Data Science Functions

Commonly Used Functions in Data Science

Table of Contents

Data Science involves analyzing and interpreting datasets using various mathematical and statistical methods. This guide explains three essential Python functions for data analysis: max(), min(), and mean(). These functions help identify maximum, minimum, and average values within datasets.

Overview of the Sports Watch Dataset

Below is an example dataset from a sports watch, including six key metrics such as duration of training, heart rate, calorie burn, and more. This dataset is commonly used in health and fitness data analysis.

Duration (minutes)	Average Pulse (BPM)	Max Pulse (BPM)	Calorie Burnage (kcal)	Hours Worked	Hours Slept
30	80	120	240	10	7
30	85	120	250	10	7
45	90	130	260	8	7
45	95	130	270	8	7
60	105	140	290	7	8

Key Variables in the Dataset

Duration: Duration of the training session in minutes.
Average Pulse: Average heart rate during the session (beats per minute).
Max Pulse: Maximum recorded heart rate (beats per minute).
Calorie Burnage: Total calories burned during the session (kcal).
Hours Worked: Hours worked before the training session.
Hours Slept: Hours of sleep before the training session.

Note: The column names use underscores (_) to separate words for better compatibility in Python.

Essential Python Functions for Data Analysis

In this tutorial, we will explore three fundamental Python functions frequently used in data analysis: max(), min(), and mean(). These functions help find maximum, minimum, and average values within datasets, enabling efficient data insights.

The `max()` Function

The Python max() function identifies the highest value in an array or list. This function is ideal for determining maximum values in a dataset.

        Example:

            Average_pulse_max = max(80, 85, 90, 95, 100, 105, 110, 115, 120, 125)

            print(Average_pulse_max)

The `min()` Function

The Python min() function finds the smallest value in an array or list. It is a quick way to determine the minimum value in a dataset.

        Example:

            Average_pulse_min = min(80, 85, 90, 95, 100, 105, 110, 115, 120, 125)

            print(Average_pulse_min)

The `mean()` Function

The mean() function from the NumPy library calculates the average value of an array. This is commonly used in data science for summarizing datasets.

        Example:

            import numpy as np

            Calorie_burnage = [240, 250, 260, 270, 280, 290, 300, 310, 320, 330]

            Average_calorie_burnage = np.mean(Calorie_burnage)

            print(Average_calorie_burnage)

Note: The prefix np. in np.mean specifies that the function is from the NumPy library.

Introduction to Data Science Written Edition English Tutorial

Curriculum

Data Science Functions

Commonly Used Functions in Data Science

Overview of the Sports Watch Dataset

Key Variables in the Dataset

Essential Python Functions for Data Analysis

The max() Function

The min() Function

The mean() Function

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The `max()` Function

The `min()` Function

The `mean()` Function