Python NumPy percentile() Function Examples
Finding Percentile Value Using NumPy
In this example, a 1D array `arr` is created with values [20, 2, 7, 1, 34]. Using NumPy’s `percentile` function, the 50th, 25th, and 75th percentiles of the array are calculated and printed, providing key statistical measures for the data distribution.
Python3
import numpy as np # 1D array arr = [ 20 , 2 , 7 , 1 , 34 ] print ( "arr : " , arr) print ( "50th percentile of arr : " , np.percentile(arr, 50 )) print ( "25th percentile of arr : " , np.percentile(arr, 25 )) print ( "75th percentile of arr : " , np.percentile(arr, 75 )) |
Output:
arr : [20, 2, 7, 1, 34]
50th percentile of arr : 7.0
25th percentile of arr : 2.0
75th percentile of arr : 20.0
Get the Percentile Value of 2-D Array Using NumPy
In this example, a 2D array `arr` is created. The np.percentile() function is applied to calculate percentiles both across the flattened array (axis=None) and along axis=0. The 50th and 0th percentiles are computed, providing statistical insights into the data distribution across different dimensions.
Python3
import numpy as np # 2D array arr = [[ 14 , 17 , 12 , 33 , 44 ], [ 15 , 6 , 27 , 8 , 19 ], [ 23 , 2 , 54 , 1 , 4 , ]] print ( "\narr : \n" , arr) # Percentile of the flattened array print ( "\n50th Percentile of arr, axis = None : " , np.percentile(arr, 50 )) print ( "0th Percentile of arr, axis = None : " , np.percentile(arr, 0 )) # Percentile along the axis = 0 print ( "\n50th Percentile of arr, axis = 0 : " , np.percentile(arr, 50 , axis = 0 )) print ( "0th Percentile of arr, axis = 0 : " , np.percentile(arr, 0 , axis = 0 )) |
Output:
arr :
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]
50th Percentile of arr, axis = None : 15.0
0th Percentile of arr, axis = None : 1.0
50th Percentile of arr, axis = 0 : [15. 6. 27. 8. 19.]
0th Percentile of arr, axis = 0 : [14. 2. 12. 1. 4.]
50th Percentile of arr, axis = 1 : [17. 15. 4.]
0th Percentile of arr, axis = 1 : [12. 6. 1.]
Get the Percentile along the Axis in NumPy
In this example, a 2D array `arr` is created. The np.percentile() function is applied along axis=1 to calculate the 50th and 0th percentiles for each row, providing insights into the distribution of values along this axis. The use of `keepdims=True` ensures that the result retains the original dimensionality, maintaining clarity in the output.
Python3
import numpy as np # 2D array arr = [[ 14 , 17 , 12 , 33 , 44 ], [ 15 , 6 , 27 , 8 , 19 ], [ 23 , 2 , 54 , 1 , 4 , ]] print ( "\narr : \n" , arr) # Percentile along the axis = 1 print ( "\n50th Percentile of arr, axis = 1 : " , np.percentile(arr, 50 , axis = 1 )) print ( "0th Percentile of arr, axis = 1 : " , np.percentile(arr, 0 , axis = 1 )) print ( "\n0th Percentile of arr, axis = 1 : \n" , np.percentile(arr, 50 , axis = 1 , keepdims = True )) print ( "\n0th Percentile of arr, axis = 1 : \n" , np.percentile(arr, 0 , axis = 1 , keepdims = True )) |
Output:
arr :
[[14, 17, 12, 33, 44], [15, 6, 27, 8, 19], [23, 2, 54, 1, 4]]
0th Percentile of arr, axis = 1 :
[[17.]
[15.]
[ 4.]]
0th Percentile of arr, axis = 1 :
[[12.]
[ 6.]
[ 1.]]
numpy.percentile() in python
numpy.percentile() function used to compute the nth percentile of the given data (array elements) along the specified axis.
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