BUG: pd.Series.isnumeric() doesn't work on decimal value strings

[sf-dcp]

Pandas version checks

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Reproducible Example

import pandas as pd

df = pd.DataFrame({"string_values": ["1", "1.0", "1.1"]})
df.string_values.str.isnumeric()

Issue Description

The series method .isnumeric() only works on integer strings. If a string number is decimal, it will return False. When running the example below, the following is returned:

This is the docs description for the method:

Expected Behavior

Running the method on decimal strings should return True.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.8
python-bits : 64
OS : Linux
OS-release : 5.15.49-linuxkit-pr
Version : #1 SMP PREEMPT Thu May 25 07:27:39 UTC 2023
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : C.UTF-8

pandas : 2.2.3
numpy : 2.2.1
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.3.1
Cython : None
sphinx : None
IPython : 8.31.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.5
lxml.etree : 5.3.0
matplotlib : 3.10.0
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : 2.9.10
pymysql : None
pyarrow : 18.1.0
pyreadstat : None
pytest : 8.3.4
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.15.1
sqlalchemy : 2.0.37
tables : None
tabulate : 0.9.0
xarray : None
xlrd : 2.0.1
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None

[akj2018]

Hi @sf-dcp,

This is not a bug in pd.Series.isnumeric() but expected behavior. As per documentation for str.isnumeric(), . (decimal point) is not a numeric character, so strings with decimal points like "1.1" fail the isnumeric() check.

str.isnumeric() method checks if:

1. All characters in the string are numeric characters.
2. There is at least one character in the string.

Numeric characters are those with the Unicode property values:

• Numeric_Type=Digit (e.g., "1", "2", "3")
• Numeric_Type=Decimal (e.g., standard decimal digits like "1", "2")
• Numeric_Type=Numeric (e.g., fractions like "⅕", Roman numerals, etc.)

Therefore, . (decimal character) does not have the Unicode numeric property; it's categorized as punctuation.

Solution 01 - Using regex

import re

def is_numeric(string):
    pattern = r'^-?\d+(\.\d+)?$'
    return bool(re.match(pattern, string))

Note:

• correctly identifies valid numeric strings like "1", "-2.5" and "0.0".
• rejects invalid strings like "1.0.0", "abc", or empty strings.
• Does not works for scientific notations, "1e10" returns False

Solution 02 - Using float()

def is_numeric(string):
    try:
        float(string)
        return True
    except ValueError:
        return False

Note:

• correctly identifies valid numeric strings like "1", "-2.5", "1e10" (scientific notation), and "0.0"
• rejects invalid strings like "1.0.0", "abc", or empty strings.

Performance Consideration

• Regex: Slower due to the regex engine's overhead of parsing and matching patterns.
• Float-based: Faster, as it directly leverages Python’s built-in C-optimized parsing.

# Test dataset (mix of numeric and non-numeric strings)
data = ["1", "123.45", "-987.65", "0", "abc", "123.45.67"] * 1_000_000

• Regex time: 5.59 seconds
• Float time: 2.23 seconds

[sf-dcp]

Hi @akj2018, thanks for the prompt reply and suggestions. I think the method name is a bit misleading then where I would expect a string number (with a dot or a negative integer string) to be returned True. But I also understand the logic behind it. At a minimum, I would recommend updating the method docs with a note or warning calling out the method's behavior when it comes to decimal numbers and/or negative numbers. Again, thanks for clarifying.

[rhshadrach]

Thanks for the report! This mirrors Python behavior:

print("1.2".isnumeric())
# False

The first line of the docstring states:

Check whether all characters in each string are numeric.

Are you saying there might be some confusion as to whether . is a numeric character?

[sf-dcp]

@rhshadrach, yep, I may be biased here but I can interpret . being numeric like in a decimal number :) I believe an explicit message about numeric values other than integers (negative and decimal string values) would be beneficial for at least a portion of pandas users.

[rhshadrach]

I'd be okay with this addition. PRs are welcome!

[akj2018]

take