This was the second project provided by DataCamp on the Data Scientist with Python Track. Such questions as display the oldest bussinesses in the world, in North America, on each continent, find countries who don't have the oldest bussiness, how many oldest bussinesses are in each category, and similar like that, were to test out our two previous lesson before this.
From the questions above, the DataCamp provides different datasets, e.g., businesses and new businesses, countries, and categories from BusinessFinancing.co.uk. and we have to overcome those using data manipulation dan data joining with Pandas, such as sorting values, merging, concatenating, grouping by, subsetting, and aggregating.
Forecasting for the future and ensuring that the firm survives shifting market conditions are crucial aspects of business. Some firms accomplish this very well and survive for hundreds of years. In this assignment, you will investigate data from BusinessFinancing.co.uk on the world's oldest firms, including when they were created and which sectors they are associated with.
The data we'll look at, like many business challenges, is spread across several datasets. To comprehend the world's oldest firms, we must first blend our data using joining procedures. From there, we may answer questions about these ancient firms by using manipulation techniques like grouping and filtering.
- The oldest business in the world
- How many businesses were founded before 1000?
- Which businesses were founded before 1000?
- Exploring the categories
- Counting the categories
- Oldest business by continent
- Joining everything for further analysis
- Counting categories by continent
- Filtering counts by continent and category