def ConvertOfferHistory(csv, as_datetime=True):
"""
Converts an offer history to human readable values
Parameters
----------
csv : list
Offer list csv obtained from ['offerCSV']
Returns
-------
times : numpy.ndarray
List of time values for an offer history.
prices : numpy.ndarray
Price (including shipping) of an offer for each time at an index
of times.
"""
# convert these values to numpy arrays
times = csv[::3]
values = np.array(csv[1::3])
values += np.array(csv[2::3]) # add in shipping
# convert to dollars and datetimes
to_datetime = True
times = keepaTime.KeepaMinutesToTime(times, to_datetime)
prices = values / 100.0
return times, prices
def ParseCSV(csv, to_datetime):
"""
Parses csv list from keepa into a python dictionary
csv is organized as the following
index item
0 Amazon Price
1 Marketplace New
2 Marketplace Used
3 Sales Rank
4 Listing Price
5 Collectable Price
11 New Offers
12 Used Offers
14 Collectable Offers
16 Rating
"""
# index in csv, key name, isfloat (is price)
indices = [[0, 'AmazonPrice', True], [1, 'MarketplaceNew', True],
[2, 'MarketplaceUsed', True], [3, 'SalesRank', False],
[4, 'ListingPrice', True], [5, 'CollectablePrice', True],
[11, 'NewOffers', False], [12, 'UsedOffers', False],
[14, 'CollectableOffers', False], [16, 'Rating', False]]
product_data = {}
for index in indices:
# Check if it exists
ind = index[0]
if csv[ind]:
key = index[1]
# Data goes [time0, value0, time1, value1, ...]
product_data[key + '_time'] = keepaTime.KeepaMinutesToTime(
csv[ind][::2], to_datetime)
# Convert to float price if applicable
if index[2]:
product_data[key] = np.array(csv[ind][1::2], np.float) / 100.0
else:
if index[1] == 'Rating':
product_data[key] = np.asarray(csv[ind][1::2],
np.float) / 10.0
else:
product_data[key] = np.asarray(csv[ind][1::2])
return product_data
def ParseCSV(csv, to_datetime):
"""
Parses csv list from keepa into a python dictionary
Parameters
----------
csv : list
csv list from keepa
Returns
-------
product_data : dict
Dictionary containing the following fields with timestamps:
AMAZON
Amazon price history
NEW
Marketplace/3rd party New price history - Amazon is considered to be part of the marketplace as well,
so if Amazon has the overall lowest new (!) price, the marketplace new price in the corresponding time interval
will be identical to the Amazon price (except if there is only one marketplace offer).
Shipping and Handling costs not included!
USED
Marketplace/3rd party Used price history
SALES
Sales Rank history. Not every product has a Sales Rank.
LISTPRICE
List Price history
5 COLLECTIBLE
Collectible Price history
6 REFURBISHED
Refurbished Price history
7 NEW_FBM_SHIPPING
3rd party (not including Amazon) New price history including shipping costs, only fulfilled by merchant (FBM).
8 LIGHTNING_DEAL
3rd party (not including Amazon) New price history including shipping costs, only fulfilled by merchant (FBM).
9 WAREHOUSE
Amazon Warehouse Deals price history. Mostly of used condition, rarely new.
10 NEW_FBA
Price history of the lowest 3rd party (not including Amazon/Warehouse) New offer that is fulfilled by Amazon
11 COUNT_NEW
New offer count history
12 COUNT_USED
Used offer count history
13 COUNT_REFURBISHED
Refurbished offer count history
14 COUNT_COLLECTIBLE
Collectible offer count history
16 RATING
The product's rating history. A rating is an integer from 0 to 50 (e.g. 45 = 4.5 stars)
17 COUNT_REVIEWS
The product's review count history.
18 BUY_BOX_SHIPPING(18, true, false, true, true),
The price history of the buy box. If no offer qualified for the buy box the price has the value -1. Including shipping costs.
19 USED_NEW_SHIPPING(19, true, true, true, true),
"Used - Like New" price history including shipping costs.
20 USED_VERY_GOOD_SHIPPING(20, true, true, true, true),
"Used - Very Good" price history including shipping costs.
21 USED_GOOD_SHIPPING(21, true, true, true, true),
"Used - Good" price history including shipping costs.
22 USED_ACCEPTABLE_SHIPPING(22, true, true, true, true),
"Used - Acceptable" price history including shipping costs.
23 COLLECTIBLE_NEW_SHIPPING(23, true, true, true, true),
"Collectible - Like New" price history including shipping costs.
24 COLLECTIBLE_VERY_GOOD_SHIPPING(24, true, true, true, true),
"Collectible - Very Good" price history including shipping costs.
25 COLLECTIBLE_GOOD_SHIPPING(25, true, true, true, true),
"Collectible - Good" price history including shipping costs.
26 COLLECTIBLE_ACCEPTABLE_SHIPPING(26, true, true, true, true),
"Collectible - Acceptable" price history including shipping costs.
27 REFURBISHED_SHIPPING
Refurbished price history including shipping costs.
30 TRADE_IN
The trade in price history. Amazon trade-in is not available for every locale.
"""
# [index in csv, key name, isfloat (is price)]
indices = [[ 0, 'AMAZON', True],
[ 1, 'NEW', True],
[ 2, 'USED', True],
[ 3, 'SALES', False],
[ 4, 'LISTPRICE', True],
[ 5, 'COLLECTIBLE', True],
[ 6, 'REFURBISHED', True],
[ 7, 'NEW_FBM_SHIPPING', True],
[ 8, 'LIGHTNING_DEAL', True],
[ 9, 'WAREHOUSE', True],
[10, 'NEW_FBA', True],
[11, 'COUNT_NEW', False],
[12, 'COUNT_USED', False],
[13, 'COUNT_REFURBISHED', False],
[14, 'CollectableOffers', False],
[16, 'RATING', False],
[17, 'COUNT_REVIEWS', False],
[18, 'BUY_BOX_SHIPPING', True],
[19, 'USED_NEW_SHIPPING', True],
[20, 'USED_VERY_GOOD_SHIPPING', True],
[21, 'USED_GOOD_SHIPPING', True],
[22, 'USED_ACCEPTABLE_SHIPPING', True],
[23, 'COLLECTIBLE_NEW_SHIPPING', True],
[24, 'COLLECTIBLE_VERY_GOOD_SHIPPING', True],
[25, 'COLLECTIBLE_GOOD_SHIPPING', True],
[26, 'COLLECTIBLE_ACCEPTABLE_SHIPPING', True],
[27, 'REFURBISHED_SHIPPING', True],
[30, 'TRADE_IN', True]]
product_data = {}
for index in indices:
# Check if it exists
ind = index[0]
if csv[ind]:
key = index[1]
# Data goes [time0, value0, time1, value1, ...]
product_data[key + '_time'] = keepaTime.KeepaMinutesToTime(csv[ind][::2], to_datetime)
# Convert to float price if applicable
if index[2]:
product_data[key] = np.array(csv[ind][1::2], np.float)/100.0
else:
if index[1] == 'Rating':
product_data[key] = np.asarray(
csv[ind][1::2], np.float)/10.0
else:
product_data[key] = np.asarray(csv[ind][1::2])
return product_data
def PlotProduct(product, keys=['AMAZON', 'USED', 'COUNT_USED', 'SALES']):
"""
Plots a product using matplotlib
Parameters
----------
product : list
Single product from keepaAPI.ProductQuery
keys : list, optional
Keys to plot. Defaults to ['AMAZON', 'USED', 'COUNT_USED', 'SALES']
"""
if not plt_loaded:
raise Exception('Plotting not available. Check matplotlib install')
# Use all keys if not specified
if not keys:
keys = product['data'].keys()
# Create three figures, one for price data, offers, and sales rank
pricefig, priceax = plt.subplots()
pricefig.canvas.set_window_title('Product Price Plot')
plt.title(product['title'])
pricelegend = []
offerfig, offerax = plt.subplots()
offerfig.canvas.set_window_title('Product Offer Plot')
plt.title(product['title'])
offerlegend = []
salesfig, salesax = plt.subplots()
salesfig.canvas.set_window_title('Product Sales Rank Plot')
plt.title(product['title'])
saleslegend = []
# Add in last update time
lstupdate = keepaTime.KeepaMinutesToTime(product['lastUpdate'])
# Attempt to plot each key
for key in keys:
# Continue if key does not exist
if key not in product['data'].keys():
continue
elif 'SALES' in key and 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
salesax.step(x, y, where='pre')
saleslegend.append(key)
elif 'COUNT_NEW' in key and 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
offerax.step(x, y, where='pre')
offerlegend.append(key)
elif 'COUNT_USED' in key and 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
offerax.step(x, y, where='pre')
offerlegend.append(key)
elif 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
priceax.step(x, y, where='pre')
pricelegend.append(key)
# Add in legends or close figure
if pricelegend:
priceax.legend(pricelegend)
else:
plt.close(pricefig)
if offerlegend:
offerax.legend(offerlegend)
else:
plt.close(offerfig)
if not saleslegend:
plt.close(salesfig)
plt.show(block=True)
plt.draw()
def ParseCSV(csv, to_datetime):
"""Parses csv list from keepa into a python dictionary
Parameters
----------
csv : list
csv list from keepa
Returns
-------
product_data : dict
Dictionary containing the following fields with timestamps:
AMAZON: Amazon price history
NEW: Marketplace/3rd party New price history - Amazon is
considered to be part of the marketplace as well, so if
Amazon has the overall lowest new (!) price, the
marketplace new price in the corresponding time interval
will be identical to the Amazon price (except if there is
only one marketplace offer). Shipping and Handling costs
not included!
USED: Marketplace/3rd party Used price history
SALES: Sales Rank history. Not every product has a Sales Rank.
LISTPRICE: List Price history
5 COLLECTIBLE: Collectible Price history
6 REFURBISHED: Refurbished Price history
7 NEW_FBM_SHIPPING: 3rd party (not including Amazon) New price
history including shipping costs, only fulfilled by
merchant (FBM).
8 LIGHTNING_DEAL: 3rd party (not including Amazon) New price
history including shipping costs, only fulfilled by
merchant (FBM).
9 WAREHOUSE: Amazon Warehouse Deals price history. Mostly of
used condition, rarely new.
10 NEW_FBA: Price history of the lowest 3rd party (not
including Amazon/Warehouse) New offer that is fulfilled
by Amazon
11 COUNT_NEW: New offer count history
12 COUNT_USED: Used offer count history
13 COUNT_REFURBISHED: Refurbished offer count history
14 COUNT_COLLECTIBLE: Collectible offer count history
16 RATING: The product's rating history. A rating is an
integer from 0 to 50 (e.g. 45 = 4.5 stars)
17 COUNT_REVIEWS: The product's review count history.
18 BUY_BOX_SHIPPING: The price history of the buy box. If no
offer qualified for the buy box the price has the value
-1. Including shipping costs.
19 USED_NEW_SHIPPING: "Used - Like New" price history
including shipping costs.
20 USED_VERY_GOOD_SHIPPING: "Used - Very Good" price history
including shipping costs.
21 USED_GOOD_SHIPPING: "Used - Good" price history including
shipping costs.
22 USED_ACCEPTABLE_SHIPPING: "Used - Acceptable" price history
including shipping costs.
23 COLLECTIBLE_NEW_SHIPPING: "Collectible - Like New" price
history including shipping costs.
24 COLLECTIBLE_VERY_GOOD_SHIPPING: "Collectible - Very Good"
price history including shipping costs.
25 COLLECTIBLE_GOOD_SHIPPING: "Collectible - Good" price
history including shipping costs.
26 COLLECTIBLE_ACCEPTABLE_SHIPPING: "Collectible - Acceptable"
price history including shipping costs.
27 REFURBISHED_SHIPPING: Refurbished price history including
shipping costs.
30 TRADE_IN: The trade in price history. Amazon trade-in is
not available for every locale.
"""
# [index in csv, key name, isfloat (is price)]
indices = [[0, 'AMAZON', True],
[1, 'NEW', True],
[2, 'USED', True],
[3, 'SALES', False],
[4, 'LISTPRICE', True],
[5, 'COLLECTIBLE', True],
[6, 'REFURBISHED', True],
[7, 'NEW_FBM_SHIPPING', True],
[8, 'LIGHTNING_DEAL', True],
[9, 'WAREHOUSE', True],
[10, 'NEW_FBA', True],
[11, 'COUNT_NEW', False],
[12, 'COUNT_USED', False],
[13, 'COUNT_REFURBISHED', False],
[14, 'CollectableOffers', False],
[16, 'RATING', True],
[17, 'COUNT_REVIEWS', False],
[18, 'BUY_BOX_SHIPPING', True],
[19, 'USED_NEW_SHIPPING', True],
[20, 'USED_VERY_GOOD_SHIPPING', True],
[21, 'USED_GOOD_SHIPPING', True],
[22, 'USED_ACCEPTABLE_SHIPPING', True],
[23, 'COLLECTIBLE_NEW_SHIPPING', True],
[24, 'COLLECTIBLE_VERY_GOOD_SHIPPING', True],
[25, 'COLLECTIBLE_GOOD_SHIPPING', True],
[26, 'COLLECTIBLE_ACCEPTABLE_SHIPPING', True],
[27, 'REFURBISHED_SHIPPING', True],
[30, 'TRADE_IN', True]]
product_data = {}
for ind, key, isfloat in indices:
if csv[ind]: # Check if entry it exists
if 'SHIPPING' in key: # shipping price is included
# Data goes [time0, value0, shipping0, time1, value1,
# shipping1, ...]
times = csv[ind][::3]
values = np.array(csv[ind][1::3])
values += np.array(csv[ind][2::3])
else:
# Data goes [time0, value0, time1, value1, ...]
times = csv[ind][::2]
values = np.array(csv[ind][1::2])
if isfloat: # Convert to float price if applicable
nanmask = values < 0
values = values.astype(np.float)/100
values[nanmask] = np.nan
if key == 'RATING':
values /= 10
timeval = keepaTime.KeepaMinutesToTime(times, to_datetime)
product_data['%s_time' % key] = timeval
product_data[key] = values
return product_data
def PlotProduct(product, keys=[], rng=None):
""" Plots a product using matplotlib """
if not plt_loaded:
raise Exception('Plotting not available. Check matplotlib install')
# Use all keys if not specified
if not keys:
keys = product['data'].keys()
# Create three figures, one for price data, offers, and sales rank
pricefig, priceax = plt.subplots()
pricefig.canvas.set_window_title('Product Price Plot')
plt.title(product['title'])
pricelegend = []
offerfig, offerax = plt.subplots()
offerfig.canvas.set_window_title('Product Offer Plot')
plt.title(product['title'])
offerlegend = []
salesfig, salesax = plt.subplots()
salesfig.canvas.set_window_title('Product Sales Rank Plot')
plt.title(product['title'])
saleslegend = []
# Add in last update time
lstupdate = keepaTime.KeepaMinutesToTime(product['lastUpdate'])
# Attempt to plot each key
for key in keys:
# Continue if key does not exist
if key not in product['data'].keys():
continue
elif 'SalesRank' in key and not 'time' in key:
x = np.append(product['data'][key + '_time'], lstupdate)
# x = ConvertToDateTime(x)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
salesax.step(x, y, where='pre')
saleslegend.append(key)
elif 'Offers' in key and not 'time' in key:
x = np.append(product['data'][key + '_time'], lstupdate)
# x = ConvertToDateTime(x)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
offerax.step(x, y, where='pre')
offerlegend.append(key)
elif not 'time' in key:
x = np.append(product['data'][key + '_time'], lstupdate)
# x = ConvertToDateTime(x)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
priceax.step(x, y, where='pre')
pricelegend.append(key)
# Add in legends or close figure
if pricelegend:
priceax.legend(pricelegend)
else:
plt.close(pricefig)
if offerlegend:
offerax.legend(offerlegend)
else:
plt.close(offerfig)
if not saleslegend:
plt.close(salesfig)
plt.show(block=True)
plt.draw()
def PlotProduct(product,
keys=['AMAZON', 'USED', 'COUNT_USED', 'SALES'],
price_limit=1000):
"""
Plots a product using matplotlib
Parameters
----------
product : list
Single product from keepaAPI.ProductQuery
keys : list, optional
Keys to plot. Defaults to ['AMAZON', 'USED', 'COUNT_USED', 'SALES']
price_limit : float, optional
Prices over this value will not be plotted. Used to ignore
extreme prices.
"""
if not plt_loaded:
raise Exception('Plotting not available. Install matplotlib with:\n' +
'pip install matplotlib')
if 'data' not in product:
product['data'] = ParseCSV[product['csv']]
# Use all keys if not specified
if not keys:
keys = product['data'].keys()
# Create three figures, one for price data, offers, and sales rank
pricefig, priceax = plt.subplots(figsize=(10, 5))
pricefig.canvas.set_window_title('Product Price Plot')
plt.title(product['title'])
plt.xlabel('Date')
plt.ylabel('Price')
pricelegend = []
offerfig, offerax = plt.subplots(figsize=(10, 5))
offerfig.canvas.set_window_title('Product Offer Plot')
plt.title(product['title'])
plt.xlabel('Date')
plt.ylabel('Listings')
offerlegend = []
salesfig, salesax = plt.subplots(figsize=(10, 5))
salesfig.canvas.set_window_title('Product Sales Rank Plot')
plt.title(product['title'])
plt.xlabel('Date')
plt.ylabel('Sales Rank')
saleslegend = []
# Add in last update time
lstupdate = keepaTime.KeepaMinutesToTime(product['lastUpdate'])
# Attempt to plot each key
for key in keys:
# Continue if key does not exist
if key not in product['data']:
print('%s not in product' % key)
continue
elif 'SALES' in key and 'time' not in key:
if product['data'][key].size == 1:
print('%s not in product' % key)
continue
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
salesax.step(x, y, where='pre')
saleslegend.append(key)
elif 'COUNT_' in key and 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y)
offerax.step(x, y, where='pre')
offerlegend.append(key)
elif 'time' not in key:
x = np.append(product['data'][key + '_time'], lstupdate)
y = np.append(product['data'][key],
product['data'][key][-1]).astype(np.float)
ReplaceInvalid(y, max_value=price_limit)
priceax.step(x, y, where='pre')
pricelegend.append(key)
# Add in legends or close figure
if pricelegend:
priceax.legend(pricelegend)
else:
plt.close(pricefig)
if offerlegend:
offerax.legend(offerlegend)
else:
plt.close(offerfig)
if not saleslegend:
plt.close(salesfig)
plt.show(block=True)
plt.draw()