PANDAS BIGGER PLOTS - Python

Introduction

Pandas is a popular Python library for data analysis and manipulation. It has a powerful plotting functionality, but sometimes the default plot size is not optimal for displaying the data. This can be especially true if you want to create bigger plots for presentations, reports or even posters. Fortunately, Pandas provides a variety of options to adjust the size of your plots.

In this article, we will cover three ways to create bigger plots using Pandas:

1. Adjusting the figure size with the figsize parameter.
2. Using the subplots function to create multiple plots in a larger figure.
3. Exporting the plot to a file and adjusting the size in an external tool.

Adjusting the figure size with the figsize parameter

The figsize parameter is used to specify the dimensions of the figure in inches. It takes a tuple of two values: width and height. By default, the figsize parameter is set to (6.4, 4.8). Here is an example of how to create a bigger plot using the figsize parameter:

import pandas as pd

# create a dataframe
df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 2, 7, 4]})

# plot the dataframe with a bigger size
df.plot(kind='bar', x='x', y='y', figsize=(10,8))

This will create a plot with a size of 10 inches in width and 8 inches in height.

Using the subplots function to create multiple plots in a larger figure

The subplots function is used to create multiple subplots in a larger figure. It takes two parameters: nrows and ncols, which specify the number of rows and columns for the subplots. You can also use the sharex and sharey parameters to align the x and y axis of the subplots. Here is an example of how to create a bigger plot with multiple subplots:

import pandas as pd

# create a dataframe
df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 2, 7, 4]})

# create the subplots
fig, axs = plt.subplots(2, 2, figsize=(10, 8), sharex=True, sharey=True)

# plot the data in the subplots
axs[0, 0].plot(df['x'], df['y'])
axs[0, 1].scatter(df['x'], df['y'])
axs[1, 0].bar(df['x'], df['y'])
axs[1, 1].plot(df['x'], df['y'], 'o--')

# set the title for each subplot
axs[0, 0].set_title('Line Plot')
axs[0, 1].set_title('Scatter Plot')
axs[1, 0].set_title('Bar Plot')
axs[1, 1].set_title('Dashed Line Plot')

# set the x and y labels
fig.text(0.5, 0.04, 'X Label', ha='center')
fig.text(0.04, 0.5, 'Y Label', va='center', rotation='vertical')

This will create a larger figure with four subplots, each with a size of 10 inches in width and 8 inches in height.

Exporting the plot to a file and adjusting the size in an external tool

Another option to create bigger plots is to export the plot to a file and adjust the size in an external tool, such as Adobe Illustrator or Inkscape. You can export the plot to different file formats, such as PNG, PDF or SVG. Here is an example of how to export a plot to a PNG file:

import pandas as pd

# create a dataframe
df = pd.DataFrame({'x': [1, 2, 3, 4], 'y': [5, 2, 7, 4]})

# plot the dataframe
ax = df.plot(kind='bar', x='x', y='y')

# export the plot to a PNG file
ax.get_figure().savefig('plot.png', dpi=300, bbox_inches='tight')

This will create a PNG file with a size of 6.4 inches in width and 4.8 inches in height. You can then open the file in an external tool and adjust the size as needed.

Conclusion

In this article, we covered three ways to create bigger plots using Pandas: adjusting the figure size with the figsize parameter, using the subplots function to create multiple plots in a larger figure, and exporting the plot to a file and adjusting the size in an external tool. By using these techniques, you can create larger and more informative plots for your data analysis and visualization projects.