# Data Management with OpenRefine and Pandas ### Exercise 1: 1. Using faceting, find out how many different interview_date values there are in the survey results. 2. Is the column formatted as Text or Date? 3. Use faceting to produce a timeline display for interview_date. You will need to use Edit cells > Common transforms > To date to convert this column to dates. 4. During what period were most of the interviews collected? ### Exercise 2 `value.replace("[", "")` Use this same strategy to remove the single quote marks ('), the right square brackets (]), and spaces from the items_owned column. ### Exercise 3 We discovered in an earlier lesson that the value for one of the village entries was given as 49. This is clearly wrong. By looking at the GPS coordinates for the entries of the other villages can we decide what village the data in that column was collected from? 1. Sort on gps_Latitude as a number with the smallest first. 2. Add a sort on gps_Longitude as a number with the smallest first. 3. Using the drop down arrow on the village column, select Edit column > Move column to end. This will allow you to compare village names with GPS coordinates. 4. Scroll through the entries until you find village 49. Can you tell from it’s GPS coordinates which village it belong to? 5. Now sort only by interview_date as date. Move the village column to the start of the table. Does the row where village is 49 group with one particular village? Is it the same village as when comparing GPS coordinates?