CASE STUDY OF PYTHON WITH DATA SCIENCE

Data Science For Beginners with Python 18 - Case Study 1 - Classify Personal Income (part 2) ProgrammingKnowledge Cross Tabulation and Data Visualization In this video, we'll explore cross tabulation and data visualization using Pandas and Seaborn libraries. Column Count We start by checking the number of columns in our data frame. We use the ns function to get all the columns. Gender Proportion We use the tab function to get the count and proportion of males and females in the population. Bivariate Analysis We explore the relationship between gender and salary status using the tab function. We create a new variable, gender_salary_stat, to get the count and proportion of males and females in different salary categories. Frequency Distribution of Salary Status We use the Seaborn library to draw a count plot of the salary status column. Frequency Distribution of Age We use the Seaborn library to draw a histogram of the age column. Bivariate Analysis for Age and Salary Status We use the Seaborn library to draw a box plot of the age and salary status columns. Further Analysis We recommend exploring bivariate analysis for job type, education, occupation, hours per week, and capital gain columns to gain more insights.