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In Python, lists are versatile mutable sequences with built-in methods for adding, removing, searching, sorting, and more—covering all common scenarios like dynamic data manipulation, queues, or stacks. Below is a complete breakdown of all list methods, each with syntax, an example, and output, plus key built-in functions for comprehensive use.

📚 Adding Elements
append(x): Adds a single element to the end.

  lst = [1, 2]
lst.append(3)
print(lst) # Output: [1, 2, 3]


extend(iterable): Adds all elements from an iterable to the end.

  lst = [1, 2]
lst.extend([3, 4])
print(lst) # Output: [1, 2, 3, 4]


insert(i, x): Inserts x at index i (shifts elements right).

  lst = [1, 3]
lst.insert(1, 2)
print(lst) # Output: [1, 2, 3]


📚 Removing Elements
remove(x): Removes the first occurrence of x (raises ValueError if not found).

  lst = [1, 2, 2]
lst.remove(2)
print(lst) # Output: [1, 2]


pop(i=-1): Removes and returns the element at index i (default: last).

  lst = [1, 2, 3]
item = lst.pop(1)
print(item, lst) # Output: 2 [1, 3]


clear(): Removes all elements.

  lst = [1, 2, 3]
lst.clear()
print(lst) # Output: []


📚 Searching and Counting
count(x): Returns the number of occurrences of x.

  lst = [1, 2, 2, 3]
print(lst.count(2)) # Output: 2


index(x[, start[, end]]): Returns the lowest index of x in the slice (raises ValueError if not found).

  lst = [1, 2, 3, 2]
print(lst.index(2)) # Output: 1


📚 Ordering and Copying
sort(key=None, reverse=False): Sorts the list in place (ascending by default; stable sort).

  lst = [3, 1, 2]
lst.sort()
print(lst) # Output: [1, 2, 3]


reverse(): Reverses the elements in place.

  lst = [1, 2, 3]
lst.reverse()
print(lst) # Output: [3, 2, 1]


copy(): Returns a shallow copy of the list.

  lst = [1, 2]
new_lst = lst.copy()
print(new_lst) # Output: [1, 2]


📚 Built-in Functions for Lists (Common Cases)
len(lst): Returns the number of elements.

  lst = [1, 2, 3]
print(len(lst)) # Output: 3


min(lst): Returns the smallest element (raises ValueError if empty).

  lst = [3, 1, 2]
print(min(lst)) # Output: 1


max(lst): Returns the largest element.

  lst = [3, 1, 2]
print(max(lst)) # Output: 3


sum(lst[, start=0]): Sums the elements (start adds an offset).

  lst = [1, 2, 3]
print(sum(lst)) # Output: 6


sorted(lst, key=None, reverse=False): Returns a new sorted list (non-destructive).

  lst = [3, 1, 2]
print(sorted(lst)) # Output: [1, 2, 3]


These cover all standard operations (O(1) for append/pop from end, O(n) for most others). Use slicing lst[start:end:step] for advanced extraction, like lst[1:3] outputs ``.

#python #lists #datastructures #methods #examples #programming

@DataScience4
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In Python, handling CSV files is straightforward using the built-in csv module for reading and writing tabular data, or pandas for advanced analysis—essential for data processing tasks like importing/exporting datasets in interviews.

# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
reader = csv.reader(file)
data = list(reader) # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]

# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
writer = csv.writer(file)
writer.writerow(['Name', 'Age']) # Header
writer.writerows([['Alice', 30], ['Bob', 25]]) # Data rows

# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv') # df = DataFrame with columns 'Name', 'Age'
print(df.head()) # Output: First 5 rows preview

# Writing with pandas
df.to_csv('output.csv', index=False) # Saves without row indices


#python #csv #pandas #datahandling #fileio #interviewtips

👉 @DataScience4
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