Description
Pandas version checks
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import xarray as xr
import numpy as np
import time
# Minimal reproducible example: Pandas Series print performance with large xarray DataArrays
print("Pandas Series Print Performance Issue")
print("=" * 40)
# Create minimal test data
np.random.seed(42)
time_coords = pd.date_range('2023-01-01', periods=2, freq='D')
x_coords = np.linspace(0, 10, 256)
y_coords = np.linspace(0, 10, 256)
# Create one large xarray DataArray
data = np.random.randn(2, 256, 256)
large_dataarray = xr.DataArray(
data,
coords={'time': time_coords, 'y': y_coords, 'x': x_coords},
dims=['time', 'y', 'x']
)
# Create minimal pandas Series with large DataArray
series = pd.Series({
'id': 1,
'data': large_dataarray,
'name': 'test_series'
})
print(f"DataArray size: {large_dataarray.nbytes / 1024 / 1024:.1f} MB")
print(f"DataArray shape: {large_dataarray.shape}")
# Method 1: Print Series directly
print("\nMethod 1: Print Series")
start_time = time.time()
print(series)
method1_time = time.time() - start_time
# Method 2: Extract DataArray first, then print it
print("\nMethod 2: Extract DataArray first, then print")
start_time = time.time()
extracted_da = series['data']
print(extracted_da)
method2_time = time.time() - start_time
# Results
print(f"\nTiming Results:")
print(f"Method 1 (print Series): {method1_time:.4f} seconds")
print(f"Method 2 (extract + print DataArray): {method2_time:.4f} seconds")
print(f"Difference: {abs(method1_time - method2_time):.4f} seconds")
if method1_time > method2_time:
ratio = method1_time / method2_time
print(f"Method 1 is {ratio:.1f}x slower than Method 2")
else:
ratio = method2_time / method1_time
print(f"Method 2 is {ratio:.1f}x slower than Method 1")
# Environment info
print(f"\nEnvironment:")
print(f"Pandas: {pd.__version__}")
print(f"XArray: {xr.__version__}")
print(f"NumPy: {np.__version__}")Issue Description
Hi Pandas team, so I was working with pandas series and was trying to put an xarray into a cell.
So when I was trying to print out the pandas series with the xarray, I found that it is extremely slow, directly printing out the pandas series is 1000X slower than getting the xarray and then print out the xarray.
The above script is an example with an xarray inside a pandas series, and the time comparison between printing the pandas series directly and get the xarray first and them print the values.
Possible issue: String formatting with xarray.
Expected Behavior
Similar time consumption for directly printing the pandas series and get the xarray and print the content.
Installed Versions
INSTALLED VERSIONS
commit : c888af6
python : 3.11.7
python-bits : 64
OS : Linux
OS-release : 6.11.0-29-generic
Version : #29~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jun 26 14:16:59 UTC 2
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 2.3.1
numpy : 2.3.1
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 23.2.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : 3.10.3
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.16.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 2025.7.0
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None
None