How to Convert Currency Strings to Floats in Pandas: Quick Methods

Rescuing pandas (7) – Converting String Columns with Currency Symbols to Float

Data Requirement

The data frame contains a price column with strings like "$1,234.56" (no NaN). The task is to remove the dollar sign $ and commas , and convert the column to float.

Requirement Breakdown

Several common pandas approaches are presented for this cleaning task.

Solution

Method One

Before using pandas, you can rely on Python built‑ins and the re module.

Using two str.replace calls

# Using two string.replace methods
data['price'] = [float(i.replace('$', '').replace(',', '')) for i in data['price']]

Using the re module

# re
import re

data['price'] = [float(re.sub(r'[$,]', '', i)) for i in data['price']]

The result is the same as above.

Method Two

Leverage pandas methods such as .map and .str operations.

Map with lambda and

replace

# Using map with chained replace

data['price'].map(lambda x: x.replace('$', '').replace(',', '')).astype(float)

Map with

re.sub

# Using map with re

data['price'].map(lambda x: re.sub(r'[$,]', '', x)).astype(float)

Split and join after stripping

# Join after split and strip

data['price'].map(lambda x: ''.join(x.lstrip('$').split(','))).astype(float)

Using pandas .str.split and

.str.strip

# .str.strip().str.split() then join

data['price'].str.strip('$').str.split(',').map(lambda x: ''.join(x)).astype(float)

Direct .str.replace with regex

# .str.replace with regex

data['price'].str.replace(r'[$,]', '', regex=True).astype(float)

Summary

Different approaches can achieve the same goal; choose the one that fits your coding style. While pandas offers concise .str.replace, other methods using Python built‑ins or re are also viable. This short guide lists several easy‑to‑understand solutions for cleaning currency strings in pandas.

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Written on February 12, 2022