9 tweets 14 reads Dec 19, 2023
How to describe distribution?
Skewness 🧡
Skewness is a measure of the asymmetry of a distribution.
Distribution is symmetric if it looks the same to the left and right of the center point.
Skewness differentiates extreme values in one versus the other tail.
The formula to get the skewness:
Left skew / Negative skew:
Negative values for the skewness indicate data that are skewed left - the left tail is long relative to the right tail.
The mean of a left-skewed distribution is almost always less than its median.
Right skew / Positive skew:
Positive values for the skewness indicate data that are skewed right - the right tail is long relative to the left tail.
Here the mean is almost always greater than the median.
Because extreme values affect the mean more than the median.
Zero skew:
When a distribution has zero skew, it is symmetrical. Its left and right sides are mirror images.
The skewness for a normal distribution is zero.
The mean = median
Pandas has a built-in method to calculate the skewness of the data.
Since the value is negative, the data is skewed to the left - the left tail is slightly longer than the right tail.
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