# bokeh 4th: server

how to write simple bokeh program that runs on a server?

# bokeh 3rd: high-level charts

Where to get Bokeh high-level charts that can be simply created through pandas DataFrame?

# bokeh 2nd: layouts

Why layout for different or similar charts is so attracting?

# bokeh 1st: fundamentals python data visualization

Let’s dive into the simple but powerful Bokeh—-create sophisticated D3.js like graphs with few Python codes!

# Visualization with Matplotlib -1 basics

Customizing plots

subplot

layout

In [1]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


data set

records of undergraduate degrees awarded to women in a variety of fields from 1970 to 2011

• physical_sciences (representing the percentage of Physical Sciences degrees awarded to women each in corresponding year)
• computer_science (representing the percentage of Computer Science degrees awarded to women in each corresponding year)
In [2]:
year=np.arange(1970,2012)

In [3]:
physical_sciences = np.array([ 13.8,  14.9,  14.8,  16.5,  18.2,  19.1,  20. ,  21.3,  22.5,
23.7,  24.6,  25.7,  27.3,  27.6,  28. ,  27.5,  28.4,  30.4,
29.7,  31.3,  31.6,  32.6,  32.6,  33.6,  34.8,  35.9,  37.3,
38.3,  39.7,  40.2,  41. ,  42.2,  41.1,  41.7,  42.1,  41.6,
40.8,  40.7,  40.7,  40.7,  40.2,  40.1])

In [4]:
computer_science = np.array([ 13.6,  13.6,  14.9,  16.4,  18.9,  19.8,  23.9,  25.7,  28.1,
30.2,  32.5,  34.8,  36.3,  37.1,  36.8,  35.7,  34.7,  32.4,
30.8,  29.9,  29.4,  28.7,  28.2,  28.5,  28.5,  27.5,  27.1,
26.8,  27. ,  28.1,  27.7,  27.6,  27. ,  25.1,  22.2,  20.6,
18.6,  17.6,  17.8,  18.1,  17.6,  18.2])

In [5]:
plt.figure(figsize=[6,3])
# Plot in blue the % of degrees awarded to women in the Physical Sciences
plt.plot(year, physical_sciences, color='blue')

# Plot in red the % of degrees awarded to women in Computer Science
plt.plot(year, computer_science, color='red')

# Display the plot
plt.show()


### Using axes()¶

• In calling plt.axes([xlo, ylo, width, height]), a set of axes is created and made active with lower corner at coordinates (xlo, ylo) of the specified width and height. Note that these coordinates are passed to plt.axes() in the form of a list.
• The coordinates and lengths are values between 0 and 1 representing lengths relative to the dimensions of the figure. After issuing a plt.axes() command, plots generated are put in that set of axes.
In [6]:
plt.figure(figsize=[9,3])

# Create plot axes for the first line plot
plt.axes([0.05,0.05,0.425,0.9])

# Plot in blue the % of degrees awarded to women in the Physical Sciences
plt.plot(year,physical_sciences, color='blue')

# Create plot axes for the second line plot
plt.axes([.525,0.05,0.425,0.9])

# Plot in red the % of degrees awarded to women in Computer Science
plt.plot(year,computer_science, color='red')

# Display the plot
plt.show()


### Using subplot()¶

• The command plt.axes() requires a lot of effort to use well because the coordinates of the axes need to be set manually. A better alternative is to use plt.subplot() to determine the layout automatically.
• plt.subplot(m, n, k) to make the subplot grid of dimensions m by n and to make the kth subplot active (subplots are numbered starting from 1 row-wise from the top left corner of the subplot grid).
In [7]:
plt.figure(figsize=[9,3])

# Create a figure with 1x2 subplot and make the left subplot active
plt.subplot(1,2,1)

# Plot in blue the % of degrees awarded to women in the Physical Sciences
plt.plot(year, physical_sciences, color='blue')
plt.title('Physical Sciences')

# Make the right subplot active in the current 1x2 subplot grid
plt.subplot(1,2,2)

# Plot in red the % of degrees awarded to women in Computer Science
plt.plot(year, computer_science, color='red')
plt.title('Computer Science')

# Use plt.tight_layout() to improve the spacing between subplots
plt.tight_layout()
plt.show()


health (representing the percentage of Computer Science degrees awarded to women in each corresponding year

education

In [8]:
health = np.array([ 77.1,  75.5,  76.9,  77.4,  77.9,  78.9,  79.2,  80.5,  81.9,
82.3,  83.5,  84.1,  84.4,  84.6,  85.1,  85.3,  85.7,  85.5,
85.2,  84.6,  83.9,  83.5,  83. ,  82.4,  81.8,  81.5,  81.3,
81.9,  82.1,  83.5,  83.5,  85.1,  85.8,  86.5,  86.5,  86. ,
85.9,  85.4,  85.2,  85.1,  85. ,  84.8])
education = np.array([ 77.1,  75.5,  76.9,  77.4,  77.9,  78.9,  79.2,  80.5,  81.9,
82.3,  83.5,  84.1,  84.4,  84.6,  85.1,  85.3,  85.7,  85.5,
85.2,  84.6,  83.9,  83.5,  83. ,  82.4,  81.8,  81.5,  81.3,
81.9,  82.1,  83.5,  83.5,  85.1,  85.8,  86.5,  86.5,  86. ,
85.9,  85.4,  85.2,  85.1,  85. ,  84.8])


### 2x2 subplot layout¶

In [9]:
# Create a figure with 2x2 subplot layout and make the top left subplot active
plt.subplot(2,2,1)

# Plot in blue the % of degrees awarded to women in the Physical Sciences
plt.plot(year, physical_sciences, color='blue')
plt.title('Physical Sciences')

# Make the top right subplot active in the current 2x2 subplot grid
plt.subplot(2,2,2)

# Plot in red the % of degrees awarded to women in Computer Science
plt.plot(year, computer_science, color='red')
plt.title('Computer Science')

# Make the bottom left subplot active in the current 2x2 subplot grid
plt.subplot(2,2,3)

# Plot in green the % of degrees awarded to women in Health Professions
plt.plot(year, health, color='green')
plt.title('Health Professions')

# Make the bottom right subplot active in the current 2x2 subplot grid
plt.subplot(2,2,4)

# Plot in yellow the % of degrees awarded to women in Education
plt.plot(year, education, color='yellow')
plt.title('Education')

# Improve the spacing between subplots and display them
plt.tight_layout()
plt.show()