This series will acquaint you with diagramming in python with Matplotlib, which is seemingly the most famous charting and information representation library for Python.

The least demanding method for introducing matplotlib is to utilize pip. Type following order in terminal:When envisioning information, frequently there is a need to plot numerous charts in a solitary figure. For example, numerous charts are valuable to picture a similar variable yet from various points (for example next to each other histogram and boxplot for a mathematical variable). In this post, I share 4 basic yet viable ways to plot different charts.

Pythonistas normally utilize the Matplotlib plotting library to show numeric information in plots, diagrams, and graphs in Python. A wide scope of use is given by matplotlib’s two APIs (Application Programming Interfaces):

Pyplot API interface, which offers an order of code protests that make matplotlib work like MATLAB.

OO (Object-Oriented) API interface, which offers an assortment of articles that can be collected with more prominent adaptability than pyplot. The OO API gives direct admittance to matplotlib’s backend layer.

The pyplot interface is simpler to execute than the OO form and is all the more usually utilized. For data about pyplot capacities and phrasing, allude to: What is Pyplot in Matplotlib

`# importing the required module` `import` `matplotlib.pyplot as plt` `# x axis values` `x ` `=` `[` `1` `,` `2` `,` `3` `]` `# corresponding y axis values` `y ` `=` `[` `2` `,` `4` `,` `1` `]` `# plotting the points` `plt.plot(x, y)` `# naming the x axis` `plt.xlabel(` `'x - axis'` `)` `# naming the y axis` `plt.ylabel(` `'y - axis'` `)` `# giving a title to my graph` `plt.title(` `'My first graph!'` `)` `# function to show the plot` `plt.show()` |

**Output: **

The code seems self-explanatory. Following steps were followed:

- Define the x-axis and corresponding y-axis values as lists.
- Plot them on canvas using
**.plot()**function. - Give a name to x-axis and y-axis using
**.xlabel()**and**.ylabel()**functions. - Give a title to your plot using
**.title()**function. - Finally, to view your plot, we use
**.show()**function.

**Plotting two or more lines on same plot**

- Python

`import` `matplotlib.pyplot as plt` `# line 1 points` `x1 ` `=` `[` `1` `,` `2` `,` `3` `]` `y1 ` `=` `[` `2` `,` `4` `,` `1` `]` `# plotting the line 1 points` `plt.plot(x1, y1, label ` `=` `"line 1"` `)` `# line 2 points` `x2 ` `=` `[` `1` `,` `2` `,` `3` `]` `y2 ` `=` `[` `4` `,` `1` `,` `3` `]` `# plotting the line 2 points` `plt.plot(x2, y2, label ` `=` `"line 2"` `)` `# naming the x axis` `plt.xlabel(` `'x - axis'` `)` `# naming the y axis` `plt.ylabel(` `'y - axis'` `)` `# giving a title to my graph` `plt.title(` `'Two lines on same graph!'` `)` `# show a legend on the plot` `plt.legend()` `# function to show the plot` `plt.show()` |

**Output: **

- Here, we plot two lines on the same graph. We differentiate between them by giving them a name(
**label**) which is passed as an argument of the .plot() function. - The small rectangular box giving information about the type of line and its color is called a legend. We can add a legend to our plot using
**.legend()**function

**Customization of Plots**

Here, we discuss some elementary customizations applicable to almost any plot.

- Python

`import` `matplotlib.pyplot as plt` `# x axis values` `x ` `=` `[` `1` `,` `2` `,` `3` `,` `4` `,` `5` `,` `6` `]` `# corresponding y axis values` `y ` `=` `[` `2` `,` `4` `,` `1` `,` `5` `,` `2` `,` `6` `]` `# plotting the points` `plt.plot(x, y, color` `=` `'green'` `, linestyle` `=` `'dashed'` `, linewidth ` `=` `3` `,` ` ` `marker` `=` `'o'` `, markerfacecolor` `=` `'blue'` `, markersize` `=` `12` `)` `# setting x and y axis range` `plt.ylim(` `1` `,` `8` `)` `plt.xlim(` `1` `,` `8` `)` `# naming the x axis` `plt.xlabel(` `'x - axis'` `)` `# naming the y axis` `plt.ylabel(` `'y - axis'` `)` `# giving a title to my graph` `plt.title(` `'Some cool customizations!'` `)` `# function to show the plot` `plt.show()` |

**Output: **

As you can see, we have done several customizations like

- setting the line-width, line-style, line-color.
- setting the marker, marker’s face color, marker’s size.
- overriding the x and y-axis range. If overriding is not done, pyplot module uses the auto-scale feature to set the axis range and scale

**Bar Chart**

- Python

`import` `matplotlib.pyplot as plt` `# x-coordinates of left sides of bars` `left ` `=` `[` `1` `, ` `2` `, ` `3` `, ` `4` `, ` `5` `]` `# heights of bars` `height ` `=` `[` `10` `, ` `24` `, ` `36` `, ` `40` `, ` `5` `]` `# labels for bars` `tick_label ` `=` `[` `'one'` `, ` `'two'` `, ` `'three'` `, ` `'four'` `, ` `'five'` `]` `# plotting a bar chart` `plt.bar(left, height, tick_label ` `=` `tick_label,` ` ` `width ` `=` `0.8` `, color ` `=` `[` `'red'` `, ` `'green'` `])` `# naming the x-axis` `plt.xlabel(` `'x - axis'` `)` `# naming the y-axis` `plt.ylabel(` `'y - axis'` `)` `# plot title` `plt.title(` `'My bar chart!'` `)` `# function to show the plot` `plt.show()` |

**Output :**

- Here, we use
**plt.bar()**function to plot a bar chart. - x-coordinates of the left side of bars are passed along with the heights of bars.
- you can also give some names to x-axis coordinates by defining
**tick_labels**

**Histogram**

- Python

`import` `matplotlib.pyplot as plt` `# frequencies` `ages ` `=` `[` `2` `,` `5` `,` `70` `,` `40` `,` `30` `,` `45` `,` `50` `,` `45` `,` `43` `,` `40` `,` `44` `,` ` ` `60` `,` `7` `,` `13` `,` `57` `,` `18` `,` `90` `,` `77` `,` `32` `,` `21` `,` `20` `,` `40` `]` `# setting the ranges and no. of intervals` `range` `=` `(` `0` `, ` `100` `)` `bins ` `=` `10` `# plotting a histogram` `plt.hist(ages, bins, ` `range` `, color ` `=` `'green'` `,` ` ` `histtype ` `=` `'bar'` `, rwidth ` `=` `0.8` `)` `# x-axis label` `plt.xlabel(` `'age'` `)` `# frequency label` `plt.ylabel(` `'No. of people'` `)` `# plot title` `plt.title(` `'My histogram'` `)` `# function to show the plot` `plt.show()` |

**Output:**

- Here, we use
**plt.hist()**function to plot a histogram. - frequencies are passed as the
**ages**list. - The range could be set by defining a tuple containing min and max values.
- The next step is to “
**bin**” the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. Here we have defined**bins**= 10. So, there are a total of 100/10 = 10 intervals.

**Scatter plot**

- Python

`import` `matplotlib.pyplot as plt` `# x-axis values` `x ` `=` `[` `1` `,` `2` `,` `3` `,` `4` `,` `5` `,` `6` `,` `7` `,` `8` `,` `9` `,` `10` `]` `# y-axis values` `y ` `=` `[` `2` `,` `4` `,` `5` `,` `7` `,` `6` `,` `8` `,` `9` `,` `11` `,` `12` `,` `12` `]` `# plotting points as a scatter plot` `plt.scatter(x, y, label` `=` `"stars"` `, color` `=` `"green"` `,` ` ` `marker` `=` `"*"` `, s` `=` `30` `)` `# x-axis label` `plt.xlabel(` `'x - axis'` `)` `# frequency label` `plt.ylabel(` `'y - axis'` `)` `# plot title` `plt.title(` `'My scatter plot!'` `)` `# showing legend` `plt.legend()` `# function to show the plot` `plt.show()` |

**Output:**

- Here, we use
**plt.scatter()**function to plot a scatter plot. - As a line, we define x and corresponding y-axis values here as well.
**marker**argument is used to set the character to use as a marker. Its size can be defined using the**s**parameter.

**Pie-chart**

- Python

`import` `matplotlib.pyplot as plt` `# defining labels` `activities ` `=` `[` `'eat'` `, ` `'sleep'` `, ` `'work'` `, ` `'play'` `]` `# portion covered by each label` `slices ` `=` `[` `3` `, ` `7` `, ` `8` `, ` `6` `]` `# color for each label` `colors ` `=` `[` `'r'` `, ` `'y'` `, ` `'g'` `, ` `'b'` `]` `# plotting the pie chart` `plt.pie(slices, labels ` `=` `activities, colors` `=` `colors,` ` ` `startangle` `=` `90` `, shadow ` `=` `True` `, explode ` `=` `(` `0` `, ` `0` `, ` `0.1` `, ` `0` `),` ` ` `radius ` `=` `1.2` `, autopct ` `=` `'%1.1f%%'` `)` `# plotting legend` `plt.legend()` `# showing the plot` `plt.show()` |

The output of above program looks like this:

- Here, we plot a pie chart by using
**plt.pie()**method. - First of all, we define the
**labels**using a list called**activities**. - Then, a portion of each label can be defined using another list called
**slices**. - Color for each label is defined using a list called
**colors**. **shadow = True**will show a shadow beneath each label in pie chart.**startangle**rotates the start of the pie chart by given degrees counterclockwise from the x-axis.**explode**is used to set the fraction of radius with which we offset each wedge.**autopct**is used to format the value of each label. Here, we have set it to show the percentage value only upto 1 decimal place.

**Plotting curves of given equation**

- Python

`# importing the required modules` `import` `matplotlib.pyplot as plt` `import` `numpy as np` `# setting the x - coordinates` `x ` `=` `np.arange(` `0` `, ` `2` `*` `(np.pi), ` `0.1` `)` `# setting the corresponding y - coordinates` `y ` `=` `np.sin(x)` `# plotting the points` `plt.plot(x, y)` `# function to show the plot` `plt.show()` |

The output of above program looks like this:

Here, we use **NumPy** which is a general-purpose array-processing package in python.

- To set the x-axis values, we use
**np.arange()**method in which the first two arguments are for range and the third one for step-wise increment. The result is a NumPy array. - To get corresponding y-axis values, we simply use the predefined
**np.sin()**method on the NumPy array. - Finally, we plot the points by passing x and y arrays to the
**plt.plot()**function.

So, in this part, we discussed various types of plots we can create in matplotlib. There are more plots that haven’t been covered but the most significant ones are discussed here –

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