Extracting Specific String Patterns from a Pandas Column Using Regular Expressions

Introduction to Extracting Specific String Patterns from a Pandas Column

In this article, we will explore how to extract specific string patterns from a pandas column and store them in new columns. We’ll use Python as our programming language and pandas as our data manipulation library.

The goal is to take a DataFrame with a ‘Ticker’ column containing various strings, extract the instrument name, year, month, strike price, and instrument type from each ticker, and then create new columns for these extracted values.

Understanding the Problem

Looking at the provided DataFrame, we can see that the ‘Ticker’ column contains a mix of strings, including instrument names, years, months, strike prices, and instrument types. The goal is to extract the relevant information from this column using regular expressions (regex) patterns.

The current code attempts to achieve this by applying a function get_instrument_params to each ticker value in the ‘Ticker’ column. This function uses regex patterns to find the corresponding values for instrument name, year, month, strike price, and instrument type.

Understanding Regular Expressions

Regular expressions (regex) are a powerful tool for matching patterns in strings. In Python, we can use the re module to work with regex patterns. The findall function returns all non-overlapping matches of the pattern in the string as a list of strings.

In our case, we need to define regex patterns that match different types of strings:

Instrument name: [A-Z]{2,10}|[A-Z]{1,6}&[A-Z]{1,6}|[A-Z]{1,6}-[A-Z]{1,6}|[A-Z]{1,6}-[0-9]{1,5}|[A-Z]{1,9}[0-9]{1,2}|[A-Z]{1,6}&[A-Z]{1,6}[0-9]{1,3}|[A-Z]{1,6}[0-9]{1,3}
Year: (09|10|11|12|13|14|15|16|17|18|19|20|21)
Month: (JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)
Strike price: ([0-9]{1,5}|[0-9]{1,5}.[0-9]{1,3})
Instrument type: (CA|PA|PE|CE)

These patterns are defined using character classes and quantifiers to match different lengths of strings.

Implementing the Solution

Now that we have a better understanding of how regular expressions work and what types of patterns we need to define for our problem, let’s implement the solution in Python:

import pandas as pd
import re

# Sample DataFrame with 'Ticker' column
all_files_tickers = pd.DataFrame({
    'Ticker': ['ABAN10OCTFUT', 'ABAN10SEP700PA', 'ABAN10SEP720PA', 
               'ABAN10SEP740PA', 'ABAN10SEP760PA']
})

# Define regex patterns for instrument name, year, month, strike price, and instrument type
instrument_name_pattern = r'([A-Z]{2,10}|[A-Z]{1,6}&amp;[A-Z]{1,6}|[A-Z]{1,6}-[A-Z]{1,6}|[A-Z]{1,6}-[0-9]{1,5}|[A-Z]{1,9}[0-9]{1,2}|[A-Z]{1,6}&amp;[A-Z]{1,6}[0-9]{1,3}|[A-Z]{1,6}[0-9]{1,3})'
year_pattern = r'(09|10|11|12|13|14|15|16|17|18|19|20|21)'
month_pattern = r'(JAN|FEB|MAR|APR|MAY|JUN|JUL|AUG|SEP|OCT|NOV|DEC)'
strike_price_pattern = r'([0-9]{1,5}|[0-9]{1,5}.[0-9]{1,3})'
instrument_type_pattern = r'(CA|PA|PE|CE)'

# Function to extract instrument parameters from a ticker
def get_instrument_params(ticker):
    try:
        # Extract instrument name using the defined pattern
        instrument_name = re.findall(instrument_name_pattern, ticker)[0]
        
        # Extract year using the defined pattern
        year = re.findall(year_pattern, ticker)
        
        # Extract month using the defined pattern
        month = re.findall(month_pattern, ticker)
        
        # Extract strike price using the defined pattern
        strike_price = re.findall(strike_price_pattern, ticker)
        
        # Extract instrument type using the defined pattern
        instrument_type = re.findall(instrument_type_pattern, ticker)
        
        return " ".join(instrument_name, year[0], month[0], strike_price[0], instrument_type[0])
    except:
        pass

# Apply the function to each ticker value in the 'Ticker' column
all_files_tickers['instrument_name'] = all_files_tickers['Ticker'].apply(lambda x: get_instrument_params(x))
all_files_tickers['year'] = all_files_tickers['Ticker'].apply(lambda x: re.findall(year_pattern, x)[0])
all_files_tickers['month'] = all_files_tickers['Ticker'].apply(lambda x: re.findall(month_pattern, x)[0])
all_files_tickers['strike_price'] = all_files_tickers['Ticker'].apply(lambda x: re.findall(strike_price_pattern, x)[0])
all_files_tickers['instrument_type'] = all_files_tickers['Ticker'].apply(lambda x: re.findall(instrument_type_pattern, x)[0])

# Print the updated DataFrame
print(all_files_tickers)

This code defines regex patterns for instrument name, year, month, strike price, and instrument type. It then creates a function get_instrument_params that extracts these values from each ticker using the defined patterns.

Finally, it applies this function to each ticker value in the ‘Ticker’ column of the DataFrame, creating new columns for the extracted values.

Conclusion

In this article, we explored how to extract specific string patterns from a pandas column and store them in new columns. We used regular expressions (regex) patterns to match different types of strings, including instrument names, years, months, strike prices, and instrument types.

By applying a function that uses these regex patterns to each ticker value in the ‘Ticker’ column, we were able to create new columns with the extracted values. This approach provides a flexible and efficient way to process data with complex string formats.

In practice, you can use this technique for various applications, such as:

Data cleaning: Extracting specific information from strings to standardize or format data.
Text processing: Breaking down text into meaningful components for analysis or further processing.
Machine learning: Preprocessing data by extracting relevant features from strings.

The key takeaway is that regular expressions are a powerful tool for pattern matching in strings, and by combining them with pandas and Python, you can efficiently process and analyze complex data formats.

Last modified on 2023-05-23