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

# # Lesson 4 - Copies and Views

# The original tutorial may be found [here](http://wiki.scipy.org/Tentative_NumPy_Tutorial#head-1529ae93dd5d431ffe3a1001a4ab1a394e70a5f2).

# When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases:
# 
# - No Copy at All
# - View or Shallow Copy
# - Deep Copy

# In[1]:


import numpy as np


# ## No Copy at All

# Simple assignments make no copy of array objects or of their data.

# In[2]:


a = np.arange(12)


# In[3]:


a


# In[4]:


b = a            # no new object is created


# In[5]:


b is a           # a and b are two names for the same ndarray object


# In[6]:


b.shape = 3, 4


# In[7]:


b


# In[8]:


a


# Python passes mutable objects as references, so function calls make no copy.

# In[9]:


def f(x):
    print id(x)


# In[10]:


id(a)


# In[11]:


f(a)


# ## View or Shallow Copy

# Different array objects can share the same data. The view method creates a new array object that looks at the same data.

# In[12]:


c = a.view()


# In[15]:


c is a


# In[16]:


c.base is a                        # c is a view of the data owned by a


# In[17]:


c.flags.owndata


# In[18]:


c.shape = 2,6                      # a's shape doesn't change


# In[19]:


a


# In[20]:


c


# In[22]:


c[0,4] = 1234     # a's data changes


# In[23]:


a


# In[25]:


c


# ## Deep Copy

# The copy method makes a complete copy of the array and its data.

# In[27]:


d = a.copy()       # a new array object with new data is created


# In[28]:


d is a


# In[29]:


d.base is a                           # d doesn't share anything with a


# In[30]:


d[0,0] = 9999


# In[31]:


a


# In[32]:


d


# # Conclusion

# We have now completed the Tutorial [Lesson 4 - Copies and Views](http://wiki.scipy.org/Tentative_NumPy_Tutorial#head-1529ae93dd5d431ffe3a1001a4ab1a394e70a5f2)