Types and Parsing

Parse Boolean Labels

Convert human-readable yes/no strings in varied case to Python booleans. By hand, normalise each string to lowercase and look it up in a mapping dict. With pandas, .str.lower() lowercases the column in one pass, then .map(dict) remaps each element to its boolean value.

By hand

Define a mapping dict covering all accepted forms ('yes', 'y', 'no', 'n'). For each label, call s.lower() before the lookup so mixed-case inputs match. The trace shows result accumulating booleans.

naive.py 
mapping = {'yes': True, 'y': True, 'no': False, 'n': False}
labels = ['yes', 'no', 'Y', 'N', 'yes']
result = []
for s in labels:
    result.append(mapping[s.lower()])
print('RESULT:', result)

With pandas

.str.lower() lowercases the entire string column, then .map(dict) replaces each value with the corresponding boolean. The dtype of the resulting Series is bool because every input maps to a value.

library.py 
import pandas as pd
from dalib.display import set_display
set_display()

labels = ['yes', 'no', 'Y', 'N', 'yes']
df = pd.DataFrame({'x': labels})
s = df['x'].str.lower().map({'yes': True, 'y': True, 'no': False, 'n': False})
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: bool
values: [True, False, True, False, True]
RESULT: [True, False, True, False, True]

Implementation notes

  • When all inputs are in the mapping, the result Series has dtype bool. If any string is unmapped, .map returns NaN for that position and the dtype becomes object — use errors='raise' or validate inputs upstream to catch surprises.
  • The normalization dict can be extended for common variants: 'true', 'false', '1', '0', 'on', 'off'.
  • Cross-reference: coerce-bad-numbers (this chapter) for the analogous pattern of mapping strings to numerics where some values may be invalid.