Numpy

13 Suicide Case Study – Numpy Import Libraries¶ In [2]: import numpy as np Read CSV¶ In [3]: data = np.genfromtxt('Suicidesindia2001-2012.csv',delimiter=',',dtype=str) data Out[3]: array([['State', 'Year', 'Type_code', ..., 'Gender', 'Age_group', 'Total'], ['A & N Islands', '2001', 'Causes', ..., 'Female', '0-14', '0'], ['A & N Islands', '2001', 'Causes', ..., 'Female', '0-14', '0'], ..., ['West …

11 – Set Operations Numpy Set Operations¶ In [1]: import numpy as np select the unique elements from an array¶ In [2]: arr = np.array([1,1,2,2,3,3,4,5,6]) print(np.unique(arr)) [1 2 3 4 5 6] In [3]: # return the number of times each unique item appears arr = np.array([1,1,2,2,3,3,4,5,6]) uniques, counts = np.unique(arr, return_counts=True) print(uniques) …

10 – Combine & Split an Array In [2]: import numpy as np In [3]: arr1 = np.array([[1,2,3,4], [1,2,3,4]]) arr2 = np.array([[5,6,7,8], [5,6,7,8]]) np.concatenate((a, b), axis=0)¶ In [4]: arr1 Out[4]: array([[1, 2, 3, 4], [1, 2, 3, 4]]) In [5]: arr2 Out[5]: array([[5, 6, 7, 8], [5, 6, 7, 8]]) In [6]: # concat along …

9 – Manipulate an Array Numpy Array Manipulation¶ In [2]: import numpy as np In [3]: arr = np.random.randint(1,10,[3,3]) arr Out[3]: array([[7, 4, 7], [5, 3, 6], [2, 1, 4]]) Transpose an array¶ In [4]: print(arr.T) [[7 5 2] [4 3 1] [7 6 4]] or¶ In [5]: print(np.transpose(arr)) [[7 5 2] [4 3 …

8 – Sort an Array Sorting of an Array¶ In [2]: import numpy as np In [3]: import numpy as np arr = np.array([[30,17,15],[19,90,16],[69,53,21]]) arr Out[3]: array([[30, 17, 15], [19, 90, 16], [69, 53, 21]]) sort an array along a specified axis¶ In [4]: # sort along the row and return a copy …

7 – Slicing & Indexing Subset, Slice, Index and Iterate through Arrays¶For one-dimensional arrays, indexing, slicing etc. is similar to python lists – indexing starts at 0. Slicing arrays¶Slicing in python means taking elements from one given index to another given index. We pass slice instead of index like this: …

6 – Math Functions NumPy Math Functions¶ In [2]: import numpy as np In [3]: arr = np.random.randint(10,99,[3,3]) arr Out[3]: array([[45, 53, 78], [81, 88, 25], [71, 55, 59]]) Element-wise addition, subtraction, multiplication and division¶ In [4]: print(arr + 10) print(arr - 10) print(arr * 10) print(arr / 10) [[55 63 88] [91 …

5 – Random Array NumPy Random Array¶ In [2]: import numpy as np In [3]: # generate a random scalar print(np.random.rand()) 0.2224104911171324 In [4]: # generate a 1-D array print(np.random.rand(3)) [0.69589689 0.9990713 0.77034202] In [5]: # generate a 2-D array print(np.random.rand(3,3)) [[0.2151302 0.64925559 0.95982155] [0.02673682 0.33937101 0.78181161] [0.39285799 0.7581885 0.15635241]] Generate a sample from …

4 – Inspect an Array Inspect NumPy Array¶ In [2]: import numpy as np In [3]: arr = np.array([[1,2,3], [4,5,6]], dtype=np.int64) arr Out[3]: array([[1, 2, 3], [4, 5, 6]], dtype=int64) Inspect general information of an array¶ In [4]: print(np.info(arr)) class: ndarray shape: (2, 3) strides: (24, 8) itemsize: 8 aligned: True contiguous: True …

3 – Numpy Data Types NumPy Data Types¶ In [2]: import numpy as np import pandas as pd Data Types in NumPy¶Numpy has the following data types: int float complex bool string unicode object The numeric data types have various precisions like 32-bit or 64-bit. Numpy data types can be represented …