Exploring and Preparing the PredictHealth Insurance Dataset

Welcome to the first lesson of the course, where we will lay the foundation for working with PredictHealth's insurance dataset. In this lesson, you will learn how to load, explore, and perform basic manipulations on a real-world dataset using Python. These are essential first steps in any data analysis project, as they help you understand the data you are working with and prepare it for deeper analysis or modeling.

By the end of this lesson, you will be able to confidently load a dataset, inspect its structure, check for data quality issues, and perform simple filtering operations. These skills are crucial for anyone interested in data science, analytics, or working with health insurance data.

To begin, we need to use some popular Python libraries that make data analysis easier and more efficient. The main libraries we will use for this course are pandas for data manipulation, numpy for numerical operations, and matplotlib.pyplot and seaborn for data visualization. In most environments, you would need to install these libraries using commands like pip install pandas, but on CodeSignal, these libraries are already installed and ready to use. This allows you to focus on learning and practicing without worrying about setup.

Here is how you import these libraries and load the PredictHealth insurance dataset:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

insurance_data = pd.read_csv('datasets/insurance.csv')

This code imports the necessary libraries and loads the dataset from a CSV file named insurance.csv into a pandas DataFrame called insurance_data. The DataFrame is a powerful data structure that allows you to easily explore and manipulate tabular data.

Once the dataset is loaded, it is important to take a first look at its contents and structure. This helps you get familiar with the data and spot any immediate issues or interesting patterns. You can use the .head() method to display the first few rows of the dataset, which gives you a quick overview of what the data looks like. By default, .head() shows 5 rows, but you can specify a different number by passing it as an argument (e.g., .head(10) to view the first 10 rows).

print("First 5 rows of the dataset:")
print(insurance_data.head())

# To view more rows if needed
print("\nFirst 10 rows of the dataset:")
print(insurance_data.head(10))

The output for the first 5 rows might look like this:

First 5 rows of the dataset:
   age     sex     bmi  children smoker     region      charges
0   19  female  27.900         0    yes  southwest  16884.9240
1   18    male  33.770         1     no  southeast   1725.5523
2   28    male  33.000         3     no  southeast   4449.4620
3   33    male  22.705         0     no  northwest  21984.4706
4   32    male  28.880         0     no  northwest   3866.8552

To get more detailed information about the dataset, such as the number of rows and columns, column names, and data types, you can use the .info() method:

print("\nDataset Information:")
print(insurance_data.info())

This will output something like:

Dataset Information:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1338 entries, 0 to 1337
Data columns (total 7 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1338 non-null   int64  
 1   sex       1338 non-null   object 
 2   bmi       1338 non-null   float64
 3   children  1338 non-null   int64  
 4   smoker    1338 non-null   object 
 5   region    1338 non-null   object 
 6   charges   1338 non-null   float64
dtypes: float64(2), int64(2), object(3)
memory usage: 73.3+ KB
None

This information helps you understand the size and structure of your data, which is important before moving on to analysis.