How to use Python regular expressions for data visualization

Python regular expressions are a powerful tool for processing text data. Regular expressions help you extract, transform, and visualize data from text. This article will introduce how to use Python regular expressions for data visualization.

1. Import related libraries

Before you start, you need to install the necessary Python libraries: Pandas, Matplotlib and Re. You can install it using pip.

pip install pandas matplotlib re

Then you need to import these libraries into the Python file.

import pandas as pd
import matplotlib.pyplot as plt
import re

2. Read data

In this article, we will use a spreadsheet file that contains data about income and expenses during the influenza pandemic. First, you need to use the read_excel function from the pandas library to read the data in the spreadsheet file.

df = pd.read_excel('data.xlsx')

3. Data Preprocessing

Before using regular expressions to visualize data, you need to perform some data preprocessing operations. This article will describe the following two preprocessing steps:

• Unformat data: Each cell in the spreadsheet file may contain formatted data, such as currency values, percentages, etc. You need to unformat these formatted data in order to proceed to the next step.
• Extract data: You need to extract data from each cell in order to visualize it. You can use regular expressions to extract certain data.

The following functions can unformat data:

def strip_currency(val):
    return re.sub(r'[^d.]', '', val)

The following functions can extract certain data:

def extract_number(val):
    return re.findall(r'd+.?d*', val)[0]

You can apply them to your spreadsheet using the apply function of each cell. Here is the code to apply the above function:

df['income'] = df['income'].apply(strip_currency).apply(extract_number).astype(float)
df['expenses'] = df['expenses'].apply(strip_currency).apply(extract_number).astype(float)

4. Visualizing Data

Once you have unformatted and extracted the data from each cell, you can now use The Matplotlib library visualizes it. In this article, we will use a scatter plot to represent the relationship between income and expenses.

plt.scatter(df['income'], df['expenses'])
plt.xlabel('Income')
plt.ylabel('Expenses')
plt.show()

This code will create a scatter plot with income on the horizontal axis and expenses on the vertical axis.

This is the basic steps on how to use Python regular expressions for data visualization. You can continue processing and visualizing the data as needed to better understand it.

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