Pandas Challenges

Participate in the pandas challenge by filtering the employee data set for August hires, extract the hire date year, count by year with value_counts, and sort by index.

Apply two boolean filters in pandas to count employees from the police department whose titles do not contain the word police, returning 896.

Engage with the pandas challenge by filtering the employee data set to return a data frame of strictly increasing salaries, using the salary column and cumulative max insights.

Filter a Pandas data frame of employee salaries to keep strictly increasing values without reordering, using drop missing values, cumulative max, and shift for above-row comparison.

Engage in daily pandas exercises by grouping data by title, counting unique departments per title, and filtering for titles that appear in multiple departments; return those titles as a set.

Tackle pandas challenge 17 by loading the employee data set, determine which hires occurred on a weekday using the weekday attribute, and sum the boolean results to count weekday hires.

apply pandas to filter the diamonds data set by price per carat over 50,000 using the query method, computing price divided by carat in one line.

Automate half-carat bin creation with pandas cut and numpy arange, group diamonds by carat bins, and compute the average price rounded to the nearest hundred.

Group diamonds by color and aggregate price to min, max, mean, median, and count, then round the mean to the nearest hundred using agg and a dictionary.