A line plot is a basic type of chart that displays data points connected by straight line segments. It is also known as a line graph
or a curve chart
. Line plots are commonly used to show trends or changes in data over time or across categories.
Advantages of line plots:
Line plots can be useful for visualizing many different types of data, including:
Time series data:
Data that is collected over time, such as stock prices, weather data, or sales data. 
Continuous data:
Data that can take on any value within a range, such as temperature or height. 
Discrete data:
Data that can only take on certain values, such as the number of students in a class or the number of cars sold in a month. 
Easy to understand:
Line plots are simple and easy to read, making them an effective way to communicate trends in data. 
Versatile:
Line plots can be used to visualize a wide variety of data types and can be customized to suit different needs. 
Useful for identifying patterns:
Line plots can be used to identify patterns and trends in data, making them useful for forecasting or predicting future outcomes.
Creating line plots:
When it comes to creating line plots in Python, you have two primary libraries to choose from: Matplotlib
and Seaborn
.
Using "Matplotlib":
Matplotlib
is a highly customizable library that can produce a wide range of plots, including line plots. With Matplotlib, you can specify the appearance of your line plots using a variety of options such as line style, color, marker, and label.
1. "Single" line plot:
A single line plot is used to display the relationship between two variables, where one variable is plotted on the xaxis and the other on the yaxis. This type of plot is best used for displaying trends over time, as it allows you to see how one variable changes in response to the other over a continuous period.

In this example, we define two lists
x
andy
containing our data points. 
We then use the
plt.plot()
function to plot these points on a line graph, and theplt.show()
function to display the plot. 
This will create a simple line plot with the xaxis displaying the values
[1, 2, 3, 4, 5]
and the yaxis displaying the values[2, 4, 6, 8, 10]
.
2. "Multiple" lines on one plot:
A plot with multiple lines is useful for comparing trends between different groups or categories. Multiple lines can be plotted on the same graph using different colors. This type of plot is particularly useful for analyzing data with multiple variables or for comparing data across different groups.

In this example, we define two lists
y1
andy2
containing our data points for two different lines. 
We use the
plt.plot()
function twice to plot both lines on the same graph. 
We add a legend using the
plt.legend()
function to distinguish between them.
3. "Customized" line plot:
Matplotlib
is a popular data visualization library in Python that allows you to create both single line plots and plots with multiple lines. With Matplotlib
, you can customize your plots with various colors, line styles, and markers to make them more visually appealing and informative.

In this example, we define
x
andy
lists as before, but then customize the line plot using various optional parameters of theplt.plot()
function. 
We change the line color to green, the line style to dashed, and the line width to 2.

We also add markers at each data point using a circular marker with a blue face color and a size of 8.

Finally, we add xaxis and yaxis labels and a title to the graph using the
plt.xlabel()
,plt.ylabel()
, andplt.title()
functions.
4. Adding a regression line:
It is possible to plot a regression line using the Matplotlib
library in Python. Although Seaborn
offers convenient functions for regression plot, Matplotlib
has the capability to create various types of visualizations, including regression plots.
 The above code imports the necessary libraries:
numpy
andmatplotlib.pyplot
.  A set of 100 random data points are generated and stored in the variables
x
andy
.  The scatter plot is created using the
scatter
function frommatplotlib
, which takesx
andy
as inputs.  The
polyfit
function fromnumpy
is used to calculate the coefficients of a linear regression line that fits the data points.  The coefficients
m
andb
of the regression line are used to plot the line using theplot
function from matplotlib, which takesx
andm*x+b
as inputs.  The
title
,xlabel
, andylabel
functions are used to set the title and axis labels of the plot.  Finally, the
show
function is called to display the plot on the screen.
Using "Seaborn":
Seaborn
is a library that specializes in statistical visualization. Seaborn provides several types of line plots, including those with regression lines, confidence intervals, and error bars.
1. "Single" line plot:
Visualizing data with a single line plot and multiple lines on one plot using Seaborn
are two ways of representing data in a graphical format. A single line plot is useful when the data being presented involves only one variable, such as time series data. It allows for the visualization of trends and patterns over time, making it an effective tool for analyzing data.
This code loads the tips
dataset from Seaborn, and creates a simple line plot with total_bill
on the xaxis and tip
on the yaxis.
2. "Multiple" lines on one plot:
When there are multiple variables involved, a line plot with multiple lines using Seaborn
can be more effective. This method allows for the comparison of different variables on the same graph, making it easier to identify patterns and relationships between them.

The above code loads the
exercise
dataset from Seaborn, and creates a line plot withtime
on the xaxis andpulse
on the yaxis. 
The
hue
parameter is used to group the data by thekind
variable, which creates multiple lines on the plot.
3. "Customized" line plot:
Seaborn
also provides various customization options, including color schemes and markers, which can be used to make the graph more visually appealing and informative.

The above code loads the
fmri
dataset from Seaborn, and creates a line plot withtimepoint
on the xaxis andsignal
on the yaxis. 
The
hue
parameter groups the data by theregion
variable, and thestyle
parameter groups the data by theevent
variable. 
The
markers
parameter is set toTrue
to display markers at each data point, and thedashes
parameter is set toFalse
to display solid lines.
4. Adding a regression Line:
Seaborn
provides a wide range of tools to create stunning and informative plots. One of its key features is the ability to add a regression line to a plot, which can help to identify the relationship between two variables and make predictions based on that relationship.

The above code loads the
anscombe
dataset from Seaborn, and creates a set of line plots withx
on the xaxis andy
on the yaxis. 
The
col
parameter is used to create a separate plot for eachdataset
, and thehue
parameter is used to color the lines by thedataset
. 
The
lmplot()
function is used to add a regression line to each plot. Other parameters, such ascol_wrap
,ci
,palette
, andscatter_kws
, are used to customize the appearance of the plot.
Limitations of line plots:

Limited data types:
Line plots are not suitable for all types of data, such as data with multiple categories or data with nonlinear relationships. 
Can be misleading:
If the scale of the yaxis is not carefully chosen, line plots can be misleading, making it important to choose appropriate scales.