1. Python Plot Multiple Figures
2. 2 Plots In Same Figure Python Ide
3. Multiple 3d Plots In One Figure Python
4. 2 Plots In Same Figure Python Programming
5. 2 Plots In One Figure Python

In this article you are going to learn multiple line plot in Python using seaborn module.

Visualization makes the data easy to understand because through it we can generate any kind of insights from the data be it mathematical, statistical, etc.

1. How to Set the Size of a Figure in Matplotlib with Python. In this article, we show how to set the size of a figure in matplotlib with Python. So with matplotlib, the heart of it is to create a figure. On this figure, you can populate it with all different types of data, including axes, a graph plot, a geometric shape, etc.
2. Plot multiple lines on one chart with different style Python matplotlib rischan Data Analysis, Matplotlib, Plotting in Python November 24, 2017 January 22, 2020 2 Minutes Sometimes we need to plot multiple lines on one chart using different styles such as dot, line, dash, or maybe with different colour as well.
3. Each line in this plot will have the same entry in the in the legend. That's why I prefer method 2: calling plot separately for each line: plt.plot(forecast) plt.plot(trainZ) plt.show or with x-values explicitly included: plt.plot(xlist, forecast) plt.plot(xlist, trainZ) plt.show or with different keyword arguments for each line.
4. Python Language Making multiple Plots in the same figure using plot superimposition with separate plot commands Example Similar to the previous example, here, a sine and a cosine curve are plotted on the same figure using separate plot commands.

Dataset link is given at the bottom of this tutorial.

That is the power of Python visualization libraries which can portray the entire story of data in just a few plots. Python has a lot of features to visualize the data. It offers a plethora of data exploring and visualizing opportunities. It has many built-in modules used for visualization like matplotlib, seaborn, plotly, etc. Working with the seaborn library is more interactive than matplotlib due to a vast variety of plots and features it offers. Multiple line plot is used to plot a graph between two attributes consisting of numeric data.

Fig, ax = plt.subplots(1, figsize=(8, 6)) # Set the title for the figure fig.suptitle('Multiple Lines in Same Plot', fontsize=15) # Draw all the lines in the same plot, assigning a.

For plotting multiple line plots, first install the seaborn module into your system.

Install seaborn using pip

pip manages packages and libraries for Python. It additionally installs all the dependencies and modules that are not in-built.

Just a single pip install command gets all your installation work done. That is how concise Python is!

It is also possible to install using conda in the anaconda terminal through the statement-

conda install seaborn

Type the following command in your terminal.

Importing the required modules and packages in Python using the ‘import’ command.

For working with this dataset, we need to import pandas, matplotlib, and seaborn module.

Python Plot Multiple Figures

• Pandas work for data manipulation, processing, and analysis. Particularly, it offers operations for manipulating data frames and time series. It helps us with the data cleaning part.
• matplotlib.pyplot function works with the figure like creating the figure, creating a plotting area in the figure, plotting lines in the plotting area and adding labels, etc.
• seaborn, an extension of Python matplotlib visualization library provides techniques for drawing attractive graphs.

Note: Matplotlib offers many basic visualizations like line, bar, scatter, pies, etc. Seaborn on the other hand offers numerous visualization options like KDE plot, rugplot, boxplot, violin plot, swarm plot, heatmap, facetgrid, regplot, and the list is endless. Seaborn works with less syntax when compared with matplotlib.

A picture is worth a thousand words. With advanced tools, such a picture is drawn in just a few lines of code.

Seaborn module contains a function ‘sns.lineplot()‘, through which we can plot a single line and multiple lines plot using its parameters. Line plots work well when you want to analyze changes in one variable concerning another

syntax: lineplot in seaborn

sns.lineplot(

)

• x, y: represent names of variables in the data set to use as input variables.
• data: data frame object pointing to the data set
• hue: grouping variables to generate lines of different colors.
• size: to specify line size.
• style: to specify line style.
• palette: colors to use for different categories of hue.
• hue_order: order for the appearance of hue variables.

Let’s begin with importing the CSV dataset on which we are going to perform the visualization. This is done through Python pandas which reads the CSV imported and converts it into a dataframe object which can be manipulated when required. We have imported an automobile data set with prices and different types of automobiles with various other characteristics.

In the above code,

• the read_csv function of pandas imports the CSV file into the dataframe object ‘data’.
• head() method displays the specified number of rows from the first row. Here, it displays the first 10 rows.

check the size of the data frame:

shape method defines the size of the data. It gives the number of rows and columns in the dataframe.

plot the single line graph:

horsepower and price are two continuous data variables in our data set. Let’s analyze the relationship between these two variables through a simple line plot.

plot the graph between the horsepower and price.

• x: represents horsepower on x-axis
• y: represents price on y-axis
• data: data frame object pointing to the entire data set.

2 Plots In Same Figure Python Ide

Insight –

Through this plot, we got to know that there is a kind of linear relationship between price and horsepower. As horsepower increases, the price of the vehicle increases as well. We get to know that for a particular variety of vehicles with horsepower 180, the price range lies near 30000.

plot the multiple line graph:

Here, it plots multiple lines on the same graph. We differentiate between them specifying a label. This label shows up at either corner of the image. If we want to use multiple line plots of seaborn for exploring the relationship between two continuous variables, we need to use hue argument. hue takes as a parameter a variable name according to which data is segregated. It renders different line plots for the segregated data.

plot the graph between horsepower and price according the fuel-type

plt.figure() specifies the size of the figure we want to create.

• plt.show()- to view the figure.

Insight-

Through this plot, we got to know that there is a kind of linear relationship between price and horsepower as we have seen earlier. As horsepower increases, the price of the vehicles increases as well. What’s different in this data is the hue argument. By specifying fuel-type in hue, we segregated the data into two groups, one with all the vehicles that run on gas and another set of diesel-driven vehicles. We get two line plots in the above figure. The orange line represents the relationship between price and horsepower of all the vehicles with fuel-type as diesel and blue represents all vehicles with fuel type as gas.

The small rectangular box at the top right corner giving information about the type of line is a legend.

Now, we can easily say that a diesel type vehicle with a horsepower power of 120 has a price value of somewhere around 25000.

Multiple 3d Plots In One Figure Python

Note:

For downloading the automobile data set and creating your visualizations, click on the link mentioned below:

Sometimes, as part of a quick exploratory data analysis, you may want to make a single plot containing two variables with different scales.

One of the options is to make a single plot with two different y-axis, such that the y-axis on the left is for one variable and the y-axis on the right is for the y-variable.

If you try to plot the two variables on a same plot without having two different y-axis, the plot would not really make sense.

If the variables have very different scales, you’ll want to make sure that you plot them in different twin Axes objects. These objects can share one axis (for example, the time, or x-axis) while not sharing the other (the y-axis).

To create a twin Axes object that shares the x-axis, we use the twinx method.

Let us import Pandas.

We will use gapminder data from Carpentries to make the plot with two different y-axis on the same plot.

Let us subset gapminder data by using Pandas query() function to filter for rows with United States.

We are interested in making a plot of how lifeExp & gdpPercap changes over the years. The variable on x-axis is year and on y-axis we are interested in lifeExp & gdpPercap.
Both lifeExp and gdpPercap have different ranges. lifeExp values are below 100 and gdpPercap values are in thousands.

Naively, let us plot both on the same plot with a single y-axis.

We can immediately see that this is a bad idea. The line for lifeExp over years is flat and really low. We don’t see any variation in it because of the scale of gdpPercap values.

One of the solutions is to make the plot with two different y-axes. The way to make a plot with two different y-axis is to use two different axes objects with the help of twinx() function.

We first create figure and axis objects and make a first plot. In this example, we plot year vs lifeExp. And we also set the x and y-axis labels by updating the axis object.

Next we use twinx() function to create the second axis object “ax2”. Now we use the second axis object “ax2” to make plot of the second y-axis variable and update their labels.

2 Plots In Same Figure Python Programming

Then we can display the plot with plt.show() as before.

Now we have what we wanted. A plot with with different y-axis made with twinx in matplotlib. This definitely help us understand the relationship of the two variables against another. We can see that both lifeExp and gdpPerCap have increased over the years.

Although a plot with two y-axis does help see the pattern, personally I feel this is bit cumbersome. A better solution to use the idea of “small multiples”, two subplots with same x-axis. We will see an example of that soon.

2 Plots In One Figure Python