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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
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5 Python Pandas Tricky Examples for Data Analysis

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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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