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Python for Data Science: Unveiling the Power of Data Manipulation

Python for Data Science: Unveiling the Power of Data Manipulation

Import and Export FilesImport and Export Files

You may be wondering: yes, playing around with dataframe is good fun but if I have a dataset of 1000000 rows I can't write down every single row. For that reason, pandas provides several different methods that can be used to read and write data from and to various file formats, such as CSV, Excel, and JSON. There are many options and parameters available for these functions, and you can use them to customize the way the data is read and written. pandas is a very powerful library for working with tabular data in Python.

P.S. These are just some of the methods available out there. Did you know that you can run SQL queries via pd.read_sql()?

Task

  1. Read a csv dataset;
  2. Print 5 rows;
  3. Export a csv dataset as an Excel file.

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Section 1. Chapter 3
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