Data Warehousing
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ETL is a process that extracts the data from different RDBMS source systems, then transforms the...
NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc from the given elements in the array. The functions are explained as follows −
Numpy is equipped with the robust statistical function as listed below
| Function | Numpy |
| Min | np.min() |
| Max | np.max() |
| Mean | np.mean() |
| Median | np.median() |
| Standard deviation | np.std() |
Consider the following Array
import numpy as np normal_array = np.random.normal(5, 0.5, 10) print(normal_array)
Output:
[5.56171852 4.84233558 4.65392767 4.946659 4.85165567 5.61211317 4.46704244 5.22675736 4.49888936 4.68731125]
Example:Statistical function
### Min print(np.min(normal_array)) ### Max print(np.max(normal_array)) ### Mean print(np.mean(normal_array)) ### Median print(np.median(normal_array)) ### Sd print(np.std(normal_array))
Output:
4.467042435266913 5.612113171990201 4.934841002270593 4.846995625786663 0.3875019367395316
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