We have already performed the first stage of EDA, which was a simple “get acquainted” step. Becoming intimate with the data and learning its relationships between its variables is an absolute must.Ĭompleting a successful and thorough EDA lays the groundwork for future stages of your data project. The goal of EDA is simple - get to know your dataset at the deepest level possible. In the previous section, we started something called “ Exploratory Data Analysis” (EDA), which is the basis for any data-related project. Performing univariate analysis with Seaborn Now that we are comfortable with the features in our dataset, we can start plotting them to uncover more insights. Looking at the mean of X and Y features, we see that diamonds, on average, have the same height and width.The minimum weight of a diamond is 0.2 carats, while the max is 5.01.The cheapest diamond in the dataset costs $326, while the most expensive costs almost 60 times more , $18,823.Here are some observations from the above output: The describe function displays some critical metrics of each numeric variable in a data frame. Now, let’s print a five-number summary of the dataset: > scribe() There are 53,940 diamonds recorded, along with their ten different features. Instead of counting all variables one by one, we can use the shape attribute of the data frame: > diamonds.shape Table: the ratio of the height of a diamond to its widest point.Depth: total depth percentage calculated as Z / average(X, Y).Clarity: the clarity of a diamond with eight clarity codes.
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