How to create a “dot plot” in Matplotlib? (not a scatter plot)

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to create a “dot plot” in Matplotlib? (not a scatter plot) in Python. So Here I am Explain to you all the possible Methods here.

How to create a “dot plot” in Matplotlib? (not a scatter plot)?

1. How to create a “dot plot” in Matplotlib? (not a scatter plot)?

You may create your dot plot by calculating the histogram and plotting a scatter plot of all possible points, the color of the points being white if they exceed the number given by the histogram.

2. create a “dot plot” in Matplotlib? (not a scatter plot)

You may create your dot plot by calculating the histogram and plotting a scatter plot of all possible points, the color of the points being white if they exceed the number given by the histogram.

Method 1

Supoose you have some data that would produce a histogram like the following,

import numpy as np; np.random.seed(13)
import matplotlib.pyplot as plt

data = np.random.randint(0,12,size=72)

plt.hist(data, bins=np.arange(13)-0.5, ec="k")

plt.show()

You may create your dot plot by calculating the histogram and plotting a scatter plot of all possible points, the color of the points being white if they exceed the number given by the histogram.

import numpy as np; np.random.seed(13)
import matplotlib.pyplot as plt

data = np.random.randint(0,12,size=72)
bins = np.arange(13)-0.5

hist, edges = np.histogram(data, bins=bins)

y = np.arange(1,hist.max()+1)
x = np.arange(12)
X,Y = np.meshgrid(x,y)

plt.scatter(X,Y, c=Y<=hist, cmap="Greys")

plt.show()

Alternatively you may set the unwanted points to nan,

Y = Y.astype(np.float)
Y[Y>hist] = np.nan

plt.scatter(X,Y)

Method 2

Pass your dataset to this function:

def dot_diagram(dataset):
values, counts = np.unique(dataset, return_counts=True)
data_range = max(values)-min(values)
width = data_range/2 if data_range<30 else 15
height = max(counts)/3 if data_range<50 else max(counts)/4
marker_size = 10 if data_range<50 else np.ceil(30/(data_range//10))
fig, ax = plt.subplots(figsize=(width, height))
for value, count in zip(values, counts):
ax.plot([value]*count, list(range(count)), marker='o', color='tab:blue',
ms=marker_size, linestyle='')
for spine in ['top', 'right', 'left']:
ax.spines[spine].set_visible(False)
ax.yaxis.set_visible(False)
ax.set_ylim(-1, max(counts))
ax.set_xticks(range(min(values), max(values)+1))