Importing the Dataset

Python in general supports many type of raw files and sources through libraries like pandas.

Pandas has many helpful read_filetype() functions to handle many file types, for example:

read_csv() read_excel() read_json() read_html() read_sql() read_pickle()

Note

See docs for detailed info: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html

In our example, the training data is in csv format and is stored in "/kaggle/input/indian-food-101/indian_food.csv". We will use read_csv() function, it accepts a filepath parameter.

The output is a DataFrame called IndianFoods.

DataFrame

• Pandas specific Data structure, to store data in tabular format;
• Looks similar to SQL table;
• Has a lot of associated functions, similar to table-level commands in SQL (SELECT, SUM etc);
• Stored in memory (RAM). In comparision, SQL tables are stored on hard-disk and pulled into memory while running commands.