100 by 100), and generate grids with linspace and meshgrid for image processing.
Dtypes7:39
Explore numpy dtypes for arrays, including float64, int64, bool, strings, and complex types. Learn to specify and convert dtypes with array constructors and astype.
Vectorized operations8:45
Explore vectorized operations in NumPy that replace nested loops by performing element-wise computations on large arrays, boosting performance for image processing and neural networks.
Reduction operations7:44
Explore reduction operations in NumPy by computing mean and std across axes. Learn how axis reduces dimensions and yields per-row, per-column, or overall summaries.
Reshaping10:41
Learn how to reshape arrays in numpy, add and remove dimensions with reshape, squeeze, and newaxis, and use broadcasting to perform matrix operations on 2D and 3D shapes.
Saving and Loading NumPy Arrays3:35
learn how numpy's save and load functions store and retrieve arrays in binary .npy files, including file naming, extension usage, and common gotchas for saving long simulations.
Image Processing10:07
Explore image processing with numpy by creating 2D and RGB images as matrices using zeros and ones, visualizing with matplotlib, and cropping, masking, and pixel-level manipulation.
Masking11:34
Learn numpy masking and vectorized operations to apply boolean masks, count true values, and replace data, including negative values, or modify image colors.
Structured Arrays5:52
Learn how NumPy structured arrays support multiple fields with strings, integers, and floats for stock data. Discover why pandas, built on NumPy, handles such heterogeneous data better.
JAX and GPUs6:23
Discover how to run numpy code on the GPU with JAX numpy, switch from numpy to JAX numpy, and use Google Colab for GPU acceleration, achieving fast matrix multiplication.

Introduction2:31
Explore pandas, the Python package built on numpy for heterogeneous data, such as booleans, strings, and numbers, with stock data examples; learn the numpy–pandas integration.
Pandas DataFrames9:54
Learn to create pandas data frames from numpy arrays, set custom columns and indices, and inspect data with df.info, df.shape, head, tail, and values.
Indexing10:56
Index Pandas data frames by column name or position, using brackets, dot notation, iloc, and loc, then apply slicing and masks to select data.
Loading & dtype8:11
Learn how to load data frames in pandas from local files or URLs, inspect dtypes and shape, handle missing values, and convert categorical columns to numeric for machine learning.
Summary statistics5:41
Compute summary statistics from a passenger dataset using pandas, including mean, standard deviation, median, min, max, and describe after dropping NaN values.
Working with textual data3:54
Manipulate text data in pandas using string methods like contains, upper, lower, and split to clean cabins and names, fill with '0', and create masks for survival by class.
Working with dates9:15
Learn to work with dates in pandas by building a data frame, converting strings to date time, and measuring differences with time delta and day counts across date columns.
Merging6:00
Merge multiple data frames in pandas using the on parameter to join by a key, manage column name collisions with suffixes, and compute time differences after casting to numeric types.
Groupby3:06
Learn how to compute group statistics in pandas by name using mask and group by, including mean and standard deviation of Alice and Tom's run times.

Description3:41
Practice with pandas by merging csv stock data into a single frame, ensuring chronological order, and plotting returns with matplotlib to explore a dca strategy.
Solution: part 115:07
Learn to merge multiple dataframes in pandas by performing two-at-a-time joins on date, rename columns to avoid collisions, and verify chronological order while plotting stock price evolution.
Solution: part 225:37
Compute stock returns with Pandas and NumPy, align by date, compute open price returns, and visualize results to compare multiple tickers in a DCA context.

Requirements

Basic programming skills

Description

Are you ready to take your data skills to the next level? Our course on Pandas and NumPy is designed to help you master these powerful libraries and unlock the full potential of your data.

Pandas and NumPy are two of the most popular Python libraries for data manipulation and analysis. Together, they provide a powerful toolset for working with structured data in Python and enable you to perform complex data tasks with ease and efficiency.

In this course, you will learn the fundamentals of Pandas and NumPy and how to use them to solve real-world data problems. You will learn how to load and manipulate data with Pandas, perform mathematical operations and statistical analyses with NumPy, and use the two libraries together to solve complex data tasks.

Our experienced instructors will guide you through the material with hands-on examples and exercises, and you will have the opportunity to apply your knowledge to real-world datasets. By the end of the course, you will have a solid understanding of Pandas and NumPy and be able to use them confidently in your own data projects.

Don't miss this opportunity to learn from the experts and take your data skills to the next level. Enroll now and join our community of data professionals who are mastering Pandas and NumPy and making a difference with data.

Who this course is for:

To those interested in data science and machine learning
For those who want to improve their Python knowledge
For those who want to improve the performance of their Python code

Crash course: Introduction to Pandas and NumPy

What you'll learn

Explore related topics

Course content

Numpy15 lectures • 1hr 51min

Pandas9 lectures • 59min

Exercice: stock market data3 lectures • 44min

Requirements

Description

Who this course is for: