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Machine Learning 
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# because the default is the line style '-', 
# nothing will be shown if we only pass in one point (3,2)
plt.plot(3, 2)

[<matplotlib.lines.Line2D at 0x226149a1ac0>]

png

# we can pass in '.' to plt.plot to indicate that we want
# the point (3,2) to be indicated with a marker '.'
plt.plot(3, 2, '.')

[<matplotlib.lines.Line2D at 0x22614a18730>]

png

# create a new figure
plt.figure()

# plot the point (3,2) using the circle marker
plt.plot(3, 2, 'o')

# get the current axes
ax = plt.gca()

# Set axis properties [xmin, xmax, ymin, ymax]
ax.axis([0,6,0,10])

(0.0, 6.0, 0.0, 10.0)

png

Scatterplots 

import numpy as np

x = np.array([1,2,3,4,5,6,7,8])
y = x

plt.figure()
plt.scatter(x, y) # similar to plt.plot(x, y, '.'), but the underlying child objects in th

<matplotlib.collections.PathCollection at 0x22614ae8cd0>

png

import numpy as np

x = np.array([1,2,3,4,5,6,7,8])
y = x

# create a list of colors for each point to have
# ['green', 'green', 'green', 'green', 'green', 'green', 'green', 'red']
colors = ['green']*(len(x)-1)
colors.append('red')

plt.figure()

# plot the point with size 100 and chosen colors
plt.scatter(x, y, s=100, c=colors)

<matplotlib.collections.PathCollection at 0x22614b5e9a0>

png

zip_generator = zip([1,2,3,4,5], [6,7,8,9,10])
# let's turn the data back into 2 lists
x, y = zip(*zip_generator) # This is like calling zip((1, 6), (2, 7), (3, 8), (4, 9), (5, 10))
print(x)
print(y)

# plot a data series in red using the first two elements of x and y
plt.scatter(x[:2], y[:2], s=100, c='red', label='type 1')
# plot a second data series in blue using the last three elements of x and y 
plt.scatter(x[2:], y[2:], s=100, c='blue', label='type 2')


# add a label to the x axis
plt.xlabel('The number of times the child kicked a ball')
# add a label to the y axis
plt.ylabel('The grade of the student')
# add a title
plt.title('Relationship between ball kicking and grades')

# add the legend to loc=4 (the lower right hand corner), also gets rid of the frame and adds a title
plt.legend(loc=4, frameon=False, title='Legend')


(1, 2, 3, 4, 5)
(6, 7, 8, 9, 10)





<matplotlib.legend.Legend at 0x22615b71f10>

png

Line plots 

import numpy as np

linear_data = np.array([1,2,3,4,5,6,7,8])
exponential_data = linear_data**2

plt.figure()
# plot the linear data and the exponential data
plt.plot(list(range(1,9)), linear_data, '-o', list(range(1,9)), exponential_data, '-o')

[<matplotlib.lines.Line2D at 0x22615c05fa0>,
 <matplotlib.lines.Line2D at 0x22615c130a0>]

png

# plot another series with a dashed red line
plt.plot([22,44,55], '--r')

[<matplotlib.lines.Line2D at 0x22615c77400>]

png

plt.figure()
plt.plot(list(range(1,9)), linear_data, '-o', list(range(1,9)), exponential_data, '-o')

# fill the area between the linear data and exponential data
plt.gca().fill_between(range(1, len(linear_data) + 1), 
                       linear_data, exponential_data, 
                       facecolor='blue', 
                       alpha=0.5)

<matplotlib.collections.PolyCollection at 0x22615cebb50>

png

import pandas as pd

plt.figure()
observation_dates = np.arange('2017-01-01', '2017-01-09', dtype='datetime64[D]')
observation_dates = list(map(pd.to_datetime, observation_dates)) # trying to plot a map will result in an error
plt.plot(observation_dates, linear_data, '-o',  observation_dates, exponential_data, '-o')

x = plt.gca().xaxis

# rotate the tick labels for the x axis
for item in x.get_ticklabels():
    item.set_rotation(45)

# adjust the subplot so the text doesn't run off the image
plt.subplots_adjust(bottom=0.25)

ax = plt.gca()
ax.set_xlabel('Date')
ax.set_ylabel('Units')
ax.set_title("Exponential ($x^2$) vs. Linear ($x$) performance")

Text(0.5, 1.0, 'Exponential ($x^2$) vs. Linear ($x$) performance')

png

Bar Charts 

plt.figure()
xvals = range(len(linear_data))
plt.bar(xvals, linear_data, width = 0.3)

new_xvals = []

# plot another set of bars, adjusting the new xvals to make up for the first set of bars plotted
for item in xvals:
    new_xvals.append(item+0.3)

plt.bar(new_xvals, exponential_data, width = 0.3 ,color='red')

from random import randint
linear_err = [randint(0,15) for x in range(len(linear_data))] 

# This will plot a new set of bars with errorbars using the list of random error values
plt.bar(xvals, linear_data, width = 0.3, yerr=linear_err)

<BarContainer object of 8 artists>

png

# stacked bar charts are also possible
plt.figure()
xvals = range(len(linear_data))
plt.bar(xvals, linear_data, width = 0.3, color='b')
plt.bar(xvals, exponential_data, width = 0.3, bottom=linear_data, color='r')

<BarContainer object of 8 artists>

png

# or use barh for horizontal bar charts
plt.figure()
xvals = range(len(linear_data))
plt.barh(xvals, linear_data, height = 0.3, color='b')
plt.barh(xvals, exponential_data, height = 0.3, left=linear_data, color='r')

<BarContainer object of 8 artists>

png

Subplots 

plt.figure()
# subplot with 1 row, 2 columns, and current axis is 1st subplot axes
plt.subplot(1, 2, 2)

linear_data = np.array([1,2,3,4,5,6,7,8])

plt.plot(linear_data, '-o')

exponential_data = linear_data**3 

# subplot with 1 row, 2 columns, and current axis is 2nd subplot axes
plt.subplot(1, 2, 2)
plt.plot(exponential_data, '-o')

# plot exponential data on 1st subplot axes
plt.subplot(1, 2, 1)
plt.plot(exponential_data, '-x')

[<matplotlib.lines.Line2D at 0x22615fdbeb0>]

png

plt.figure()
ax1 = plt.subplot(1, 2, 1)
plt.plot(linear_data, '-o')
# pass sharey=ax1 to ensure the two subplots share the same y axis
ax2 = plt.subplot(1, 2, 2, sharey=ax1)
plt.plot(exponential_data, '-x')

[<matplotlib.lines.Line2D at 0x22616092c10>]

png

plt.figure()
# the right hand side is equivalent shorthand syntax
plt.subplot(1,2,1) == plt.subplot(121)

True

png

# create a 3x3 grid of subplots
fig, ax = plt.subplots(3, 3, sharex=True, sharey=True)
# plot the linear_data on the 5th subplot axes 
ax[1][1].plot(linear_data, '-')

[<matplotlib.lines.Line2D at 0x22616337430>]

png

x = np.linspace(0, 2, 100)

fig = plt.figure(figsize=(10, 8))
fig.suptitle('A figure with subplots', fontsize=16)

ax_1 = fig.add_axes([0, 0, 1, 0.4])
ax_2 = fig.add_axes([0, 0.5, 0.4, 0.4])
ax_3 = fig.add_axes([0.5, 0.5, 0.5, 0.4])

ax_1.plot(x, x, label='linear')
ax_2.plot(x, x**2, label='quadratic')
ax_3.plot(x, x**3, label='cubic')

ax_1.set_xlabel('x')
ax_2.set_ylabel('y')
ax_3.set_title('Comparison')

ax_2.legend(loc='best')

<matplotlib.legend.Legend at 0x22616417100>

png

x = np.linspace(0, 2, 100)

fig = plt.figure(figsize=(10, 8))

ax_1 = fig.add_axes([0, 0, 1, 1])
ax_2 = fig.add_axes([0.1, 0.5, 0.4, 0.4])

ax_1.plot(x, x**2, label='quadratic', color='orange', linestyle='dashed')
ax_1.text(1.3, 1.5, r"$y=x^2$", fontsize=20, color="orange")
ax_1.grid(True)

ax_2.plot(x, x, label='linear')
ax_2.plot(x, x**2, label='quadratic')
ax_2.plot(x, x**3, label='cubic')

ax_1.set_xlabel('x')
ax_1.set_ylabel('y')

ax_1.legend(loc='best')
ax_2.legend(loc='best')

<matplotlib.legend.Legend at 0x2261493e280>

png

Histograms 

# create 2x2 grid of axis subplots
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)
axs = [ax1,ax2,ax3,ax4]

# draw n = 10, 100, 1000, and 10000 samples from the normal distribution and plot corresponding histograms
for n in range(0,len(axs)):
    sample_size = 10**(n+1)
    sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)
    axs[n].hist(sample)
    axs[n].set_title('n={}'.format(sample_size))

png

# repeat with number of bins set to 100
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex=True)
axs = [ax1,ax2,ax3,ax4]

for n in range(0,len(axs)):
    sample_size = 10**(n+1)
    sample = np.random.normal(loc=0.0, scale=1.0, size=sample_size)
    axs[n].hist(sample, bins=100)
    axs[n].set_title('n={}'.format(sample_size))

png

plt.figure()
Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
plt.scatter(X,Y)

<matplotlib.collections.PathCollection at 0x226171d6f10>

png

# use gridspec to partition the figure into subplots
import matplotlib.gridspec as gridspec

plt.figure()
gspec = gridspec.GridSpec(3, 3)

top_histogram = plt.subplot(gspec[0, 1:])
side_histogram = plt.subplot(gspec[1:, 0])
lower_right = plt.subplot(gspec[1:, 1:])

png

plt.figure()
gspec = gridspec.GridSpec(3, 3)

top_histogram = plt.subplot(gspec[0, 1:])
side_histogram = plt.subplot(gspec[1:, 0])
lower_right = plt.subplot(gspec[1:, 1:])

Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
lower_right.scatter(X, Y)
top_histogram.hist(X, bins=100)
s = side_histogram.hist(Y, bins=100, orientation='horizontal')

png

Heatmaps 

plt.figure()

Y = np.random.normal(loc=0.0, scale=1.0, size=10000)
X = np.random.random(size=10000)
_ = plt.hist2d(X, Y, bins=25)
plt.colorbar()

<matplotlib.colorbar.Colorbar at 0x22616902d30>

png

plt.hist2d(X, Y, bins=100)
plt.colorbar()
plt.show()

png

Pandas Visualization 

plt.style.use('seaborn-colorblind')

np.random.seed(123)

df = pd.DataFrame({'A': np.random.randn(365).cumsum(0), 
                   'B': np.random.randn(365).cumsum(0) + 20,
                   'C': np.random.randn(365).cumsum(0) - 20}, 
                  index=pd.date_range('1/1/2017', periods=365))
df.head()
A B C
2017-01-01 -1.085631 20.059291 -20.230904
2017-01-02 -0.088285 21.803332 -16.659325
2017-01-03 0.194693 20.835588 -17.055481
2017-01-04 -1.311601 21.255156 -17.093802
2017-01-05 -1.890202 21.462083 -19.518638 
df.plot(); # add a semi-colon to the end of the plotting call to suppress unwanted output

png

df.plot('A','B', kind = 'scatter');

png

You can also choose the plot kind by using the DataFrame.plot.kind methods instead of providing the kind keyword argument.

kind :

  • 'line' : line plot (default)
  • 'bar' : vertical bar plot
  • 'barh' : horizontal bar plot
  • 'hist' : histogram
  • 'box' : boxplot
  • 'kde' : Kernel Density Estimation plot
  • 'density' : same as ‘kde’
  • 'area' : area plot
  • 'pie' : pie plot
  • 'scatter' : scatter plot
  • 'hexbin' : hexbin plot
df.plot.scatter('A', 'C', c='B', s=df['B'], colormap='viridis')

<AxesSubplot:xlabel='A', ylabel='C'>

png

df.plot.box();

png

df.plot.hist(alpha=0.7);

png

Kernel density estimation plots are useful for deriving a smooth continuous function from a given sample.

df.plot.kde()

<AxesSubplot:ylabel='Density'>

png

Seaborn 

import seaborn as sns

np.random.seed(1234)

v1 = pd.Series(np.random.normal(0,10,1000), name='v1')
v2 = pd.Series(2*v1 + np.random.normal(60,15,1000), name='v2')

# plot a kernel density estimation over a stacked barchart
plt.figure()
sns.histplot([v1, v2]);
v3 = np.concatenate((v1,v2))
sns.kdeplot(v3);

#! ???? why kde does not showing

png

sns.distplot(v3, hist_kws={'color': 'Teal'}, kde_kws={'color': 'Navy'});

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

png

sns.jointplot(v1, v2, alpha=0.4);

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

png

sns.jointplot(v1, v2, kind='hex');

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

png

sns.set_style('white')

sns.jointplot(v1, v2, kind='kde', space=0);

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

png

iris = pd.read_csv('iris.csv')
iris.head()
sepal.length sepal.width petal.length petal.width variety
0 5.1 3.5 1.4 0.2 Setosa
1 4.9 3.0 1.4 0.2 Setosa
2 4.7 3.2 1.3 0.2 Setosa
3 4.6 3.1 1.5 0.2 Setosa
4 5.0 3.6 1.4 0.2 Setosa 
sns.pairplot(iris, hue='variety', diag_kind='kde', size=2);

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.
  warnings.warn(msg, UserWarning)

png

plt.figure(figsize=(8,6))
plt.subplot(121)
sns.swarmplot('variety', 'petal.length', data=iris);
plt.subplot(122)
sns.violinplot('variety', 'petal.length', data=iris);

c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 8.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
c:\Users\LENOVO\AppData\Local\Programs\Python\Python39\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

png

tips = sns.load_dataset('tips')

sns.barplot(x='sex', y='total_bill', data=tips)

<AxesSubplot:xlabel='sex', ylabel='total_bill'>

png

sns.boxplot(x='time', y='total_bill', data=tips)

<AxesSubplot:xlabel='time', ylabel='total_bill'>

png

sns.violinplot(x='time', y='total_bill', data=tips, hue='sex', split=True)

<AxesSubplot:xlabel='time', ylabel='total_bill'>

png

sns.lmplot(x='total_bill', y='tip', data=tips, hue='sex', height=8, aspect=1)

<seaborn.axisgrid.FacetGrid at 0x226167563d0>

png

sns.relplot(data=tips, x="total_bill", y="tip", hue="day", col="time", row="sex")

<seaborn.axisgrid.FacetGrid at 0x2261c488370>

png

tips_mean = tips.total_bill.mean()
tips_sd = tips.total_bill.std()

ax = sns.displot(data=tips, x="total_bill", kde=True, height=8)

plt.axvline(x=tips_mean, color='black', linestyle='dashed')

plt.axvline(x=tips_mean + tips_sd, color='red', linestyle='dotted')
plt.axvline(x=tips_mean - tips_sd, color='red', linestyle='dotted')

plt.title('$\mu = {}$ | $\sigma = {}$'.format(round(tips_mean, 2), round(tips_sd, 2)))

Text(0.5, 1.0, '$\\mu = 19.79$ | $\\sigma = 8.9$')

png

sns.jointplot(x=tips['total_bill'], y=tips['tip'], height=10)

<seaborn.axisgrid.JointGrid at 0x2261c61d6d0>

png

sns.pairplot(tips, hue='sex', height=3)

<seaborn.axisgrid.PairGrid at 0x2261c748bb0>

png

Bad Visualization

  1. inconsistent scale
x_1 = np.linspace(0, 1, 80, endpoint=False)
x_2 = np.linspace(1, 2, 20)
x = np.concatenate((x_1, x_2))
plt.figure(figsize=(10, 8), dpi=80)
plt.plot(x, label='linear')
plt.plot(x**2, label='quadratic')
plt.plot(x**3, label='cubic')
plt.xlabel('x label')
plt.xticks([0, 20, 40, 60, 80, 90, 100], [0, 0.25, 0.5, 0.75, 1, 1.5, 2])
plt.ylabel('y label')
plt.title("Simple Plot")
plt.grid(True)
plt.legend()

<matplotlib.legend.Legend at 0x2261aaf5d60>

png

This is broken. in this plot the range from 1 to 2 on the x-axis is denser than the same range from 0 to 1. this easily leads you to misinterpretation.

Don’t get it wrong, it is advised to use other scales rather than linear space when it’s beneficial. just be consistent and declare that the plot isn’t drawn on linear space.

x = np.linspace(0, 5, 100)
fig, axes = plt.subplots(1, 2, figsize=(10,5))
      
axes[0].plot(x, x, label='linear')
axes[0].plot(x, x**2, label='quadratic')
axes[0].plot(x, x**3, label='cubic')
axes[0].plot(x, np.exp(x), label='cubic')
axes[0].set_title("Normal scale")
axes[0].grid(True)

axes[1].plot(x, x, label='linear')
axes[1].plot(x, x**2, label='quadratic')
axes[1].plot(x, x**3, label='cubic')
axes[1].plot(x, np.exp(x), label='cubic')
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale")
axes[1].grid(True)

axes[1].legend(loc='best')

<matplotlib.legend.Legend at 0x2261acd2040>

png

  1. Too many variables
diamonds = sns.load_dataset("diamonds")

fig = plt.figure(figsize=(10,8))
ax = diamonds.color.value_counts().plot(kind='pie')

png

Too many variables, especially in the pie chart make it useless. use other categories or bar charts.

  1. Wrong type of chart
flights = sns.load_dataset("flights")
flights_1957 = flights[flights.year == 1957]

fig, axe = plt.subplots(1, figsize=(10,5))
axe.scatter(list(flights_1957.month), list(flights_1957.passengers), color='orange')
axe.plot(list(flights_1957.month), list(flights_1957.passengers), linestyle='dashed')

[<matplotlib.lines.Line2D at 0x2261be13d00>]

png

Data is not continuous. so we’re not allowed to draw a line between two consecutive months.

References

  • datatoviz website
  • Applied data science with python by Michigan university, Coursera
  • Python for data analysis book by O’Reilly
  • Pandas Bootcamp by Udemy
  • https://sk7w4tch3r.github.io/CS-SBU-DataScience/

Machine Learning - Computer Science Faculty of Shahid Beheshti University. Winter 2023 - Contact us at saeidcheshmi@outlook.com