Install & Compatibility
Where this runs
tested against v2.3.3 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
pandas
✓ import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': ['x', 'y']})
# Modify using loc in one step
df.loc[df['a'] > 1, 'b'] = 'z'
✗ df['b'][df['a'] > 1] = 'z' # chained assignment — silently fails in pandas 3.0 (CoW)
Copy-on-Write is now mandatory in 3.0. Chained assignment (df['col'][mask] = val) no longer modifies the DataFrame — it silently does nothing.
string dtype
✓ ser = pd.Series(['a', 'b'])
ser.dtype # dtype('str') in pandas 3.0
# Check for string dtype in 3.0-compatible way:
if pd.api.types.is_string_dtype(ser):
...
✗ if df['col'].dtype == object: # no longer true for string columns in pandas 3.0
...
String columns now infer to str dtype instead of numpy object. Code checking dtype == object or dtype == 'O' for string detection will silently miss string columns.
pandas 3.0 patterns. Use loc for in-place modification. Strings are str dtype not object.
import pandas as pd
import numpy as np
# Create DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie'],
'score': [85, 92, 78],
'dept': ['eng', 'eng', 'mkt']
})
# Correct modification in pandas 3.0 (CoW)
df.loc[df['score'] > 80, 'grade'] = 'pass'
# Or use assign() for derived columns (returns new DataFrame)
df = df.assign(grade=lambda x: np.where(x['score'] > 80, 'pass', 'fail'))
# Check dtypes — strings are now 'str', not 'object'
print(df.dtypes)
# name str
# score int64
# dept str
Debug
Known issues
breakingCopy-on-Write (CoW) is now the only mode in pandas 3.0. Chained assignment df['col'][mask] = value silently does nothing — no error, no warning, no modification. This is the most common invisible bug when upgrading.fixUse df.loc[mask, 'col'] = value for conditional assignment. Use df = df.assign(col=...) for derived columns. Remove all defensive .copy() calls added to silence old SettingWithCopyWarning.
affects: >= 3.0
breakingString columns now default to str dtype instead of numpy object dtype. Code checking dtype == object or dtype == 'O' to detect string columns will fail silently in pandas 3.0.fixReplace dtype == object checks with pd.api.types.is_string_dtype(col) or dtype == 'str'. For library code: handle both 'object' and 'str' dtypes during transition.
affects: >= 3.0
breakingPython 3.10 and below dropped. pandas 3.0 requires Python >=3.11.fixPin pandas<3.0 for Python <=3.10 environments. Upgrade Python to 3.11+ to use pandas 3.0.
affects: >= 3.0
breakingDatetime default resolution changed from nanoseconds to microseconds (or input resolution). pd.Timestamp arithmetic and comparisons with nanosecond precision may produce different results.fixExplicitly pass unit='ns' where nanosecond precision is required: pd.to_datetime(arr, unit='ns').
affects: >= 3.0
breakingDataFrame.groupby() observed parameter default changed to True for Categorical columns. Previously unobserved categories were included by default, causing silent behavior changes on groupby aggregations.fixPass observed=False explicitly to restore old behavior if unobserved categories are needed.
affects: >= 2.2
deprecatedmode.copy_on_write option deprecated — setting it has no effect in pandas 3.0 and will be removed in 4.0.fixRemove pd.options.mode.copy_on_write = True/False from your code — CoW is always on.
affects: >= 3.0
gotchapyarrow is not required but strongly recommended for pandas 3.0. Without pyarrow, the new str dtype falls back to numpy object-backed storage, losing most performance benefits.fixpip install pyarrow alongside pandas. Verified by: pd.Series(['a']).dtype shows 'str' regardless, but performance differs significantly.
affects: >= 3.0
gotchaMany third-party libraries (scikit-learn, seaborn, statsmodels, SHAP) had pandas 3.0 compatibility issues at release. Check library versions when upgrading.fixTest your full dependency stack against pandas 3.0 before upgrading. Use pandas 2.3.x as a stepping stone to surface deprecation warnings first.
affects: >= 3.0
gotchaBuilding `psycopg2` from source fails due to missing PostgreSQL development headers and libraries (`pg_config`). This is a common issue in minimal environments.fixEnsure PostgreSQL development headers are installed, or install the `psycopg2-binary` package instead of `psycopg2` (e.g., `pip install psycopg2-binary`). For Debian/Ubuntu, `apt-get install libpq-dev`.
affects: N/A (dependency issue, not pandas version specific)
gotchaInstalling `pandas[performance]` (which requires numba and llvmlite) may fail in minimal environments (e.g., Alpine Linux) due to missing system build tools like `gcc` and `cmake`, which are necessary for compiling these dependencies.fixEnsure that essential build tools are installed in your environment before installing `pandas[performance]`. For Alpine Linux, use `apk add build-base cmake`.
affects: >= 3.0
Errors
Common errors & fixes
ModuleNotFoundError: No module named 'pandas'
The pandas library is not installed in the Python environment where the code is being executed.
fixInstall pandas using pip: `pip install pandas` (or `conda install pandas` if using Anaconda).
KeyError: 'some_column_name'
Attempting to access a DataFrame column or index label that does not exist in the DataFrame, often due to typos, incorrect casing, or hidden whitespace in column names.
fixVerify the exact column names using `df.columns` and correct any typos or casing issues. It's often helpful to strip whitespace from column names: `df.columns = df.columns.str.strip()`.
AttributeError: 'DataFrame' object has no attribute 'append'
The `append()` method for DataFrames and Series was deprecated in pandas 1.4.0 and completely removed in pandas 2.0 and later versions.
fixReplace `df.append()` with `pd.concat()` for combining DataFrames or Series. Example: `new_df = pd.concat([df1, df2])`.
AttributeError: module 'pandas' has no attribute 'dataframe'
Incorrect capitalization when trying to create a DataFrame; the class name for DataFrame must start with a capital 'D'.
fixUse `pd.DataFrame()` with a capital 'D' for DataFrame. Example: `df = pd.DataFrame({'col1': [1, 2]})`. ChainedAssignmentError: A value is trying to be set on a copy of a slice from a DataFrame.
In pandas 3.0, Copy-on-Write (CoW) is enabled by default, making chained assignments (e.g., `df[condition]['column'] = value`) reliably operate on a temporary copy, not the original DataFrame. This error prevents silent, incorrect modifications that previously might have only issued a `SettingWithCopyWarning`.
fixUse `.loc` for a single-step, explicit assignment to ensure modification of the original DataFrame. Example: `df.loc[df['column_a'] > 5, 'column_b'] = new_value`.
Upgrade
Version history
3.0.5latest on PyPI · released Jul 22, 2026
Audit
Dependencies
numpy>=1.23.2requiredRequired. Installed automatically.
python-dateutil>=2.8.2requiredRequired. Installed automatically.
pyarrow>=10.0.1optionalStrongly recommended. Backs the new str dtype for better performance. Not required but highly beneficial.
openpyxloptionalRequired for read_excel() and to_excel() with .xlsx files.