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‎MachineLearning/Numpy/CreationOfNumpy.md‎

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## Numpy Array Create
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### Import Required Module Numpy and Rename
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```python
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import numpy as np
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```
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#### Numpy Array Using List
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```python
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#Define Simple List
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lst=[10,23,2,3,7,8]
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#Create numpy array using lst
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np_array=np.array(lst)
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#print numpy array
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print("Create Numpy Array Using List:\n",np_array)
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```
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Output:
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Create Numpy Array Using List:
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[10 23 2 3 7 8]
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#### Numpy Array Using Tuple
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```python
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#Define Simple Tuple
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tple=(10,23,2,3,7,8)
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#Create numpy array using tuple
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np_array=np.array(tple)
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#print numpy array
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print("Create a Numpy Array Using Tuple:\n",np_array)
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```
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Output:
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Create a Numpy Array Using Tuple:
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[10 23 2 3 7 8]
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#### Numpy String Array
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```python
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tple=np.array(['P','Y','T','H','O','N'])
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#Create numpy array using tuple
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np_array=np.array(tple)
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#print numpy array
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print("Create Numpy Array Using Characters :\n",np_array)
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```
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Output:
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Create Numpy Array Using Characters :
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['P' 'Y' 'T' 'H' 'O' 'N']
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#### Create Numpy array using arange() function
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```python
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#Simple Array Here Only End is Specified
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arr=np.arange(6)
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print("Array With stop Parameter: ",arr)
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print("Type of an Array: ",arr.dtype)
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#Create Float Array
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arr=np.arange(6.0)
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print("\nArray With stop Parameter: ",arr)
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print("Type of an Array: ",arr.dtype)
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#Numpy Array From Specific Range
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arr=np.arange(4,9) #it will include 4 and exclude 9
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print("\nArray With Specified Range: ",arr)
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print("Type of an Array: ",arr.dtype)
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#Simple Numpy array with Step counter(print 5 table)
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arr=np.arange(5,51,5)
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print("\nArray of 5 Table: ",arr)
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print("Type of an Array: ",arr.dtype)
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```
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Output:
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Array With stop Parameter: [0 1 2 3 4 5]
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Type of an Array: int32
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Array With stop Parameter: [0. 1. 2. 3. 4. 5.]
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Type of an Array: float64
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Array With Specified Range: [4 5 6 7 8]
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Type of an Array: int32
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Array of 5 Table: [ 5 10 15 20 25 30 35 40 45 50]
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Type of an Array: int32
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## important Terminologies Used In Numpy array
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```python
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#Simple list
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lst=[1,2,3]
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np_ar=np.array(lst)
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```
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#### Print The Numpy Array
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```python
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# print Normal numpy array
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print(np_ar)
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```
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Output:
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[1 2 3]
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#### Get Shape of Numpy array
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Use the following syntax to get the shape of numpy array
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numpy_array.shape
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```python
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#This is 1d Array So Only
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shape=np_ar.shape
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print("Shape Of an Array(row,columns):",shape)
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```
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Output:
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Shape Of an Array(row, columns): (3,)
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#### Get element data types in numpy array
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numpy_array.dtype
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```python
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data_type=np_ar.dtype
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print("Data Type Of Numpy Array:",data_type)
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```
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Output:
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Data Type Of Numpy Array: int32
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#### Get Dimension Of Numpy Array
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- To get the Dimension of numpy array simple call *ndim* with numpy array
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numpy_array.ndim
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```python
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dim=np_ar.ndim
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print("The dimension of the Numpy Array:",dim)
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```
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Output:
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The dimension of the Numpy Array: 1
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#### Get Element Count(Size How many Element in numpy array)
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- To simply get the Count of the present element in the numpy array use *size* as follows
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numpy_array.size
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```python
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elem_count=np_ar.size
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print("Element Count:",elem_count)
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```
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Output:
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Element Count: 3
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#### Get the element Size in Bytes
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```python
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size=np_ar.itemsize
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print("Item Size in Bytes:",size)
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```
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Output:
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Item Size in Bytes: 4
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## Type Of Numpy Array
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```
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1. Single Dimensional
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2. Two Dimensional
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3. Three Dimensional
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```
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### 1. Single Dimensional
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![1d](https://github.com/chavarera/PythonScript/blob/master/MachineLearning/Numpy/img/np1d.png)
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```python
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import numpy as np
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#Simple list
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lst=[1,2,3,4]
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np_1d=np.array(lst)
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print("1D Numpy Array:\n",np_1d)
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#dimensssions
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print("\nDimenssion of np_1d: ",np_1d.ndim)
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#Second value from tuple is blank for shape becaue it is 1d array
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print("Shape Of Array(row,colums) :",np_1d.shape)
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#Get Type of Array Elements
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print("Type Of Numpy Array Elements : ",np_1d.dtype)
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#get Item Size
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print("Item Size: ",np_1d.itemsize)
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#get objects Size
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print("Object Size: ",np_1d.size)
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```
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Output:
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1D Numpy Array:
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[1 2 3 4]
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Dimenssion of np_1d: 1
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Shape Of Array(row,colums) : (4,)
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Type Of Numpy Array Elements : int32
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Item Size: 4
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Object Size: 4
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### 2.Two Dimensssional
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![2d](https://github.com/chavarera/PythonScript/blob/master/MachineLearning/Numpy/img/np2d.png)
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```python
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import numpy as np
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#2d List
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lst2=[
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[1,2],
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[3,4],
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[5,6]
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]
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np_2d=np.array(lst2)
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print("2D Numpy Array:\n",np_2d)
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#dimensssions
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print("\nDimenssion of np_2d: ",np_2d.ndim)
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#three rows 2 columns
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print("Shape Of Array(row,colums) :",np_2d.shape)
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#Get Type of Array Elements
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print("Type Of Numpy Array Elements : ",np_2d.dtype)
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#get Item Size
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print("Size of Object Size: ",np_2d.itemsize)
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#get objects Size
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print("element Count : ",np_2d.size)
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```
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Output:
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2D Numpy Array:
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[[1 2]
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[3 4]
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[5 6]]
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Dimenssion of np_2d: 2
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Shape Of Array(row,colums) : (3, 2)
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Type Of Numpy Array Elements : int32
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Size of Object Size: 4
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element Count : 6
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### 3. Three dimensional
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![3d](https://github.com/chavarera/PythonScript/blob/master/MachineLearning/Numpy/img/np3d.png)
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```python
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import numpy as np
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#3d List
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lst3=[
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[
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[3,4,5],
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[6,7,8],
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[9,10,11]
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],
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[
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[0,1,2],
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[12,13,14],
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[15,16,17]
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],
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[
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[18,19,20],
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[21,22,23],
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[24,25,27]
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]
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]
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np_3d=np.array(lst3)
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print(np_3d)
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#dimensssions
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print("\nDimenssion of np_3d: ",np_3d.ndim)
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#three rows 2 columns
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print("Shape Of Array(row,colums) :",np_3d.shape)
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#Get Type of Array Elements
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print("Type Of Numpy Array Elements : ",np_3d.dtype)
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#get Item Size
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print("Size of Object Size: ",np_3d.itemsize)
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#get objects Size
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print("Element Count: ",np_3d.size)
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```
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Output
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[[[ 3 4 5]
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[ 6 7 8]
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[ 9 10 11]]
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[[ 0 1 2]
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[12 13 14]
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[15 16 17]]
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[[18 19 20]
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[21 22 23]
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[24 25 27]]]
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Dimenssion of np_3d: 3
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Shape Of Array(row,colums) : (3, 3, 3)
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Type Of Numpy Array Elements : int32
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Size of Object Size: 4
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Element Count: 27
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