Loading and storing data from a DataFrame
It is fairly unusual to create a DataFrame object from the raw data in a Python session. In practice, the data will often come from an external source, such as an existing spreadsheet or CSV file, database, or API endpoint. For this reason, pandas provides numerous utilities for loading and storing data to file. Out of the box, pandas supports loading and storing data from CSV, Excel (xls or xlsx), JSON, SQL, Parquet, and Google BigQuery. This makes it very easy to import your data into pandas and then manipulate and analyze this data using Python.
In this recipe, we will learn how to load and store data in a CSV file. The instructions will be similar for loading and storing data in other file formats.
Getting ready
For this recipe, we will need to import the pandas package under the pd alias and the NumPy library as np. We must also create a default random number generator from NumPy using the following commands:
from numpy.random...
