Plot – Python(matplotlib)

Let’s start with the most fundamental type of chart—the line chart (often referred to as a plot, which shows how data changes over time).

A line chart is a graph that visualizes data points by connecting them with a line.

Each data point is typically plotted along the x and y axes, and a straight or curved line connects them to illustrate trends or changes in the overall dataset. This makes line charts particularly useful for showing patterns over time or identifying fluctuations in data.

• Tracking Changes Over Time
  • Often used to show trends in data over time, such as stock prices, temperature changes, or website visitors.
• Economic Data Visualization
  • Commonly used to represent economic indicators like GDP growth, unemployment rates, or inflation trends.
• Scientific Research
  • Helps visualize changes in experimental or observational data.
  • For example, a line chart can illustrate temperature or pressure variations in a chemical reaction.
• Business Performance Monitoring
  • Useful for tracking company performance over time, such as sales growth, revenue trends, or product output.

Plot Code

import matplotlib.pyplot as plt
import numpy as np

under_y = [-8, -6, -1, 5, 11, 17, 22, 22, 15, 8, 1, -6]
high_y = [2, 5, 11, 18, 23, 27, 29, 30, 26, 20, 12, 4]

x = ['January', 'February', 'March', 'April', 'May', 'June',
     'July', 'August', 'September', 'October', 'November', 'December' ]

plt.plot(x, under_y, 'ro--', label="High C°")
plt.plot(x, high_y, 'bo--', label="Low C°")

plt.xticks(rotation=-60)
plt.xlabel('Month')
plt.ylabel('C°')
plt.legend()

plt.show()

Plots are widely used across various fields because they effectively visualize relationships between data and how they change over time.

In fact, line charts are often the first type of visualization used in data analysis.

This is because they provide a quick and simple way to preprocess data—allowing you to observe trends just by looking at the numbers and their flow. They serve as a great starting point before diving deeper into detailed analysis.

However, line charts rarely reveal the most critical insights. If key information is immediately obvious in a line chart, it’s usually something people can intuitively grasp without deep analysis.

My Approach to Data Analysis

1. Plot Different X and Y Values
   • The first thing I do when analyzing data is plot different variables on a line chart and compare them to the standard deviation.
   • If I can extract insights at this stage, that’s great—but often, this step is just about getting a feel for the data’s behavior.
2. Deep Dive Analysis
   • Once I understand the general patterns, I start analyzing the data in more detail to uncover deeper insights.
3. Using Line Charts in Reports
   • Whenever I write a report, I prioritize line charts to make the information as intuitive as possible.
   • Since others need to understand my findings, I aim for clarity and simplicity in my visualizations.

Line charts are a great starting point for data exploration, helping to build a foundation before moving into deeper analysis.