Crash course: Data analytics in Python using Pandas

Analyze sample data with pandas in Python to compare salaries by gender across jobs and cities, calculating max and average salaries to explore location and gender pay patterns.

Import pandas, load a comma-separated values file into a data frame, clean salary by removing the dollar sign and converting to float, then group by gender to compute mean salary.

Explore how to compute and compare average salaries by gender across job titles in pandas, creating female and male salary columns and using group by for insights.

Extend pandas analysis by grouping by job title, city, and gender to compare average salaries across groups. Adjust pandas display options to show all columns in the terminal.

Import data with pandas using read_csv, set separators and headers, and convert dictionaries to data frames with pd.DataFrame across various sources.

Learn to inspect a pandas dataframe with head and tail to view records, shape for size, and describe and info for numeric stats, data types, and memory usage.

Demonstrates handling missing values in pandas by filling city with London in place, imputing median latitude, and optionally dropping incomplete rows to preserve analysis accuracy.

Learn how to identify and remove duplicate rows in a dataset using Pandas, including whole-row duplicates and non-unique first name cases, and understand how the shape changes after dropping duplicates.

Explore where statements to filter a data frame by salary conditions, combine multiple criteria, sort results, and use grouping by job title to reduce processing and focus on marketing campaigns.

Learn how to select specific fields from a pandas dataframe using single and double brackets. Create a focused subset to reduce data frame size and improve computational efficiency.

Learn how to replace values in a pandas dataframe to standardize gender labels, using a list-based mapping to convert male and female to M and F in a single step.

Apply group by to summarize salaries by gender and city, using mean, sum, median, or max. Group by multiple fields yields max salaries by gender in each city.

Rank a dataframe of salaries by cleaning the salary field, converting to float, and creating a clean salary column. Sort by rank in descending order to identify the top earners.

Use the apply function to clean salary data, remove the dollar sign, and convert values to float, creating new columns like clean salary and female salary average.

Export the data frame back to files, writing to csv and json, showcasing the new columns clean salary, female salary, and male salary with job titles preserved.

Practice data analytics in Python with pandas by analyzing a weblog dataset to compute bytes per domain, add gender-based totals, and compare bytes by country.

Learn to analyze data with pandas by reading a CSV into a data frame, applying groupby, reset_index, and sort, then create gender-based bytes columns and summarize by URL and country.

Use pandas to process contract data, rename columns, compute days until renewal with format_date, apply a status_calc to flag overdue or urgent renewals, and email results via the grid API.

Celebrate completing the course by applying pandas to real business problems using the building blocks provided. Engage with the Q&A and leave a review to share feedback and keep coding.