Converting String to Numeric Data Types in Pandas

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In this article, we will explore how to convert string data types to numeric data types in pandas. Specifically, we will focus on the common issue of converting a list of non-numeric strings into an integer or float data type.

Introduction

Pandas is a powerful library used for data manipulation and analysis in Python. One of its key features is the ability to convert data types between different categories. However, when dealing with string data that contains non-numeric characters, this process can be challenging.

In this article, we will delve into the world of pandas and explore the various methods available for converting string data to numeric data types.

The Problem

Let’s consider an example where we have a csv file containing grades received by students in different courses. We want to convert the grades_received column to numeric data types so that we can find the greatest and least value in each category.

Course    Grades_Recieved
098321      A,B,D
324323      C,B,D,F
213323      A,B,D,F

We want to create new categories that list the highest grade received and the lowest grade received in each course. However, when we try to convert grades_received to int64 using pandas’ astype method, we get an error:

import pandas as pd

df = pd.read_csv('grades.csv')

df.astype({'Grades_Recieved':'int64'}).dtypes

Output:

Grades_Recieved    object
dtype: object

The error message indicates that the int64 data type is not compatible with the string data in grades_received.

Solution 1: Using str.split()

One solution to this problem is to use the str.split() method, which splits a string into a list of substrings based on a specified delimiter. In our case, we can split the grades_received string by commas (,).

df['Highest_Grade'] = df['Grades_Recieved'].str.split(',').apply(lambda x: min(x))
df['Lowest_Grade'] = df['Grades_Recieved'].str.split(',').apply(lambda x: max(x))

This code creates two new columns, Highest_Grade and Lowest_Grade, which contain the minimum and maximum grade values in each course, respectively.

Solution 2: Using to_numeric()

Another solution is to use the to_numeric() method, which attempts to convert a string to a numeric data type. However, this method raises an error if the conversion fails.

df['Grades_Recieved'] = df['Grades_Recieved'].apply(lambda x: pd.to_numeric(x))

However, in our example, this approach will not work because we have multiple grades separated by commas. The to_numeric() method only converts a single value to a numeric data type.

Solution 3: Using map()

We can use the map() function to apply a custom mapping function to each element in the series.

def convert_grade(grade):
    # If the grade contains commas, split it into individual grades
    if ',' in grade:
        return min(grade.split(','))
    else:
        # Otherwise, return the original grade as an integer
        return int(grade)

df['Grades_Recieved'] = df['Grades_Recieved'].map(convert_grade)

This code defines a custom mapping function convert_grade() that splits each comma-separated grade into individual grades and returns the minimum value. The map() function applies this conversion to each element in the series.

Conclusion

In conclusion, converting string data types to numeric data types in pandas requires careful consideration of the potential errors that may occur during the conversion process. By using a combination of methods such as str.split(), to_numeric(), and map(), we can effectively convert non-numeric strings into numeric data types and perform various analysis tasks.

Example Use Cases

Converting grades received by students in different courses to numeric data types.
Finding the highest and lowest grade values in each course.
Creating new categories that list the highest and lowest grade values in each course.

By following these steps and using the methods discussed in this article, you can successfully convert string data types to numeric data types in pandas.

Last modified on 2025-03-12