Skewness measures the symmetry of a distribution. It shows how much the distribution deviates from a normal distribution. Its values can be zero, positive, and negative. A zero value represents a perfectly normal shape of a distribution. Positive skewness is shown by the tails pointing toward the right—that is, outliers are skewed to the right and data stacked up on the left. Negative skewness is shown by the tails pointing toward the left—that is, outliers are skewed to the left and data stacked up on the right. Positive skewness occurs when the mean is greater than the median and the mode. Negative skewness occurs when the mean is less than the median and mode. Let's compute skewness in the following code block:
# skewness of communication_skill_score column
data['communcation_skill_score'].skew()
Output:
-1.704679180800373
In the preceding code block, we have computed the skewness of the communication skill score column using the skew...
