pandas

Common errors

Warnings

Install

Install compatibility verified last tested: 2026-05-12

Imports

• pandas
  wrong

  df['b'][df['a'] > 1] = 'z'  # chained assignment — silently fails in pandas 3.0 (CoW)

  correct

  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'

  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
  wrong

  if df['col'].dtype == object:  # no longer true for string columns in pandas 3.0
      ...

  correct

  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):
      ...

  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.

Quickstart verified last tested: 2026-04-23

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