Exploring Tech Salaries with Pandas: Pivot and CrossTab Techniques

This project teaches essential data analysis techniques using Pandas Pivot Table and CrossTab on the 2016 Hacker News Salary Survey dataset. Students will learn to group data, apply aggregation functions, and handle missing values. The hands-on activities are designed to build skills in exploring and analyzing tech industry salary data, enabling students to make informed decisions based on their findings.

codevalidated

Calculate the average `annual_base_pay` by `location_state `.

Index the salaries_df dataframe by location_state and set the values to annual_base_pay. Use the mean aggregation function to calculate the average annual base pay by location state.

Store the resulting dataframe in the variable avg_base_pay.

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Using Pivot Table: Calculate the maximum `annual_bonus` and minimum `total_experience_years` by `job_title_rank` and `location_state`.

Store the resulting dataframe in the variable max_annual_bonus_min_experience_by_rank_state.

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Using Pivot Table: Calculate the count of `job_title` by `job_title_category`.

Store the resulting dataframe in the variable job_titles_by_category.

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Using Pivot Table: Calculate the average `annual_base_pay` and the count of records (`salary_id`) for job of `Senior` rank by `location_latitude` and `location_longitude`.

Store the resulting dataframe in the variable avg_base_pay_count_records_senior_by_latitude_longitude.

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Using Pivot Table: calculate the average `annual_base_pay` by `location_country`.

Store the resulting dataframe in the variable base_pay_by_country.

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Using Crosstab: Calculate the median `annual_base_pay` by `location_country` and `job_title_rank`.

Perform crosstabulation to calculate the median annual base pay by location country and job title rank. Also, fill missing values with the overall median annual base pay.

Store the resulting dataframe in the variable median_base_pay.

Exploring Tech Salaries with Pandas: Pivot and CrossTab Techniques

Anurag Verma

Project Activities

Calculate the average `annual_base_pay` by `location_state `.

Using Pivot Table: Calculate the maximum `annual_bonus` and minimum `total_experience_years` by `job_title_rank` and `location_state`.

Using Pivot Table: Calculate the count of `job_title` by `job_title_category`.

Using Pivot Table: Calculate the average `annual_base_pay` and the count of records (`salary_id`) for job of `Senior` rank by `location_latitude` and `location_longitude`.

Using Pivot Table: calculate the average `annual_base_pay` by `location_country`.

Using Crosstab: Calculate the median `annual_base_pay` by `location_country` and `job_title_rank`.

Anurag Verma

Data Wrangling with Pandas

Set Operations using Sakila

LIKE Operator using World

Membership and Range Operators with World Database