Beautification of chart style

1. Overview of chart styles

1.1 Default chart styles

import matplotlib

print(matplotlib.rc_params())

Common configuration items

Configuration items	Instructions	The default value
lines.color	Line color	‘C0’
lines.linestyle	Line type	‘-‘
lines.linewidth	Line width	1.5
lines.marker	Tag line	‘None’
lines.markeredgecolor	Mark border color	‘auto’
lines.markeredgewidth	Mark border width	1.0
lines.markerfacecolor	Tag color	auto
lines.markersize	Markup size	6.0
font.family	The system font	[‘sans-serif’]
font.sans-serif	Sans serif font	[‘DejaVu Serif’, ‘Bitstream Vera Serif’, ‘Computer Modern Roman’, ‘New Century Schoolbook’, ‘Century Schoolbook L’, ‘Utopia’, ‘ITC Bookman’, ‘Bookman’, ‘Nimbus Roman No9 L’, ‘Times New Roman’, ‘Times’, ‘Palatino’, ‘Charter’, ‘serif’]
font.size	The font size	10.0
font.style	The font style	‘normal’
axes.unicode_minus	Uses the Unicode encoded minus sign	True
axes.prop_cycle	Property circulator	\cycler(‘color’, [‘#1f77b4’, ‘#ff7f0e’, ‘#2ca02c’, ‘#d62728’, ‘#9467bd’, ‘#8c564b’, ‘#e377c2’, ‘#7f7f7f’, ‘#bcbd22’, ‘#17becf’])
figure.constrained_layout.use	Using constraint layouts	False

1.2 Chart style modification

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], linewidth=3)

plt.rcParams['lines.linewidth'] = 3

plt.show()

or:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], linewidth=3)

plt.rc('lines', linewidth=3)

plt.show()

2. Use color

2.1 Use basic color

Word abbreviation	Word
c	cyan
m	megenta
y	yellow
k	black
r	red
g	green
b	blue
w	white

View all colors:

import matplotlib

for name, hex in matplotlib.colors.cnames.items():
    print(name, hex)

2.2 Using color mapping tables

import matplotlib.pyplot as plt

print(plt.colormaps())

2.3 Demo1: Procurement of different types of books in the two regions

import matplotlib.pyplot as plt
import numpy as np

# Support Chinese
import matplotlib.pyplot as plt
import numpy as np

# Support Chinese
plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False

x = np.arange(5)
y1 = [1200, 2400, 1800, 2200, 1600]
y2 = [1050, 2100, 1300, 1600, 1340]
bar_width = 0.6
tick_label = ['家庭', '小说', '心理', '科技', '儿童']

fig = plt.figure()
ax = fig.add_subplot(111)
ax.bar(x, y1, bar_width, color="#FFCC00", align="center", label="地区1")
ax.bar(x, y1, bar_width, bottom=y1, color="#B0C4DE", align="center",
       label="地区2")
ax.set_xlabel("图书种类")
ax.set_ylabel("采购数量(本)")
ax.set_title("地区1和地区2对各类图书的采购情况")
ax.grid(True, axis="y", color="gray", alpha=0.2)
ax.set_xticks(x)
ax.set_xticklabels(tick_label)
ax.legend()

plt.show()

3. Select line type

3.1 Select the type of line

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], linestyle='--')

plt.show()

or

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], ls='--')

plt.show()

3.2 Demo2: US dollar / RMB exchange rate trend in the international foreign exchange market in July 2017 and July 2019

import matplotlib.pyplot as plt
import numpy as np

# Chinese support
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# Data
eurcny_2017 = np.array([6.8007, 6.8007, 6.8015, 6.8015, 6.8060,
                        6.8060, 6.8060, 6.8036, 6.8025, 6.7877,
                        6.7835, 6.7758, 6.7700, 6.7463, 6.7519,
                        6.7511, 6.7511, 6.7539, 6.7265])
eurcny_2019 = np.array([6.8640, 6.8705, 6.8697, 6.8697, 6.8697,
                        6.8881, 6.8853, 6.8856, 6.8677, 6.8662,
                        6.8662, 6.8662, 6.8827, 6.8761, 6.8635,
                        6.8860, 6.8737, 6.8796, 6.8841])
date_x = np.array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
                   13, 14, 17, 18, 19, 24, 25, 26, 31])

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(date_x, eurcny_2017, color="#006374", linewidth=2,
        label='2017年7月美元/人民币汇率')
ax.plot(date_x, eurcny_2019, color="#8a2e76", linestyle='--',
        linewidth=2, label='2019年7月美元/人民币汇率')

ax.set_title('2017年7月与2019年7月美元/人民币汇率走势')
ax.set_xlabel('日期')
ax.set_ylabel('汇率')
ax.legend()

plt.show()

4. Add data marker

4.1 Add data markers for line or scatter charts

matplotlib has many built-in data markers. Using these data markers, you can easily mark the data points for line chart or scatter chart. Data markers can be divided into filled data markers and unfilled data markers.

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], marker='*', markersize=20, markerfacecolor='y')

plt.show()

Learn more: matplotlib format string
Format string is a string that quickly sets the abbreviation of the basic style of lines.

[color][marker][linestyle]

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5], 'mo--')

plt.show()

4.2 Demo3: Mark quarterly sales of different products

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False

sale_a = [2144, 4617, 7674, 6666]
sale_b = [853, 1214, 2414, 4409]
sale_c = [153, 155, 292, 680]

fig = plt.figure()
ax = fig.add_subplot(111)

ax.plot(sale_a, 'D-', sale_b, '^:', sale_c, 's--')
ax.grid(alpha=0.3)
ax.set_ylabel('销售额(万元)')
ax.set_xticks(np.arange(len(sale_c)))
ax.set_xticklabels(['第1季度', '第2季度', '第3季度', '第4季度'])
ax.legend(['产品A', '产品B', '产品C'])

plt.show()

5. Set font

5.1 Set font style

Common properties of the Text class

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [3, 4, 5])
plt.text(1.9, 3.75, 'y=x+2', bbox=dict(facecolor='y'), family='serif',
         fontsize=18, fontstyle='normal', rotation=-60)

plt.show()

5.2 Demo4: Maximum and minimum temperature in the next 15 days

import matplotlib.pyplot as plt
import numpy as np

plt.rcParams["font.sans-serif"] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False

x = np.arange(4, 19)
y_max = [32, 33, 34, 34, 33, 31, 30, 29, 30, 29, 26, 23, 21, 25, 31]
y_min = [19, 19, 20, 22, 22, 21, 22, 16, 18, 18, 17, 14, 15, 16, 16]

plt.plot(x, y_max, marker='o', label='最高温度')
plt.plot(x, y_min, marker='o', label='最低温度')

x_temp = 4
for y_h, y_l in zip(y_max, y_min):
    plt.text(x_temp - 0.2, y_h + 0.7, y_h, family='SimHei',
             fontsize=8, fontstyle='normal')
    plt.text(x_temp - 0.2, y_l + 0.7, y_l, family='SimHei',
             fontsize=8, fontstyle='normal')
    x_temp += 1
plt.title('未来15天最高气温和最低气温的走势')
plt.xlabel('日期')
plt.ylabel(r'温度($^\circ$C)')
plt.ylim(0, 40)

plt.legend()
plt.show()

6. Switch theme style

Show available style

import matplotlib.style as ms

print(ms.available)

import matplotlib.pyplot as plt
import matplotlib.style as ms

ms.use('seaborn-dark')

plt.plot([1, 2, 3], [3, 4, 5], marker='*', markersize=20, markerfacecolor='y')

plt.show()

7. Fill area

7.1 Fills the area between polygons or curves

1. The fill() function is used to fill the polygon

ax.fill(x, y)                    # a polygon with default color
ax.fill(x, y, "b")               # a blue polygon
ax.fill(x, y, x2, y2)            # two polygons
ax.fill(x, y, "b", x2, y2, "r")  # a blue and a red polygon

2. The fill_between() function is used to fills the area between two horizontal curves

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 25, 500, endpoint=False)
cos_y = 0.5 * np.cos(0.5 * x)
sin_y = np.sin(x)

plt.fill_between(x, cos_y, sin_y, cos_y < sin_y, color='dodgerblue', alpha=0.5)
plt.fill_between(x, cos_y, sin_y, cos_y > sin_y, color='orangered', alpha=0.5)

plt.show()

3. The fill_betweenx() function is used to fills the area between two vertical curves

fill_betweenx(y, x1, x2=0, where=None, step=None, interpolate=False, *, data=None, **kwargs)

7.2 Demo5: Colored “snowflakes”

import matplotlib.pyplot as plt
import numpy as np


def koch_snowflake(order, scale=10):
    def _koch_snowflake_complex(order):
        if order == 0:
            # Initial triangle
            angles = np.array([0, 120, 240]) + 90
            return scale / np.sqrt(3) * np.exp(np.deg2rad(angles) * 1j)
        else:
            ZR = 0.5 - 0.5j * np.sqrt(3) / 3
            p1 = _koch_snowflake_complex(order - 1)  # Start
            p2 = np.roll(p1, shift=-1)  # End
            dp = p2 - p1  # Connect vector
            new_points = np.empty(len(p1) * 4, dtype=np.complex128)
            new_points[::4] = p1
            new_points[1::4] = p1 + dp / 3
            new_points[2::4] = p1 + dp * ZR
            new_points[3::4] = p1 + dp / 3 * 2
            return new_points

    points = _koch_snowflake_complex(order)
    x, y = points.real, points.imag
    return x, y


x, y = koch_snowflake(order=5)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.fill(x, y, facecolor='lightsalmon', edgecolor='orangered', linewidth=1)

plt.show()