#!/usr/bin/env python
# coding: utf-8

# ## What is Numpy

# NumPy is a python library used for working with arrays.
# 
# It also has functions for working in domain of linear algebra, fourier transform, and matrices.
# 
# NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.
# 
# NumPy stands for Numerical Python.
# 
# Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great

# ## Installation Instructions
# 
# **It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:**
#     
#     conda install numpy
#     
# **If you do not have Anaconda and can not install it, please refer to [Numpy's official documentation on various installation instructions.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**

# In[2]:


my_list= [9,8,7]
my_list


# In[3]:


import numpy as np
np.array(my_list)


# In[4]:


list1=[1,2,3,4,5]
np.array(list1)


# In[5]:


matrix=[[1,2,3],[4,5,6],[6,7,7]]
print(matrix)
print ('\n')

print (np.array(matrix))


# ## Built-in Methods
# 
# There are lots of built-in ways to generate Arrays

# #### NumPy is used to work with arrays. The array object in NumPy is called ndarray.

# In[6]:


arr = np.array([1, 2, 3, 4, 5])

print(arr)

print(type(arr))


# In[7]:


np.arange(0,20)


# In[8]:


np.arange(0,50,3)


# ### linspace
# Return evenly spaced numbers over a specified interval.

# In[9]:


np.linspace (0,50,6)


# In[10]:


np.linspace (0,10,40)


# ### zeros and ones
# 
# Generate arrays of zeros or ones

# In[11]:


np.zeros(9)


# In[12]:


np.zeros((3,4))


# In[13]:


np.ones(4)


# In[14]:


np.ones((3,4))


# ## eye
# 
# Creates an identity matrix

# In[15]:


np.eye(5)


# In[16]:


np.eye(4,5)


# # Random
# ### What is a Random Number?
# Random number does NOT mean a different number every time. Random means something that can not be predicted logically.

# ### rand
# Create an array of the given shape and populate it with
# random samples from a uniform distribution
# over ``[0, 1)``.

# In[17]:


np.random.rand(3)


# In[18]:


np.random.rand(4,4)


# ### randn
# 
# Return a sample (or samples) from the "standard normal" distribution. Unlike rand which is uniform:

# In[19]:


np.random.randn(2)


# In[20]:


np.random.randn(2,3)


# ### randint
# Return random integers from `low` (inclusive) to `high` (exclusive).

# In[21]:


np.random.randint(1,7)


# In[22]:


np.random.randint(1,100,10)


# In[23]:


np.random.randint (1,111)


# ### Some of the Array Attributes and methods
# 
# Have a look some of the main attributes and methods or an array

# In[25]:


arr = np.arange(50)
ranarr=np.random.randint (0,100,10)


# In[26]:


arr


# In[27]:


ranarr


# ## Reshape 
# Covert existing array into new shape with same data.

# In[29]:


arr.reshape(5,10)


# In[31]:


arr.reshape (2,25)


# In[34]:


arr1= np.arange(24)


# In[35]:


arr1


# In[37]:


arr1.reshape(3,2,4)


# ### max,min,argmax,argmin
# 
# These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax

# In[39]:


arr


# arr.min()

# In[41]:


arr.max()


# In[42]:


ranarr


# In[43]:


ranarr.max()


# In[44]:


ranarr.argmax()


# In[45]:


ranarr.argmin()


# ## Shape
# The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it

# In[46]:


# Shape is an attribute that array have 
# Vector
arr.shape


# In[48]:


# Notice the the two set of array
arr.reshape(10,5)


# In[49]:


arr.reshape (1,50)


# In[51]:


arr.reshape(50,1).shape


# In[52]:


# You Can find the datatype of object in the array 
arr.dtype


# A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data:
# 
# Type of the data (integer, float, Python object, etc.)
# Size of the data (how many bytes is in e.g. the integer)
# Byte order of the data (little-endian or big-endian)
# If the data type is structured, an aggregate of other data types, (e.g., describing an array item consisting of an integer and a float),
# what are the names of the “fields” of the structure, by which they can be accessed,
# what is the data-type of each field, and
# which part of the memory block each field takes.
# If the data type is a sub-array, what is its shape and data type.
# 
# https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.dtypes.html

# ### Numpy Indexing and Selection
# We will explore how we are going to grab a prticular elements of an array 

# In[54]:


arr


# ## Bracket Indexing and Selection
# The simplest way to pick one or some elements of an array looks very similar to python lists:

# In[55]:


# Get the value at the index 24
arr[24]


# In[58]:


# Get the value in the range 
print (arr[4:24])
print ('\n')
print (arr[0:10])


# ## Broadcasting
# 
# Here Numpy is different from normal List in python since it is having ability to broadcast

# In[61]:


# Setting the value with index range (Broadcasting)
arr[0:10]=88
# Show 
arr


# In[62]:


arr=np.arange (0,21)
arr


# In[63]:


# Slicing is Very important 
slice_of_arr=arr[0:16]
#Show slice 
slice_of_arr


# In[66]:


# Change the value of Slice 
slice_of_arr[:]=50
slice_of_arr


# #### Note : Now the change is also there in original array 

# In[67]:


arr


# Data is not copied, it's a view of the original array! This avoids memory problems!

# In[69]:


# To get a copy , need to be explicit 
arr_copy=arr.copy ()
arr_copy 


# ## Indexing & Selection for 2D Array(Matrix)
# Two-dimensional (2D) arrays are indexed by two subscripts, one for the row and one for the column. Each element in the 2D array must by the same type, either a primitive type or object type.
# 
# 
# The general format is arr_2d[row][col] or arr_2d[row,col]. I recommend usually using the comma notation for clarity.

# In[2]:


import numpy as np
matrix= np.array(([1,2,3],[4,5,6],[7,8,9]))
# Show the value 
matrix


# In[4]:


# Indexing Rows 
# Grab 3nd row
matrix[2]


# In[5]:


#row[1][2]    #row[1,2]
# Indexing Column 
# Grab 2nd column 
matrix[0:,1:2]


# In[6]:


# Format is arr_2d[row][col] or arr_2d[row,col]

# Getting individual element value
matrix[1][0]


# In[7]:


matrix[1][2]


# In[8]:


# Getting individual element value
matrix[1,0]


# In[9]:


matrix


# In[10]:


# Matrix slicing 
# Shape (2,2) from top left corner 
matrix[0:2,0:2]


# In[11]:


# Matrix slicing 
# Shape (2,2) from top right corner 
matrix[0:2,1:3]


# In[12]:


# Matrix slicing 
# Shape (2,2) from bottom right corner 
matrix[1:,1:]


# In[13]:


arr=np.arange(25)


# In[14]:


b=arr.reshape(5,5)


# In[15]:


b


# In[16]:


b[3:,3:]


# In[17]:


# Grab all the elements in 4th column
b[0:,3:4]


# ## Fancing Indexing 
# Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once.
# 
# lets see an example 

# In[18]:


# Set up matrix 
matrix1 = np.zeros ((11,11))
# Length of matrix
matrix1_length = matrix1.shape[1]


# In[20]:


for i in range (matrix1_length):
    matrix1[i]+=i
matrix1


# In[21]:


# Fancy Indexing allows The following 
matrix1[[2,3,4,5]]


# In[22]:


matrix1[[5,6,7,8]]


# ## More Indexing Help
# Indexing a 2d matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching NumPy indexing to fins useful images, like this one:

# 
# 
# ## Selection
# 
# Let's briefly go over how to use brackets for selection based off of comparison operators.

# In[24]:


arr = np.arange(1,22)
arr


# In[25]:


arr>4


# In[26]:


bool_arr= arr>5


# In[27]:


bool_arr


# In[28]:


arr[bool_arr]


# In[29]:


arr[arr>3]


# In[30]:


x=6
arr[arr>x]


# ## NumPy Operations
# 
# ### Arithmetic 
# Arithmetic Operations. Input arrays for performing arithmetic operations such as add(), subtract(), multiply(), and divide() must be either of the same shape or should conform to array broadcasting rules.
# 
# You Can easily perform array with array arithematic , or scalar with array arithematic 
# 
# lets see some examples 
# 

# In[31]:


arr= np.arange (0,25)


# In[32]:


arr


# In[34]:


arr+arr


# In[35]:


arr*arr


# In[36]:


arr-arr


# In[147]:


# Warning on division by zero, but not an error!
# Just replaced with nan
arr/arr


# In[148]:


# Also warning, but not an error instead infinity
1/arr


# In[149]:


arr**3


# ## Universal Array Functions
# 
# Numpy comes with many [universal array functions](http://docs.scipy.org/doc/numpy/reference/ufuncs.html), which are essentially just mathematical operations you can use to perform the operation across the array. Let's show some common ones:

# In[150]:


# Taking square Roots 
np.sqrt (arr)


# In[151]:


np.max(arr)   


# In[152]:


# Calculating exponential (e^)
np.exp(arr)


# In[153]:


np.max(arr) #same as arr.max()


# In[154]:


np.sin(arr)


# In[155]:


np.log(arr)


# ## Some Valueble insight of numpy lot more to explore  
# https://numpy.org/

# In[ ]: