Closed
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this issue exists on the latest version of pandas.
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I have confirmed this issue exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import numpy as np
# This configuration option makes this code slow
pd.options.future.infer_string = True
# Define dimensions
n_rows = 10000
n_cols = 10000
# Generate random IDs for the rows
ids = [f"string_id_{i}" for i in range(1, n_rows + 1)]
# Generate a random sparse matrix with 10% non-NaN values
data = np.random.choice([np.nan, 1], size=(n_rows, n_cols), p=[0.9, 0.1])
# Create a DataFrame from the sparse matrix and add the 'Id' column
df = pd.DataFrame(data, columns=[f"column_name_{i}" for i in range(1, n_cols + 1)])
df.insert(0, 'Id', ids)
# Melt the DataFrame
df_melted = df.melt(id_vars=['Id'], var_name='Column', value_name='Value')
# Display the first few rows of the melted DataFrame
df_melted.head()
Installed Versions
INSTALLED VERSIONS
------------------
commit : d9cdd2ee5a58015ef6f4d15c7226110c9aab8140
python : 3.12.5.final.0
python-bits : 64
OS : Darwin
OS-release : 23.6.0
Version : Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:30 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : pl_PL.UTF-8
LOCALE : pl_PL.UTF-8
pandas : 2.2.2
numpy : 2.1.0
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 73.0.1
pip : 24.1.2
Cython : None
pytest : 8.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.4
IPython : 8.26.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.6.1
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 17.0.0
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2024.6.1
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
Prior Performance
This code with pd.options.future.infer_string = False runs in:
5.23 s ± 1.35 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
Memory consumption is around 14 GB.
Enabling pd.options.future.infer_string = True makes it 2 times slower:
10.6 s ± 40.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Also memory consumption is bigger with peak around 25GB.