matplotlib.md

Matplotlib

Table of contents

• Table of contents
• 1. Introduction
• 2. Concepts
  • 2.1. Figure
  • 2.2. Axes
  • 2.3. Axis
• 3. Common Plots
• 4. Box Plot
• 5. Contour Plot

1. Introduction

• Matplotlib is a python library to create data visualisations.
• Pyplot module within matplotlib provides a MATLAB-like interface.
• It creates figures and axes to achieve the desired plot.

2. Concepts

• Figure: It may contain one or multiple axes (plots).
• Axes: Known as plots. It contains two (2D) or three (3D) axis objects.
• Axis: Lines with numbers.

2.1. Figure

• Figure object is the overall window where everything is drawn.
• When run plt.show(), python will create a default figure if there’s no existing one.
• The default figure only contains one axes and the user is unable to call the axes.

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

2.2. Axes

2.2.1. Create axes one by one

• Create axes with add_subplot

fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(2,2,1)# (n_rows, n_cols, index)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
ax4 = fig.add_subplot(2,2,4)

ax1.set(title = 'Axes 1')
ax2.set(title = 'Axes 2')
ax3.set(title = 'Axes 3')
ax4.set(title = 'Axes 4')

fig.tight_layout() #to ensure no overlaps
plt.show()

2.2.2. Create all axes at one time

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8))
axes[0,0].set(title = 'Axes 1')
axes[0,1].set(title = 'Axes 2')
axes[1,0].set(title = 'Axes 3')
axes[1,1].set(title = 'Axes 4')
plt.show()

#OR

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))
ax1.set(title = 'Scatter')
ax2.set(title = 'Line')
ax3.set(title = 'Bar')
ax4.set(title = 'Bar with Artists')
plt.show()

2.3. Axis

2.3.1. Axis Limits

fig = plt.figure()
ax = fig.add_subplot()
ax.set(title = "Exercise 2", xlim=[-6,6], ylim=[-6,6])
#OR
ax.set_xlim([-1,4])
ax.set_ylim([-6,6])

2.3.2. Axis Ticks

• Customize axis ticks

ax.set_xlabel(‘angle’)
x = [0,2,4,6]
label = ['zero','two','four','six']

#Method 1:
ax.set_xticks(x) #denote the positions on corresponding action where ticks will be displayed.
ax.set_xticklabels(label) #labels corresponding to tick marks

#Method 2:
ax.set(xticks=x, xticklabels=label)

2.3.4. Legend

• Label when there are multiple plots on same axes

plt.plot([1, 2, 3], [10, 20, 25], color = 'black', label='city1') 
plt.plot([1, 2, 3], [30, 23, 13], color = 'orange', label='city2') 
plt.scatter([1, 2, 3], [20, 10, 30], color = 'salmon', label='city3') 

plt.legend() #Need to call .legend() to display label
#plt.legend(loc='upper left') #To display where to put the label box
plt.show()

3. Common Plots

• Scatter
• Line
• Bar
• Histogram

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10, 10), sharex='all', sharey='all') #sharex='all', sharey='all' to share same axis x, y
ax1.set(title = 'Scatter')
ax2.set(title = 'Line')
ax3.set(title = 'Bar')
ax4.set(title = 'Bar with Artists')

ax1.scatter(x,y, marker="+", color="red")
ax2.plot(x,y,marker='*', linestyle='dashed', color="black")
ax3.bar(x,y, edgecolor='black', color='lightblue')

bars = ax4.bar(x,y, color='lightblue')
for bar, height in zip(bars, y):
    if height < 0:
        bar.set(color = 'salmon') #To change the color of each bar based on Height values

plt.show();

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4. Box Plot

• A boxplot is a standardized way of displaying the distribution of data as below:
  • whis (whisker): whis = 1.5, by default.
  • “minimum”: Q3 + whis*IQR
  • first quartile (Q1/25th Percentile): the middle number between the smallest number (not the “minimum”) and the median of the dataset.
  • median: middle value of the dataset (i.e: half of the numbers in the dataset below the median, half of the numbers in the dataset above the median)
  • interquartile range (IQR): Q3-Q1 25th to the 75th percentile.
  • third quartile (Q3/75th Percentile): the middle value between the median and the highest value (not the “maximum”) of the dataset.
  • “maximum”: Q3 + whis*IQR
• It can tell you about your outliers and what their values are.
  • outliers: those points > (Q3 + whis * IQR) = "maximum" or those points < (Q1 – whis * IQR) = “minimum”
• It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed.

x = np.random.randn(100) 
plt.boxplot(x) 
plt.show()

5. Contour Plot

• Contour plots show the relationship between a fitted response and two continuous variables

def f(x, y):
  return np.sin(np.pi*x)*np.sin(np.pi*y)
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y) #create positions Z = f(X, Y)

plt.contour(X, Y, Z)
plt.show()

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