In simple language, the standard deviation is the average/mean of the difference between each value in the dataset with its average/mean; that is, how data is spread out from the mean. If the standard deviation of the dataset is low, then the data points tend to be close to the mean of the dataset, otherwise, the data points are spread out over a wider range of values.

Different Python libraries have functions to get the standard deviation of the dataset. The NumPy library has the numpy.std(dataset) function. The statistics library has the statistics.stdev(dataset). function. Using the pandas library, we calculate the standard deviation in our df data frame using the df.std() function:

#standard variance of dataset using std() function
std_dev =df.std()
print(std_dev)
# standard variance of the specific column
sv_height=df.loc[:,"height"].std()
print(sv_height)

The output of the preceding code is as follows:

Next, let's look at variance.