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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: 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

5 Python Pandas Tricky Examples for Data Analysis

Here are five tricky Python Pandas examples. These provide detailed insights to work with Pandas in Python,

Pandas examples

#1 Dealing with datetime data (parse_dates pandas example)

import pandas as pd

# Convert a column to datetime format

data['date_column'] = pd.to_datetime(data['date_column'])

# Extract components from datetime (e.g., year, month, day)

data['year'] = data['date_column'].dt.year

data['month'] = data['date_column'].dt.month

# Calculate the time difference between two datetime columns

data['time_diff'] = data['end_time'] - data['start_time']

#2 Working with text data


# Convert text to lowercase

data['text_column'] = data['text_column'].str.lower()

# Count the occurrences of specific words in a text column

data['word_count'] = data['text_column'].str.count('word')

# Extract information using regular expressions

data['extracted_info'] = data['text_column'].str.extract(r'(\d+)')

#3 Handling large datasets efficiently

# Read a large dataset in chunks

chunk_size = 100000

data_chunks = pd.read_csv('large_data.csv', chunksize=chunk_size)

# Process data in chunks

for chunk in data_chunks:

    # Perform calculations or manipulations on each chunk

# Append data from multiple files

file_list = ['file1.csv', 'file2.csv', 'file3.csv']

combined_data = pd.concat([pd.read_csv(file) for file in file_list])

#4 Pivot tables and reshaping data

# Create a pivot table

pivot_table = data.pivot_table(values='column2', index='column1', columns='column3', aggfunc='mean')

# Unstack a multi-index DataFrame

unstacked_data = pivot_table.unstack().reset_index()

# Melt a DataFrame from wide to long format

melted_data = pd.melt(data, id_vars=['id'], value_vars=['var1', 'var2'], var_name='variable', value_name='value')

#5 Efficient memory usage

# Optimize memory usage of DataFrame columns

data['numeric_column'] = pd.to_numeric(data['numeric_column'], downcast='integer')

data['category_column'] = data['category_column'].astype('category')

# Load a subset of columns from a large dataset

selected_columns = ['column1', 'column2', 'column3']

data_subset = pd.read_csv('large_data.csv', usecols=selected_columns)

These examples demonstrate more advanced techniques for handling datetime data, text data, large datasets, reshaping data, and optimizing memory usage. They highlight some of the powerful features that pandas provide for complex data analysis tasks.



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