Demo: Matplotlib, Backends

UW Geospatial Data Analysis
CEE467/CEWA567
David Shean

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(101)

x

array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,
        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,
        26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,
        39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,
        52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,
        65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,
        78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,
        91,  92,  93,  94,  95,  96,  97,  98,  99, 100])

y = 2*x
y2 = x**2

y

array([  0,   2,   4,   6,   8,  10,  12,  14,  16,  18,  20,  22,  24,
        26,  28,  30,  32,  34,  36,  38,  40,  42,  44,  46,  48,  50,
        52,  54,  56,  58,  60,  62,  64,  66,  68,  70,  72,  74,  76,
        78,  80,  82,  84,  86,  88,  90,  92,  94,  96,  98, 100, 102,
       104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128,
       130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154,
       156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180,
       182, 184, 186, 188, 190, 192, 194, 196, 198, 200])

y2

array([    0,     1,     4,     9,    16,    25,    36,    49,    64,
          81,   100,   121,   144,   169,   196,   225,   256,   289,
         324,   361,   400,   441,   484,   529,   576,   625,   676,
         729,   784,   841,   900,   961,  1024,  1089,  1156,  1225,
        1296,  1369,  1444,  1521,  1600,  1681,  1764,  1849,  1936,
        2025,  2116,  2209,  2304,  2401,  2500,  2601,  2704,  2809,
        2916,  3025,  3136,  3249,  3364,  3481,  3600,  3721,  3844,
        3969,  4096,  4225,  4356,  4489,  4624,  4761,  4900,  5041,
        5184,  5329,  5476,  5625,  5776,  5929,  6084,  6241,  6400,
        6561,  6724,  6889,  7056,  7225,  7396,  7569,  7744,  7921,
        8100,  8281,  8464,  8649,  8836,  9025,  9216,  9409,  9604,
        9801, 10000])

Plot using %matplotlib inline

%matplotlib inline

#plt.plot?

#Using pyplot interface
plt.plot(x,y)

[<matplotlib.lines.Line2D at 0x7ff44b01e4f0>]

lines = plt.plot(x,y)

lines[0]

<matplotlib.lines.Line2D at 0x7ff442f1bcd0>

# Don't usually interact with these objects, but can get/set properties
#https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html
lines[0].get_color()

'#1f77b4'

#Add a trailing semicolon to prevent returning lines or axes object
plt.plot(x,y);

Multiple plots on same axes

• Must be in same notebook cell with inline backend!

plt.plot(x,y, color='red')
plt.scatter(x,y2, marker='o')

<matplotlib.collections.PathCollection at 0x7ff442ea65b0>

Subplots

• Convenience function to create new figure and axes

• https://matplotlib.org/stable/tutorials/introductory/usage.html#parts-of-a-figure

• https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html

• Note returned objects

plt.subplots()

(<Figure size 432x288 with 1 Axes>, <AxesSubplot:>)

#Using object-oriented interface (OOI)
fig, ax = plt.subplots()
ax.plot(x,y)
ax.set_title('my title')

Text(0.5, 1.0, 'my title')

fig

ax

<AxesSubplot:title={'center':'my title'}>

# Two-panel figure
fig, axa = plt.subplots(2, 1)
axa[0].plot(x,y)
axa[1].plot(x,y2);
axa[1].set_ylabel('Y values')
axa[1].set_title("$x^2$")

Text(0.5, 1.0, '$x^2$')

#Try to set the title on existing plot - doesn't work!
axa[1].set_title("$x^2$")

Text(0.5, 1.0, '$x^2$')

#Can modify axes and set title in same cell
fig, ax = plt.subplots(1)
ax.plot(x,y)
ax.set_title("$x^2$")
ax.set_xlabel('x')
ax.set_xlim(40,70)

(40.0, 70.0)

fig, axa = plt.subplots(1, 2)
axa[0].plot(x,y)
axa[0].set_title("$2x$")
axa[1].plot(x,y2);
axa[1].set_title("$x^2$")

Text(0.5, 1.0, '$x^2$')

#Save figure
fig, ax = plt.subplots(1)
ax.plot(x,y)
fig.savefig('my_figure.jpg', dpi=150)

Using %matplotlib widget backend for interactive plotting

#May need to execute this cell again if using "Run all cells"
%matplotlib widget

plt.plot(x,y)

[<matplotlib.lines.Line2D at 0x7ff442d48e50>]

#Add to the figure above!
plt.plot(x,y2);

Using Seaborn

• https://seaborn.pydata.org/

import seaborn as sns
sns.set_theme()

fig, ax = plt.subplots()
ax.plot(x,y)

[<matplotlib.lines.Line2D at 0x7ff43a5c6760>]

#Turn off Seaborn for now
sns.reset_orig()

Pandas built-in plotting

import pandas as pd

# Create a Pandas DataFrame
df = pd.DataFrame(np.array([y, y2]).T, index=x, columns=['y', 'y2'])

df

	y	y2
0	0	0
1	2	1
2	4	4
3	6	9
4	8	16
...	...	...
96	192	9216
97	194	9409
98	196	9604
99	198	9801
100	200	10000

101 rows × 2 columns

# Use the convenient, built-in Pandas plot function (matplotlib under the hood)
df.plot()

<AxesSubplot:>

#Scatterplot
df.plot(kind='scatter', x='y', y='y2', color='k')

<AxesSubplot:xlabel='y', ylabel='y2'>

Using hvPlot (Holoviews and Bokeh)

• https://holoviews.org/getting_started/Introduction.html

• https://docs.bokeh.org/en/latest/index.html

import hvplot.pandas

df.hvplot()