Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution.
Use df.skew() or df['column1'].skew() to calculate skewness in pandas.
Kurtosis is a statistical measure that defines how heavily the tails of distribution differ from the tails of a normal distribution. In other words, kurtosis identifies whether the tails of a given distribution contain extreme values.
Use df.kurtosis() or df['column1'].kurtosis() to calculate kurtosis in pandas.
The ideal value for both is close to 0 for a normal distribution when calculated using pandas.