Dot graph in python

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

How to create dot plots in Python

Pjarzabek/DotPlotPython

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

How to create dot plots in Python

A little tweak to classic Seaborn scatterplot

In this repository you can find how to create a dot plot using Python and Seaborn package. Dot plots are not included in Seaborn, but you can use a little tweak to the scatterplot and convert it with few simple steps.

If you are interested just in the code, you should check the Python script used to create a dot plot of movies I watched sorted by release year and rating. Raw dataset will help you recreate my findings.

You should also read this Jupyter notebook, which explains how the loop let to convert scatterplot to a dot plot.

Источник

Draw Dot Plot Using Python and Matplotlib

A dot plot is a simple graph that uses solid circles, or dots, to show the frequency of each unique data value.

Let’s illustrate it with an example. Suppose a group of 23 people participated in a 10K race. The below list has their finish times (in minutes):

57, 59, 56, 54, 56, 59, 60, 62, 61, 59, 60, 59, 55, 60, 56, 58, 58, 59, 61, 58, 58, 57, 54

We can count the number of runners for each unique finish time:

Finish Time	54	55	56	57	58	59	60	61	62
Runners	2	1	3	2	4	5	3	2	1

And draw a dot plot to visualize this information:

You can get the frequency of a finish time by counting the dots corresponding to it. Two runners completed the race in 54 minutes, only one took 55 minutes, three finished in 56 minutes, and so on.

Now that you know what dot plots are, let me show you how to draw them.

The dotplot() function 🔗

Matplotlib is the most popular Python library for visualization. But it doesn’t have a native function for dot plots.

No need to worry, though! You can use the below dotplot() function. It’ll draw dot plots for any given list of numbers (passed in as input_x ):

# standard numpy and matplotlib library imports import numpy as np import matplotlib.pyplot as plt def dotplot(input_x, **args): # Count how many times does each value occur unique_values, counts = np.unique(input_x, return_counts=True) # Convert 1D input into 2D array scatter_x = [] # x values scatter_y = [] # corresponding y values for idx, value in enumerate(unique_values): for counter in range(1, counts[idx]+1): scatter_x.append(value) scatter_y.append(counter) # draw dot plot using scatter() plt.scatter(scatter_x, scatter_y, **args) # Optional - show all unique values on x-axis. # Matplotlib might hide some of them plt.gca().set_xticks(unique_values) 

Here’s how this function works:

• It transforms the given list input_x into a 2D array.
• It counts how many times each unique value occurs and creates as many 2D points. For example, if the value 60 appears three times, we’ll have three 2D points — (60, 1), (60, 2), and (60, 3).
• Finally, it uses Matplotlib’s scatter() and the 2D array to draw the dot plot.
• Notice that the function passes all the inputs ( **args ) to scatter() . Thus, you can customize the dot plot using any parameters that work with scatter() .

Let’s see a few ways in which you can use this function.

Example 1: dotplot() in Action 🔗

Suppose the below list contains the heights (in inches) of 50 high school basketball players:

hs_heights = np.array([ 71, 67, 64, 72, 65, 69, 66, 68, 69, 72, 69, 73, 69, 72, 73, 74, 76, 68, 66, 63, 67, 71, 72, 74, 68, 69, 75, 71, 72, 72, 65, 66, 72, 74, 66, 62, 75, 75, 64, 63, 64, 66, 74, 67, 72, 70, 71, 70, 74, 68 ]) 

How frequently does each height measurement occur? We can find the answer by drawing the dot plot:

# Draw dot plot using our new function dotplot(input_x=hs_heights) 

We have a basic dot plot that shows the distribution of heights. But the graph is too small and too bland. Let’s jazz it up.

Remember, we can use any argument available in scatter() . So here’s what we’ll do:

• Use the parameter marker to draw stars (★) instead of dots (●). You can use any of the Matplotlib markers.
• Change the marker color using the parameter color .
• The parameter s controls the marker size. Let’s set it to 100.
• Make the plot bigger and sharper using Matplotlib’s figure().

# dpi - controls sharpness plt.figure(figsize=(10, 6), dpi=150) dotplot(input_x=hs_heights, marker='*', color='#C44E52', s=100) plt.xlabel("Height (Inches)", fontsize=14, labelpad=15) plt.ylabel("Number of Players", fontsize=14, labelpad=15) plt.title("High School Basketball Players", fontsize=14, pad=15) plt.show() 

Example 2: Without Frame or Y-Axis 🔗

You might have seen dot plots without the rectangular frame around them. Sometimes the y-axis is omitted as well. Let’s learn how to do that using our dotplot() function.

Suppose the list below contains students’ scores for a high school final exam. And we want to know how these scores are distributed.

exam_scores = np.array([ 82, 82, 76, 84, 76, 82, 79, 83, 75, 78, 85, 77, 78, 82, 77, 86, 87, 76, 77, 86, 85, 78, 86, 77, 78, 84, 79, 78, 75, 85, 85, 86, 78, 83, 84, 82, 78, 77, 82, 85 ]) 

The below code visualizes the exam scores as a dot plot without the frame or y-axis:

# Seaborn for better styling import seaborn as sns # Line2D will be needed to draw x-axis line from matplotlib.lines import Line2D # Use seaborn to scale up font size sns.set_theme(style="ticks", font_scale=1.75) # Dimensions of the plot plt.figure(figsize=(20, 8)) # Draw the dot plot dotplot(input_x=exam_scores, s=400, color='#0054A6') axes = plt.gca() # Remove the rectangle around the plot axes.set_frame_on(False) # Remove y-axis values axes.axes.get_yaxis().set_visible(False) # Removing frame also removed x-axis line # let's add it back xmin, xmax = axes.get_xlim() ymin, ymax = axes.get_ylim() xaxis_line = Line2D( (xmin, xmax), (ymin, ymin), linewidth=2, color='black' ) axes.add_artist(xaxis_line) # No one scored 80 or 81. So Matplotlib won't show these # two values on the x-axis # Below code ensures that every possible value in the score # range is visible on the x-axis score_range = range(exam_scores.min(), exam_scores.max()+1) axes.set_xticks(score_range) plt.xlabel("Final Exam Scores", labelpad=20) plt.show() 

The plot looks cleaner without the surrounding box and the y-axis.

You can experiment as per your needs. For example, what if you want to keep the y-axis? You could draw it using Line2D , as we did above for the x-axis.

Example 3: Multicolored Dot Plot 🔗

This one is strictly for fun 😀.

So far, we’ve been drawing dots with the same color. But you can specify a different color for each dot.

Let’s visualize the heights of basketball players again. We’ll generate random colors using NumPy’s default_rng and draw a colorful dot plot:

# import random number generator from numpy.random import default_rng # Get random colors rng = default_rng() # We need colors for all data points # And each color consists of 3 numbers (RGB) # Hence input size = (hs_heights.size, 3) random_colors = rng.random(size=(hs_heights.size, 3)) # Set seaborn style and plot size sns.set_theme(style='ticks') plt.figure(figsize=(10, 6), dpi=150) # draw dot plot # Set random colors using the 'color' parameter dotplot(input_x=hs_heights, s=200, color=random_colors) plt.show() 

Summary & Next Steps 🔗

Matplotlib doesn’t support dot plots natively. So we wrote our own function to draw dot plots. This post also showed you how to customize these plots with various options — the dot shape, size, color, axes lines, etc.

The dot plot is a great tool to visualize the distribution of smaller datasets. But you’ll need different techniques and graphs to handle larger datasets. You can read all about that here.

Источник

How to create a Dot Plot in Plotly Python

In this post, you will learn how to create a Dot plot in Plotly Python.

1 . Create a Dot plot with Plotly Express –

Dot plots (also known as Cleveland dot plots) are scatter plots with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a bar chart, dot plots can be less cluttered and allow for an easier comparison between conditions.

import pandas as pd url = "https://raw.githubusercontent.com/bprasad26/lwd/master/data/BestPayingDegrees.csv" df = pd.read_csv(url) df.head()

To create a Dot plot in plotly express we use the px.scatter() method.

import plotly.express as px fig = px.scatter(df, y='Degree', x='Starting Salary') fig.update_layout(title="Earnings By Degree") fig.show()

2 . Create a Dot Plot with Plotly Graph Objects –

To create a Dot plot with Plotly Graph Objects, we use the go.Scatter() method.

import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(y=df['Degree'], x=df['Starting Salary'],mode='markers', marker=)) fig.update_layout(title="Earnings By Degree", xaxis_title="Starting Salary", yaxis_title="Degree") fig.show()

Related Posts –

Share this:

Like this:

Leave a Reply Cancel reply

Newsletter

Tags

To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behavior or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.

The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.

The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.

The technical storage or access that is used exclusively for statistical purposes. The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.

The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.

Источник