12 Python programs they love to ask

Strings

Warm up. Everyone asks these.

1. 01

   Reverse a string

   s = "analyst"
   print(s[::-1])  # tsylana

2. 02

   Check a palindrome

   def is_pal(s):
       s = s.lower()
       return s == s[::-1]

Lists

The logic round starts.

1. 03

   Top 3 most frequent words

   from collections import Counter
   words = text.split()
   print(Counter(words).most_common(3))

2. 04

   Second largest number

   nums = [4, 9, 2, 9, 7]
   print(sorted(set(nums))[-2])  # 7

Lists

Where freshers freeze.

1. 05

   Find duplicates in a list

   seen, dups = set(), set()
   for x in nums:
       if x in seen:
           dups.add(x)
       seen.add(x)

2. 06

   Missing number from 1 to n

   n = len(nums) + 1
   missing = n * (n + 1) // 2 - sum(nums)

Logic

Clean code wins here.

1. 07

   FizzBuzz, the classic filter

   for i in range(1, 16):
       if i % 15 == 0: print("FizzBuzz")
       elif i % 3 == 0: print("Fizz")
       elif i % 5 == 0: print("Buzz")
       else: print(i)

2. 08

   Flatten a nested list

   grid = [[1, 2], [3, 4], [5]]
   flat = [x for row in grid for x in row]

pandas

Now the analyst part.

1. 09

   Top 3 cities by revenue

   (df.groupby("city")["rev"].sum()
      .nlargest(3))

2. 10

   Fill missing values with median

   med = df["age"].median()
   df["age"] = df["age"].fillna(med)

pandas

The ones that get you hired.

1. 11

   Remove duplicate customers

   df = df.drop_duplicates(
       subset=["email"], keep="first")

2. 12

   Month-wise sales

   df["m"] = df["date"].dt.to_period("M")
   df.groupby("m")["sales"].sum()

How to practise these