The dataset includes information about each individual slave, information useful for their upcoming sale. The data has numeric information, text and geographic information. The spreadsheet consists of nine columns. The geographic information is the state and county column, the states shown are Georgia, Louisiana, Virginia, North/South Carolina, Mississippi, and Maryland. The county column lists a variety of counties within each state where a slave was sold to give a more accurate account of where in the state the slave was sold. The numeric information is split up in the date of the entry of slave information column, the age of the slave in years and month’s column and the appraised value column. For the year column the dates start at 1775 and continue to 1865 but the spreadsheet doesn’t go in order by date so the numbers jump around quite a bit. The other two columns describe the slaves age in years which tends to vary from old to young but more often doesn’t have any information at all and months column is completely empty I believe due to the fact that very few infants were sold. The last numeric information is regarding the appraised value of the slave which is varied based on the age and skill and defect of the slave. The final text information is in three columns that include the slave’s sex, skills and defects. The sex of the slave is broken into male and female. The next text column is the skills column, in which the men had skills listed as cabinet makers or gardeners and women would be cooks or midwives. The final column is the defects column. This column shows slaves that were noted with flaws. These could be as simple as too old or too young, any type of sickness they might have or if they were disruptive.
This information draws a lot of connections between the rows and columns. Many can be found and expanded upon. I believe the most notable relationship is the appraised value and the rest of the columns. The amount of money willing to be paid on a particular slave is changes often depending on the other columns information; gender, age, skill and defect can alter the price in any given state or county. A young male with a skill would be much higher price than an older female with a defect. Another relationship found is the one between the date in which the slave was sold and the location. Possibly revealing that in certain states and specific counties experienced a much later or earlier slave trade. Could be from slavery expanding to other states more aggressively or slowing down much later in other states. Maryland and South Carolina have some of the earliest dates on the spreadsheet, then every other state tends to pick up during the 19th century. Could be due to policy changes that America was facing that effected slave trade. The next relationship found is an obvious one between the male skills and the female skills. The males had skills that were using their hands like cabinet maker and gardener while the women had more domestic jobs like cooks or caring for children. A relationship I would be interested in discovering is one that would relate defects to either age or gender, specifically a defect that dealt with disobedience.