In [1]:

%matplotlib inline

In [2]:

from matplotlib import pyplot as plt

In [3]:

# availables styles
plt.style.available

Out[3]:

['bmh',
 'classic',
 'dark_background',
 'fast',
 'fivethirtyeight',
 'ggplot',
 'grayscale',
 'seaborn-bright',
 'seaborn-colorblind',
 'seaborn-dark-palette',
 'seaborn-dark',
 'seaborn-darkgrid',
 'seaborn-deep',
 'seaborn-muted',
 'seaborn-notebook',
 'seaborn-paper',
 'seaborn-pastel',
 'seaborn-poster',
 'seaborn-talk',
 'seaborn-ticks',
 'seaborn-white',
 'seaborn-whitegrid',
 'seaborn',
 'Solarize_Light2',
 'tableau-colorblind10',
 '_classic_test']

In [4]:

plt.style.use('seaborn-white')

In [5]:

# Median Developer Salaries by Age
ages_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]

dev_y = [38496, 42000, 46752, 49320, 53200,
         56000, 62316, 64928, 67317, 68748, 73752]

# Median Python Developer Salaries by Age

py_dev_y = [45372, 48876, 53850, 57287, 63016,
            65998, 70003, 70000, 71496, 75370, 83640]

# Median JavaScript Developer Salaries by Age
js_dev_y = [37810, 43515, 46823, 49293, 53437,
            56373, 62375, 66674, 68745, 68746, 74583]

In [6]:

plt.plot(ages_x, dev_y, label='All Devs')
plt.plot(ages_x, py_dev_y, label='Python')

plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")

# adding legend
# method 1
# plt.legend(['All Devs', 'Python'])
# method 2 - pass a label argument to plot

plt.legend()

Out[6]:

<matplotlib.legend.Legend at 0x1e7c4ffcf08>

Format Strings

A format string consists of a part for color, marker and line:

fmt = '[marker][line][color]'

Markers

character	description
.	point marker
,	pixel marker
o	circle marker
v	triangle_down marker
^	triangle_up marker
<	triangle_left marker
>	triangle_right marker
1	tri_down marker
2	tri_up marker
3	tri_left marker
4	tri_right marker
s	square marker
p	pentagon marker
*****	star marker
h	hexagon1 marker
H	hexagon2 marker
+	plus marker
x	x marker
D	diamond marker
d	thin_diamond marker
'	vline marker
_	hline marker

Line Styles

character	description
-	solid line style
--	dashed line style
-.	dash-dot line style
:	dotted line style

Example	format strings:
b	blue markers with default shape
or	red circles
-g	green solid line
--	dashed line with default color
^k:	black triangle_up markers connected by a dotted line

Colors

The supported color abbreviations are the single letter codes

character	color
b	blue
g	green
r	red
c	cyan
m	magenta
y	yellow
k	black
w	white

In [7]:

# change formatting of the plot
# method 1

plt.plot(ages_x, dev_y, 'k--', label='All Devs')
plt.plot(ages_x, py_dev_y, 'b', label='Python')


plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")
plt.legend()

Out[7]:

<matplotlib.legend.Legend at 0x1e7c50e5b88>

In [8]:

# proper method / more desirable

plt.plot(ages_x, dev_y, color='k', linestyle='--', marker='.', label='All Devs')
plt.plot(ages_x, py_dev_y, color='b', marker='o', label='Python')
plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")
plt.legend()

Out[8]:

<matplotlib.legend.Legend at 0x1e7c515cac8>

In [9]:

# using hexadecimal value for colors and linewidth

plt.plot(ages_x, py_dev_y, linewidth=3, label='Python')

plt.plot(ages_x, js_dev_y, linewidth=3, label='JavaScript')

plt.plot(ages_x, dev_y, color='#444444', linestyle='--', label='All Devs')

plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")
plt.legend()

# adding a grid
plt.grid(True)

# Automatically adjust subplot parameters to give specified padding.
plt.tight_layout()
plt.show()

In [10]:

# using xkcd format

plt.xkcd()
plt.plot(ages_x, py_dev_y, linewidth=3, label='Python')
plt.plot(ages_x, js_dev_y, linewidth=3, label='JavaScript')
plt.plot(ages_x, dev_y, color='#444444', linestyle='--', label='All Devs')
plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")
plt.legend()
plt.grid(True)
plt.tight_layout()
# saving file
plt.savefig(r'plots/plot1-1.png')

In [11]:

# ages 18-55

# Ages 18 to 55
ages_x = [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]

py_dev_y = [20046, 17100, 20000, 24744, 30500, 37732, 41247, 45372, 48876, 53850, 57287, 63016, 65998, 70003, 70000, 71496, 75370, 83640, 84666,
            84392, 78254, 85000, 87038, 91991, 100000, 94796, 97962, 93302, 99240, 102736, 112285, 100771, 104708, 108423, 101407, 112542, 122870, 120000]

js_dev_y = [16446, 16791, 18942, 21780, 25704, 29000, 34372, 37810, 43515, 46823, 49293, 53437, 56373, 62375, 66674, 68745, 68746, 74583, 79000,
            78508, 79996, 80403, 83820, 88833, 91660, 87892, 96243, 90000, 99313, 91660, 102264, 100000, 100000, 91660, 99240, 108000, 105000, 104000]

dev_y = [17784, 16500, 18012, 20628, 25206, 30252, 34368, 38496, 42000, 46752, 49320, 53200, 56000, 62316, 64928, 67317, 68748, 73752, 77232,
         78000, 78508, 79536, 82488, 88935, 90000, 90056, 95000, 90000, 91633, 91660, 98150, 98964, 100000, 98988, 100000, 108923, 105000, 103117]

In [12]:

plt.plot(ages_x, py_dev_y, linewidth=3, label='Python')
plt.plot(ages_x, js_dev_y, linewidth=3, label='JavaScript')
plt.plot(ages_x, dev_y, color='#444444', linestyle='--', label='All Devs')
plt.xlabel("Ages")
plt.ylabel("Median Salary (USD)")
plt.title("Median Salary (USD) by Age")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig(r'plots/plot1-2.png')