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

# ![](../docs/banner.png)

# # NumPy
# 
# **Tomas Beuzen, September 2020**

# These exercises complement [Chapter 5](../chapters/chapter5-numpy.ipynb) and [Chapter 6](../chapters/chapter6-numpy-addendum.ipynb).

# ## Exercises

# ### 1.

# Import numpy under the alias `np`.

# In[1]:


# Your answer here.


# ### 2.

# Create the following arrays:
# 
# 1. Create an array of 5 zeros.
# 2. Create an array of 10 ones.
# 3. Create an array of 5 3.141s.
# 4. Create an array of the integers 1 to 20.
# 5. Create a 5 x 5 matrix of ones with a dtype `int`.

# In[2]:


# Your answer here.


# ### 3.

# Use numpy to:
# 1. Create an 3D matrix of 3 x 3 x 3 full of random numbers drawn from a standard normal distribution (hint: `np.random.randn()`)
# 2. Reshape the above array into shape (27,)

# In[3]:


# Your answer here.


# ### 4.

# Create an array of 20 linearly spaced numbers between 1 and 10.

# In[4]:


# Your answer here.


# ### 5.

# Run the following code to create an array of shape 4 x 4 and then use indexing to produce the outputs shown below.

# In[5]:


import numpy as np
a = np.arange(1, 26).reshape(5, -1)


# ```python
# 20
# ```

# In[6]:


# Your answer here.


# ```python
# array([[ 9, 10],
#        [14, 15],
#        [19, 20],
#        [24, 25]])
# ```

# In[7]:


# Your answer here.


# ```python
# array([ 6,  7,  8,  9, 10])
# ```

# In[8]:


# Your answer here.


# ```python
# array([[11, 12, 13, 14, 15],
#        [16, 17, 18, 19, 20]])
# ```

# In[9]:


# Your answer here.


# ```python
# array([[ 8,  9],
#        [13, 14]])
# ```

# In[10]:


# Your answer here.


# ### 6.

# Calculate the sum of all the numbers in `a`.

# In[11]:


# Your answer here.


# ### 7.

# Calculate the sum of each row in `a`.

# In[12]:


# Your answer here.


# ### 8.

# Extract all values of `a` greater than the mean of `a` (hint: use a boolean mask).

# In[13]:


# Your answer here.


# ### 9.

# Find the location of the minimum value in the following array `b`:

# In[14]:


np.random.seed(123)
b = np.random.randn(10)
b


# In[15]:


# Your answer here.


# ### 10.

# Find the location of the maximum value in the following 2D array `c` (hint: there are many ways to do this question, but a quick search on stackoverflow.com will typically help you find the optimum solution for a problem, for example see [post](https://stackoverflow.com/questions/3584243/get-the-position-of-the-biggest-item-in-a-multi-dimensional-numpy-array)):

# In[16]:


np.random.seed(123)
c = np.random.randn(3, 2)
c


# In[17]:


# Your answer here.


# <hr>
# <hr>
# <hr>

# ## Solutions

# ### 1.

# Import numpy under the alias `np`.

# In[18]:


import numpy as np


# ### 2.

# Create the following arrays:
# 
# 1. Create an array of 5 zeros.
# 2. Create an array of 10 ones.
# 3. Create an array of 5 3.141s.
# 4. Create an array of the integers 1 to 20.
# 5. Create a 5 x 5 matrix of ones with a dtype `int`.

# In[19]:


print(np.zeros(5))
print(np.ones(10))
print(np.full(5, 3.141))
print(np.array(range(21)))
print(np.ones((5, 5), dtype=int))


# ### 3.

# Use numpy to:
# 1. Create an 3D matrix of 3 x 3 x 3 full of random numbers drawn from a standard normal distribution (hint: `np.random.randn()`)
# 2. Reshape the above array into shape (27,)

# In[20]:


x = np.random.randn(3, 3, 3)
x


# In[21]:


x.reshape(-1) # or x.reshape(27)


# ### 4.

# Create an array of 20 linearly spaced numbers between 1 and 10.

# In[22]:


np.linspace(1, 10, 20)


# ### 5.

# Below I've defined an array of shape 4 x 4. Use indexing to procude the given outputs.

# In[23]:


a = np.arange(1, 26).reshape(5, -1)
a


# ```python
# 20
# ```

# In[24]:


a[3,4]


# ```python
# array([[ 9, 10],
#        [14, 15],
#        [19, 20],
#        [24, 25]])
# ```

# In[25]:


a[1:,3:]


# ```python
# array([ 6,  7,  8,  9, 10])
# ```

# In[26]:


a[1,:]


# ```python
# array([[11, 12, 13, 14, 15],
#        [16, 17, 18, 19, 20]])
# ```

# ```python
# array([[ 8,  9],
#        [13, 14]])
# ```

# In[27]:


a[1:3,2:4]


# ### 6.

# Calculate the sum of all the numbers in `a`.

# In[28]:


a.sum()


# ### 7.

# Calculate the sum of each row in `a`.

# In[29]:


a.sum(axis=1)


# ### 8.

# Extract all values of `a` greater than the mean of `a` (hint: use a boolean mask).

# In[30]:


a[a > a.mean()]


# ### 9.

# Find the location of the minimum value in the following array `b`:

# In[31]:


np.random.seed(123)
b = np.random.randn(10)
b


# In[32]:


b.argmin()


# ### 10.

# Find the location of the maximum value in the following 2D array `c` (hint: there are many ways to do this question, but a quick search on stackoverflow.com will typically help you find the optimum solution for a problem, for example see [post](https://stackoverflow.com/questions/3584243/get-the-position-of-the-biggest-item-in-a-multi-dimensional-numpy-array)):

# In[33]:


np.random.seed(123)
c = np.random.randn(3, 2)
c


# In[34]:


print(f"Location of maximum: {np.unravel_index(c.argmax(), c.shape)}")
print(f"   Value of maximum: {c.max():.2f}")