Adventures in Machine Learning

Master Data Manipulation: Extracting Numbers from Strings in Pandas

Extracting Numbers from a String in Pandas

As data analytics and machine learning become increasingly important, the ability to manipulate data effectively has become an essential skill. In this article, we will focus on one particularly common task in data manipulation: extracting numbers from strings in pandas.

Syntax for Extracting Numbers

Fortunately, extracting numbers from strings in pandas is relatively straightforward. The primary keyword we need to use is extract.

Here is the basic syntax:

df[‘new_column_name’] = df[‘original_column_name’].str.extract(r'(d+)’)

In this example, df is the name of our dataframe, ‘new_column_name’ is the name of the new column we want to create, and ‘original_column_name’ is the name of the column that contains the string we want to extract numbers from. The regular expression d+ matches one or more consecutive digits.

Example for Extracting Numbers

Now let’s look at a more detailed example. Suppose we have a simple dataframe with two columns: name and age, where age is stored as a string.

“`

import pandas as pd

df = pd.DataFrame({

‘name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘Dan’, ‘Eli’],

‘age’: [’20’, ’25’, ’30’, ’35’, ’40’]

})

“`

Our goal is to extract the age values from the age column and store them as integers in a new column called ‘age_int’. Here’s how we can do it:

“`

df[‘age_int’] = df[‘age’].str.extract(r'(d+)’).astype(int)

“`

The result is a new column called ‘age_int’ that contains the integer values of the age column.

Additional Resources

While extracting numbers from strings in pandas is a common operation, there are many other useful operations you can perform using pandas. Whether you’re manipulating data, cleaning datasets, or preparing data for machine learning models, pandas offers a wide range of powerful tools.

If you’re new to pandas, there are many great tutorials and online resources available to help you get started. Some popular resources include the pandas documentation, online courses such as DataCamp, and tutorials on sites like Kaggle and Medium.

Additionally, many textbooks on data science and machine learning feature detailed examples of using pandas for data manipulation and analysis. In conclusion, extracting numbers from strings in pandas is a fundamental task in data manipulation.

Using the extract function in pandas, you can quickly and easily extract numerical values from strings, store them as integers, and perform further analysis or manipulate data in other ways. With pandas’ powerful tools and a wealth of online resources available, anyone can master these essential data manipulation skills and become a more effective data scientist or analyst.

In summary, extracting numbers from strings in pandas is a crucial task in data manipulation, and it can be accomplished easily and efficiently using the extract function. By following the syntax and examples outlined above, you can extract numerical values from strings in your data and perform further analysis or manipulate your data in other ways.

Familiarizing yourself with pandas’ powerful tools and exploring the many online resources available can help you develop essential data manipulation skills and become a more effective data scientist or analyst. Remember that data manipulation is a fundamental skill in today’s data-driven world, and mastering these skills can have a significant impact on your career.

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