# line chart

The line chart is to use lines to represent data. It is generally used to see the trend of the data, whether it is a positive ratio (increasing) or an inverse ratio (decreasing)

For example, here is a piece of data about income and outlay

```python
income = [1200, 1400, 1600, 1800, 2000, 2200, 2400]
outlay = [1000, 1240, 1320, 1500, 1790, 1924, 2218.75]
```

Each income corresponds to an outlay

If we use the following procedure, we can draw a clear diagram of the relationship between income and expenditure:

```python
income = [1200, 1400, 1600, 1800, 2000, 2200, 2400]
outlay = [1000, 1240, 1320, 1500, 1790, 1924, 2218.75]

import matplotlib.pyplot as plt

plt.plot(income, outlay)

plt.title('The relationship between income and outlay')
plt.xlabel('income')
plt.ylabel('outlay')

plt.grid()
plt.show()
```

![The relationship between income and outlay](/files/lGIK6sPtCGTugQsc2xWj)

In this graph, we will find that when income increases, expenditures will increase accordingly.

A typical use scheme of line chart is to draw function image.

For example, we can use the following code to draw an image of a quadratic function.

```python
import matplotlib.pyplot as plt
import numpy as np
 
x = np.arange(-10, 10, 0.1)
y = x ** 2
 

plt.plot(x, y)

plt.title('Draw function graph')
plt.xlabel('x')
plt.ylabel('y')

plt.grid()
plt.show()
```

![quadratic function](/files/08ZaXvq9aWTRUGZ28Ki2)

We can also draw images of two functions in the same picture.

For example, in the figure below, I have drawn the sine and cosine images at the same time.

```python
import matplotlib.pyplot as plt
import numpy as np
 
x = np.arange(-np.pi, np.pi, 0.1)
y1 = np.sin(x)
y2 = np.cos(x)
 

plt.plot(x, y1, label = 'sin')
plt.plot(x, y2, label = 'cos')

plt.title('Draw function graph')
plt.xlabel('x')
plt.ylabel('y')

plt.grid()
plt.legend()
plt.show()
```

![Plot two functions at the same time](/files/4IYEaHlkaEvzWAXZFSBj)

The following code block can draw multiple functions with gradually color:

```python
import numpy as np

import matplotlib
import matplotlib.pyplot as plt

x = np.arange( 0, 5, 0.01 )
N = 30
cmap = plt.get_cmap( 'jet', N )

for i, n in enumerate( np.linspace(0, 0.5, N) ):
    y = x ** n
    plt.plot( x, y, c=cmap(i) )

plt.xlabel('x')
plt.ylabel('y')
plt.title('Automatically add multiple functions')


# Draw colorbar
norm = matplotlib.colors.Normalize( 0, 0.5 )
sm = plt.cm.ScalarMappable( cmap=cmap, norm=norm )
plt.colorbar(sm)


plt.grid()
plt.show()
```

![N = 10](/files/OLtcyc6UQbb9F7Vq3VnM)

![N = 20](/files/O1Y8IqC1axCXPp4j45OY)

![N = 30](/files/C9eUWY0ja1tfdCsTiuQQ)

![N = 50](/files/JxmhNZYbVhnQ70BIePqd)

![N = 100](/files/LThkSEbqiDKMdTo542RR)

![N = 1000](/files/POn7qJ5rMitfJO0Al7Rx)

This method is very useful when drawing a large number of function images, and can show the difference between different function images.

## Statistics

Start time of this page: December 19, 2021

Completion time of this page: December 19, 2021


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