# Matplotlib Tutorial

Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. For examples, see the sample plots and thumbnail gallery.

It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython.

## 2D plots

Conventionally, the package is imported into the Python script by adding the following statement

`from `

matplotlib import pyplot as plt

Here `pyplot()`

is the most important function in matplotlib library, which is used to plot 2D data. The following script plots the equation y = 2x + 5

```
import numpy as np
from matplotlib import pyplo
```

t as plt
x = np.arange(1,11)
y = 2 * x + 5
plt.title("Matplotlib demo")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
plt.plot(x,y)
plt.show()

An ndarray object x is created from `np.arange()`

function as the values on the x axis. The corresponding values on the y axis are stored in another ndarray object y. These values are plotted using `plot()`

function of pyplot submodule of matplotlib package. The graphical representation is displayed by `show() `

function.

The above code should produce the following output

Instead of the linear graph, the values can be displayed discretely by adding a format string to the `plot()`

function. Following formatting characters can be used.

Character | Description |
---|---|

'-' | Solid line style |

'--' | Dashed line style |

'-.' | Dash-dot line style |

':' | Dotted line style |

'.' | 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 |

The following color abbreviations are also defined.

Character | Color |
---|---|

'b' | Blue |

'g' | Green |

'r' | Red |

'c' | Cyan |

'm' | Magenta |

'y' | Yellow |

'k' | Black |

'w' | White |

To display the circles representing points, instead of the line in the above example, use “ob” as the format string in `plot()`

function.

```
import numpy as np
from matplotlib import pyplot as plt
x = np.arange(1,11)
y = 2 * x + 5
plt.title("Matplotlib demo")
plt.xlabel("x axis caption")
plt.ylabel("y axis caption")
plt.plot(x,y,"ob")
plt.show()
```

The above code should produce the following output

### Sine Wave Plot

The following script produces the sine wave plot using matplotlib.

```
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on a sine curve
x = np.arange(0, 3 * np.pi, 0.1)
y = np.sin(x)
plt.title("sine wave form")
# Plot the points using matplotlib
plt.plot(x, y)
plt.show()
```

###
`subplot()`

The `subplot()`

function allows you to plot different things in the same figure. In the following script, sine and cosine values are plotted.

```
import numpy as np
import matplotlib.pyplot as plt
# Compute the x and y coordinates for points on sine and cosine curves
x = np.arange(0, 3 * np.pi, 0.1)
y_sin = np.sin(x)
y_cos = np.cos(x)
# Set up a subplot grid that has height 2 and width 1,
# and set the first such subplot as active.
plt.subplot(2, 1, 1)
# Make the first plot
plt.plot(x, y_sin)
plt.title('Sine')
# Set the second subplot as active, and make the second plot.
plt.subplot(2, 1, 2)
plt.plot(x, y_cos)
plt.title('Cosine')
# Show the figure.
plt.show()
```

###
`bar()`

The pyplot submodule provides `bar()`

function to generate bar graphs. The following example produces the bar graph of two sets of x and y arrays.

```
from matplotlib import pyplot as plt
x = [5,8,10]
y = [12,16,6]
x2 = [6,9,11]
y2 = [6,15,7]
plt.bar(x, y, align = 'center')
plt.bar(x2, y2, color = 'g', align = 'center')
plt.title('Bar graph')
plt.ylabel('Y axis')
plt.xlabel('X axis')
plt.show()
```

## Histogram Using Matplotlib

NumPy has a `numpy.histogram()`

function that is a graphical representation of the frequency distribution of data. Rectangles of equal horizontal size corresponding to class interval called bin and variable height corresponding to frequency.

###
`numpy.histogram()`

The `numpy.histogram()`

function takes the input array and bins as two parameters. The successive elements in bin array act as the boundary of each bin.

```
import numpy as np
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
np.histogram(a,bins = [0,20,40,60,80,100])
hist,bins = np.histogram(a,bins = [0,20,40,60,80,100])
print hist
print bins
```

It will produce the following output

[3 4 5 2 1]
[0 20 40 60 80 100]
###
`plt()`

Matplotlib can convert this numeric representation of histogram into a graph. The `plt()`

function of pyplot submodule takes the array containing the data and bin array as parameters and converts into a histogram.

```
from matplotlib import pyplot as plt
import numpy as np
a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])
plt.hist(a, bins = [0,20,40,60,80,100])
plt.title("histogram")
plt.show()
```