Useful code snippets

• Creating a local HTTP server (Python 3.x):

#go to the website directory on the computer, and type 'cmd'

python -m http.server 8000

#open a web browser and go to http://localhost:8000
#if port 8000 is already in use, type another port number such as '8080'
#to stop the server, go back to the command prompt where the server is running and press Ctrl+C.

• to read files contain foreign languages, use "ISO-8859-1", also known as "latin1":

import pandas as pd

data_set = pd.read_csv("file_path/file_name", encoding="ISO-8859-1")

print(data_set.head(5))

or
• use "windows-1252":

import pandas as pd

data_set = pd.read_csv("file_path/file_name", encoding="windows-1252")

print(data_set.head(5))

• to filter strings with AND operators:

# Assuming df is a pandas DataFrame

# to filter strings with 'meet' but not 'nice to meet you' and 'meeting'
filtered_df = df[(df['English'].str.contains('meet', case=False)) & ~(df['English'].str.contains('nice to meet you|meeting', case=False))]

print(filtered_df)

'case=False' means it is not case-sensitive.
**When you're combining multiple conditions with logical operators in pandas, especially with bitwise operators like & (AND) and | (OR) that are used for element-wise logical operations on pandas series, it's important to use parentheses to group conditions. This is because the bitwise logical operators have a higher precedence than comparison operators like <, >, or ==. Without parentheses, Python might attempt to evaluate the expression in an order that doesn't match your intended logic, leading to errors or unexpected results
• to filter strings with OR operators:

df = df[df['English'].str.contains('meet|Nice to meet you|meeting', case=False)]

• Reading a file:

with open(r'C:\Users\Owner\Desktop\PYTHON\auto.csv', 'r') as file:
content = file.read()

('r' before the file path tells Python not to treat backslashes as escape characters)
or you can use forward slashes to avoid getting 'unicode errors'.
Using open( ) for reading files is memory-efficient for large plain text files, such as reading line by line or for quick reads or simple data processing

with open('C:/Users/Owner/Desktop/PYTHON/auto.csv', 'r') as file:
content = file.read()

• Writing to a file:

with open('filename.txt', 'w') as file:
file.write('Hello, world!')

• Create a list based on existing lists:

squares = [x**2 for x in range(10)]

• Flattening a list of lists:

flat_list = [item for sublist in list_of_lists for item in sublist]

• Chunking a list into n-sized chunks:

def chunk_list(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
chunks = list(chunk_list([1, 2, 3, 4, 5], 2))

• Removing duplicates from a list:

unique_items = list(set(your_list))

• Merging two dictionaries:

dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}

merged_dict = {**dict1, **dict2}
print(merged_dict)

• Using 'update' to merge two dictionaries:

dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}

dict1.update(dict2)
print(dict1)

• Using enumerate to get index/value pairs:

for index, value in enumerate(your_list):
print(index, value)

• Checking memory usage of an object:

import sys
print(sys.getsizeof(your_object), "bytes")

• Getting the most frequent value in a list:

from collections import Counter
most_common = Counter(your_list).most_common(1)[0][0]

• Getting five most frequent values in a list:

from collections import Counter

def most_frequent_numbers(lst, n=5):
counts = Counter(lst)
return counts.most_common(n)

# Example usage:
my_list = [1, 2, 3, 4, 1, 2, 1, 5, 2, 2, 3]
result = most_frequent_numbers(my_list)

# Display result in two columns
print("Number\tFrequency")
for number, frequency in result:
print(f"{number}\t{frequency}")

• Counting the frequency of all the numbers in a list in decending order:

from collections import Counter

def sort_numbers_by_frequency(numbers):
# Count the frequency of each number
freq_counter = Counter(numbers)

# Sort by frequency in descending order
sorted_freq = sorted(freq_counter.items(), key=lambda x: x[1], reverse=True)

# Display in two columns (Number, Frequency)
print("Numbers\tFrequency")
for number, frequency in sorted_freq:
print(f"{number}\t{frequency}")

# Example usage:
numbers_list = [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 3, 4, 4]
sort_numbers_by_frequency(numbers_list)

• Formatting the frequency of all the numbers in a list in decending order:

from collections import Counter

def sort_numbers_by_frequency(numbers):
# Count the frequency of each number
freq_counter = Counter(numbers)

# Sort by frequency in descending order
sorted_freq = sorted(freq_counter.items(), key=lambda x: x[1], reverse=True)

# Display in two columns (Number, Frequency) with separator and dash lines
print("Numbers | Frequency")
print("-" * 20)  # Dash line
for number, frequency in sorted_freq:
print(f"{number}{' '*(7-len(str(number)))}| {frequency}")

# Example usage:
numbers_list = [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 3, 4, 4]
sort_numbers_by_frequency(numbers_list)

• Swapping two variables:

a, b = b, a

• Checking if a file exists:

import os
os.path.exists('filename.txt')

• List slicing tricks: reversing a list

reversed_list = your_list[::-1]

• JSON serialization and deserialization:

import json

# Serialization
json_string = json.dumps(your_object)

# Deserialization
your_object = json.loads(json_string)