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Advanced String method in Pandas

Advanced String method in Pandas

By Sneha kumari
8 mins read
Last updated: 4 May 2023
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Overview

The process of analyzing and modifying the string data type is called String Manipulation. Python, especially pandas, have a number of functions that help us with interpreting strings. This article will give you some insights into the advanced string methods in pandas.

Introduction

Some of the basic string operations include converting or checking the case and length of the string, checking whether the string contains only digits or not, and so many more. However, there are some other operations that come in very handy while dealing with strings, such as replacing characters and removing extra spaces. Let's look at a few of them!

Advanced String Methods

We have already dealt with common string methods in Pandas. Moving on to advanced string methods, what you really need to know is that these methods are called advanced methods not because it is a tough nut to crack but because they aren't used as frequently as the common methods. Once you get the knack of it, you will understand it on your own. Just like the previous article, we first need to create a dataframe to experiment all these methods with. Let's do that.

Code Example 1:

Output:

Code Example 2:

Output:

Series.str.replace()

It is written as Series.str.replace(a,b). It replaces the value a with the value b and then returns the string. We will work on an example that will replace 'Grace' with 'Golu'.

Code Example 3:

Output:

Series.str.count()

It returns the count of the appearance of a character or pattern in each element in DataFrame. In the example given below, we can see that we are counting the occurrence of 'n', and only in the data at index 1 do we have n occurring twice, and hence the output is 2. In the rest of the cases, it is 0.

Code Example 4:

Output:

Series.str.match()

This function determines if each string starts with a match of a regular expression.

Syntax:

Parameters:

  • patstr: Character sequence or regular expression.
  • case: It takes in bool values. The default value is set to True. If the value is True, it is case sensitive.
  • flags: It takes in integer values where the default is set to 0, which implies no flags.
  • na: It takes in scalar values that are single values. It is an optional parameter. It fills in values for missing values. The default value depends on the dtype of the array. For object-dtype, numpy.nan is used. For StringDtype, pandas.NA is used.

Return type: Series/Index/array of boolean values

Series.str.strip()

This method removes leading and trailing characters, be it whitespaces, newlines, or a set of characters specified as parameter values from each string from both the left and right sides. If you closely observe the outputs of the code given below, you will be able to differentiate how the method has worked on the given data.

Code Example 6:

Output:

Series.str.lstrip()

This method is a more customized version of strip() method. It removes all the leading whitespaces or any other character specified.

Its syntax is:

Here the to_strip parameter specifies the character to be removed. If it is specified as None, it will only remove leading whitespaces. Look for elements that had leading whitespaces and then see the output. You will easily spot the difference.

Code Example 7:

Output:

Series.str.rstrip()

This method is a more customized version of strip() method. It removes all the trailing whitespaces or any other character specified.

Its syntax is:

Here the to_strip parameter specifies the character to be removed. If it is specified as None, it will only remove trailing whitespaces. Look for elements that had trailing whitespaces and then see the output. You will easily spot the difference.

Code Example 8:

Output:

Series.str.swapcase()

This method swaps the case of the string data from upper to lower and vice-versa. For the given example, we can see that cases of every character have been swapped after using the swapcase() method.

Code Example 9:

Output:

Series.str.find(pattern)

This method returns the first position of the first occurrence of the pattern. In the given example, we look for the first occurrence of 'm', which has occurred at index 3 in 'Thomas'.

Note: This method is case-sensitive. Thus 'M' and 'm' are different and hence will have different outputs.

Code Example 10:

Output:

Series.str.findall(pattern)

This method returns a list of all occurrences of the given pattern in the data. Its return type is a list with all the occurrences of the given element in the data. In the example used below, we are searching for all occurrences of 's', and the output is displayed as a list.

Code Example 11:

Output:

Series.str.split(‘ ‘)

It splits each string with the given pattern. It returns a list. After the strings are split the new elements are stored in a list and are returned as output.

Code Example 12:

Output:

Code Example 13:

Output:

Series.str.cat(sep=’ ‘)

This method performs concatenation operations. It will concatenate the elements in the data with the given separator value. As we have used '/' as a separator in the example given below, we can see that the elements are concatenated using the same.

Code Example 14:

Output:

Series.str.get_dummies()

This method performs One hot encoding and then returns the dataframe. It will search for the values on a given index. If it exists, it will return value 1 else it return 0.

Note: One hot encoding is the method of converting categorical values as '0', 1's, 2's, and so on depending on the number of categories. It helps in better prediction using ML algorithms.

Code Example 15:

Output:

Series.str.startswith(pattern)

As the name suggests, if the data in the dataframe starts with a particular pattern, it will return True; else, it will return False. In the given example, we are looking for data that starts with 'G' and thus in the case of 'Grace', the output displayed is True, and if we experiment with 'g' the output is False.

Note: startswith() is a case-sensitive function.

Code Example 16:

Output:

Series.str.endswith(pattern)

This method behaves exactly like startswith() just that it will return the value True if the data ends with a particular pattern. The output that we have received is different for 's' and 'S'. Thus it is a case-sensitive function.

Code Example 17:

Output:

Series.str.repeat(value)

It repeats each element with a given number of times as the mentioned value. If you look closely, the parameter value for repeat is given as 2, and thus all the elements in the output are repeated twice. Since whitespace was not included, there will be no space between the last element and the first repeated element.

Code Example 18:

Output:

Conclusion

In this article, we learned about the advanced string methods in Pandas and their functions. We started with:

  • replace() - written as replace(a,b) helps replace value a with given value b.
  • count() - counts the occurrence of a character in the data.
  • match() - checks if strings start with the given regular expression.
  • strip() - removes leading and trailing whitespaces or any other character specified. If the specification is mentioned only leading or only trailing, we use lstrip() and rstrip(), respectively.
  • swapcase() - swaps the case from upper to lower and vice versa and then returns the string.
  • find() and findall() - help find the position of the first occurrence of a character or all occurrences of a character, respectively, in the form of a list.
  • split() - helps split the data on the basis of a given pattern and then returns the new data as a list.
  • cat() - helps in concatenation on string with the given separator value.
  • get_dummies() - perform one hot encoding, and then with reference to a relative index it will return 1 or 0 if the element exists at the particular index and if it does not exist respectively.
  • startswith() and endswith() - check whether the data starts or ends with a particular pattern. If yes, it will return True else return False.
  • repeat() - helps repeat the occurrence of the element for the specified value.

Finally, we have reached the end of all these methods. As already mentioned, the best way to learn is to use these methods on the data and see how it behaves.