The following csv data file contains data of diamonds with following characteristics. "diamonds.csv"
carat - diamond's apparent size. cut - how good the diamond shaping is . color - colour of a diamond as letter representation. clarity - clarity of diamond. depth - depth of diamond. table - top surface of diamond. price - price of diamond. x - x dimension. y - y dimension. z - z dimension.
Read the file and find the following:
- Drop the unnamed column from the dataset once you read it.
- The occurences of all unique colors of diamonds, bar chart of the same.
- Average price of each cut of diamond.
- Find the volume of every diamond and store it in a new column in the dataset. (volume = xyz)
- Histograms of Price and depths colums as two separate plots
- A scatter plot of carat vs Price, volume vs Price, depth vs Price in a subplot of 1x3.
- Convert the categorical values of color, to numbers. i.e D, E, F, H, I, J to 0, 1, 2, 3, 4, 5 using Pandas methods only
- Which clarity(i.e the category) diamond has maximum mean depth?
- Find the diamond(i.e row) with minimum price per volume.