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Introduction
Nowadays, when we discuss Pandas in Python for data manipulation, one of the important things that come to our mind is regex filtering. It is one of the best ways to filter the data and retrieve accurate information.
In this article, we will discuss regex filtering with Pandas. We will discuss what Pandas and regex are. Then we will discuss important functions that play important roles in regex filtering. In the last of this article, we will discuss an example of all those functions on some data set to filter the data.
So, let us get started!!
A Brief about Pandas
Pandas is one of the most used libraries of Python. It is used to work with data sets. It provides several functions which we can use in Data Manipulation, Data Cleaning, and Data Analysis.
Pandas consist of two data structures, these data structures help to handle and analyze the tabular data:
Series: It looks like a column in a table. It is a 1D(One Dimensional) array. It can hold any type of data
DataFrame: It looks like a table which has rows and columns. It is a 2D(Two Dimensional) array
What is Regex Filtering?
Regex stands for Regular Expressions. It is a sequence of characters that is used to form a pattern. This pattern can be used to extract, replace and delete the data from a given data set.
Suppose we want to filter the email, phone number, or date of birth in a given unstructured data, then we can use Regex filtering.
There are various types of filtering, but regex filtering is the one which can provide accurate and fast results compared to others.
There is a meaning for each and every symbol or character in the regex:
“.”: It is used to match any character except a new line
“*”: It is used to match zero or more occurrences of the preceding character
“+”: It is used to match one or more occurrences of the preceding character
“?”: It is used to match zero or one occurrence of the preceding character
“[]”: It is used to define a character class. It allows a match of any character within the brackets
“()”: It is used to group characters together
Now, you might be confused here about what are the functions used in regex filtering.
Functions Used in Regex Filtering with Pandas
There are several functions that can be used in regex filtering, and a few of them are mentioned below:
contains(): This function is used to check if a pattern is present in a Series
match(): This function is used to determine if a pattern matches the beginning of a given string
extract(): This function is used to extract matching patterns from a Series
replace(): This function is used to replace matching patterns with a specified text
findall(): This function returns all non-overlapping matches of a regex pattern in each element of a Series
Now, you might be wondering how we can use these functions, let us understand this with the help of an example.
Time for an Example
Let us discuss an example based on all the above-mentioned functions that are used in regex filtering.
Suppose we have a dataset called ninjasinformation. In which we have information about our ninjas. We have their names, ages, courses, and questions solved by them. We also have some ninjas to which we have given the name ‘NoName’.
Let us try the above-mentioned functions to extract, replace and analyze the ninjas’ information.
contains() Function
Let us discuss an example of contains() function based on a given dataset of ninjasinformation.
Python
Python
import pandas as pd
# Loading the ninjasinformation CSV
dataframe = pd.read_csv("ninjasinformation.csv")
# contains() function for finding 'Name' with 'an'
In this example, we have used information about our ninjas. We are trying to implement the regex filtering function on a given CSV. Firstly, we have imported the library of Pandas. Then we loaded the ninjasinformation.csv into a dataframe. Then we used contains() function to check if any name contains ‘an’ in their name. We have passed three parameters in that function, ‘an’, the case(upper or lower) should be false, and na as false for missing values.
match() Function
Let us discuss an example of match() function based on a given dataset of ninjasinformation.
Python
Python
import pandas as pd
# Loading the ninjasinformation CSV
dataframe = pd.read_csv("ninjasinformation.csv")
# match() function for finding people whos 'age' equal to 21
In this example, we have used information about our ninjas. We are trying to implement the regex filtering function on a given CSV. Firstly, we have imported the library of Pandas. Then we loaded the ninjasinformation.csv into a dataframe. Then we used the match() function to give the details of ninjas whose age is 21. We convert the 'Age' values to strings using .astype(str) to ensure compatibility with the regex pattern.
extract() Function
Let us discuss an example of the extract() function based on a given dataset of ninjasinformation.
Python
Python
import pandas as pd
# Loading the ninjasinformation CSV
dataframe = pd.read_csv("ninjasinformation.csv")
# extract() function for taking 3 letters of the courses
In this example, we have used information about our ninjas. We are trying to implement the regex filtering function on a given CSV. Firstly, we have imported the library of Pandas. Then we used the extract() function to extract the 3 letters of the course name.
replace() Function
Let us discuss an example of the extract() function based on a given dataset of ninjasinformation.
In this example, we have used information about our ninjas. We are trying to implement the regex filtering function on a given CSV. Firstly, we have imported the library of Pandas. Then we used the replace() function to replace the NoName with a given name which we have passed as a parameter in the function.
findall() Function
Let us discuss an example of the extract() function based on a given dataset of ninjasinformation.
In this example, we have used information about our ninjas. We are trying to implement the regex filtering function on a given CSV. Firstly, we have imported the library of Pandas. Then we implemented the findall() function to find all the names which are containing the ending letter ‘a’.
Frequently Asked Questions
Can we use regex only with text data?
No, regex can also be applied to numerical data. It can be used in filtering the patterns in numbers.
Are regex patterns case-sensitive?
By default, regex patterns are case-sensitive. We can also make them case-insensitive using appropriate flags.
Can regex filtering slow down data processing?
Regex filtering can be resource-intensive, especially for large datasets. If we are following an efficient pattern designing and optimizing the pattern, then we can make it faster.
Can we combine regex with other filtering methods?
Yes, we can combine regex filtering with other Pandas filtering methods. We can use this to get more precise results.
Is regex filtering limited to Pandas?
No, regex filtering is a versatile concept. It is applicable to various programming languages and text-processing tools.
Conclusion
In this blog, we have discussed regex filtering with Pandas. We have also discussed all the important functions that are used in regex filtering. By using regex filtering, we can easily extract, replace and analyze the data. If you want to learn more about the Pandas in Python, then you can check out our blogs:
We hope this blog helps you to get knowledge about regex filtering with Pandas. You can refer to our guided paths on the Coding Ninjas Studio platform. You can check our course to learn more aboutDSA, DBMS, Competitive Programming, Python, Java, JavaScript, etc.
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