Joining Data Sets¶
Let us perform few tasks to understand how to perform joins over multiple collections using loops and conditionals.
- There are different strategies for joins.
- Nested Loops
- Sort Merge
- Hash Join
- We will be using Nested Loops approach using orders and order_items.
- Build dict for one data set – orders.
- Iteratively lookup into the orders data set while processing the other one – order_items
- Develop a function get_daily_revenue which takes orders, order_items and order_status as arguments and return dict containing order_date and order_revenue. We need to get revenue considering only those orders which satisfy the status passed.
In [1]:
%run 07_preparing_data_sets.ipynb
In [2]:
orders[:10]
Out[2]:
['1,2013-07-25 00:00:00.0,11599,CLOSED', '2,2013-07-25 00:00:00.0,256,PENDING_PAYMENT', '3,2013-07-25 00:00:00.0,12111,COMPLETE', '4,2013-07-25 00:00:00.0,8827,CLOSED', '5,2013-07-25 00:00:00.0,11318,COMPLETE', '6,2013-07-25 00:00:00.0,7130,COMPLETE', '7,2013-07-25 00:00:00.0,4530,COMPLETE', '8,2013-07-25 00:00:00.0,2911,PROCESSING', '9,2013-07-25 00:00:00.0,5657,PENDING_PAYMENT', '10,2013-07-25 00:00:00.0,5648,PENDING_PAYMENT']
In [3]:
len(orders)
Out[3]:
68883
In [4]:
order_items[:10]
Out[4]:
['1,1,957,1,299.98,299.98', '2,2,1073,1,199.99,199.99', '3,2,502,5,250.0,50.0', '4,2,403,1,129.99,129.99', '5,4,897,2,49.98,24.99', '6,4,365,5,299.95,59.99', '7,4,502,3,150.0,50.0', '8,4,1014,4,199.92,49.98', '9,5,957,1,299.98,299.98', '10,5,365,5,299.95,59.99']
In [5]:
len(order_items)
Out[5]:
172198
In [6]:
def get_orders_dict(orders, order_status):
orders_dict = {}
for order in orders:
order_details = order.split(',')
if order_details[3] == order_status:
orders_dict[int(order_details[0])] = order_details[1]
return orders_dict
In [7]:
list(get_orders_dict(orders, 'COMPLETE').items())[:10]
Out[7]:
[(3, '2013-07-25 00:00:00.0'), (5, '2013-07-25 00:00:00.0'), (6, '2013-07-25 00:00:00.0'), (7, '2013-07-25 00:00:00.0'), (15, '2013-07-25 00:00:00.0'), (17, '2013-07-25 00:00:00.0'), (22, '2013-07-25 00:00:00.0'), (26, '2013-07-25 00:00:00.0'), (28, '2013-07-25 00:00:00.0'), (32, '2013-07-25 00:00:00.0')]
In [8]:
get_orders_dict(orders, 'COMPLETE')[3]
Out[8]:
'2013-07-25 00:00:00.0'
In [9]:
get_orders_dict(orders, 'COMPLETE').get(3)
Out[9]:
'2013-07-25 00:00:00.0'
In [10]:
len(get_orders_dict(orders, 'COMPLETE'))
Out[10]:
22899
In [11]:
orders_dict = get_orders_dict(orders, 'COMPLETE')
In [12]:
order_item = order_items[0]
order_item
Out[12]:
'1,1,957,1,299.98,299.98'
In [13]:
int(order_item.split(',')[1]) # order_id
Out[13]:
1
In [14]:
int(order_item.split(',')[1]) in orders_dict
Out[14]:
False
In [15]:
3 in orders_dict
Out[15]:
True
In [16]:
def get_daily_revenue(orders, order_items, order_status):
orders_dict = get_orders_dict(orders, order_status) # dict with order_id as key and order_date as value
daily_revenue = {} # Initializing empty dict to have date and corresponding revenue
for order_item in order_items:
# Processing all order_items
order_item_order_id = int(order_item.split(',')[1])
order_item_subtotal = float(order_item.split(',')[4])
if order_item_order_id in orders_dict:
# Look up into orders_dict using order_id to get order_date
orders_dict_date = orders_dict[order_item_order_id]
if orders_dict_date in daily_revenue:
# If order_date exists in orders_dict then add order_item_subtotal to the existing value
# based up on the key in daily_revenue
daily_revenue[orders_dict_date] = round(daily_revenue[orders_dict_date] + order_item_subtotal, 2)
else:
# If order_date does not exists then add new element to daily_revenue
daily_revenue[orders_dict_date] = order_item_subtotal
return daily_revenue
In [17]:
orders_sample = [
'4,2013-07-25 00:00:00.0,8827,CLOSED',
'5,2013-07-25 00:00:00.0,11318,COMPLETE',
'7,2013-07-25 00:00:00.0,4530,COMPLETE',
'105,2013-07-26 00:00:00.0,8220,COMPLETE'
]
In [18]:
order_items_sample = [
'5,4,897,2,49.98,24.99',
'6,4,365,5,299.95,59.99',
'7,4,502,3,150.0,50.0',
'8,4,1014,4,199.92,49.98',
'9,5,957,1,299.98,299.98',
'10,5,365,5,299.95,59.99',
'11,5,1014,2,99.96,49.98',
'12,5,957,1,299.98,299.98',
'13,5,403,1,129.99,129.99',
'14,7,1073,1,199.99,199.99',
'15,7,957,1,299.98,299.98',
'16,7,926,5,79.95,15.99',
'239,105,403,1,129.99,129.99',
'240,105,365,3,179.97,59.99',
'241,105,365,2,119.98,59.99'
]
In [19]:
get_daily_revenue(orders_sample, order_items_sample, 'COMPLETE')
Out[19]:
{'2013-07-25 00:00:00.0': 1709.78, '2013-07-26 00:00:00.0': 429.94}
{note}
Use the function to get daily revenue considering only COMPLETE orders.In [20]:
get_daily_revenue(orders, order_items, 'COMPLETE')
Out[20]:
{'2013-07-25 00:00:00.0': 20030.32,
'2013-07-26 00:00:00.0': 42165.88,
'2013-07-27 00:00:00.0': 33156.21,
'2013-07-28 00:00:00.0': 27012.91,
'2013-07-29 00:00:00.0': 45898.65,
'2013-07-30 00:00:00.0': 40590.21,
'2013-07-31 00:00:00.0': 46503.83,
'2013-08-01 00:00:00.0': 38231.41,
'2013-08-02 00:00:00.0': 36633.44,
'2013-08-03 00:00:00.0': 34828.71,
'2013-08-04 00:00:00.0': 26161.97,
'2013-08-05 00:00:00.0': 25804.33,
'2013-08-06 00:00:00.0': 41413.79,
'2013-08-07 00:00:00.0': 31533.1,
'2013-08-08 00:00:00.0': 27359.07,
'2013-08-09 00:00:00.0': 22091.95,
'2013-08-10 00:00:00.0': 39038.72,
'2013-08-11 00:00:00.0': 21302.69,
'2013-08-12 00:00:00.0': 41139.11,
'2013-08-13 00:00:00.0': 12468.53,
'2013-08-14 00:00:00.0': 29760.88,
'2013-08-15 00:00:00.0': 37473.12,
'2013-08-16 00:00:00.0': 25547.74,
'2013-08-17 00:00:00.0': 52940.94,
'2013-08-18 00:00:00.0': 31304.59,
'2013-08-19 00:00:00.0': 10603.87,
'2013-08-20 00:00:00.0': 32680.05,
'2013-08-21 00:00:00.0': 20326.67,
'2013-08-22 00:00:00.0': 30542.99,
'2013-08-23 00:00:00.0': 25676.67,
'2013-08-24 00:00:00.0': 39566.76,
'2013-08-25 00:00:00.0': 24176.98,
'2013-08-26 00:00:00.0': 32764.19,
'2013-08-27 00:00:00.0': 29917.18,
'2013-08-28 00:00:00.0': 15508.83,
'2013-08-29 00:00:00.0': 29657.13,
'2013-08-30 00:00:00.0': 16826.35,
'2013-08-31 00:00:00.0': 23916.02,
'2013-09-01 00:00:00.0': 28224.43,
'2013-09-02 00:00:00.0': 36572.45,
'2013-09-03 00:00:00.0': 33598.37,
'2013-09-04 00:00:00.0': 14672.73,
'2013-09-05 00:00:00.0': 43742.2,
'2013-09-06 00:00:00.0': 42171.36,
'2013-09-07 00:00:00.0': 32440.08,
'2013-09-08 00:00:00.0': 26988.11,
'2013-09-09 00:00:00.0': 37805.85,
'2013-09-10 00:00:00.0': 37044.81,
'2013-09-11 00:00:00.0': 13959.96,
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'2013-09-22 00:00:00.0': 32105.07,
'2013-09-23 00:00:00.0': 36075.92,
'2013-09-24 00:00:00.0': 36712.37,
'2013-09-25 00:00:00.0': 44628.96,
'2013-09-26 00:00:00.0': 51404.02,
'2013-09-27 00:00:00.0': 43653.31,
'2013-09-28 00:00:00.0': 27721.5,
'2013-09-29 00:00:00.0': 26244.91,
'2013-09-30 00:00:00.0': 37202.86,
'2013-10-01 00:00:00.0': 18622.05,
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'2013-10-03 00:00:00.0': 31431.93,
'2013-10-04 00:00:00.0': 43874.09,
'2013-10-05 00:00:00.0': 33440.09,
'2013-10-06 00:00:00.0': 38882.82,
'2013-10-07 00:00:00.0': 18696.85,
'2013-10-08 00:00:00.0': 38930.45,
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'2013-10-10 00:00:00.0': 31290.32,
'2013-10-11 00:00:00.0': 35022.43,
'2013-10-12 00:00:00.0': 28034.86,
'2013-10-13 00:00:00.0': 48405.18,
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'2013-10-28 00:00:00.0': 22706.3,
'2013-10-29 00:00:00.0': 23544.26,
'2013-10-30 00:00:00.0': 16798.86,
'2013-10-31 00:00:00.0': 29819.85,
'2013-11-01 00:00:00.0': 45791.7,
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'2014-05-07 00:00:00.0': 35490.3,
'2014-05-08 00:00:00.0': 19789.73,
'2014-05-09 00:00:00.0': 43436.97,
'2014-05-10 00:00:00.0': 31074.21,
'2014-05-11 00:00:00.0': 36289.84,
'2014-05-12 00:00:00.0': 47296.15,
'2014-05-13 00:00:00.0': 33047.27,
'2014-05-14 00:00:00.0': 25645.86,
'2014-05-15 00:00:00.0': 31806.22,
'2014-05-16 00:00:00.0': 49846.26,
'2014-05-17 00:00:00.0': 29198.83,
'2014-05-18 00:00:00.0': 33001.06,
'2014-05-19 00:00:00.0': 16558.01,
'2014-05-20 00:00:00.0': 43688.15,
'2014-05-21 00:00:00.0': 25794.95,
'2014-05-22 00:00:00.0': 17414.02,
'2014-05-23 00:00:00.0': 25661.97,
'2014-05-24 00:00:00.0': 30707.18,
'2014-05-25 00:00:00.0': 19127.84,
'2014-05-26 00:00:00.0': 26552.53,
'2014-05-27 00:00:00.0': 13517.47,
'2014-05-28 00:00:00.0': 39338.9,
'2014-05-29 00:00:00.0': 25546.25,
'2014-05-30 00:00:00.0': 14286.25,
'2014-05-31 00:00:00.0': 25137.29,
'2014-06-01 00:00:00.0': 39940.35,
'2014-06-02 00:00:00.0': 41520.41,
'2014-06-03 00:00:00.0': 24381.6,
'2014-06-04 00:00:00.0': 24597.44,
'2014-06-05 00:00:00.0': 14154.59,
'2014-06-06 00:00:00.0': 29931.83,
'2014-06-07 00:00:00.0': 36834.15,
'2014-06-08 00:00:00.0': 35127.21,
'2014-06-09 00:00:00.0': 44563.2,
'2014-06-10 00:00:00.0': 16817.27,
'2014-06-11 00:00:00.0': 28803.69,
'2014-06-12 00:00:00.0': 27426.44,
'2014-06-13 00:00:00.0': 39116.97,
'2014-06-14 00:00:00.0': 43824.5,
'2014-06-15 00:00:00.0': 20881.44,
'2014-06-16 00:00:00.0': 35070.53,
'2014-06-17 00:00:00.0': 30644.41,
'2014-06-18 00:00:00.0': 32682.51,
'2014-06-19 00:00:00.0': 47391.75,
'2014-06-20 00:00:00.0': 25706.96,
'2014-06-21 00:00:00.0': 17196.91,
'2014-06-22 00:00:00.0': 23103.45,
'2014-06-23 00:00:00.0': 23146.55,
'2014-06-24 00:00:00.0': 23427.4,
'2014-06-25 00:00:00.0': 37621.4,
'2014-06-26 00:00:00.0': 24306.14,
'2014-06-27 00:00:00.0': 47910.89,
'2014-06-28 00:00:00.0': 22260.18,
'2014-06-29 00:00:00.0': 19895.91,
'2014-06-30 00:00:00.0': 31446.39,
'2014-07-01 00:00:00.0': 28576.8,
'2014-07-02 00:00:00.0': 19860.78,
'2014-07-03 00:00:00.0': 30640.73,
'2014-07-04 00:00:00.0': 17784.68,
'2014-07-05 00:00:00.0': 35675.93,
'2014-07-06 00:00:00.0': 13465.18,
'2014-07-07 00:00:00.0': 24332.8,
'2014-07-08 00:00:00.0': 34845.64,
'2014-07-09 00:00:00.0': 28610.76,
'2014-07-10 00:00:00.0': 30247.47,
'2014-07-11 00:00:00.0': 21528.33,
'2014-07-12 00:00:00.0': 29528.6,
'2014-07-13 00:00:00.0': 29041.92,
'2014-07-14 00:00:00.0': 24056.18,
'2014-07-15 00:00:00.0': 38500.98,
'2014-07-16 00:00:00.0': 32893.29,
'2014-07-17 00:00:00.0': 23240.27,
'2014-07-18 00:00:00.0': 34192.71,
'2014-07-19 00:00:00.0': 32981.55,
'2014-07-20 00:00:00.0': 45338.95,
'2014-07-21 00:00:00.0': 37885.36,
'2014-07-22 00:00:00.0': 24028.45,
'2014-07-23 00:00:00.0': 25482.51,
'2014-07-24 00:00:00.0': 34552.03}
In [21]:
len(get_daily_revenue(orders, order_items, 'COMPLETE'))
Out[21]:
364
In [22]:
get_daily_revenue(orders, order_items, 'COMPLETE')['2014-07-10 00:00:00.0']
Out[22]:
30247.47