Definition

A box plot, also known as a box and whisker plot, is a statistical graph used to represent the distribution of a dataset. It is called a box plot because the graph consists of a box that represents the middle 50% of the data and "whiskers" that extend out from the box to show the range of the data.

Importance

Box plots are important because they help us understand the distribution of a dataset simply and visually. By showing the median, quartiles, and outliers, box plots provide a quick summary of the key features of the data.

Box plots can help us identify if the data is skewed, if there are any extreme values or outliers, and if there are any differences between groups of data. This information can be very useful for making decisions, such as identifying areas for improvement in a process, detecting anomalies in a data set, or comparing different groups or data sets.

Box plots are also helpful because people with different levels of statistical knowledge can easily interpret them. This makes them a valuable tool for communicating findings to various audiences, including managers, stakeholders, and the general public.

Understanding

Box plots consist of five main components:

1. Minimum: The lowest value in the dataset.

2. Maximum: The highest value in the dataset.

3. Median: The middle value of the dataset, which separates the data into two halves.

4. Quartiles: The lower quartile (Q1) and upper quartile (Q3), divide the data into four equal parts.

5. Outliers: Data points that lie outside the typical range of the dataset, often indicating unusually high or low values.

Sample BoxPlot

import pandas as pd
import matplotlib.pyplot as plt

import random

data = [random.randint(1, 100) for i in range(100)]
df = pd.DataFrame(data, columns=["Value"])

df.boxplot(column=["Value"])
plt.show()

This shows 100 random values and with the help of boxplot you can understand the IQR, Median, Mean, Q1, Q2 and Q3

That's the end of the article readers!

Will be explaining more in my following blogs!

"Boxplots unveil the hidden tale within data, showcasing range, median, outliers, and distribution in a concise visual." - Florence Nightingale

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