Extracting Substrings in Pandas DataFrame
Data processing is a fundamental technique that is used in nearly every field. Data scientists are required to collect, analyze, and extract meaningful information from data sets, which can be a daunting task.
However, with the advent of data-processing tools like Pandas, working with large amounts of data has become much easier. In this article, we will delve into one of the most common tasks in Pandas: extracting substrings.
Understanding Substring Extraction
A substring can be defined as a part of a string that is extracted or isolated from a larger string. For instance, extracting the first three characters from a string is a substring operation.
In Pandas, a substring can be extracted using the .str method as follows:
The above syntax will create a new column in the data frame, which is a substring of the specified column. For example, let’s say we have a data frame called ‘points’ with the following columns:
If we want to extract the first character of the Team column, we can use the following code:
points[‘First’] = points[‘Team’].str
The resulting data frame will look like this:
Team Points First
A 120 A
B 150 B
C 178 C
D 136 D
Notice that we created a new column called ‘First,’ which contains the first character of the ‘Team’ column. The .str method is instrumental in extracting substrings.
Using Syntax in Practice for Creating New Column
One of the most common applications of substring extraction is to create a new column in a data frame. Suppose that we have a data frame called ‘team’ with the following columns:
Hyderabad Sunrisers 95
Mumbai Indians 89
Chennai Super Kings 90
Kolkata Knight Riders 92
If we want to extract only the team names (i.e., the words before the first space in the ‘Team’ column), we can use the following code:
team[‘Team Name’] = team[‘Team’].str.split().str
The above code employs the .split() method with an argument of zero, which indicates the first word in the string. The resulting data frame will look like this:
Team Points Team Name
Hyderabad Sunrisers 95 Hyderabad
Mumbai Indians 89 Mumbai
Chennai Super Kings 90 Chennai
Kolkata Knight Riders 92 Kolkata
Converting Numeric Column to String before Getting Substring
Sometimes we might need to extract substrings from a numeric column. In such cases, we need to convert the column to a string before using the .str method.
Suppose we have a data frame called ‘grades’ with a column containing the students’ scores:
If we want to extract only the first digit of each score, we can use the following code:
grades[‘First Digit’] = grades[‘Grade’].astype(str).str
The above code uses the .astype(str) function to convert the numeric column to a string. The resulting data frame will look like this:
Grade First Digit
Other Common Tasks in Pandas
In addition to extracting substrings, there are other tasks that are commonly performed in Pandas. These may include:
Cleaning and preprocessing data
2. Renaming columns
Handling missing data
4. Filtering data
Merging data frames
6. Handling duplicates
It is important to note that there are numerous tutorials online that can guide you through the process of performing each task.
These tutorials are designed to cater to users of all levels of proficiency- from beginners to experts. Utilizing these tutorials will greatly enhance your command of Pandas and make your data processing tasks much easier.
Extracting substrings in Pandas is a task that is frequently performed by data scientists and analysts. The .str method is one of the most effective ways to extract substrings in a data frame, and it can be used to create new columns containing relevant information.
In addition, there are several other common tasks that are performed in Pandas, and it is important to familiarize oneself with these tasks so as to become proficient in data manipulation. With the aid of online tutorials, you can easily acquire the necessary skill set to become a proficient data analyst.
In summary, extracting substrings in Pandas is a crucial task for data scientists and analysts who need to manipulate large amounts of data. The article outlines the process for using the .str method to extract a substring from a column and demonstrates how to create a new column containing relevant information.
Additionally, there are other common tasks, such as cleaning and preprocessing data, renaming columns, filtering data, and merging data frames, that Pandas can perform with ease. Utilizing online tutorials is a great way to gain proficiency in manipulating data.
Overall, Pandas is a versatile and powerful tool that can greatly enhance your ability to process and analyze data efficiently.