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Data Science

May 2026×Notebook lesson

Notebook converted from Jupyter for blog publishing.

07-Text-Methods

Driptanil DattaSoftware Developer

Text Methods

A normal Python string has a variety of method calls available:

mystring = 'hello'

mystring.capitalize()

RESULT

'Hello'

mystring.isdigit()

RESULT

False

help(str)

STDOUT

Help on class str in module builtins:

class str(object)
 |  str(object='') -> str
 |  str(bytes_or_buffer[, encoding[, errors]]) -> str

Pandas and Text

Pandas can do a lot more than what we show here. Full online documentation on things like advanced string indexing and regular expressions with pandas can be found here: https://pandas.pydata.org/docs/user_guide/text.html (opens in a new tab)

Text Methods on Pandas String Column

import pandas as pd

names = pd.Series(['andrew','bobo','claire','david','4'])

names

RESULT

0    andrew
1      bobo
2    claire
3     david
4         4

names.str.capitalize()

RESULT

0    Andrew
1      Bobo
2    Claire
3     David
4         4

names.str.isdigit()

RESULT

0    False
1    False
2    False
3    False
4     True

Splitting , Grabbing, and Expanding

tech_finance = ['GOOG,APPL,AMZN','JPM,BAC,GS']

len(tech_finance)

RESULT

tickers = pd.Series(tech_finance)

tickers

RESULT

0    GOOG,APPL,AMZN
1        JPM,BAC,GS
dtype: object

tickers.str.split(',')

RESULT

0    [GOOG, APPL, AMZN]
1        [JPM, BAC, GS]
dtype: object

tickers.str.split(',').str[0]

RESULT

0    GOOG
1     JPM
dtype: object

tickers.str.split(',',expand=True)

HTML

0
1
2
0
GOOG

Cleaning or Editing Strings

messy_names = pd.Series(["andrew  ","bo;bo","  claire  "])

# Notice the "mis-alignment" on the right hand side due to spacing in "andrew  " and "  claire  "
messy_names

RESULT

0      andrew  
1         bo;bo
2      claire  
dtype: object

messy_names.str.replace(";","")

RESULT

0      andrew  
1          bobo
2      claire  
dtype: object

messy_names.str.strip()

RESULT

0    andrew
1     bo;bo
2    claire
dtype: object

messy_names.str.replace(";","").str.strip()

RESULT

0    andrew
1      bobo
2    claire
dtype: object

messy_names.str.replace(";","").str.strip().str.capitalize()

RESULT

0    Andrew
1      Bobo
2    Claire
dtype: object

Alternative with Custom apply() call

def cleanup(name):
    name = name.replace(";","")
    name = name.strip()
    name = name.capitalize()
    return name

messy_names

RESULT

0      andrew  
1         bo;bo
2      claire  
dtype: object

messy_names.apply(cleanup)

RESULT

0    Andrew
1      Bobo
2    Claire
dtype: object

Which one is more efficient?

import timeit 
  
# code snippet to be executed only once 
setup = '''
import pandas as pd
import numpy as np
messy_names = pd.Series(["andrew  ","bo;bo","  claire  "])
def cleanup(name):
    name = name.replace(";","")
    name = name.strip()
    name = name.capitalize()
    return name
'''
  
# code snippet whose execution time is to be measured 
stmt_pandas_str = ''' 
messy_names.str.replace(";","").str.strip().str.capitalize()
'''
 
stmt_pandas_apply = '''
messy_names.apply(cleanup)
'''
 
stmt_pandas_vectorize='''
np.vectorize(cleanup)(messy_names)
'''

timeit.timeit(setup = setup, 
                    stmt = stmt_pandas_str, 
                    number = 10000)

RESULT

3.931618999999955

timeit.timeit(setup = setup, 
                    stmt = stmt_pandas_apply, 
                    number = 10000)

RESULT

1.2268500999999787

timeit.timeit(setup = setup, 
                    stmt = stmt_pandas_vectorize, 
                    number = 10000)

RESULT

0.28283379999993485

Wow! While .str() methods can be extremely convienent, when it comes to performance, don't forget about np.vectorize()! Review the "Useful Methods" lecture for a deeper discussion on np.vectorize()

06 Combining Dataframes 08 Time Methods