This course teaches you how to build pipelines to import data kept in common storage formats. You’ll use pandas, a major Python library for analytics, to get data from a variety of sources, from spreadsheets of survey responses, to a database of public service requests, to an API for a popular review site. Along the way, you’ll learn how to fine-tune imports to get only what you need and to address issues like incorrect data types. Finally, you’ll assemble a custom dataset from a mix of sources.

• read_csv() and read_excel
• Setting data types, choosing data to load, handling missing data and errors
• usecols, nrows, skiprows, names, dtype, na_values, .isna(), sheet_name, parse_dates(for standard datetime), pd.to_datetime()(Parse non-standard date formats)
• Putting multiple spreadsheets together (Using iterate through dataframe in dictionary)
• Setting custom true/false values

• read_sql() and sqlalchemy
• SQL SELECT, WHERE, aggregate functions and joins
• Install SQLite and import .db in it
• sqlalchemy's create_engine() makes an engine to handle database connections

• read_json(), json_normalize(), and requests
• Working with APIs and nested JSONs
• Appending and merging datasets
• requests.get()(get the data from API), .json()(extract JSON data), .append(), .merge()