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

# # Module 3
# 
# ## Video 12: Slicing Arrays
# **Python for the Energy Industry**
# 
# ## 1D Arrays
# 
# Arrays such as those seen in the previous video can be sliced in a similar way to lists:

# In[1]:


import numpy as np

my_array = np.arange(10)

print('my_array -',my_array)

# get values between index 2 (included) and 6 (excluded)
print('my_array[2:6] -',my_array[2:6])

# get values between index 4 (included) the end of the array
print('my_array[4:] -',my_array[4:])

# get values between the start of the array and index 7 (excluded)
print('my_array[:7] -',my_array[:7])


# Slicing with the form a:b:c, accesses indices between a and b, and indices that are multiples of c. For example:

# In[2]:


print('my_array[2:9:2] -',my_array[2:9:2])


# ## 2D Arrays
# 
# ### Generating Arrays
# 
# Numpy arrays can have any number of dimensions. Working with 2D arrays is common, and most functions for generating 1D arrays also work for multiple dimensions:

# In[3]:


# 4 by 4 array of zeros
print(np.zeros((4,4)))


# In[4]:


# 6 by 3 array of ones
print(np.ones((6,3)))


# In[5]:


# 2 by 10 array of random numbers
print(np.random.rand(2,10))


# Note that the when specifying the shape of the array, the first argument is the number of rows and the second argument is columns.
# 
# You can get the shape of an array:

# In[6]:


x = np.ones((6,3))

print(x.shape)


# ### Slicing Arrays
# 
# Accessing elements in 2D arrays is done by separating the indices with a comma. The element in the second row, third column of an array would be accessed with the slice [1,2].

# In[7]:


a = np.random.rand(3,3)
print(a)

print('second row, third column:',a[1,2])


# This applies when slicing as well, so getting all columns of the second row would be: [1,:]

# In[10]:


a = np.random.rand(3,3)
print(a)

print('second row, all columns:',a[1,:])


# ### Filtering Arrays
# 
# You can also access the values in an array that meet certain conditions:

# In[11]:


print(a[a > 0.5])


# In[12]:


print(a[np.logical_and(a > 0.5, a < 0.8)])


# ### Exercise 
# 
# Create a 5 by 5 array of random numbers between 0 and 10. 

# In[ ]: