How To Convert String To Numeric In Python

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convert string to numeric in python

Welcome to our blog on how to convert string to numeric in Python. Python is a worldwide known programming language that offers various data manipulation capabilities. One common task is converting strings that contain numerical values into actual numeric data types such as integers or floats.

Either you are dealing with user inputs, data from external sources, or simply need to perform calculations on numeric strings, understanding how to convert them to numeric values is a crucial skill.

In this blog, we will explore different approaches and sample problems to convert strings to numeric data types in Python.

Why is converting string to numeric in python needed?

Converting strings to numeric values in Python is a crucial task in many data processing and analysis scenarios. Here are some reasons why is converting string to numeric in python is needed:

  1. Data Processing: When dealing with data from external sources such as files, APIs, or databases, the data may come in the form of strings. In order to perform numerical operations or analysis on this data, it’s important to convert them to numeric data types.
  2. Data Validation: String inputs may also need to be validated to ensure they represent valid numeric values.
  3. Data Manipulation: String representations of numbers may be part of larger datasets that need to be processed or analyzed. Converting them to numeric data types allows you to perform arithmetic operations, comparisons, and other data manipulation tasks accurately.
  1. Output Formatting: Sometimes, you may need to format numeric values as strings for display purposes, such as in reports or visualizations.

How To Convert String To Numeric In Python

There are six different approaches to converting string to numeric in python:

  1. Custom conversion logic
  2. Using regular expressions
  3. Using third-party libraries
  4. Handling Edge Cases
  5. Using Type Casting
  6. Using Arithmetic Operations

Let’s dive in more with examples to each approach.

Approach 1: Custom Conversion Logic

In this approach, you can implement your own conversion logic using string manipulation techniques to extract and convert numeric values from strings.

Pros:

  • Offers flexibility to handle specific formatting or parsing requirements.
  • Provides full control over the conversion process.
  • Can be tailored to suit specific data types or patterns.

Cons:

  • May require additional coding and testing effort.
  • Can be time-consuming for complex string formats.
  • May not be suitable for large-scale data processing tasks.

Code:

string_num = "123.45"  # Example string with a decimal number

# Custom conversion logic
num = float(string_num.replace(",", ""))  # Removes comma and converts to float

print(num)
#

Output:

123.45

Code Explanation:

  1. A string variable string_num is defined with the value “123.45”.
  2. A custom conversion logic is applied to convert the string to a float value without the commas.
  3. The replace() method is used to remove any commas in the string by replacing them with an empty string “”.
  4. The resulting string without commas is then converted to a float value using the float() function.
  5. The resulting float value is stored in the variable num.
  6. The value of num is printed to the console using the print() function.

Approach 2: Using Regular Expressions

In this approach, you can utilize the power of regular expressions to match and extract numeric values from strings based on specific patterns or formats.

Pros:

  • Provides flexibility in handling complex string formats.
  • Offers precise and fine-grained control over pattern matching.
  • Can be used for advanced data extraction tasks.

Cons:

  • Requires familiarity with regular expressions.
  • Can be difficult to implement for complex patterns.
  • May not be as efficient for large-scale data processing tasks.

Code:     

import re

def convert_to_numeric(string):
    # Use regular expression to substitute all non-numeric characters with an empty string
    numeric_string = re.sub(r'[^0-9]', '', string)
    return int(numeric_string)

# Test with a sample string
string = "1234abc5678def"
numeric_value = convert_to_numeric(string)
print(numeric_value)

Output:

12345678

Code Explanation:

  1. The re module is imported to use regular expressions in Python.
  2. The convert_to_numeric() function takes a string as input.
  3. re.sub() function is used to substitute all non-numeric characters with an empty string using the regular expression pattern r'[^0-9]’. This pattern matches any character that is not a digit (0-9).
  4. The resulting numeric_string is returned.
  5. int(numeric_string) is used to convert the numeric_string to an integer.
  6. The string variable is assigned a sample string “1234abc5678def”.
  7. The convert_to_numeric() function is called with the string variable as input.
  8. The numeric_value is printed, which is the converted numeric value of the string “12345678”.

Approach 3: Using Third-Party Libraries

When it comes to handling strings and numeric values, there’s an approach that can really amp up your game. It’s all about leveraging third-party libraries like NumPy or Pandas. These powerhouses come packed with specialized functions that can effortlessly convert strings into numeric values.

Pros:

  • Offers pre-built functions for efficient and accurate conversion.
  • Provides additional functionality for data manipulation and analysis.
  • Can handle complex data types and formats.

Cons:

  • Requires installation and import of external libraries.
  • May introduce dependencies in your code.
  • May have a learning curve for using library-specific functions.

Code:

import numpy as np

string_num = "498"  # Example string with a decimal number

# Using third-party library (NumPy)
num = np.asarray(string_num, dtype=float)  # Converts to float using NumPy

print(num)
#

Output:

498.0

Code Explanation:

  1. The NumPy library is imported using the statement “import numpy as np”.
  2. A string variable “string_num” is defined with the value “498”, which represents a decimal number in string form.
  3. The NumPy function “np.asarray()” is used to convert the string “string_num” to a float value. The “dtype=float” argument specifies that the resulting array should have a data type of float.
  4. The converted float value is stored in a variable called “num”.
  5. The “print()” function is used to display the value of “num” on the screen.

Approach 4: Handling Edge Cases

In this approach, you can handle specific edge cases or formatting inconsistencies in strings before converting them to numeric values. This may involve removing unwanted characters, handling decimal points, or extracting specific patterns.

Pros:

  • Allows for handling unique cases that may not be covered by other approaches.
  • Provides an opportunity to clean and preprocess data before conversion.
  • Can be combined with other approaches for more accurate results.

Cons:

  • May require additional conditional checks and string manipulations.
  • Can be time-consuming for complex edge cases.
  • May need constant updates to handle evolving data pattern

Code:

string_num = "$1,234.56"  # Example string with a formatted number

# Handling Edge Cases
num = float(string_num.replace("$", "").replace(",", ""))  # Removes "$" and "," and converts to float

print(num)
#

Output:

1234.56

Code Explanation:

  1. The variable string_num is assigned the value of “$1,234.56”, which is a string representing a formatted number with a dollar sign and commas.
  2. The string_num is processed to remove the “$” and “,” characters using the replace() method. The first replace() call removes the “$” character, and the second replace() call removes the “,” character. The resulting string is then converted to a float using the float() function, which removes any leading zeros and converts the remaining string to a floating-point number.
  3. The resulting float value is stored in the variable num, which now contains the numeric value of the original formatted number string without the dollar sign and commas.
  4. Finally, the value of num is printed using the print() function, which displays the numeric value on the screen.

Approach 5: Using Type Casting

In this approach, you can directly cast the string to the desired numeric data type, such as int or float, using the corresponding type casting functions.

Pros:

  • Simple and straightforward approach.
  • Requires minimal code and can be efficient for small-scale data processing tasks.
  • Suitable for cases where the string format aligns with the desired data type.

Cons:

  • May raise errors for strings that do not conform to the desired data type.
  • Can be less flexible in handling complex string formats.
  • May not be suitable for cases where data cleaning or preprocessing is required.

Code:

string_num = "12345"  # Example string with an integer number

# Using Type Casting
num = int(string_num)  # Converts to int

print(num)
#

Output:

12345

Code Explanation:

  1. string_num is a variable that holds the value “12345”, which is a string containing an integer number.
  2. The int() function is used to perform type casting, converting the string value of string_num into an integer.
  3. The result of the type casting is stored in a new variable num, which now holds the integer value of “12345”.
  4. The print() function is then used to display the value of num on the screen.

Approach 6: Using Arithmetic Operations

In this approach, you can utilize arithmetic operations to perform mathematical operations on strings that contain numeric values, which can result in a converted numeric value.

Pros:

  • Can handle simple mathematical operations on numeric strings.
  • Provides an opportunity to perform custom calculations during conversion.
  • Can be combined with other approaches for unique use cases.

Cons:

  • Limited to simple arithmetic operations.
  • May not be efficient for large-scale data processing tasks.
  • Requires careful handling of edge cases to avoid errors.

Code:

string_num = "100"  # Example string with a numeric value

# Using Arithmetic Operations
num = int(string_num) * 2  # Multiplies the integer value by 2

print(num)
#

Output:

200

Code explanation:

  1. string_num is a variable that holds the string value “100”, which is a string representation of a numeric value.
  2. The int() function is used to convert the string representation of the numeric value to an integer. The resulting integer is stored in the variable num.
  3. The * operator performs a multiplication operation, and 2 is multiplied with the integer value stored in num.
  4. The result of the multiplication operation is stored in the num variable, which now holds the value of 200 (since 100 * 2 = 200).
  5. The print() function is used to display the value of num on the screen.

Best Approach of Converting String To Numeric In Python

The third-party library is the best approach for converting string to numeric in python. Here are the some key features of this method are:

  • Efficiency and accuracy: These libraries offer pre-built functions that are designed for efficient and accurate conversion of string data to numeric values. The functions are optimized for performance and can handle large datasets with ease.
  • Additional functionality: These libraries provide additional functionality for data manipulation and analysis, such as handling missing values, filtering data, and performing mathematical operations on arrays.
  • Flexibility: The libraries offer a range of options and settings for customizing the conversion process, such as specifying the data type of the resulting array and handling errors and exceptions.

Sample Problems For Converting String To Numeric In Python

Sample Problem 1:

Scenario: You are a mobile phone retailer and have a dataset of phone prices in string format that needs to be converted to numeric format.

Problem: The string values contain currency symbols and commas that need to be removed before conversion.

Solution Steps:

  1. A sample dataset of phone prices is created in string format and assigned to the variable phone_prices.
  2. A function named clean_string is defined to clean the string values in the dataset. It takes a single argument price_str, which represents a string value.
  3. The first line of the clean_string function removes the currency symbol (‘₹’) from the input string using the replace method.
  4. The second line of the clean_string function removes the commas (‘,’) from the input string using the replace method.
  5. The clean_string function returns the cleaned string value.
  6. The clean_string function is applied to each value in the phone_prices dataset using a list comprehension, and the results are assigned to the variable cleaned_prices.
  7. The float function is used to convert each cleaned string value in the cleaned_prices dataset to a numeric value, and the results are assigned to the variable numeric_prices.
  8. The print function is used to display the contents of the numeric_prices dataset.

Code:

# Sample dataset of phone prices in string format
phone_prices = ["₹23,999", "₹34,999", "₹45,999", "₹56,999", "₹67,999"]

# Function to clean string values
def clean_string(price_str):
    # Remove currency symbol
    price_str = price_str.replace("₹", "")
    # Remove commas
    price_str = price_str.replace(",", "")
    return price_str

# Apply clean_string function to each value in phone_prices dataset
cleaned_prices = [clean_string(price) for price in phone_prices]

# Convert cleaned string values to numeric values
numeric_prices = [float(price) for price in cleaned_prices]

# Display numeric prices
print(numeric_prices)

Output:

[23999.0, 34999.0, 45999.0, 56999.0, 67999.0]

Sample Problem 2:

Scenario: You are a laptop retailer and have a dataset of laptop model numbers that include letters, numbers, and special characters.

Problem: The model numbers need to be converted to a numeric format that represents the year the laptop was released.

Solution Steps:

  1. Import the regular expression module “re”.
  2. Define a list of laptop model numbers called “model_numbers”.
  3. Define a regular expression pattern that matches any four-digit number called “pattern”.
  4. Use a list comprehension to apply the regular expression pattern to each model number in “model_numbers” and extract the year as a string using the “re.findall()” method.
  5. Use another list comprehension to convert each year string into an integer using the “int()” function.
  6. Print the resulting list of numeric years.

Code:

import re

# Sample dataset of laptop model numbers
model_numbers = ['Dell XPS 13 (2021)', 'MacBook Air M1 (2020)', 'Lenovo ThinkPad X1 Carbon (2019)']

# Define regular expression pattern to extract year
pattern = r'\d{4}'

# Apply pattern to each model number and extract year
years = [re.findall(pattern, model)[0] for model in model_numbers]

# Convert year strings to numeric format
numeric_years = [int(year) for year in years]

# Display results
print(numeric_years)

Output:

[2021, 2020, 2019]

Sample Problem 3:

Scenario: You are a speaker manufacturer and have a dataset of speaker wattage ratings in string format that needs to be converted to numeric format.

Problem: The wattage values include units of measurement (Watts) that need to be removed before conversion.

Solution Steps:

  1. The NumPy library is imported as “np”.
  2. A list of wattage ratings in string format is created and stored in the variable “wattages_str”.
  3. A NumPy array of the wattage ratings in numeric format is created using a list comprehension and the “replace” and “strip” string methods. The “replace” method removes the “Watts” substring from each wattage rating, while the “strip” method removes any whitespace characters before and after the rating. The resulting numeric values are stored in the variable “wattages_numeric”.
  4. The resulting NumPy array is printed using the “print” function.

Code:

import numpy as np

# Sample dataset of speaker wattage ratings in string format
wattages_str = ['200 Watts', '1000 Watts', '500 Watts']

# Convert string values to numeric format using NumPy
wattages_numeric = np.asarray([wattage.replace('Watts', '').strip() for wattage in wattages_str], dtype=float)

# Display results
print(wattages_numeric)

Output:

[ 200. 1000.  500.]

Sample Problem 4:

Scenario: You are a social media marketer and have a dataset of Facebook ad click-through rates in string format that needs to be converted to numeric format.

Problem: Some of the string values in the dataset are missing or contain non-numeric characters, which will cause errors during conversion.

Solution Steps:

  1. Define a sample dataset of Facebook ad click-through rates in string format as a list of strings.
  2. Define a function named clean_string that takes in a click rate as a string and returns None if the string is empty or “N/A”, and otherwise removes the percentage symbol from the string.
  3. Apply the clean_string function to each click rate in the sample dataset using a list comprehension, and store the results in a new list named cleaned_rates.
  4. Convert the cleaned click rates from strings to numeric format using another list comprehension, and store the results in a new list named numeric_rates. If a cleaned click rate is None, store None in the numeric_rates list.
  5. Print out the resulting numeric_rates list.

Code:

# Sample dataset of Facebook ad click-through rates in string format
click_rates_str = ['0.2%', '0.5%', '', '1.0%', 'N/A']

# Define function to handle missing or invalid values
def clean_string(click_rate_str):
    if click_rate_str == '' or click_rate_str == 'N/A':
        return None  # Return None for missing or invalid values
    else:
        return click_rate_str.replace('%', '')  # Remove percentage symbol

# Apply function to dataset
cleaned_rates = [clean_string(rate) for rate in click_rates_str]

# Convert cleaned strings to numeric format
numeric_rates = [float(rate) if rate is not None else None for rate in cleaned_rates]

# Display results
print(numeric_rates)

Output:

[0.2, 0.5, None, 1.0, None]

Sample Problem 5:

Scenario: You are a market analyst and have a dataset of stock prices in string format that needs to be converted to numeric format.

Problem: The string values contain commas and dollar signs that need to be removed before conversion.

Solution Steps:

  1. Initialize a list of stock prices in string format: stock_prices = [‘$100.50’, ‘$50.25’, ‘$200,000’]
  2. Remove commas and dollar signs from the string values using replace() method: cleaned_prices = [price.replace(‘,’, ”).replace(‘$’, ”) for price in stock_prices]. This creates a new list of cleaned strings with the commas and dollar signs removed.
  3. Convert the cleaned strings to numeric format using type casting: numeric_prices = [float(price) for price in cleaned_prices]. This creates a new list of floats.
  4. Display the resulting list of numeric prices: print(numeric_prices). This prints the list to the console.

Code:

# Sample dataset of stock prices in string format
stock_prices = ['$100.50', '$50.25', '$200,000']

# Remove commas and dollar signs from string values
cleaned_prices = [price.replace(',', '').replace('$', '') for price in stock_prices]

# Convert cleaned strings to numeric format using type casting
numeric_prices = [float(price) for price in cleaned_prices]

# Display results
print(numeric_prices)

Output:

[100.5, 50.25, 200000.0]

Sample Problem 6:

Scenario: You are a Facebook developer and have a dataset of post engagement rates in string format that needs to be converted to numeric format.

Problem: The string values represent engagement rates as a percentage and need to be divided by 100 to convert to a decimal format.

Solution Steps:

  1. A list of engagement rates is defined as engagement_rates and is initialized with the string values ‘25%’, ‘50%’, ‘75%’.
  2. A list comprehension is used to iterate through each string value in engagement_rates.
  3. For each string value, the ‘%’ symbol is removed using the replace() method, and the resulting string is converted to a float value using the float() function.
  4. The float value is divided by 100 to convert the percentage to a decimal.
  5. The resulting float values are stored in a new list called numeric_rates.
  6. The print() function is used to display the contents of numeric_rates.

Code:

# Sample dataset of post engagement rates in string format
engagement_rates = ['25%', '50%', '75%']

# Remove percentage symbol from string values and divide by 100
numeric_rates = [(float(rate.replace('%', '')) / 100) for rate in engagement_rates]

# Display results
print(numeric_rates)

Output:

[0.25, 0.5, 0.75]

Conclusion

After thorough analysis of the methods available for converting string to numeric in python, it becomes apparent that each approach boasts its own unique set of advantages and disadvantages.

The third-party library is often considered  as the most proficient method due to its efficiency, accuracy, flexibility and ease of use. It is important to choose the appropriate method which depends as per the need of the project.

Author:

Vikas Panwar

I am well-versed individual in the field of computer programming, having knowledge and experience in multiple programming languages including Python, Java, and C++. Furthermore, you have a solid understanding of data structures and algorithms, showcasing your proficiency in the fundamentals of computer science. Follow:  
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