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How to Check Column Nulls and Replace: Pandas

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Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations:


Regular expression examples


01 Matching a Simple Pattern


import re


text = "Hello, World!"

pattern = r"Hello"

result = re.search(pattern, text)

if result:

    print("Pattern found:", result.group())

Output:


Output:

Pattern found: Hello

This example searches for the pattern "Hello" in the text and prints it when found.


02 Matching Multiple Patterns


import re


text = "The quick brown fox jumps over the lazy dog."

patterns = [r"fox", r"dog"]

for pattern in patterns:

    if re.search(pattern, text):

        print(f"Pattern '{pattern}' found.")

Output:


Pattern 'fox' found.

Pattern 'dog' found.

It searches for both "fox" and "dog" patterns in the text and prints when they are found.


03 Matching Any Digit

 

import re


text = "The price of the product is $99.99."

pattern = r"\d+"

result = re.search(pattern, text)

if result:

    print("Price:", result.group())

Output:


Price: 99

This example extracts digits (numbers) from the text.


04 Matching Email Addresses


import re


text = "Contact us at support@example.com or info@example.org."

pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,7}\b"

emails = re.findall(pattern, text)

for email in emails:

    print("Email:", email)

Output:

Email: support@example.com

Email: info@example.org

It extracts email addresses from the text using a common email pattern.


05. Replacing Text

 

import re

text = "Please visit our website at http://www.example.com."

pattern = r"http://www\.[A-Za-z]+\.[A-Za-z]+"

replacement = "https://www.example.com"

updated_text = re.sub(pattern, replacement, text)

print("Updated Text:", updated_text)

Output:

Updated Text: Please visit our website at https://www.example.com.

This example replaces a URL with a different URL in the text.


These are just a few examples of what you can do with regular expressions in Python. Regex is a versatile tool for text processing, and you can create complex patterns to match specific text structures or extract information from text data.

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