All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
First, we need to find the first 1M prime numbers. But there's no need to waste time writing functions or waiting for computation. There are lists of Prime numbers out there. Let's start by reading the first 1M prime numbers that are stored in the file primes1.csv in the variable primes_df. Your DataFrame should look something like:
Let's start by just generating a list of the first 100,000 sequential integers numbers in a dataframe named exp_df. Just that, a single column named n containing all integers in the range 1 to 100_000.
WARNING: Make sure
100_000IS included in your dataframe:
Your dataframe should look something like:
We'll now start approaching the Mersenne formula 2^n - 1.
First, create a new column named 2^n that contains the results of exactly that formula. The column n elevated to the power of 2. You'll need to use vectorized operations to do so.
Your dataframe will look something like:
Now, create the column 2^n - 1 that completes the formula. Your dataframe should now look something like:
Initialize a new column in primes_df named is_mersenne_prime that contains ONLY False values.
Your dataframe should look something like:
Now is the time to flip the switch! Use the values in exp_df to correctly identify which values in primes_df are ACTUALLY Mersenne prime. You'll need to turn the value of is_mersenne_prime to True for those prime numbers.
Warning! If you make a mistake and you have modified your primes_df dataframe, you'll need to start from scratch. So, it might be handy to create a copy of your dataframe first just as a backup. Example:
>>> primes_df_backup = primes_df.copy() # Execute this ONLY ONCE!
>>> # I made a mistake and the activity doesn't work, I want to go back!
>>> primes_df = primes_df_backup.copy() # We're back!
>>> # Try again
Your dataframe should look something like:
