NumPy Functions

Beyond basic arithmetic and array creation, NumPy provides a rich set of functions for sorting, searching, and summarizing data - tools you'll reach for constantly while preparing data for analysis.

Sorting Functions

import numpy as np

arr = np.array([5, 2, 8, 1, 9])

print(np.sort(arr))          # [1 2 5 8 9]
print(np.argsort(arr))       # [3 1 0 2 4] - indices that would sort the array

Searching Functions

import numpy as np

arr = np.array([10, 20, 30, 40, 50])

print(np.where(arr > 25))       # (array([2, 3, 4]),) - indices matching condition
print(np.searchsorted(arr, 35)) # 3 - index where 35 would be inserted to keep order

Unique Values and Counting

import numpy as np

arr = np.array([1, 2, 2, 3, 3, 3, 4])

print(np.unique(arr))                          # [1 2 3 4]
values, counts = np.unique(arr, return_counts=True)
print(values, counts)                          # [1 2 3 4] [1 2 3 1]

Statistical Functions

import numpy as np

data = np.array([12, 45, 7, 23, 56, 9])

print(np.median(data))     # median value
print(np.percentile(data, 90))   # 90th percentile
print(np.var(data))        # variance

Coming Up Next

Next, you'll learn about Array Manipulation - reshaping, joining, and splitting NumPy arrays.