How pandas to_numeric works in Python? Best example

How pandas to_numeric works in Python? Best example
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When working with numerical data in Python, especially in Pandas, we often encounter situations where numbers are stored as strings. This can cause issues while performing calculations. Thankfully, the pandas.to_numeric() function helps to efficiently convert these string-based numbers into actual numeric types. In this article, I’ll walk you through how pandas.to_numeric() works and demonstrate its best usage.

What is pandas.to_numeric()?

pandas.to_numeric() is a built-in function that converts a given argument (such as a Pandas Series or list) into a numeric type. It is particularly useful when dealing with messy datasets where numbers might be stored as strings.

The function can handle different scenarios, including:

  • Converting integer or float-like strings into proper numeric values
  • Handling missing or non-convertible values gracefully
  • Forcing conversions with specific error-handling approaches

Basic Syntax of pandas.to_numeric()

Here’s the basic syntax of the function:

pandas.to_numeric(arg, errors='raise', downcast=None)

The function parameters include:

  • arg – The input data (a list, Pandas Series, or DataFrame column).
  • errors – Defines how to handle invalid parsing.
  • downcast – Optimizes memory by converting numeric data to the smallest possible type.

Handling Errors with pandas.to_numeric()

The errors parameter determines the behavior when non-numeric values are encountered:

Value Behavior
'raise' Throws an error if conversion fails (default setting).
'coerce' Converts invalid values to NaN.
'ignore' Leaves invalid entries unchanged.

Best Example: Converting a Series to Numeric

Let me demonstrate pandas.to_numeric() with a practical example:

import pandas as pd

# Sample DataFrame with mixed-type numbers
df = pd.DataFrame({'values': ['10', '20.5', '30', 'error', '50']})

# Applying pandas.to_numeric()
df['values_numeric'] = pd.to_numeric(df['values'], errors='coerce')

print(df)

Output:

  values  values_numeric
0     10            10.0
1   20.5            20.5
2     30            30.0
3  error             NaN
4     50            50.0

In this example:

  • Valid numeric strings are converted correctly.
  • The invalid entry 'error' is replaced with NaN due to the errors='coerce' parameter.

Optimizing Memory Usage with Downcasting

The downcast parameter allows us to convert numbers into smaller data types to save memory. Let’s take a look at how it works:

df['values_optimized'] = pd.to_numeric(df['values_numeric'], downcast='integer')

print(df.dtypes)

Output:

values               object
values_numeric      float64
values_optimized      int8
dtype: object

By using downcast='integer', Pandas automatically sets the smallest possible integer type.

Key Takeaways

  1. pandas.to_numeric() is a powerful tool for converting strings into numbers.
  2. The errors parameter allows handling of invalid values flexibly.
  3. Using downcast optimizes memory usage significantly.

By understanding and leveraging pandas.to_numeric(), you can ensure that your numerical data is in the correct format and efficiently managed within Pandas.

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