14 matplotlib

14.1 Library

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

from plydata import define, query, select, group_by, summarize, arrange, head, rename
import plotnine
from plotnine import *

14.2 Sample Data

This chapter uses the sample data generate with below code. The idea is to simulate two categorical-alike feature, and two numeric value feature:

com is random character between ?C1?, ?C2? and ?C3?
dept is random character between ?D1?, ?D2?, ?D3?, ?D4? and ?D5?
grp is random character with randomly generated ?G1?, ?G2?
value1 represents numeric value, normally distributed at mean 50
value2 is numeric value, normally distributed at mean 25

n = 200
comp = ['C' + i for i in np.random.randint( 1,4, size  = n).astype(str)] # 3x Company
dept = ['D' + i for i in np.random.randint( 1,6, size  = n).astype(str)] # 5x Department
grp =  ['G' + i for i in np.random.randint( 1,3, size  = n).astype(str)] # 2x Groups
value1 = np.random.normal( loc=50 , scale=5 , size = n)
value2 = np.random.normal( loc=20 , scale=3 , size = n)
value3 = np.random.normal( loc=5 , scale=30 , size = n)

mydf = pd.DataFrame({
    'comp':comp, 
    'dept':dept, 
    'grp': grp,
    'value1':value1, 
    'value2':value2,
    'value3':value3 })
mydf.head()

#:>   comp dept grp     value1     value2     value3
#:> 0   C1   D3  G1  47.343508  16.623546   1.741223
#:> 1   C2   D1  G1  61.737449  22.592145  29.889468
#:> 2   C2   D4  G1  48.773299  22.211320  17.476382
#:> 3   C2   D2  G1  47.856641  18.504218  35.166332
#:> 4   C3   D3  G1  51.066041  19.154196  -2.138135

mydf.info()

#:> <class 'pandas.core.frame.DataFrame'>
#:> RangeIndex: 200 entries, 0 to 199
#:> Data columns (total 6 columns):
#:>  #   Column  Non-Null Count  Dtype  
#:> ---  ------  --------------  -----  
#:>  0   comp    200 non-null    object 
#:>  1   dept    200 non-null    object 
#:>  2   grp     200 non-null    object 
#:>  3   value1  200 non-null    float64
#:>  4   value2  200 non-null    float64
#:>  5   value3  200 non-null    float64
#:> dtypes: float64(3), object(3)
#:> memory usage: 9.5+ KB

14.3 MATLAB-like API

The good thing about the pylab MATLAB-style API is that it is easy to get started with if you are familiar with MATLAB, and it has a minumum of coding overhead for simple plots.
However, I’d encourrage not using the MATLAB compatible API for anything but the simplest figures.
Instead, I recommend learning and using matplotlib’s object-oriented plotting API. It is remarkably powerful. For advanced figures with subplots, insets and other components it is very nice to work with.

14.3.1 Sample Data

# Sample Data
x = np.linspace(0,5,10)
y = x ** 2

14.3.2 Single Plot

plt.figure()
plt.xlabel('x')
plt.ylabel('y')
plt.plot(x,y,'red')
plt.title('My Good Data')
plt.show()

14.3.3 Multiple Subplots

Each call lto subplot() will create a new container for subsequent plot command

plt.figure()
plt.subplot(1,2,1) # 1 row, 2 cols, at first box
plt.plot(x,y,'r--')
plt.subplot(1,2,2) # 1 row, 2 cols, at second box
plt.plot(y,x,'g*-')
plt.show()

14.4 Object-Oriented API

14.4.1 Sample Data

# Sample Data
x = np.linspace(0,5,10)
y = x ** 2

14.4.2 Single Plot

One figure, one axes

fig = plt.figure()
axes = fig.add_axes([0,0,1,1]) # left, bottom, width, height (range 0 to 1)
axes.plot(x, y, 'r')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('title')
plt.show()

14.4.3 Multiple Axes In One Plot

This is still considered a single plot, but with multiple axes

fig = plt.figure()
ax1 = fig.add_axes([0, 0, 1, 1])         # main axes
ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes

ax1.plot(x,y,'r')
ax1.set_xlabel('x')
ax1.set_ylabel('y')

ax2.plot(y, x, 'g')
ax2.set_xlabel('y')
ax2.set_ylabel('x')
ax2.set_title('insert title')
plt.show()

14.4.4 Multiple Subplots

One figure can contain multiple subplots
Each subplot has one axes

14.4.4.1 Simple Subplots - all same size

subplots() function return axes object that is iterable.

Single Row Grid
Single row grid means axes is an 1-D array. Hence can use for to iterate through axes

fig, axes = plt.subplots( nrows=1,ncols=3 )
print (axes.shape)

for ax in axes:
    ax.plot(x, y, 'r')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')
    ax.text(0.2,0.5,'One')
plt.show()

Multiple Row Grid
Multile row grid means axes is an 2-D array. Hence can use two levels of for loop to iterate through each row and column

fig, axes = plt.subplots(2, 3, sharex='col', sharey='row')
print (axes.shape)

for i in range(axes.shape[0]):
    for j in range(axes.shape[1]):
        axes[i, j].text(0.5, 0.5, str((i, j)),
                      fontsize=18, ha='center')
plt.show()

14.4.4.2 Complicated Subplots - different size

GridSpec specify grid size of the figure
Manually specify each subplot and their relevant grid position and size

plt.figure(figsize=(5,5))
grid = plt.GridSpec(2, 3, hspace=0.4, wspace=0.4)
plt.subplot(grid[0, 0])  #row 0, col 0
plt.subplot(grid[0, 1:]) #row 0, col 1 to :
plt.subplot(grid[1, :2]) #row 1, col 0:2 
plt.subplot(grid[1, 2]); #row 1, col 2
plt.show()

plt.figure(figsize=(5,5))
grid = plt.GridSpec(4, 4, hspace=0.8, wspace=0.4)
plt.subplot(grid[:3, 0])    # row 0:3, col 0
plt.subplot(grid[:3, 1: ])  # row 0:3, col 1:
plt.subplot(grid[3, 1: ]);  # row 3,   col 1:
plt.show()

-1 means last row or column

plt.figure(figsize=(6,6))
grid = plt.GridSpec(4, 4, hspace=0.4, wspace=1.2)
plt.subplot(grid[:-1, 0 ])  # row 0 till last row (not including last row), col 0
plt.subplot(grid[:-1, 1:])  # row 0 till last row (not including last row), col 1 till end
plt.subplot(grid[-1, 1: ]); # row last row, col 1 till end
plt.show()

14.4.5 Figure Customization

14.4.5.1 Avoid Overlap - Use tight_layout()

Sometimes when the figure size is too small, plots will overlap each other. - tight_layout() will introduce extra white space in between the subplots to avoid overlap.
- The figure became wider.

fig, axes = plt.subplots( nrows=1,ncols=2)
for ax in axes:
    ax.plot(x, y, 'r')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')
fig.tight_layout() # adjust the positions of axes so that there is no overlap
plt.show()

14.4.5.2 Avoid Overlap - Change Figure Size

fig, axes = plt.subplots( nrows=1,ncols=2,figsize=(12,3))
for ax in axes:
    ax.plot(x, y, 'r')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')
plt.show()

14.4.5.3 Text Within Figure

fig = plt.figure()
fig.text(0.5, 0.5, 'This Is A Sample',fontsize=18, ha='center');
axes = fig.add_axes([0,0,1,1]) # left, bottom, width, height (range 0 to 1)
plt.show()

14.4.6 Axes Customization

14.4.6.1 Y-Axis Limit

fig = plt.figure()
fig.add_axes([0,0,1,1], ylim=(-2,5));
plt.show()

14.4.6.2 Text Within Axes

fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')
for i in range(2):
    for j in range(3):
        ax[i, j].text(0.5, 0.5, str((i, j)),
                      fontsize=18, ha='center')
plt.show()

plt.text(0.5, 0.5, 'one',fontsize=18, ha='center')
plt.show()

14.4.6.4 Create Subplot Individually