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

# # Series Vectorization and Broadcasting
# 
# Just like NumPy, pandas offers powerful vectorized methods and leans on broadcasting.
# 
# Let's explore!

# In[1]:


import pandas as pd

test_balance_data = {
    'pasan': 20.00,
    'treasure': 20.18,
    'ashley': 1.05,
    'craig': 42.42,
}

test_deposit_data = {
    'pasan': 20,
    'treasure': 10,
    'ashley': 100,
    'craig': 55,   
}

balances = pd.Series(test_balance_data)
deposits = pd.Series(test_deposit_data)


# ## Vectorization
# While it is indeed possible to loop through each item and apply it to another...

# In[2]:


for label, value in deposits.iteritems():
    balances[label] += value
balances


# ...it's important to remember to lean on vectorization and skip the loops altogether.

# In[3]:


# Undo the change using inplace subtraction
balances -= deposits

# This is the same as the loop above using inplace addition
balances += deposits
balances


# ## Broadcasting

# ### Broadcasting a Scalar
# Also just like NumPy arrays, the mathematical operators have been overridden to use the vectorized versions of the same opration.

# In[4]:


# 5 is brodacsted and added to each and every value. This returns a new Series.
balances + 5


# ### Broadcasting a Series
# Labels are used to line up entries. When the label only exists in one side, a `np.nan` (<b>n</b>ot <b>a</b> <b>n</b>umber ) is put in place.
# 
# CashBox is giving out free coupons that user's can scan into the app to get $1 added to their accounts.

# In[5]:


coupons = pd.Series(1, ['craig', 'ashley', 'james'])
coupons


# Now we are going to add the coupons to people who cashed them in. This addition will return a new `Series`. 

# In[6]:


# Returns a new Series
balances + coupons


# Notice how values that are not in both `Series` are set to `np.nan`. This isn't what we want! Pasan had $45.00 and now he has nothing. He is going to be so bummed!
# 
# Also take note that James is not in the **`balances`** `Series` but he is in the **`coupons`** `Series`. Note how he is now added to the new `Series`, but his value is also set to `np.nan`.

# #### Using the `fill_value`
# It is possible to fill missing values so that everything aligns. The concept is to use the `add` method directly along with the the keyword argument `fill_value`.

# In[7]:


# Returns a new Series
balances.add(coupons, fill_value=0)