# 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()
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()

#### 14.4.6.3 Share Y Axis Label

fig, ax = plt.subplots(2, 3, sharex='col', sharey='row') # removed inner label
plt.show()

#### 14.4.6.4 Create Subplot Individually

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

plt.subplot(2,4,1)
plt.text(0.5, 0.5, 'one',fontsize=18, ha='center')

plt.subplot(2,4,8)
plt.text(0.5, 0.5, 'eight',fontsize=18, ha='center')
plt.show()

Iterate through subplots (ax) to populate them

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()

## 14.5 Histogram

plt.hist(mydf.value1, bins=12);
plt.show()

## 14.6 Scatter Plot

plt.scatter(mydf.value1, mydf.value2)
plt.show()

## 14.7 Bar Chart

com_grp = mydf.groupby('comp')
grpdf = com_grp['value1'].sum().reset_index()
grpdf
plt.bar(grpdf.comp, grpdf.value1);
plt.xlabel('Company')
plt.ylabel('Sum of Value 1')
plt.show()