Vectorized String Operations: A Guide to Pandas String Methods

An Introduction to Pandas String Operations

In previous investigations, we laboriously discussed the methods of creating Pandas data structures, an article which may be found here. Furthermore, we got deeper into the discussion by examining the methods of indexing these data structures. For greater insight into this topic, you may find such here. Our most recent topic we examine is the application of Ufuncs for rapid, vectorized operations, which you can examine here. In conjunction with these Ufuncs, Pandas provides additional vectorized operations for working with string data in Pandas data structures. This article presently investigates this vectorization of string operations and Pandas string methods in a variety of contexts.

An Overview of Pandas String Methods

The utility of Pandas string methods is its close relationship to the methods when used in basic Python code. Thus, providing an individual has previous experience with these techniques, then their application with Pandas will be rather easy. However, where these techniques differ is their vectorized methodology, which greatly enhances the efficiency of these operations. The ultimate utility of these functions is rapid alteration of string objects stored in data structures. The various string operations include Pandas-Python cognates, string methods regular expressions, miscellaneous string methods, and function-free item accessing. Let’s investigate these categories.

For examining all of these different functions, we will utilize a Pandas Series data structure filled with several string objects. When we apply any of these string methods, the data structure is typically followed by the syntax ‘.str.method()’. When we execute the string methods, the output is another data structure associating value with index. Examine the Pandas Series we use below, which is a data structure comprised of an array of movies.

Pandas-Python String Cognate Functions

Movies.str.len()
pandas.Series.str.len()

Firstly, the syntax above demonstrates the len() function. As in typical Python code, this Pandas cognate returns the length of each string object in a Pandas data structure.

Movies.str.lower()
pandas.Series.str.lower()

Secondly, the syntax above demonstrates the lower() function. As in typical Python code, this Pandas cognate returns modified string objects from the Pandas data structure where all substrings are converted to lower-case characters.

Movies.str.islower()
pandas.Series.str.islower()

Thirdly, the syntax above demonstrates the lower() function. As in typical Python code, this Pandas cognate returns a Boolean value which validates that the substrings are lower-case characters.

Movies.str.upper()
pandas.Series.str.upper()

The syntax above demonstrates the upper() function. As in typical Python code, this Pandas cognate returns modified substrings from each string object in a Pandas data structure, and converts these sub-strings to upper-case characters.

Movies.str.isupper()
pandas.Series.str.isupper()

The syntax above demonstrates the isupper() function. As in typical Python code, this Pandas cognate returns a Boolean value which validates that the substrings are upper-case characters.

Movies.str.startswith()
pandas.Series.str.startswith()

The syntax above demonstrates the startswith() function. As in typical Python code, this Pandas cognate returns a Boolean value which validates that the substrings begins with a specified substring.

Movies.str.find()
pandas.Series.str.find()

The syntax above demonstrates the find() function. As in typical Python code, this Pandas cognate returns whether or not a particular item is found in each string of a Pandas data structure. If the unit is found, the code returns a 0. If the unit is not found, then the output is the value of -1.

Movies.str.endswith()
pandas.Series.str.endswith()

The syntax above demonstrates the len() function. As in typical Python code, this Pandas cognate returns a Boolean value which validates that the substrings ends with a specified substring.

Movies.str.isnumeric()
pandas.Series.str.isnumeric()

The syntax above demonstrates the isnumeric() function. As in typical Python code, this Pandas cognate returns a Boolean value which validates that the substrings are numerical characters.

Movies.str.isalnum()
pandas.Series.str.isalnum()

The syntax above demonstrates the isalnum() function. As in typical Python code, this Pandas cognate returns a Boolean value for each string object in a Pandas data structure and validates that it is an alphanumerical character.

Movies.str.isdecimal()
pandas.Series.str.isdecimal()

The syntax above demonstrates the isdecimal() function. As in typical Python code, this Pandas cognate returns a Boolean value for each string object in a Pandas data structure and validates that it is an decimal character.

Movies.str.index()
pandas.Series.str.index()

The syntax above demonstrates the index() function. As in typical Python code, this Pandas cognate returns the index position of a particular sub-string object in a Pandas data structure.

Movies.str.isalpha()
pandas.Series.str.isalpha()

The syntax above demonstrates the isalpha() function. As in typical Python code, this Pandas cognate returns a Boolean value for each string object in a Pandas data structure and validates that it is a letter character.

Movies.str.split()
pandas.Series.str.split()

The syntax above demonstrates the split() function. As in typical Python code, this Pandas cognate alters each string object in a Pandas data structure by splitting on a particular delimiter.

Movies.str.strip()
pandas.Series.str.strip()

The syntax above demonstrates the strip() function. As in typical Python code, this Pandas cognate returns a modified string object in a Pandas data structure by removing a particular object or part of the string.

Movies.str.capitalize()
pandas.Series.str.capitalize()

The syntax above demonstrates the capitalize() function. As in typical Python code, this Pandas cognate returns a modified string object in a Pandas data structure wherein each string character, or a specified slice of characters, is capitalized.

Pandas String Regular Expressions

The utility of regular expressions is well beyond the scope of this article. It will be presented as its own topic. Nevertheless, we here examine the utility of these string methods as they pertain to Pandas data structures.

Movies.str.match()
Pandas.Series.str.match()

The str.match() regular expression takes a regular expression as input and identifies string objects in the data structure that match the input regular expression. If a match is found, the function returns a Boolean value indicating that the string object in that location in fact has the regular expression within it.

Movies.str.extract()
Pandas.Series.str.extract()

The str.extract regular expression is quite similar to that of the str.match() function. Like this function, str.extract takes a regular expression as input. If a match is found, rather than returning a Boolean value, the string itself is returned. If one is not found at a particular location, then the function returns NaN.

Movies.str.findall()
Pandas.Series.str.findall()

The str.match() regular expression takes a regular expression as input and identifies all string objects in the data structure that match the input regular expression. If a match is found, the function returns a Boolean value indicating that the string object in that location in fact has the regular expression within it.

Movies.str.replace()
Pandas.Series.str.replace()

The str.replace() function takes two inputs, one is a substring which is to be replaced, another which is a substring to replace the original substring.

Movies.str.count()
Pandas.Series.str.count()

The str.count() regular expression counts the number of instances in which a match to the regular expression specified is made.

Pandas Miscellaneous String Methods

Movies.str.get()
pandas.Series.str.get(pos)

The syntax above demonstrates the str.get() function. This function takes as input a particular index position, and from this, will return the string object that occurs at this position in the data structure.

Movies.str.slice()
pandas.Series.str.slice(start,end)

The syntax above demonstrates the str.slice() function. This function takes as input a slice, two numbers which indicate the start and end position. The function then returns the items occurring within these boundaries in the data structure

Movies.str.slice_replace()
pandas.Series.str.slice_replace(pos, substring)

The syntax above demonstrates the str.slice_replace() function. This function takes two inputs. The first, is a position which specifies the string we want to replace in each string of the data structure. We then specify a substring we want to replace it with.

Movies.str.cat()
pandas.Series.str.cat()

The syntax above demonstrates the str.cat() function. This function does not require any inputs. What it does is it concatenates all of the string objects in a Pandas data structure into one large string.

Movies.str.repeat()
pandas.Series.str.repeat(scale factor)

The syntax above demonstrates the str.repeat() function. This function takes an input which specifies the number of times each string in a data structure must be repeated.

Movies.str.join()
pandas.Series.str.join('delimiter')

The syntax above demonstrates the string.join() function. This function takes as input a particular delimiter, and subsequently joins the strings together on this delimiter.

Discussion of Pandas String Methods

The methods presented here are surely not an exhaustive list of all the string methods avaialble to utilize when working with Pandas. However, together, they constitute approximately 95% of all methods that are most often used when altering string-based data structures. If an individual is sure to master these methods, then operations on complex Pandas data structures is sure to be much more efficient. For more information regarding string methods, the Pandas manual may be found here.

If this tutorial has been helpful for you, check out the rest of our series which discusses various elements of operations using Pandas. Additionally, Pandas is all about working with data. Our website also has several series that may prove beneficial for complex data analysis using Pandas. With that being said, consider out checking some of the articles below:

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