13.2. String Manipulation

There are a few basic string manipulation tools that we use all the time when working with text:

  • Transform upper case characters to lower case (or vice versa).

  • Replace a substring with another or delete a substring.

  • Split a string into pieces at a particular character.

  • Slice a string at specified locations.

We’ll show how we can combine these tools to clean up the county names data. Remember that we have two tables that we want to join, but the county names are spelled inconsistently. Below, we’ve displayed the election and census dataframes.

County State Voted
0 De Witt County IL 97.8
1 Lac qui Parle County MN 98.8
2 Lewis and Clark County MT 95.2
3 St John the Baptist Parish LA 52.6
County State Population
0 DeWitt IL 16,798
1 Lac Qui Parle MN 8,067
2 Lewis & Clark MT 55,716
3 St. John the Baptist LA 43,044

13.2.1. Converting Text to a Standard Format with Python String Methods

We need to address the following inconsistencies between the county names in the two tables.

  1. Capitalization: qui vs Qui

  2. Omission of words: County and Parish are absent from the census table

  3. Different abbreviation conventions: & vs and

  4. Different punctuation conventions: St. vs St

  5. Use of whitespace: DeWitt vs De Witt

When we clean text, we usually start by converting all of the characters to lowercase—it’s easier to work with all lowercase characters than to try to track combinations of uppercase and lowercase. Next, we want to fix inconsistent words by replacing & with and and removing County and Parish. Finally, we’ll fix up the punctuation and whitespace inconsistencies.

Following these steps, we create a method called clean_county that cleans an input county name using two of Python’s string methods.

def clean_county(county):
    return (county
            .replace('county', '')
            .replace('parish', '')
            .replace('&', 'and')
            .replace('.', '')
            .replace(' ', ''))

Python provides a variety of methods for basic string manipulation. Although simple, these methods are the primitives that piece together to form more complex string operations. These methods are conveniently defined on all Python strings and do not require importing other modules. Although it is worth familiarizing yourself with the complete list of string methods, we describe a few of the most commonly used methods in the table below.




Returns a copy of a string with all letters converted to lowercase

str.replace(a, b)

Replaces all instances of the substring a in str with the substring b


Removes leading and trailing whitespace from str


Returns substrings of str split at a substring a


Slices str, returning indices x (inclusive) to y (not inclusive)

We next verify that the clean_county method produces matching counties for all the counties in both tables:

([clean_county(county) for county in election['County']],
 [clean_county(county) for county in census['County']])
(['dewitt', 'lacquiparle', 'lewisandclark', 'stjohnthebaptist'],
 ['dewitt', 'lacquiparle', 'lewisandclark', 'stjohnthebaptist'])

Since each county name in both tables now has the same transformed representation, we can successfully join the two tables.

13.2.2. String Methods in pandas

In the code above we used a loop to transform each county name. pandas Series objects provide a convenient way to apply string methods to each item in the series.

The .str property on pandas Series exposes the same string methods as Python does. Calling a method on the .str property calls the method on each item in the series. This allows us to transform each string in the series without using a loop. We save the transformed counties back into their originating tables:

election['County'] = (election['County']
 .str.replace('parish', '')
 .str.replace('county', '')
 .str.replace('&', 'and')
 .str.replace('.', '', regex=False)
 .str.replace(' ', ''))

census['County'] = (census['County']
 .str.replace('parish', '')
 .str.replace('county', '')
 .str.replace('&', 'and')
 .str.replace('.', '', regex=False)
 .str.replace(' ', ''))

Now, the two tables should contain the same string representation of the county names and we can join these tables.

election.merge(census, on=['County','State'])
County State Voted Population
0 dewitt IL 97.8 16,798
1 lacquiparle MN 98.8 8,067
2 lewisandclark MT 95.2 55,716
3 stjohnthebaptist LA 52.6 43,044


Note that we merged on two columns: the county name and the state. We did this because some states have counties with the same name. For example, California and New York both have a county called King.

Python’s string methods form a set of simple and useful operations for string manipulation. pandas Series implement the same methods that apply the underlying Python method to each string in the series. To see the complete list of methods, we recommend looking at the Python documentation on str methods and the Pandas documentation for the .str accessor. We did the canonicalization task above using only str.lower() and multiple calls to str.replace(). Next, we’ll extract text with another string method, str.split().

13.2.3. Splitting Strings to Extract Pieces of Text

Let’s say we want to extract the date from a web server’s log entry shown below.

log_entry - - [26/Jan/2004:10:47:58 -0800]"GET /stat141/Winter04 HTTP/1.1"
301 328 "http://anson.ucdavis.edu/courses""Mozilla/4.0 (compatible; MSIE 6.0;
Windows NT 5.0; .NET CLR 1.1.4322)"

String splitting can help us narrow in on the pieces of information that form the date. For example, when we split the string on the left bracket, we get two strings:

[' - - ',
 '26/Jan/2004:10:47:58 -0800]"GET /stat141/Winter04 HTTP/1.1" 301 328 "http://anson.ucdavis.edu/courses""Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.0; .NET CLR 1.1.4322)"']

The second string has the date information, and if we want the day, month, and year, we can split that string on a colon.


To separate out these three parts of the date, we can split on the forward slash. All together we split the original string three times, each time keeping only the pieces we are interested in.

['26', 'Jan', '2004']

By repeatedly using split(), we can extract out all the parts of the log entry. But this approach is complicated—if we wanted to also get the hour, minute, second, and time zone of the activity, we would need to use split() six times in total. There’s a simpler way to extract out the parts:

import re

pattern = r'[ \[/:\]]' 
re.split(pattern, log_entry)[4:11]
['26', 'Jan', '2004', '10', '47', '58', '-0800']

This alternative approach uses a powerful tool called a regular expression, which we’ll cover in the next section.