Data Visualization | Matplotlib | Python

January 17, 2021

Matplotlib Data Visualization | Code Snippets

Installing `matplotlib`

matplotlib is a third party library and is not part of standard Python library
It can be installed with pip utility
```
pip install matplotlib
```
matplotlib is available directly in distributions such as Anaconda, and WinPython

upgrading `matplotlib`

pip install --upgrade matplotlib

loading `matplotlib` using `import`

import matplotlib

`matplotlib` version

print(matplotlib.__version__)

Parts of a Matplotlib Figure

Figure : Whole area chosen for plotting
Axes : Area where data is plotted
Axis : Number-like objects, which define graph limits
Artist : Every element on the figure is an artist

Creating a Figure

import matplotlib.pyplot as plt
fig = plt.figure()

Executing the above code doesn’t display any figure.
You should explicitly tell pyplot to display it.

import matplotlib.pyplot as plt
fig = plt.figure()
plt.show()

Output:

<Figure size 432x288 with 0 Axes>

The output simply shows the figure object.
You will be able to view a picture only when a figure contains at least one Axes element.

Creating Axes

An Axes is the region of the figure, available for plotting data.
An Axes object is associated with only one Figure.
A Figure can contain one or more number of Axes elements.
An Axes contains two Axis objects in case of 2D plots and three Axis objects in case of 3D plots.
An Axes can be added to a figure using add_subplot methods.

add_subplot(nrows, ncols, index)

When these argument values are less than 10, they all can be clubbed and passed as a single three-digit number.
add_subplot(1, 1, 1) and add_subplot(111) are same

generate a figure with one `axes`

fig = plt.figure()
ax = fig.add_subplot(111)
plt.show()

The default width and height of a figure are 6 and 4 inches respectively.
You can change the size of a figure using figsize argument.
the expression fig = plt.figure(figsize=(8,6)) generates a figure having 8 inches width and 6 inches height.

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
plt.show()

Setting Title and Axis Labels

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='My First Plot',
      xlabel='X-Axis', ylabel='Y-Axis',
      xlim=(0, 5), ylim=(0,10))
plt.show()

Setting an attribute (`set_<paramter_name>`)

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set_title("My First Plot")
ax.set_xlabel("X-Axis"); ax.set_ylabel('Y-Axis')
ax.set_xlim([0,5]); ax.set_ylim([0,10])
plt.show()

Plotting Data (`plot` function)

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='My First Plot',
      xlabel='X-Axis', ylabel='Y-Axis',
      xlim=(0, 5), ylim=(0,10))
x = [1, 2, 3, 4]; y = [2, 4, 6, 8]
plt.plot(x, y)
plt.show()

implicit vs explicit

Plotting data or setting attributes can also be done by calling functions like plot, and title directly on plt.
This would plot the data on axes, which is active currently.
However, Explicit is better than implicit. Hence prefer former style of plotting.

fig = plt.figure(figsize=(8,6))

x = [1, 2, 3, 4]; y = [2, 4, 6, 8]
plt.plot(x, y)
plt.title('My First Plot')
plt.xlabel('X-Axis'); plt.ylabel('Y-Axis')
plt.xlim(0,5); plt.ylim(0,10)
plt.plot(x, y)
plt.show()

# Note this is implicit plotting example. 
# Explicit is better than implicit. Hence prefer the formet styple of plotting.

Adding a Legend

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='My First Plot',
      xlabel='X-Axis', ylabel='Y-Axis',
      xlim=(0, 5), ylim=(0,10))
x = [1, 2, 3, 4]; y = [2, 4, 6, 8]
plt.plot(x, y, label='linear-growth')
plt.legend()
plt.show()

Line Plot

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Daily Temperature in Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
ax.plot(days, temp)
plt.show()

Common Parameters of ‘plot’ Function

color: Sets the color of the line.
linestyle: Sets the line style, e.g., solid, dashed, etc.
linewidth: Sets the thickness of a line.
marker: Chooses a marker for data points, e.g., circle, triangle, etc.
markersize: Sets the size of the chosen marker.
label: Names the line, which will come in legend.

Setting ‘plot’ Parameters

A green dashed line, having width 3

ax.plot(days, temp, color='green', linestyle='--', linewidth=3)

Marking Data Points (`marker` argument)

Data points are made visible using marker argument.
plot a green colored line with data points marked in circles

ax.plot(days, temp, color='green', marker='o')

Plotting multiple lines in Line plot

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
location1_temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
location2_temp = [26.4, 26.8, 26.1, 26.4, 27.5, 27.3, 26.9, 26.8, 27.0]
ax.plot(days, location1_temp, color='green', marker='o', linewidth=3)
ax.plot(days, location2_temp, color='red', marker='o', linewidth=3)
plt.show()

Scatter plot (`scatter` function)

scatter plot only marks the data points with the chosen marker.

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
ax.scatter(days, temp)
plt.show()

Common Parameters of ‘scatter’

c: Sets color of markers.
s: Sets size of markers.
marker: Selects a marker. e.g: circle, triangle, etc
edgecolor: Sets the color of lines on edges of markers.
Parameters c and s can take a list of values.
If the number of values is less than the number of data points considered, then the list is repeated.
plot green colored circles of size 60, with black edges

ax.scatter(days, temp, marker='o', c=['green'], s=[60], edgecolor='black')

Example:

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
ax.scatter(days, temp, marker='o', c=['green'], s=[60], edgecolor='black')
plt.show()

Scatter Plot Using ‘plot’

plot function can also create a scatter plot when linestyle is set to none, and a marker is chosen

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
ax.plot(days, temp, marker='o', linestyle='none')
plt.show()

Sine wave plot

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)

t = np.linspace(0.0, 2.0, num=200)
v = np.sin(2.5*np.pi*t)

ax.plot(t,v, color='red', label='sin(t)')
ax.set_xlabel('Time (seconds)')
ax.set_ylabel('Voltage (mV)')
ax.set_title("Sine Wave")
ax.set_xlim([0,2])
ax.set_ylim([-1,1])

# Mark major ticks on X-Axis at 0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0.
ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0])
# Mark major ticks on Y-Axis at -1, 0, and 1.
ax.set_yticks([-1, 0, 1])

ax.grid(linestyle='--')
ax.legend()

plt.show()

Multi Curve Plot

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)

x = np.linspace(0.0, 5.0, num=20)
y1 = x
y2 = x**2
y3 = x**3

ax.plot(x, y1, color='red', marker='o', label='y = x')
ax.plot(x, y2, color='green', marker='s', label='y = x**2')
ax.plot(x, y3, color='blue', marker='^', label='y = x**3')


ax.set_xlabel('X')
ax.set_ylabel('f(X)')

ax.set_title("Linear, Quadratic, & Cubic Equations")
ax.legend()

plt.show()

Scatter Plot (Cars sold by a company)

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)

s = [50, 60, 55, 50, 70, 65, 75, 65, 80, 90, 93, 95]
# print(s)
months = list(range(1, 13))
# print(months)

ax.scatter(months, s, c=['green'])

ax.set_xlim([0,13])
ax.set_ylim([20,100])

ax.set_xticks([1, 3, 5, 7, 9, 11])
ax.set_xticklabels(['Jan', 'Mar', 'May', 'Jul', 'Sep', 'Nov'])

ax.set_xlabel('Months')
ax.set_ylabel('No. of Cars Sold')

ax.set_title("Cars Sold by Company 'X' in 2017")

plt.show()

Bar Plot using `bar`

Common Parameters of ‘bar’

color: Sets the color of bars.
edgecolor: Sets the color of the border line of bars.
width: Sets the width of bars
align: Aligns the bars w.r.t x-coordinates
label: Sets label to a bar, appearing in legend.

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales',
      xlabel='Quarter', ylabel='Sales (in millions)')
quarters = [1, 2, 3]
sales_2017 = [25782, 35783, 36133]
ax.bar(quarters, sales_2017)
ax.set_xticks(quarters)
ax.set_xticklabels(['Q1-2017', 'Q2-2017', 'Q3-2017'])
plt.show()

Draw Red color bars with black edges

ax.bar(quarters, sales_2017, color='red', width=0.6, edgecolor='black')

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales',
      xlabel='Quarter', ylabel='Sales (in millions)')
quarters = [1, 2, 3]
sales_2017 = [25782, 35783, 36133]
ax.bar(quarters, sales_2017, color='red', width=0.6, edgecolor='black')
ax.set_xticks(quarters)
ax.set_xticklabels(['Q1-2017', 'Q2-2017', 'Q3-2017'])
plt.show()

Plotting Multiple Groups

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales',
      xlabel='Quarter', ylabel='Sales (in millions)')
quarters = [1, 2, 3]
x1_index = [0.8, 1.8, 2.8]; x2_index = [1.2, 2.2, 3.2]
sales_2016 = [28831, 30762, 32178]; sales_2017 = [25782, 35783, 36133]
ax.bar(x1_index, sales_2016, color='yellow', width=0.4, edgecolor='black', label='2016')
ax.bar(x2_index, sales_2017, color='red', width=0.4, edgecolor='black', label='2017')
ax.set_xticks(quarters)
ax.set_xticklabels(['Q1', 'Q2', 'Q3'])
ax.legend()
plt.show()

Barplot Using ‘barh’

barh draws the bars horizontally as shown in above image.
height parameter is used to adjust the height of each bar.
Horizontal bar plots are used while comparing values of one category at a time.

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales',
      xlabel='Sales (in millions)', ylabel='Quarter')
quarters = [1, 2, 3]
sales_2017 = [25782, 35783, 36133]
ax.barh(quarters, sales_2017, height=0.6, color='red')
ax.set_yticks(quarters)
ax.set_yticklabels(['Q1-2017', 'Q2-2017', 'Q3-2017'])
plt.show()

Pie Plot

fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales')
sales_2017 = [25782, 35783, 36133]
ax.pie(sales_2017)
plt.show()

Common Parameters of ‘pie’

colors: Sets the colors of portions.
labels: Sets the labels of portions.
startangle: Sets the start angle at which portion drawing starts.
autopct: Sets the percentage display format of an area, covering portions.

fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(111)
ax.set(title='Avg. Quarterly Sales')
sales_2017 = [25782, 35783, 36133]
ax.pie(sales_2017)
plt.show()

Create Bar Plot iris sepal length

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)

species = ['setosa', 'versicolor', 'viriginica']
index = [0.2, 1.2, 2.2]
sepal_len = [5.01, 5.94, 6.59]

ax.bar(index, sepal_len, color='red', width=0.5, edgecolor='black')

ax.set(title='Mean Sepal Length of Iris Species',
      xlabel='Species', ylabel='Sepal Length (cm)')

ax.set_xlim([0,3])
ax.set_ylim([0,7])

ax.set_xticks([ 0.45, 1.45, 2.45])
ax.set_xticklabels(['setosa', 'versicolor', 'viriginica'])

plt.show()

Bar plot iris measurements

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)

sepal_len = [5.01, 5.94, 6.59]
sepal_wd = [3.42, 2.77, 2.97]
petal_len = [1.46, 4.26, 5.55]
petal_wd = [0.24, 1.33, 2.03]
species = ['setosa', 'versicolor', 'viriginica']
species_index1 = [0.7, 1.7, 2.7]
species_index2 = [0.9, 1.9, 2.9]
species_index3 = [1.1, 2.1, 3.1]
species_index4 = [1.3, 2.3, 3.3]

ax.bar(species_index1, sepal_len, color='c', width=0.2, edgecolor='black', label='Sepal Length')
ax.bar(species_index2, sepal_wd, color='m', width=0.2, edgecolor='black', label='Sepal Width')
ax.bar(species_index3, petal_len, color='y', width=0.2, edgecolor='black', label='Petal Length')
ax.bar(species_index4, petal_wd, color='orange', width=0.2, edgecolor='black', label='Petal Width')

ax.set(title='Mean Measurements of Iris Species',
      xlabel='Species', ylabel='Iris Measurements (cm)')

ax.set_xlim([0.5,3.7])
ax.set_ylim([0,10])

ax.set_xticks([1.1, 2.1, 3.1])
ax.set_xticklabels(['setosa', 'versicolor', 'viriginica'])

ax.legend()

plt.show()

hbar plot of iris petal length

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)

species = ['setosa', 'versicolor', 'viriginica']
index = [0.2, 1.2, 2.2]
petal_len = [1.46, 4.26, 5.55]

ax.barh(index, petal_len, height=0.5, color='c', edgecolor='black')

ax.set(title='Mean Petal Length of Iris Species',
      xlabel='Petal Length (cm)', ylabel='Species')

ax.set_yticks([0.45, 1.45, 2.45])
ax.set_yticklabels(['setosa', 'versicolor', 'viriginica'])

plt.show()

Histogram Using ‘hist’

import numpy as np
np.random.seed(100)
x = 60 + 10*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Distribution of Student's Percentage",
      ylabel='Count', xlabel='Percentage')
ax.hist(x)
plt.show()

Common Parameters of ‘hist’

color: Sets the color of bars.
bins: Sets the number of bins to be used.
normed: Sets to True where bins display fraction and not the count.

import numpy as np
np.random.seed(100)
x = 60 + 10*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Distribution of Student's Percentage",
      ylabel='Proportion', xlabel='Percentage')
ax.hist(x, color='blue', bins=30, normed=True)
plt.show()

Boxplot Using ‘boxplot’

import numpy as np
np.random.seed(100)
x = 50 + 10*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Box plot of Student's Percentage",
      xlabel='Class', ylabel='Percentage')
ax.boxplot(x)
plt.show()

Common Parameters of ‘boxplot’

labels: Sets the labels for box plots.
notch: Sets to True if notches need to be created around the median.
bootstrap: Number set to indicate that notches around the median are bootstrapped.
vert: Sets to False for plotting Box plots horizontally.
Box plot of Student Percentages can be redrawn by setting notch, bootstrap and labels using the below-shown expression.

ax.boxplot(x, labels=['A'], notch=True, bootstrap=10000)

import numpy as np
np.random.seed(100)
x = 50 + 10*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Box plot of Student's Percentage",
      xlabel='Class', ylabel='Percentage')
ax.boxplot(x, labels=['A'], notch=True, bootstrap=10000)
plt.show()

Multiple boxplots

import numpy as np
np.random.seed(100)
x = 50 + 10*np.random.randn(1000)
y = 70 + 25*np.random.randn(1000)
z = 30 + 5*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Box plot of Student's Percentage",
      xlabel='Class', ylabel='Percentage')
ax.boxplot([x, y, z], labels=['A', 'B', 'C'], notch=True, bootstrap=10000)
plt.show()

Horizontal boxplots

import numpy as np
np.random.seed(100)
x = 50 + 10*np.random.randn(1000)
y = 70 + 25*np.random.randn(1000)
z = 30 + 5*np.random.randn(1000)
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
ax.set(title="Box plot of Student's Percentage",
      xlabel='Percentage', ylabel='Class')
ax.boxplot([x, y, z], labels=['A', 'B', 'C'], vert=False, notch=True, bootstrap=10000)
plt.show()

Histogram normal distribution

import numpy as np


fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)

np.random.seed(100)
x1 = 25 + 3.0*np.random.randn(1000)

# x, color='blue', bins=30, normed=True
ax.hist(x1, bins=30)
ax.set(title="Histogram of a Single Dataset",
      ylabel='Bin Count', xlabel='X1')

plt.show()

Box Plot of 4 normal distributions

import numpy as np

fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)

np.random.seed(100)
x1 = 25 + 3.0*np.random.randn(1000)
x2 = 35 + 5.0*np.random.randn(1000)
x3 = 55 + 10.0*np.random.randn(1000)
x4 = 45 + 3.0*np.random.randn(1000)

labels = ['X1', 'X2', 'X3', 'X4']
ax.boxplot([x1, x2, x3, x4], labels=labels, notch=True, patch_artist=True, sym='+')

ax.set(title="Box plot of Multiple Datasets",
      xlabel='Dataset', ylabel='Value')
          
plt.show()

Matplotlib Styles

matplotlib.pyplot comes with a lot of styles. Based on the chosen style, the display of figure changes.
You can view various styles available in pyplot by running the following commands.

import matplotlib.pyplot as plt
print(plt.style.available)

Output:

['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']

Using a Style

A specific style can be invoked with either of the two expressions shown below.

plt.style.use('ggplot')

or

plt.style.context('ggplot')
Using the later expression with a keyword, with is recommended.

Matplotlib `ggplot` style

with plt.style.context('ggplot'):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
    days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
    temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
    ax.plot(days, temp, color='green', linestyle='--', linewidth=3)
    plt.show()

Composing Styles

Multiple style sheets can be used together in matplotlib.

with plt.style.context(['dark_background', 'seaborn-poster']):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
    days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
    temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
    ax.plot(days, temp, color='green', linestyle='--', linewidth=3)
    plt.show()

Creating a Custom Style

A style sheet is a text file having extension .mplstyle.
All custom style sheets are placed in a folder, stylelib, present in the config directory of matplotlib.
Use the below expression for knowing the Config folder.

import matplotlib
print(matplotlib.get_configdir())

Now, create a file mystyle.mplstyle with the below-shown contents and save it in the folder <matplotlib_configdir/stylelib/.

axes.titlesize : 24
axes.labelsize : 20
lines.linewidth : 8
lines.markersize : 10
xtick.labelsize : 16
ytick.labelsize : 16

Reload the matplotlib library with the subsequent expression.

matplotlib.style.reload_library()

A custom style can also be used similar to builtin styles, after reloading the style library.
use mystyle along with dark_background.

with plt.style.context(['dark_background', 'mystyle']):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    ax.set(title='Avg. Daily Temperature of Jan 2018',
      xlabel='Day', ylabel='Temperature (in deg)',
      xlim=(0, 30), ylim=(25, 35))
    days = [1, 5, 8, 12, 15, 19, 22, 26, 29]
    temp = [29.3, 30.1, 30.4, 31.5, 32.3, 32.6, 31.8, 32.4, 32.7]
    ax.plot(days, temp, color='green', linestyle='--', linewidth=3)
    plt.show()

`matplotlibrc` file

matplotlib uses all the settings specified in matplotlibrc file.
These settings are known as rc settings or rc parameters.
For customization, rc settings can be altered in the file or interactively.
The location of active matplotlibrc file used by matplotlib can be found with below expression.

import matplotlib
matplotlib.matplotlib_fname()

Matplotlib rcParams

All rc settings, present in matplotlibrc file are stored in a dictionary named matplotlib.rcParams.
Any settings can be changed by editing values of this dictionary.
For example, if you want to change linewidth and color, the following expressions can be used.

import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.color'] = 'r'

Generate plot with style

with plt.style.context('ggplot'):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    
    sepal_len = [5.01, 5.94, 6.59]
    sepal_wd = [3.42, 2.77, 2.97]
    petal_len = [1.46, 4.26, 5.55]
    petal_wd = [0.24, 1.33, 2.03]
    species = ['setosa', 'versicolor', 'viriginica']
    species_index1 = [0.7, 1.7, 2.7]
    species_index2 = [0.9, 1.9, 2.9]
    species_index3 = [1.1, 2.1, 3.1]
    species_index4 = [1.3, 2.3, 3.3]
    
    ax.bar(species_index1, sepal_len, width=0.2, label='Sepal Length')
    ax.bar(species_index2, sepal_wd, width=0.2, edgecolor='black', label='Sepal Width')
    ax.bar(species_index3, petal_len, width=0.2, edgecolor='black', label='Petal Length')
    ax.bar(species_index4, petal_wd, width=0.2, edgecolor='black', label='Petal Width')

    ax.set(title='Mean Measurements of Iris Species',
          xlabel='Species', ylabel='Iris Measurements (cm)')

    ax.set_xlim([0.5,3.7])
    ax.set_ylim([0,10])

    ax.set_xticks([1.1, 2.1, 3.1])
    ax.set_xticklabels(['setosa', 'versicolor', 'viriginica'])

    ax.legend()

    plt.show()

with plt.style.context('seaborn-colorblind'):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    
    sepal_len = [5.01, 5.94, 6.59]
    sepal_wd = [3.42, 2.77, 2.97]
    petal_len = [1.46, 4.26, 5.55]
    petal_wd = [0.24, 1.33, 2.03]
    species = ['setosa', 'versicolor', 'viriginica']
    species_index1 = [0.7, 1.7, 2.7]
    species_index2 = [0.9, 1.9, 2.9]
    species_index3 = [1.1, 2.1, 3.1]
    species_index4 = [1.3, 2.3, 3.3]
    
    ax.bar(species_index1, sepal_len, width=0.2, label='Sepal Length')
    ax.bar(species_index2, sepal_wd, width=0.2, edgecolor='black', label='Sepal Width')
    ax.bar(species_index3, petal_len, width=0.2, edgecolor='black', label='Petal Length')
    ax.bar(species_index4, petal_wd, width=0.2, edgecolor='black', label='Petal Width')

    ax.set(title='Mean Measurements of Iris Species',
          xlabel='Species', ylabel='Iris Measurements (cm)')

    ax.set_xlim([0.5,3.7])
    ax.set_ylim([0,10])

    ax.set_xticks([1.1, 2.1, 3.1])
    ax.set_xticklabels(['setosa', 'versicolor', 'viriginica'])

    ax.legend()

    plt.show()

with plt.style.context('grayscale'):
    fig = plt.figure(figsize=(8,6))
    ax = fig.add_subplot(111)
    
    sepal_len = [5.01, 5.94, 6.59]
    sepal_wd = [3.42, 2.77, 2.97]
    petal_len = [1.46, 4.26, 5.55]
    petal_wd = [0.24, 1.33, 2.03]
    species = ['setosa', 'versicolor', 'viriginica']
    species_index1 = [0.7, 1.7, 2.7]
    species_index2 = [0.9, 1.9, 2.9]
    species_index3 = [1.1, 2.1, 3.1]
    species_index4 = [1.3, 2.3, 3.3]
    
    ax.bar(species_index1, sepal_len, width=0.2, label='Sepal Length')
    ax.bar(species_index2, sepal_wd, width=0.2, edgecolor='black', label='Sepal Width')
    ax.bar(species_index3, petal_len, width=0.2, edgecolor='black', label='Petal Length')
    ax.bar(species_index4, petal_wd, width=0.2, edgecolor='black', label='Petal Width')

    ax.set(title='Mean Measurements of Iris Species',
          xlabel='Species', ylabel='Iris Measurements (cm)')

    ax.set_xlim([0.5,3.7])
    ax.set_ylim([0,10])

    ax.set_xticks([1.1, 2.1, 3.1])
    ax.set_xticklabels(['setosa', 'versicolor', 'viriginica'])

    ax.legend()

    plt.show()

Creating Suplots

subplot(nrows, ncols, index)
# 'index' is the position in a virtual grid with 'nrows' and 'ncols'
# 'index' number varies from 1 to `nrows*ncols`.

subplot creates the Axes object at index position and returns it.

fig = plt.figure(figsize=(10,8))
axes1 = plt.subplot(2, 2, 1, title='Plot1')
axes2 = plt.subplot(2, 2, 2, title='Plot2')
axes3 = plt.subplot(2, 2, 3, title='Plot3')
axes4 = plt.subplot(2, 2, 4, title='Plot4')
plt.show()

The above shown code creates a figure with four subplots, having two rows and two columns.
The third argument, index value varied from 1 to 4, and respective subplots are drawn in row-major order.
create a figure with three subplots, where the first subplot spans all columns of first row

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

axes1 = plt.subplot(2, 2, (1,2), title='Plot1')
axes1.set_xticks([]); axes1.set_yticks([])

axes2 = plt.subplot(2, 2, 3, title='Plot2')
axes2.set_xticks([]); axes2.set_yticks([])

axes3 = plt.subplot(2, 2, 4, title='Plot3')
axes3.set_xticks([]); axes3.set_yticks([])

plt.show()

Subplots Using ‘GridSpec’

GridSpec class of matplotlib.gridspec can also be used to create Subplots.
Initially, a grid with given number of rows and columns is set up.
Later while creating a subplot, the number of rows and columns of grid, spanned by the subplot are provided as inputs to subplot function.

import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10,8))
gd = gridspec.GridSpec(2,2)

axes1 = plt.subplot(gd[0,:],title='Plot1')
axes1.set_xticks([]); axes1.set_yticks([])
axes2 = plt.subplot(gd[1,0])
axes2.set_xticks([]); axes2.set_yticks([])
axes3 = plt.subplot(gd[1,-1])
axes3.set_xticks([]); axes3.set_yticks([])

plt.show()

Creating a Complex Layout

fig = plt.figure(figsize=(12,10))
gd = gridspec.GridSpec(3,3)

axes1 = plt.subplot(gd[0,:],title='Plot1')
axes1.set_xticks([]); axes1.set_yticks([])

axes2 = plt.subplot(gd[1,:-1], title='Plot2')
axes2.set_xticks([]); axes2.set_yticks([])

axes3 = plt.subplot(gd[1:, 2], title='Plot3')
axes3.set_xticks([]); axes3.set_yticks([])

axes4 = plt.subplot(gd[2, :-1], title='Plot4')
axes4.set_xticks([]); axes4.set_yticks([])

plt.show()

Create multiple plots

import numpy as np

t = np.arange(0.0, 5.0, 0.01)
s1 = np.sin(2*np.pi*t)
s2 = np.sin(4*np.pi*t)

fig = plt.figure(figsize=(8,6))
axes1 = plt.subplot(2, 1, 1, title='Sin(2*pi*x)')
axes1.plot(t, s1)

axes2 = plt.subplot(2, 1, 2, title='Sin(4*pi*x)', sharex=axes1, sharey=axes1)
axes2.plot(t, s2)

plt.show()

import numpy as np


np.random.seed(1000)
x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)

fig = plt.figure(figsize=(8,6))
axes1 = plt.subplot(2, 2, 1, title='Scatter plot with Upper Traingle Markers')
axes1.scatter(x,y, s=80, c=z, marker='^')
axes1.set_xticks([0.0, 0.4, 0.8, 1.2])
axes1.set_yticks([-0.2, 0.2, 0.6, 1.0])

axes2 = plt.subplot(2, 2, 2, title='Scatter plot with Plus Markers')
axes2.scatter(x, y, s=80, c=z, marker='+')
axes2.set_xticks([0.0, 0.4, 0.8, 1.2])
axes2.set_yticks([-0.2, 0.2, 0.6, 1.0])

axes3 = plt.subplot(2, 2, 3, title='Scatter plot with Circle Markers')
axes3.scatter(x, y, s=80, c=z, marker='o')
axes3.set_xticks([0.0, 0.4, 0.8, 1.2])
axes3.set_yticks([-0.2, 0.2, 0.6, 1.0])

axes4 = plt.subplot(2, 2, 4, title='Scatter plot with Diamond Markers')
axes4.scatter(x, y, s=80, c=z, marker='d')
axes4.set_xticks([0.0, 0.4, 0.8, 1.2])
axes4.set_yticks([-0.2, 0.2, 0.6, 1.0])

plt.tight_layout()


plt.show()

import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt

x = np.arange(1, 101)
y1 = x
y2 = x**2
y3 = x**3

fig = plt.figure(figsize=(8,6))
g = gridspec.GridSpec(2,2)

axes1 = plt.subplot(g[0,0], title='y = x')
axes1.plot(x, y1)

axes2 = plt.subplot(g[1,0], title='y = x**2')
axes2.plot(x, y2)

axes3 = plt.subplot(g[:,1], title='y = x**3')
axes3.plot(x, y3)

plt.tight_layout()

plt.show()

Adding text

Text can be added to any part of the figure using text function.

Syntax

text(x, y, s)
# 'x', 'y' represent x and y coordinates of a position.
# 's' is the text or string to be written

Example of adding text

fig = plt.figure(figsize=(8,6))

ax = fig.add_subplot(111)

ax.set(title='Writing Text',
      xlabel='X-Axis', ylabel='Y-Axis',
      xlim=(0, 5), ylim=(0, 9))

x = [1, 2, 3, 4]
y = [2, 4, 6, 8]

ax.scatter(x, y, c=['green'], s=[60], edgecolor='black')

for i in range(len(x)):
    str_temp = '({}, {})'.format(x[i] - 0.2, y[i] + 0.4)
    ax.text(x[i] - 0.4, y[i] + 0.4, str_temp, fontsize=16)

plt.show()

Matplotlib Backend

matplotlib can generate plots in different outputs.
In general, backend refers to everything that occurs from the time of executing a plotting code to generating the figure.
Backends are of two types.
- interactive backends: Graphical user interfaces like PyGTK, wxPython, Tkinter, qt4, etc.
- non-interactive backends: Image files such as PNG, JPEG, etc.

Choosing a Backend

The default backend chosen by matplotlib is available as backend setting in matplotlibrc file.
If you want to alter the backend of many figures, change the value of backend setting.
The below expression chooses WXAgg backend.
```
backend : WXAgg
```

You can also use use method if you want to go with a specific backend.

import matplotlib
matplotlib.use('WXAgg') 
# Above expression must be used before importing pylot

Saving Figures

Once a backend is chosen, a matplotlib figure can be saved in any format supported by it.
The below shown example saves the plot in png format.

fig = plt.figure(figsize(8,6))
ax = fig.add_subplot(111)
ax.set(title='My First Plot',
      xlabel='X-Axis', ylabel='Y-Axis',
      xlim=(0, 5), ylim=(0,10))
x = [1, 2, 3, 4]; y = [2, 4, 6, 8]
plt.plot(x, y)
plt.figsave('myplot.png')

References

matplotib website: https://matplotlib.org/
matplotlib documentation: https://matplotlib.org/contents.html
matplotlib examples: https://matplotlib.org/gallery/index.html
matplotlib tutorials: https://matplotlib.org/tutorials/index.html

Matplotlib Data Visualization | Code Snippets

Installing matplotlib

upgrading matplotlib

loading matplotlib using import

matplotlib version

Parts of a Matplotlib Figure

Creating a Figure

Creating Axes

generate a figure with one axes

Setting Title and Axis Labels

Setting an attribute (set_<paramter_name>)

Plotting Data (plot function)

implicit vs explicit

Adding a Legend

Line Plot

Common Parameters of ‘plot’ Function

Setting ‘plot’ Parameters

Marking Data Points (marker argument)

Plotting multiple lines in Line plot

Scatter plot (scatter function)

Common Parameters of ‘scatter’

Scatter Plot Using ‘plot’

Sine wave plot

Multi Curve Plot

Scatter Plot (Cars sold by a company)

Bar Plot using bar

Common Parameters of ‘bar’

Plotting Multiple Groups

Barplot Using ‘barh’

Pie Plot

Common Parameters of ‘pie’

Create Bar Plot iris sepal length

Bar plot iris measurements

hbar plot of iris petal length

Histogram Using ‘hist’

Common Parameters of ‘hist’

Boxplot Using ‘boxplot’

Common Parameters of ‘boxplot’

Multiple boxplots

Horizontal boxplots

Histogram normal distribution

Box Plot of 4 normal distributions

Matplotlib Styles

Using a Style

Matplotlib ggplot style

Composing Styles

Creating a Custom Style

matplotlibrc file

Matplotlib rcParams

Generate plot with style

Creating Suplots

Subplots Using ‘GridSpec’

Creating a Complex Layout

Create multiple plots

Adding text

Syntax

Matplotlib Backend

Choosing a Backend

Saving Figures

References

Installing `matplotlib`

upgrading `matplotlib`

loading `matplotlib` using `import`

`matplotlib` version

generate a figure with one `axes`

Setting an attribute (`set_<paramter_name>`)

Plotting Data (`plot` function)

Marking Data Points (`marker` argument)

Scatter plot (`scatter` function)

Bar Plot using `bar`

Matplotlib `ggplot` style

`matplotlibrc` file