# Graph Plotting in Python | Set 1

This series will introduce you to graphing in python with Matplotlib, which is arguably the most popular graphing and data visualization library for Python.

**Installation**

Easiest way to install matplotlib is to use pip. Type following command in terminal:

pip install matplotlib

OR, you can download it from here and install it manually.

**Getting started ( Plotting a line)**

`# 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**

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

**C****ustomization of Plots**

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

`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 auto-scale feature to set the axis range and scale.

**Bar Chart**

`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 left side of bars are passed along with heights of bars.
- you can also give some name to x-axis coordinates by defining
**tick_labels**

**Histogram**

`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. - Range could be set by defining a tuple containing min and max value.
- 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**

`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. - Like a line, we define x and corresponding y – axis values here as well.
**marker**argument is used to set the character to use as marker. Its size can be defined using**s**parameter.

**Pie-chart**

`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()` |

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, 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**

`# 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)` ` ` `# potting the points` `plt.plot(x, y)` ` ` `# function to show the plot` `plt.show()` |

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 first two arguments are for range and third one for step-wise increment. The result is a numpy array. - To get corresponding y-axis values, we simply use 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 which haven’t been covered but the most significant ones are discussed here –

This article is contributed by **Nikhil Kumar**. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

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