IMG_5673.JPG – An image file called IMG_5673.shanes_file.TXT – a simple text file called shanes_file.
project1.DOCX – a Microsoft Word file called Project1.So, a filename is typically in the form “.”.
Computers determine how to read files using the “file extension”, that is the code that follows the dot (“.”) in the filename.Each file contains data of different types – the internals of a Word document is quite different from the internals of an image.Data is stored on your computer in individual “files”, or containers, each with a different name.The first step to working with comma-separated-value (CSV) files is understanding the concept of file types and file extensions. CSV data formats and errors – common errors with the function.Įach of these topics is discussed below, and we finish this tutorial by looking at some more advanced CSV loading mechanisms and giving some broad advantages and disadvantages of the CSV format.Understanding the Python path and how to reference a file – what is the absolute and relative path to the file you are loading? What directory are you working in?.Understanding how data is represented inside CSV files – if you open a CSV file, what does the data actually look like?.Understanding file extensions and file types – what do the letters CSV actually mean? What’s the difference between a.While this code seems simple, an understanding of three fundamental concepts is required to fully grasp and debug the operation of the data loading procedure if you run into issues: # Preview the first 5 lines of the loaded data # Control delimiters, rows, column names with read_csv (see later) # (in the same directory that your python process is based) You can watch the course below, or watch it on the YouTube channel (12 hour watch).The basic process of loading data from a CSV file into a Pandas DataFrame (with all going well) is achieved using the “read_csv” function in Pandas: # Load the Pandas libraries with alias 'pd' The Matplotlib Data Science Python LibraryĪnd finally, you'll see all of these tools working in concert as part of a basic COVID-19 trend analyzer app.Other important Python Data Structures: Lists, Tuples, Sets, and Dictionaries.Here are some other topics this course will cover: This has made them a long-time favorite tool of the data science community. This makes them easy to share and to use – even for non-programmers. Jupyter Notebooks run right inside of peoples' browsers. This is a popular way of creating documents with interactive code embedded. Next, the course will show you how to launch your own Jupyter Notebook.
Then it walks you through how to install both Python and the powerful Anaconda data science platform. It kicks off with a one-hour introduction to basic programming concepts, problem solving, and pseudocode.
This course includes a full codebase for your reference. This is a hands-on course and you will practice everything you learn step-by-step. You'll learn basic Python, along with powerful tools like Pandas, NumPy, and Matplotlib. This free 12-hour Python Data Science course will take you from knowing nothing about Python to being able to analyze data.