Pandas Challenges

Tackle the dunder data panda's challenge by loading an employee dataframe, selecting the title and salary columns, and returning the first five rows to practice pandas data selection and testing.

Select the year, content rating, and duration for all movies between The Lion King and The Little Prince using the pandas loc indexer on the movie dataset.

Select every 100th row from an employee data set using pandas iloc and slice notation. Validate results by comparing data frames for equality with a final check.

Read the employee data set, use the hire date column, filter for hires after March 1, 2010, and sort by date with merge sort to get the first ten.

Tackle pandas five challenges by returning the top employee per department by salary. Select department title and salary, sort by salary descending, then drop duplicates by department.

Read the employee dataset, filter for hires on or after 2010-01-01 using a boolean series in pandas, and return the median salary from the salary column.

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.

Compute the percent of employees whose titles contain the word senior, case-insensitive; group by department, take the mean, multiply by 100, round to nearest tenth, and return a sorted series.

Tackle the Dunder Data Pandas challenge #12 by filtering the employee dataset to count hires on February 29th across any year, using month and day conditions and a boolean sum.

This challenge demonstrates computing the percentage of each sex by department using pandas crosstab, with normalization by department and rounding to one decimal place.

Group data by department and extract the last three employees per group using head and tail, demonstrating dataframe order of appearance in the pandas challenge.

Tackle Dunder Data Pandas Challenge #15 by using the cross tab function to count the frequency of each race within each sex in the employee data set.

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.

tackle pandas challenge 18 by loading the diamonds dataset, using the size attribute to get the total values, and applying shape to reveal rows and columns, then verify the solution.

Filter the diamonds dataset with Pandas by color and clarity using the query method, handle string quotes correctly, and verify results with an assert statement.

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.

Use pandas value_counts on clarity and color to identify the fifth most common diamond combination in the diamonds dataset, then retrieve it from the index by its integer location.

Filter the diamonds dataset to carat between 2 and 2.5 and cut in good or very good using a single query with the in operator, returning the first five results.

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.

Dunder data pandas challenge #24 guides you to filter diamonds by matching cut, polish, and symmetry using a query with chain comparisons, then show the last ten results and assert.

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.