I’m facing a problem with cleaning and preprocessing email data in Python using Pandas. I have a large dataset containing email addresses and need to filter out the invalid or improperly formatted ones. I have heard about regular expressions and how you can define a pattern and filter valid emails based on that pattern but I am not sure how to do this in Python or how to write the code for it. Can anyone provide me with example codes and techniques of how I can use this pattern to filter my data?
@mubashir_rizvi, you can use the
str.contains() method with the
regex=True parameter to check if each element in your series matches the regular expression pattern and this method returns a boolean result. You can also add the
na=False parameter that ensures missing values are not considered valid email addresses.
Then, you can use the resulting boolean mask to filter out the invalid emails from the original series. Let’s see the example given below for a better understanding:
Hi @mubashir_rizvi one way to filter valid emails from a Panda series is by using
re.match()method searches for a pattern at the beginning of a string and returns a match object if the pattern is found; otherwise, it returns
re.match()method is used in a simple lambda function, which is then applied to the series using the
apply()method that allows applying functions to each element of a Pandas dataframe or series.
Hey @mubashir_rizvi , you can also use the built-in
filter() function along with the
re.match() method to filter out valid email addresses from a Pandas series. The result is in the form of a list and to convert it into a series, simply have to use the