Gannon Dunkerley & Co. Interview Questions and Answers

The code will output an empty list as a result of slicing from -1 to -5 in the list 'a'.

• Slicing in Python allows you to access a subset of elements in a list or tuple.

• When slicing, the start index is inclusive and the end index is exclusive.

• In this case, a[-1:-5] will result in an empty list because the start index -1 is greater than the end index -5.

Read sample data in text, check for null and unique values, create new column by multiplying two features

• Save text data as CSV and read in Python using pandas

• Use isnull() to check for null values

• Use nunique() to check for unique values

• Create a new column by multiplying two existing columns

• Add the new column to the existing dataframe

Fibonacci series is a sequence of numbers where each number is the sum of the two preceding ones.

• Use a loop or recursion to generate Fibonacci series in Python.

• Start with 0 and 1 as the first two numbers in the series.

• Add the previous two numbers to get the next number in the series.

To measure 9 minutes using 7 and 4 minutes hourglasses

• Start both hourglasses at the same time

• When the 4 minutes hourglass runs out, flip it immediately

• When the 7 minutes hourglass runs out, flip the 4 minutes hourglass

• When the 4 minutes hourglass runs out again, a total of 9 minutes have passed

Remove duplicates from an array without using any functions

• Create an empty array to store unique values

• Loop through the original array and check if the value is already in the unique array before adding it

• Use nested loops to compare each element with all other elements in the array

To create a single dimensional array from a multi dimensional array, use the flatten method in Python.

• Use the flatten method in Python to convert a multi dimensional array into a single dimensional array.

• Example: multi_array = [[1, 2], [3, 4], [5, 6]]

• single_array = [item for sublist in multi_array for item in sublist]

Use dropDuplicates() function in Spark to remove duplicates from a DataFrame.

• Use dropDuplicates() function on the DataFrame to remove duplicates based on all columns

• Specify columns to check for duplicates by passing them as arguments to dropDuplicates() function

• Example: df.dropDuplicates() or df.dropDuplicates(['col1', 'col2'])