A Q-Q plot, short for 'quantile-quantile' plot, is often used to assess whether or not a set of data potentially came from some theoretical distribution. In most cases, this type of plot is used to determine whether or not a set of data follows a normal distribution.
This tutorial explains how to create a Q-Q plot for a set of data in Python.
Example: Q-Q Plot in Python
Suppose we have the following dataset of 100 values:
To create a Q-Q plot for this dataset, we can use the qqplot() function from the statsmodels library:
The plot method is used to plot almost any kind of data in Python. It tells Python what to plot and how to plot it, and also allows customization of the plot being generated such as color, type, etc. In Python matplotlib, a line plot can be plotted using the plot method. It plots Y versus X as lines and/or markers. Scatteplot is a classic and fundamental plot used to study the relationship between two. To create a Q-Q plot for this dataset, we can use the qqplot function from the statsmodels library: import statsmodels.api as sm import matplotlib.pyplot as plt #create Q-Q plot with 45-degree line added to plot fig = sm.qqplot(data, line='45') plt.show In a Q-Q plot, the x-axis displays the theoretical quantiles. This means it doesn't. This python Line chart tutorial also includes the steps to create multiple line chart, Formatting the axis, using labels and legends. Lets see with an example for each. Create simple Line chart in Python: import matplotlib.pyplot as plt values = 1, 5, 8, 9, 7, 11, 8, 12, 14, 9 plt.plot(values) plt.show.
In a Q-Q plot, the x-axis displays the theoretical quantiles. This means it doesn't show your actual data, but instead it represents where your data would be if it were normally distributed.
The y-axis displays your actual data. This means that if the data values fall along a roughly straight line at a 45-degree angle, then the data is normally distributed.
We can see in our Q-Q plot above that the data values tend to closely follow the 45-degree, which means the data is likely normally distributed. This shouldn't be surprising since we generated the 100 data values by using the numpy.random.normal() function.
Consider instead if we generated a dataset of 100 uniformally distributed values and created a Q-Q plot for that dataset:
The data values clearly do not follow the red 45-degree line, which is an indication that they do not follow a normal distribution.
Scatter Plots With Python
Notes on Q-Q Plots
Keep in mind the following notes about Q-Q plots:
- Although a Q-Q plot isn't a formal statistical test, it offers an easy way to visually check whether or not a data set is normally distributed.
- Be careful not to confuse Q-Q plots with P-P plots, which are less commonly used and not as useful for analyzing data values that fall on the extreme tails of the distribution.
You can find more Python tutorials here.
Today we'll learn about plotting 3D-graphs in Python using matplotlib. Matplotlib is an amazing module which not only helps us visualize data in 2 dimensions but also in 3 dimensions. 3D graphs represent 2D inputs and 1D output. The submodule we'll be using for plotting 3D-graphs in python is mplot3d which is already installed when you install matplotlib. So, you need to make sure you have installed matplotlib to implement this tutorial.
So, Let's get started!
Imports:
In this tutorial, we will be using the 3D plots in matplotlib. There are also other options like pandas3D. Feel free to play around with that too.
The submodule of matplotlib called mpl_toolkits is used to plot our 3D graphs. Check out its documentation here. We will also import matplotlib.pyplot itself too.
Making our dataset for 3d graph plotting
Now we need to get our x, y, and z values so that we can plot them. You can also make use of a csv or excel dataset to make it easier to visualize. Here, we'll create three numpy arrays representing x, y, and z values.
For this, first import numpy and randint() function to create random datavalues: Slots online for cash.
We then define our numpy arrays by using the randint() function and list comprehension.
Now, let's see what our values are :
Output:
Plotting our 3d graph in Python with matplotlib
Let's first start by defining our figure
Now, to create a blank 3D axes, you just need to add 'projection='3d' ' to plt.axes()
The output will look something like this:
Now we add label names to each axis. To keep it simple, we're just naming them ‘x', ‘y'and ‘z' respectively. Also, note that the function is ‘set_xlabel' unlike in 2D plot where it is just ‘xlabel ‘
Finally, we get to the part where we plot the graph. The function used is plot3D().
Interactive Plots With Python
Output:
We plotted the line graph here. There are a number of plotting techniques we can use like contour3D, scatter3D, plot_wireframe, and plot_surface, etc.
Also read:
How do I plot in 3D
a polyhedron using 3d vectors as vertices and arrays of vectors as faces Web services.
Notes on Q-Q Plots
Keep in mind the following notes about Q-Q plots:
- Although a Q-Q plot isn't a formal statistical test, it offers an easy way to visually check whether or not a data set is normally distributed.
- Be careful not to confuse Q-Q plots with P-P plots, which are less commonly used and not as useful for analyzing data values that fall on the extreme tails of the distribution.
You can find more Python tutorials here.
Today we'll learn about plotting 3D-graphs in Python using matplotlib. Matplotlib is an amazing module which not only helps us visualize data in 2 dimensions but also in 3 dimensions. 3D graphs represent 2D inputs and 1D output. The submodule we'll be using for plotting 3D-graphs in python is mplot3d which is already installed when you install matplotlib. So, you need to make sure you have installed matplotlib to implement this tutorial.
So, Let's get started!
Imports:
In this tutorial, we will be using the 3D plots in matplotlib. There are also other options like pandas3D. Feel free to play around with that too.
The submodule of matplotlib called mpl_toolkits is used to plot our 3D graphs. Check out its documentation here. We will also import matplotlib.pyplot itself too.
Making our dataset for 3d graph plotting
Now we need to get our x, y, and z values so that we can plot them. You can also make use of a csv or excel dataset to make it easier to visualize. Here, we'll create three numpy arrays representing x, y, and z values.
For this, first import numpy and randint() function to create random datavalues: Slots online for cash.
We then define our numpy arrays by using the randint() function and list comprehension.
Now, let's see what our values are :
Output:
Plotting our 3d graph in Python with matplotlib
Let's first start by defining our figure
Now, to create a blank 3D axes, you just need to add 'projection='3d' ' to plt.axes()
The output will look something like this:
Now we add label names to each axis. To keep it simple, we're just naming them ‘x', ‘y'and ‘z' respectively. Also, note that the function is ‘set_xlabel' unlike in 2D plot where it is just ‘xlabel ‘
Finally, we get to the part where we plot the graph. The function used is plot3D().
Interactive Plots With Python
Output:
We plotted the line graph here. There are a number of plotting techniques we can use like contour3D, scatter3D, plot_wireframe, and plot_surface, etc.
Also read:
How do I plot in 3D
a polyhedron using 3d vectors as vertices and arrays of vectors as faces Web services.