String Cleaning

Strip Whitespace

Remove leading and trailing whitespace from each string in a column. By hand, call s.strip() on each element in a loop. With pandas, Series.str.strip() applies the same operation across the entire column at once.

By hand

With pandas

df['x'].str.strip() returns a new Series with every string stripped. The dtype stays object (strings). The snapshot shows the cleaned values alongside the original dtype.

naive.py 
strings = ['  Alice  ', 'Bob ', '  Carol', 'Dave', '  Eve  ']
result = []
for s in strings:
    result.append(s.strip())
print('RESULT:', result)
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

strings = ['  Alice  ', 'Bob ', '  Carol', 'Dave', '  Eve  ']
df = pd.DataFrame({'x': strings})
s = df['x'].str.strip()
result = s.tolist()
print('index:', s.index.tolist())
print('dtype:', s.dtype)
print('values:', s.tolist())
print('RESULT:', result)

index: [0, 1, 2, 3, 4]
dtype: object
values: ['Alice', 'Bob', 'Carol', 'Dave', 'Eve']
RESULT: ['Alice', 'Bob', 'Carol', 'Dave', 'Eve']

Implementation notes

  • str.strip() removes all leading and trailing whitespace characters (spaces, tabs, newlines). Use str.lstrip() to strip only the left side, or str.rstrip() for only the right.
  • Pass a character string to strip specific characters instead of whitespace: s.strip('.') removes leading/trailing dots.
  • Whitespace inside the string is unaffected — use str.replace or regex to collapse internal spaces.
  • Cross-reference: normalize-case (this chapter) — strip and lowercase are often applied together as the first cleaning step.