Broken line diagram

One . Introduction to line chart （Line Chart）

Line chart is used to show the change of data in a continuous time interval or time span , Its characteristic is to reflect The tendency of things to change over time or ordered categories .

In the line chart, you can clearly see whether the data is increasing or decreasing 、 The rate of increase and decrease 、 The law of increase and decrease and the characteristics of peak value . Line charts are often used to analyze the trend of data over time , It can also be used to analyze the interaction and interaction of multiple groups of data over time . In a line chart, the horizontal axis is usually used to represent the time span and the time interval is the same , The vertical axis represents the data values at different times .

Two . Composition of line chart

The composition of a line chart includes ：

1. The horizontal axis ： Time
2. The vertical axis ： Represents the value
3. spot ： Indicates the location of the data
4. Line ： Indicates the trend relationship between data

3、 ... and . Use scenarios

Applicable scenarios ：

1. Apply to Continuous independent variable , A scenario in which dependent variables are ordered .

Scenarios that are not applicable ：

1. When the data type of the horizontal axis is disordered classification or the data type of the vertical axis is continuous time , Line chart is not suitable .

Four . Realization

stay matplotlib Use in plot Method to realize line graph .plot The method is introduced as follows ：

plot(args,scalex = True,scaley = True,data = None,** kwargs)

Parameters 1：x： Iteratable numeric type （ The length must be equal to Y equal ）, Appoint x Axis data , Default range(len(y))

Parameters 2：y： Iteratable numeric type , Appoint y Axis data

Parameters 3：fmt ： String type （ Optional ）, Specifies the format string , The format is [marker][linestyle][color]

• marker： Specifies the marker point style
• linestyle： Specifies the polyline linearity
• color： Specify the line color

Parameters 4：scalex ： bool type , （ Optional ）, Default True, Appoint X Whether the view is applicable to data restrictions

Parameters 5：scalex： bool type , （ Optional ）, Default True, Appoint Y Whether the view is applicable to data restrictions .

Parameters 6：data ： Specify the data to be marked （ Optional ）

Parameters 7：** kwargs： The accepted keyword parameters are passed to Line2D Class instance .

Return value ： Plotting data Line2D Class instance list

notes ：

1. *args The variable parameters passed include x, y, fmt
2. Line2D Class explanation

Implementation code ：

import matplotlib.pyplot as plt

import numpy as np

x_data = np.array(['2011', '2012', '2013', '2014', '2015', '2016', '2017'])
y_data = np.array([58000, 60200, 63000, 71000, 84000, 90500, 107000])
y_data2 = np.array([52000, 54200, 51500, 58300, 56800, 59500, 62700])

#  Initialize drawing frame 
figure, ax = plt.subplots(1, 2)

#  draw y_data
line1 = ax[0].plot(y_data, marker='o', lw=2, ls='--', markersize=6, markerfacecolor='r', label="line1")
ax[0].legend()
ax[0].set_xticks(range(len(y_data)))
ax[0].set_xticklabels(x_data)
ax[0].set_title("line1", fontsize=20, fontweight="bold")
ax[0].set_xlabel("year", fontsize=15)
ax[0].set_ylabel("amount", fontsize=15)

#  draw y_data2
line2 = ax[1].plot(y_data2, marker='o', lw=2, ls='-', color='k', markersize=6, markerfacecolor='b', label="line2")
ax[1].legend()
ax[1].set_xticks(range(len(y_data)))
ax[1].set_xticklabels(x_data)
ax[1].set_title("line2", fontsize=20, fontweight="bold")
ax[1].set_xlabel("year", fontsize=15)
ax[1].set_ylabel("amount", fontsize=15)

plt.show()

The effect is as follows ：

If you want to be more beautiful, you can make the following changes

1. ax[0].grid(True)： Draw grid
2. ax[1].grid(True)： Draw grid
3. plt.style.use('ggplot')： Use ggplot style

The effect is as follows ：

have access to text Mark the value of each marked point , The complete procedure is as follows ：

#  draw y_data
line1 = ax[0].plot(y_data, lw=2, color='k', label="line1", marker='o', markerfacecolor='g')
ax[0].legend()
ax[0].set_xticks(range(len(y_data)))
ax[0].set_xticklabels(x_data)
ax[0].set_title("line1", fontsize=20, fontweight="bold")
ax[0].set_xlabel("year", fontsize=15)
ax[0].set_ylabel("amount", fontsize=15)
ax[0].grid(True)

#  Tag the data 
for x_, y_ in zip(range(len(y_data)), y_data):
    ax[0].text(x_, y_, y_, ha='left', va='top', color='red')

#  draw y_data2
line2 = ax[1].plot(y_data2, lw=2, color='k', label="line2", marker='o', markerfacecolor='g')
ax[1].legend()
ax[1].set_xticks(range(len(y_data)))
ax[1].set_xticklabels(x_data)
ax[1].set_title("line2", fontsize=20, fontweight="bold")
ax[1].set_xlabel("year", fontsize=15)
ax[1].set_ylabel("amount", fontsize=15)
ax[1].grid(True)

#  Tag the data 
for x_, y_ in zip(range(len(y_data2)), y_data2):
    ax[1].text(x_, y_, y_, ha='left', va='top', color="red")

plt.show()

The effect is as follows ：

5、 ... and . Reference resources

1. Introduction to stacking diagram