Lists — Operations & Methods

cols = ["name", "age", "salary"]
cols[0]    # "name"
cols[-1]   # "salary"  (last element)
cols[1:3]  # ["age", "salary"]  (slicing)

Negative indexing (-1 for last) and slicing are Python-specific. In data work, you frequently build lists of column names to select or drop from a DataFrame.

lst = [1, 2, 3]
lst.append(4)      # [1, 2, 3, 4]
lst.extend([5, 6]) # [1, 2, 3, 4, 5, 6]
lst.insert(0, 0)   # [0, 1, 2, 3, 4, 5, 6]
lst.remove(3)      # removes first occurrence of 3
lst.pop()          # removes and returns last item

append() adds one item; extend() adds multiple. pop() is useful when processing items one by one. remove() deletes by value, pop() by position.

nums = [3, 1, 4, 1, 5, 9]
nums.sort()              # sorts in place: [1, 1, 3, 4, 5, 9]
sorted(nums)             # returns new sorted list
nums.sort(reverse=True)  # descending

.sort() modifies the original list. sorted() returns a new one and works on any iterable. In data analysis, you often sort value lists before mapping to charts.

# Without comprehension:
squares = []
for x in range(5):
    squares.append(x**2)

# With list comprehension (preferred):
squares = [x**2 for x in range(5)]   # [0, 1, 4, 9, 16]

# With condition:
evens = [x for x in range(10) if x % 2 == 0]

List comprehensions are faster and more Pythonic. Interviewers love asking this. Use them to transform or filter lists in one line — e.g. [c.upper() for c in col_names].

nums = [10, 20, 30, 40]
len(nums)   # 4
min(nums)   # 10
max(nums)   # 40
sum(nums)   # 100

These built-ins work on any iterable. Before Pandas, these are your quick stats on a raw list. They also work on Pandas Series.

cols = ["name", "age", "salary"]
"age" in cols      # True
"gender" in cols   # False

The in operator for lists has O(n) time complexity — it scans every element. For large collections with frequent lookups, use a set instead, which is O(1).