Master JSON Using Python

Learn to work with Python files in JupyterLab, write and run a simple function using a Python console, execute with shift+enter, and safely shut down kernels and terminals.

Access and use terminals in JupyterLab to run commands, install packages with pip (for example pandas), and manage terminal sessions by closing and reopening them.

Learn to use interactive Python notebooks with kernels to run code in blocks, restart kernels, import pandas, and create data frames, while mixing markdown notes.

Learn json objects by exploring curly brackets, key-value pairs, and double-quoted string keys, while using strings, numbers, true or false, null, objects, and arrays with correct comma rules.

Explore json arrays, their syntax with straight brackets, value types such as strings, numbers, true, false, null, objects, or arrays, and comma rules.

Discover nested JSON by nesting objects and arrays and visualize data as a tree. Use tools like tree viewers to navigate depth and organize complex structures in Python.

Explore data type compatibility between JSON and Python using the built in json library, converting objects to dictionaries, arrays to lists and tuples, and handling strings, numbers, and booleans.

Master the built-in JSON library to deserialize JSON, converting JSON data into Python dictionaries and lists from files and strings, including json.load and json.loads.

Serialize Python structures to JSON and explore pretty printing, indentation, and sorted keys while loading a weather alert JSON file from an API and exporting to files or strings.

Learn to navigate dictionaries and lists in Python by using keys and values, deserializing JSON, and traversing nested structures with a custom display function to access specific values.

Master JSON using Python by exploring built-in list methods like append, insert, pop, remove, extend, reverse, sort, copy, and clear.

Learn to manipulate data with Python dictionary methods—clear, copy, items, pop, pop item, set default, and update—on a sample Einstein quote JSON.

Learn how to normalize json data with pandas json_normalize to flatten nested structures into a tabular format, using record_path, meta, and max_level to shape the data frame.

Master json using Python: export dataframes to json with the pandas to_json function, use indent for pretty printing, and explore six orientations—split, records, index, columns, values, and table—with their structures.

Learn to make your first API request with Python's requests library to the Open Weather API's current weather endpoint using latitude, longitude, and an API key, then deserialize the JSON.

Explore how to build and customize http requests by configuring headers, including user agent and authorization headers with an api key, and by sending json bodies for post operations.

Explore response components by inspecting headers, status code and reason, and elapsed time for the Open Weather API; learn about the date/time, JSON format, endpoint, query string, and allowed methods.