Why and when to use Sheets as your backend.
Python
- Getting your Sheets-backed app online.
- Turn your spreadsheet into a JSON API.
- Fetch data from a spreadsheet into Python.
- How to enable the Google Sheets API and get your credentials.
- Update your spreadsheets programmatically with Python.
- Speed up your app by caching expensive operations.
- Remember values between reruns.
- Organize your app with layout elements.
- Visualize data with built-in charts.
- Show pandas DataFrames beautifully.
- Collect input with widgets.
- Show text, titles, and markdown.
- Build web apps with pure Python.
- Apply multiple decorators to one function.
- Use classes instead of functions for stateful decorators.
- A practical decorator example for measuring performance.
- Pass configuration to your decorators.
- Use @functools.wraps to keep the original function info.
- Handle functions that take arguments.
- The basic pattern for creating decorators.
- Modify function behavior without changing function code.
- Handle connections safely with context managers.
- Calculate sums, averages, and counts.
- Combine data from multiple tables.
- Define table structure with CREATE TABLE.
- Modify or remove existing rows.
- Add new rows to database tables.
- Retrieve data from database tables.
- Connect to databases from Python code.
- Share field selections across queries.
- The contract between client and server.
- Query GraphQL APIs from Python.
- Parameterize queries for reuse and safety.
- Create, update, and delete with mutations.
- Request data with the query syntax.
- A query language for APIs that gives clients control.
- Send and receive JSON from web APIs.
- Serialize Python objects JSON doesn't understand.
- Deal with invalid JSON gracefully.
- How Python types become JSON types.
- Make JSON readable with proper formatting.
- Convert between JSON strings and Python objects.
- The universal data format for the web.
- Write your own validation logic.
- Convert models to dictionaries and JSON.
- Models inside models for complex data.
- Add validation rules to individual fields.
- Make fields optional or give them defaults.
- Pydantic validates and coerces data automatically.
- Define your data schema as a Python class.
- Data validation using Python type hints.
- Pre-fill some arguments to a function.
- Functions that take or return functions.
- Pythonic way to transform and filter.
- Small anonymous functions for quick tasks.
- Combine all items into a single value.
- Keep only items that pass a test.
- Apply a function to every item in a sequence.
- Functions without side effects.
- A different way to think about code.
- Scrape responsibly and avoid getting blocked.
- Scrape data across multiple pages.
- Get href, src, and other HTML attributes.
- Use CSS selector syntax to find elements.
- Find one element or many elements.
- Turn messy HTML into navigable Python objects.
- Download web pages using the requests library.
- Extracting data from websites automatically.
- Methods that work on the class itself, not instances.
- Automatically generate __init__, __repr__, and more.
- Create new classes based on existing ones.
- Create computed attributes that look like regular attributes.
- Control how objects display as strings.
- Call methods from the parent class.
- Initialize objects with starting values.
- Methods are functions that belong to a class.
- self refers to the current object instance.
- Classes are blueprints for creating objects.
- Run custom functions on DataFrame rows or columns.
- Stack DataFrames vertically or horizontally.
- Combine two DataFrames based on common columns.
- Inner, left, right, and outer joins explained.
- Reshape data into summary tables.
- Count occurrences of unique values in a column.
- Pull year, month, day from datetime columns.
- Convert strings to datetime objects.
- Work with text data using the str accessor.
- Pull specific parts from strings using patterns.
- Apply multiple functions to grouped data.
- Group rows by column values for aggregate analysis.
- Find null and NaN values in your data.
- Remove rows or columns with null values.
- Replace null values with meaningful substitutes.
- Create new columns in a DataFrame.
- Calculate new columns from existing ones.
- Remove unwanted columns from a DataFrame.
- Change column names in a DataFrame.
- Order rows based on column values.
- Select rows that match specific criteria.
- Check if values are in a list of options.
- Combine conditions with AND and OR logic.
- Write filters as readable strings.
- Access single or multiple columns from a DataFrame.
- Access specific rows using iloc for position-based selection.
- Build DataFrames from dictionaries, lists, and other sources.
- Load data from CSV files into a DataFrame.
- Quickly inspect your data with head, tail, and info.
- Understanding the core data structure in pandas.