DS Data Preparation

Before analyzing data, a Data Scientist must extract, clean, and transform the data to make it valuable and suitable for analysis.

Extract and Read Data With Pandas

Before data can be analyzed, it must be imported or extracted.

In the example below, we demonstrate how to import data using Pandas in Python.

We use the read_csv() function to load a CSV file containing health data.

import pandas as pd

health_data = pd.read_csv(“data.csv”, header=0, sep=“,”)

print(health_data)

Import the Pandas library.
Name the data frame health_data.
header=0 indicates that the variable names are located in the first row (note that 0 refers to the first row in Python).
sep=”,” specifies that a comma is used as the separator between values, which is typical for a .csv file (comma-separated values).

Tip: For large CSV files, you can use the head() function to display only the top 5 rows.

import pandas as pd

health_data = pd.read_csv(“data.csv”, header=0, sep=“,”)

print(health_data.head())