Pandas BUG: DataFrame.mul() corrupts data by setting values to zero

Pandas version checks

• [x] I have checked that this issue has not already been reported.

• [x] I have confirmed this bug exists on the latest version of pandas.

• [x] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import numpy as np
import sys


# Create DataFrame with datetime index and multiple columns
# This reproduces the bug with ~6 years of hourly data (2033-2038)
np.random.seed(42)
date_range = pd.date_range('2033-01-01', '2038-12-31 23:00:00', freq='H')
n_cols = 40
data = np.random.rand(len(date_range), n_cols) * 0.1  # Values between 0 and 0.1

df = pd.DataFrame(data, index=date_range, columns=range(n_cols))

# Create a Series of ones with the same index
ones_series = pd.Series(1.0, index=df.index)

print(f"DataFrame shape: {df.shape}")
print(f"Memory usage (MB): {df.memory_usage(deep=True).sum() / 1024**2:.2f}")
print(f"Original data sample (should be > 0):")
print(df.iloc[32:37, 23])  # Show some sample values

# Perform the multiplication that causes corruption
print("\nPerforming multiplication...")
result = df.mul(ones_series, axis=0)

# Check for corruption
print(f"After multiplication (should be identical):")
print(result.iloc[32:37, 23])

# Verify corruption
are_equal = df.equals(result)
print(f"\nDataFrames equal: {are_equal}")

if not are_equal:
    # Count corrupted values
    diff_mask = df.values != result.values
    n_corrupted = diff_mask.sum()
    print(f"CORRUPTION DETECTED: {n_corrupted} values corrupted!")

    # Show corruption details
    corrupted_rows, corrupted_cols = np.where(diff_mask)
    if len(corrupted_rows) > 0:
        print(f"\nCorruption sample:")
        for i in range(min(5, len(corrupted_rows))):
            row, col = corrupted_rows[i], corrupted_cols[i]
            original = df.iloc[row, col]
            corrupted = result.iloc[row, col]
            date = df.index[row]
            print(f"  {date}, Col {col}: {original:.4f} -> {corrupted:.4f}")

    # Verify that corrupted values are zeros
    corrupted_values = result.values[diff_mask]
    all_zeros = np.all(corrupted_values == 0.0)
    print(f"\nAre all corrupted values zero? {all_zeros}")

    # Show which columns are affected
    unique_affected_cols = np.unique(corrupted_cols)
    print(f"Number of affected columns: {len(unique_affected_cols)}")
    print(f"Affected columns: {unique_affected_cols}")

# Demonstrate that numpy approach works correctly
print(f"\nTesting numpy workaround...")
numpy_result = pd.DataFrame(
    df.to_numpy() * ones_series.to_numpy()[:, None],
    index=df.index,
    columns=df.columns
)

numpy_works = df.equals(numpy_result)
print(f"Numpy approach works correctly: {numpy_works}")

Issue Description

The DataFrame.mul() method is corrupting data by setting non-zero values to zero when multiplying a DataFrame with datetime index by a Series of ones. This occurs only under specific conditions related to DataFrame size and affects data integrity.

Expected Behavior

When multiplying a DataFrame by a Series of ones using df.mul(ones_series, axis=0), all original values should be preserved (multiplied by 1.0).

Installed Versions

Comment From: MarceloVelludo

This bug doesn't occurs on recent versions of pandas. Our test runs perfectly fine on the last version and was related with the numexrp that is activated when the elements is more than 1M for optimizations.

Comment From: jbrockmendel

I also cannot reproduce on main (though on mac)

Comment From: rhshadrach

@MarceloVelludo: what does recent versions of pandas mean explicitly? OP is reporting on 2.2.3, I just want to make sure you're reporting not seeing this issue there. Also, what architecture are you on? Posting pd.show_versions() would be helpful.

Comment From: MarceloVelludo

@MarceloVelludo: what does recent versions of pandas mean explicitly? OP is reporting on 2.2.3, I just want to make sure you're reporting not seeing this issue there. Also, what architecture are you on? Posting pd.show_versions() would be helpful.

INSTALLED VERSIONS
------------------
commit                : c888af6d0bb674932007623c0867e1fbd4bdc2c6
python                : 3.13.5
python-bits           : 64
OS                    : Linux
OS-release            : 6.15.6-1-MANJARO
Version               : #1 SMP PREEMPT_DYNAMIC Thu, 10 Jul 2025 15:38:04 +0000
machine               : x86_64
processor             : 
byteorder             : little
LC_ALL                : None

pandas                : 2.3.1
numpy                 : 2.3.1
pytz                  : 2025.2
dateutil              : 2.9.0.post0
pip                   : 25.1.1
Cython                : None
sphinx                : None
IPython               : 9.4.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.13.4
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : None
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.6
lxml.etree            : None
matplotlib            : 3.10.3
numba                 : None
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : 19.0.1
pyreadstat            : None
pytest                : None
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : 1.16.0
sqlalchemy            : 2.0.41
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : 0.23.0
tzdata                : 2025.2
qtpy                  : None
pyqt5                 : None

Comment From: rhshadrach

Thanks @MarceloVelludo - I also tested this on 2.2.x and that is fine as well.