# Place plots side by side in Matplotlib

Last Updated : 09 Aug, 2022

Matplotlib is the most popular Python library for plotting graphs and visualizing our data. In Matplotlib we can create multiple plots by calling them once. To create multiple plots we use the subplot function of pyplot module in Matplotlib.

Syntax: plt.subplot(nrows, .ncolumns, index)

Parameters:

• nrows is for number of rows means if the row is 1 then the plots lie horizontally.
• ncolumns stands for column means if the column is 1 then the plot lie vertically.
• and index is the count/index of plots. It starts with 1.

Approach:

• Import libraries and modules.
• Create data for plot.
• Now, create a subplot using above function.
• Give the parameters to the function according to the requirement.

Example 1:

## Python3

 `# importing libraries` `import` `numpy as np` `import` `matplotlib.pyplot as plt`     `# creating an array of data for x-axis` `x ``=` `np.array([``2``, ``4``, ``6``, ``8``, ``10``, ``12``, ``14``, ``16``, ``18``, ``20``])`   `# data for y-axis` `y_1 ``=` `2``*``x`   `# data for y-axis for another plot` `y_2 ``=` `3``*``x`   `# using subplot function and creating plot one` `plt.subplot(``1``, ``2``, ``1``)  ``# row 1, column 2, count 1` `plt.plot(x, y_1, ``'r'``, linewidth``=``5``, linestyle``=``':'``)` `plt.title(``'FIRST PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# using subplot function and creating plot two` `# row 1, column 2, count 2` `plt.subplot(``1``, ``2``, ``2``)`   `# g is for green color` `plt.plot(x, y_2, ``'g'``, linewidth``=``5``)` `plt.title(``'SECOND PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# space between the plots` `plt.tight_layout(``4``)`   `# show plot` `plt.show()`

Output:

Example 2: In vertical form.

## Python3

 `# importing libraries` `import` `numpy as np` `import` `matplotlib.pyplot as plt`   `# creating an array of data for x-axis` `x ``=` `np.array([``2``, ``4``, ``6``, ``8``, ``10``, ``12``, ``14``, ``16``, ``18``, ``20``])`   `# data for y-axis` `y_1 ``=` `2``*``x`   `# data for y-axis for another plot` `y_2 ``=` `3``*``x`   `# using subplot function and creating plot one` `# row 2, column 1, count 1` `plt.subplot(``2``, ``1``, ``1``)` `plt.plot(x, y_1, ``'r'``, linewidth``=``5``, linestyle``=``':'``)` `plt.title(``'FIRST PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# using subplot function and creating plot two` `# row 2, column 1, count 2` `plt.subplot(``2``, ``1``, ``2``)` `plt.plot(x, y_2, ``'g'``, linewidth``=``5``)` `plt.title(``'SECOND PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# space between the plots` `plt.tight_layout()`   `# show plot` `plt.show()`

Output:

To increase the size of the plots we can write like this

`plt.subplots(figsize(l, b))`

Example 3:

## Python3

 `# importing libraries` `import` `numpy as np` `import` `matplotlib.pyplot as plt`   `# creating an array of data for x-axis` `x ``=` `np.array([``2``, ``4``, ``6``, ``8``, ``10``, ``12``, ``14``, ``16``, ``18``, ``20``])`   `# data for y-axis` `y_1 ``=` `2``*``x`   `# data for y-axis for another plot` `y_2 ``=` `3``*``x`   `# figsize() function to adjust the size` `# of function` `plt.subplots(figsize``=``(``15``, ``5``))`   `# using subplot function and creating ` `# plot one` `plt.subplot(``1``, ``2``, ``1``)` `plt.plot(x, y_1, ``'r'``, linewidth``=``5``, linestyle``=``':'``)` `plt.title(``'FIRST PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# using subplot function and creating plot two` `plt.subplot(``1``, ``2``, ``2``)` `plt.plot(x, y_2, ``'g'``, linewidth``=``5``)` `plt.title(``'SECOND PLOT'``)` `plt.xlabel(``'x-axis'``)` `plt.ylabel(``'y-axis'``)`   `# space between the plots` `plt.tight_layout(``4``)`   `# show plot` `plt.show()`

Output: