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Read the code comments as well. Comments explained the sections.

Calculate sell prices (and related) for ebay

In [46]:

ebay_profit_tax = U2 * income_taxAB2 = price_calculation_df['ebay_cost'] = round(L2 + N2 + O2 + P2 + S2 + ebay_profit_tax, 2)W2[:5], AB2[:5]L2[:1], M2[:1], N2[:1], P2[:1], S2[:1], V2[:1]

Out[46]:

(0    548.29
 dtype: float64, 0    54.829
 Name: Total with ship, dtype: float64, 0    16.7487
 Name: Total with ship, dtype: float64, 0    5.4829
 Name: Total with ship, dtype: float64, 0    16.4487
 Name: Total with ship, dtype: float64, 0    27.4145
 Name: shopforsoul_profit, dtype: float64)

In [47]:

AC2 = price_calculation_df['ebay_list_price'] = round(AB2 + U2 -F2, 2) # - F2, 2)AC2[:5]

Out[47]:

0    262.43
1    263.43
2    258.56
3    258.43
4    259.65
dtype: float64

In [48]:

AD2 = price_calculation_df['Buyer pay from Mall'] = round(Y2+R2,2)

In [49]:

AE2 = price_calculation_df['Ebay Buyers Pay'] = round(AC2+R2+F2,2)AF2 = price_calculation_df['Ebay vs Mall'] = round(AE2-AD2, 2)

account for amazon selling cost and calculate your sell price for amazon

#amazon has sales commission to pay

#you might want to account for monthly subscription fee (based on how much you sell in a month and how much subscription fee you pay)

In [50]:

# possible income taxAG2 = price_calculation_df['NA-1'] = round(W2-R2+L2*0.2,2)AI2 = price_calculation_df['amazon_profit'] = round(L2*amazon_profit, 2)AJ2 = price_calculation_df['amazon_our_cost'] = round(L2+S2+P2+R2+AI2*income_tax,2)AH2 = price_calculation_df['Amazon Final Value Fee'] = round( (AI2 + AJ2) * amazon_final_fee_rate, 2)AK2 = price_calculation_df['amazon_list_price'] = round(AJ2+AI2+AH2, 2)AL2 = price_calculation_df['amazon_special_price'] = round(L2*amazon_special_profit+AJ2+(L2*amazon_special_profit+AJ2)*income_tax+L2*amazon_special_profit*income_tax-L2*amazon_profit*income_tax, 2)AM2 = price_calculation_df['Amazon Final Value Fee USD'] = round(AH2*cad_to_usd, 2)AN2 = price_calculation_df['amazon_profit_usd'] = round(AI2*cad_to_usd, 2)AO2 = price_calculation_df['amazon_our_cost_usd'] = round(AJ2*cad_to_usd, 2)AP2 = price_calculation_df['amazon_list_price_usd'] = round(AK2*cad_to_usd, 2)AQ2 = price_calculation_df['amazon_special_price_usd'] = round(AL2*cad_to_usd, 2)

In [51]:

AE2[:2], AF2[:2] , AG2[:2] , AH2[:2] , AI2[:2] , AJ2[:2] , AK2[:2] ,AL2[:2] , AM2[:2] , AN2[:2] , AO2[:2] , AP2[:2] , AQ2[:2]

Out[51]:

(0    935.52
 1    936.76
 dtype: float64, 0    96.42
 1    96.55
 dtype: float64, 0    115.82
 1    115.97
 dtype: float64, 0    526.97
 1    527.67
 dtype: float64, 0    739.21
 1    740.19
 dtype: float64, 0    658.71
 1    659.59
 dtype: float64)

In [52]:

AR2 = price_calculation_df['amazon_list_price_gbp'] = round(AK2*cad_to_gbp, 2)AS2 = price_calculation_df['amazon_special_price_gbp'] = round(AL2*cad_to_gbp, 2)AT2 = price_calculation_df['amazon_list_price_eu'] = round(AK2*cad_to_euro,2)AU2 = price_calculation_df['amazon_special_price_eu'] = round(AL2*cad_to_euro, 2)AV2 = price_calculation_df['amazon_list_price_mxn'] = round(AK2*cad_to_mxn, 2)AW2 = price_calculation_df['amazon_special_price_mxn'] = round(AL2*cad_to_mxn, 2)AR2[:2], AS2[:2], AT2[:2], AU2[:2], AV2[:2], AW2[:2]

Out[52]:

(0    602.25
 1    603.04
 dtype: float64, 0    536.66
 1    537.37
 dtype: float64, 0    684.14
 1    685.05
 dtype: float64, 0    609.64
 1    610.45
 dtype: float64, 0    18468.44
 1    18492.89
 dtype: float64, 0    16457.20
 1    16479.01
 dtype: float64)

In [53]:

#D2 = 1BD2 = raw_df['Weight Kg']AX2 = price_calculation_df['ama_safe_refund_amount'] = round(AL2-H2-I2-K2-S2-P2-R2-AH2-D2-18, 2)AY2 = price_calculation_df['Weight'] = round(BD2*2.2, 2)BJ2 = price_calculation_df['original in stock'] = raw_df['Stock status']AZ2 = price_calculation_df['Stock status'] = (BJ2=="In Stock").astype(int)#AZ2 = AZ2.astype(int)AX2[:2], AY2[:2], AZ2[:2], BJ2[:2], BD2[:2]

Out[53]:

(0    573.29
 1    573.41
 dtype: float64, 0    0.09
 1    0.06
 Name: Weight Kg, dtype: float64, 0    0
 1    0
 Name: Stock status, dtype: int64, 0    Closed
 1       NaN
 Name: Stock status, dtype: object, 0    0.043
 1    0.025
 Name: Weight Kg, dtype: float64)

In [55]:

AY2[:5]

Out[55]:

0    0.09
1    0.06
2    0.09
3    0.09
4    0.09
Name: Weight Kg, dtype: float64

In [56]:

min_raw_price_to_show_on_amazon = 10BA2 = price_calculation_df['categories'] = raw_df['Category Name'] + '/' + raw_df['Subcategory Name']BB2 = price_calculation_df['_root_category'] = 'All'BC2 = price_calculation_df['visibility'] = 4vis = []for x in D2:if x < min_raw_price_to_show_on_amazon:#vis.append(1)# temporaryvis.append('Catalog, Search')else:vis.append('Catalog, Search')BC2 = price_calculation_df['visibility'] = vis# temporary# BC2 = price_calculation_df['visibility'] = 4

In [57]:

BC2[:5]

Out[57]:

['Catalog, Search',
 'Catalog, Search',
 'Catalog, Search',
 'Catalog, Search',
 'Catalog, Search']

In [61]:

# take width height from supplier to push to Amazon

In [62]:

BE2 = price_calculation_df['Height mm'] = raw_df['Height mm']BF2 = price_calculation_df['Width mm'] = raw_df['Width mm']BG2 = price_calculation_df['Depth mm'] = raw_df['Depth mm']

In [64]:

# We do not want to sell products that has weight > 40 lbs

# status = 2 = disabled on Amazon# based on 'weight right' products will be disabled or enabled

In [65]:

# default valueBH2 = price_calculation_df['status'] =  2BN2 = price_calculation_df['weight right'] = 0vis = []for x in AY2:if x <= 20:vis.append(1)else:vis.append(0)BN2 = price_calculation_df['weight right'] = visBN2[:5]

Out[65]:

[1, 1, 1, 1, 1]

In [66]:

# based on shipping cost, you might not want to sell products.

# we will not sell if the shipping cost is more than $40

In [67]:

vis = []for x in F2.astype(float):if x >= 40:vis.append(0)else:vis.append(1)BO2 = price_calculation_df['Ship Right'] = vis#if (F2 >= 40) and (D2/F2 <= 2):#BO2 = price_calculation_df['Ship Right'] = 0BO2[:5]

Out[67]:

[0, 0, 0, 0, 0]

In [60]:

# F2[2531]

In [68]:

# if the product will be discontinued soon, we are not selling.

# will set status = 2. initially will set Is Normal = No = 0

In [69]:

BM2 = price_calculation_df['Soon Discontinued'] = raw_df['Soon Discontinued']BP2 = price_calculation_df['Is Normal'] = 0vis = []for x in BM2:if x == 'Soon Discontinued':vis.append(0)else:vis.append(1)BP2 = price_calculation_df['Is Normal'] = visBP2[10603:10610]

Out[69]:

[0, 1, 1, 1, 1, 0, 0]

In [70]:

'''BH2 = [1] * len(BN2)for index in range(0, len(BN2) ):if (BN2[index]==1):if (BO2[index]==1):if (BP2[index]==1):if (AZ2[index]==1):BH2[index] =  1price_calculation_df['status'][index] = 1'''

Out[70]:

"\nBH2 = [1] * len(BN2)\nfor index in range(0, len(BN2) ):\n    if (BN2[index]==1):\n        if (BO2[index]==1):\n            if (BP2[index]==1):\n                if (AZ2[index]==1):\n                    BH2[index] =  1\n                    price_calculation_df['status'][index] = 1\n\n"

In [71]:

# set status for some or until now. Status = 2 = disabled

# status = enabled if all conditions are right i.e. BN2 and BO2 and BP2 and AZ2# i.e. price right, ship right, weight right, not discontinuedBH2 = BN2 and BO2 and BP2 and AZ2BH2.replace(0, 2, inplace=True)#sorted(BH2, reverse=True)price_calculation_df['status'] = BH2

In [72]:

# keep track of supplier retail price (recommended retail) and retail+shipping priceBI2 = price_calculation_df['Retail Price'] = raw_df['Retail Price']

In [73]:

BH2[1]  , raw_df[1:2]['Model Code']

Out[73]:

(2, 1    A01AL3303110
 Name: Model Code, dtype: object)

In [74]:

price_calculation_df['Retail Price'] = price_calculation_df['Retail Price'].astype(str)

In [75]:

#list(price_calculation_df['Retail Price'].unique()[0:len(price_calculation_df['Retail Price'])+1])BI2 = price_calculation_df['Retail Price'] = price_calculation_df['Retail Price'].str.replace(',','')#max(sorted(price_calculation_df['Retail Price'].astype(str)))BK2 = price_calculation_df['R+shipp'] = BI2.astype(float) + F2.astype(float) #price_calculation_df['Retail Price'] + price_calculation_df['cheapest_courier_shipping']

In [76]:

BK2[:5]

Out[76]:

0    516.77
1    517.48
2    513.00
3    512.92
4    513.97
dtype: float64

In [77]:

#F2# compare our retail shop = magento price with supplier recommended retail priceBL2 = price_calculation_df['Shop vs retail'] = Y2 - BK2 #round(W2+L2*0.15,22) #- price_calculation_df['Retail Price'] #- price_calculation_df['cheapest_courier_shipping']

Out[77]:

(0    234.68
 1    234.97
 2    234.65
 3    234.60
 4    234.74
 dtype: float64, 0    691.10
 1    692.04
 2    687.49
 3    687.38
 4    688.51
 dtype: float64, 0    486.61
 1    487.28
 2    484.07
 3    483.99
 4    484.79
 dtype: float64)

In [78]:

# calculate prices for Amazon

In [79]:

BQ2 = price_calculation_df['amazon_ca_minimum_price'] = round(AJ2-H2-K2-I2-P2-S2,2)usd_rate = 0.761242BR2 = price_calculation_df['amazon_usd_minimum_price'] = round(BQ2 * cad_to_usd, 2)BL2[:5], BQ2[:5], BR2[:5]

Out[79]:

(0    234.68
 1    234.97
 2    234.65
 3    234.60
 4    234.74
 dtype: float64, 0    691.10
 1    692.04
 2    687.49
 3    687.38
 4    688.51
 dtype: float64, 0    486.61
 1    487.28
 2    484.07
 3    483.99
 4    484.79
 dtype: float64)

In [80]:

BS2 = price_calculation_df['amazon_gbp_minimum_price'] = round(BQ2 * cad_to_gbp, 2)BT2 = price_calculation_df['amazon_eu_minimum_price'] = round(BQ2 * cad_to_euro, 2)BU2 = price_calculation_df['amazon_mxn_minimum'] = round(BQ2 * cad_to_mxn, 2)"""E2 = [500] * len(raw_df)min_e2_f2 = E2if F2.any() < min_e2_f2.any():min_e2_f2 = F2"""#BV2 = price_calculation_df['amazon_ca_abs_minimum'] = round(BQ2-R2+Q2-F2+ F2 )BW2 = price_calculation_df['expedited_cost'] = 45BU2[1]#[:5], BT2[:5], BU2[:5], BW2[:5]

Out[80]:

12174.02

In [81]:

BV2 = price_calculation_df['amazon_ca_abs_minimum'] = round ( BQ2 - R2 + Q2, 2)BV2[:5]

Out[81]:

0    625.31
1    626.16
2    622.03
3    621.93
4    622.96
dtype: float64

In [ ]:

# output Price Calculation

In [82]:

output_folder = './data-supplier-2020-04-14/output/'

In [83]:

price_calculation_df.to_csv( output_folder + 'calculated_price_' + input_file_name + today + '.csv')

In [84]:

df_calculated = pd.read_csv(output_folder + 'calculated_price_' + input_file_name + today + '.csv')df_calculated.head(2)

Out[84]:

Unnamed: 0product_idskuNameraw_pricePostal Shipping Pricecheapest_courier_shippingproduct_eanShip RiskPrice Risk…Retail PriceR+shippShop vs retailamazon_ca_minimum_priceamazon_usd_minimum_priceamazon_gbp_minimum_priceamazon_eu_minimum_priceamazon_mxn_minimumexpedited_costamazon_ca_abs_minimum0030399shopforsoul-A01AL3301111Black 3x3x3 MoYu AoLong V2 Puzzle-A01AL330111112.90500.0500.06.941378e+1215.00.3870…16.77516.77234.68691.10486.61396.45450.3612157.4945625.311138649shopforsoul-A01AL3303110Qiyun AoLong V2 3x3x3 Speed Cube Enhanced Edit…13.61500.0500.06.941378e+1215.00.4083…17.48517.48234.97692.04487.28396.99450.9712174.0245626.16

2 rows × 75 columns

In [85]:

price_calculation_df[['product_id', 'sku', 'Name']][:5]

Out[85]:

product_idskuName030399shopforsoul-A01AL3301111Black 3x3x3 MoYu AoLong V2 Puzzle-A01AL3301111138649shopforsoul-A01AL3303110Qiyun AoLong V2 3x3x3 Speed Cube Enhanced Edit…239296shopforsoul-A01CL3301111New , 5.46cm Smaller Sized Fangshi (Funs) Shua…337397shopforsoul-A01CL3302113Fangshi Shuanren 3 x 3 x 3 with Sticker 54.6 m…438354shopforsoul-A01CL3302120FangShi 3x3x3 Speed Cube Puzzle White Fully As…

# # Calculate sell prices (and related) for ebay# In[46]:ebay_profit_tax = U2 * income_tax
AB2 = price_calculation_df[‘ebay_cost’] = round(L2 + N2 + O2 + P2 + S2 + ebay_profit_tax, 2)
W2[:5], AB2[:5]
L2[:1], M2[:1], N2[:1], P2[:1], S2[:1], V2[:1]# In[47]:AC2 = price_calculation_df[‘ebay_list_price’] = round(AB2 + U2 -F2, 2) # — F2, 2)
AC2[:5]# In[48]:AD2 = price_calculation_df[‘Buyer pay from Mall’] = round(Y2+R2,2)# In[49]:AE2 = price_calculation_df[‘Ebay Buyers Pay’] = round(AC2+R2+F2,2)
AF2 = price_calculation_df[‘Ebay vs Mall’] = round(AE2-AD2, 2)# # account for amazon selling cost and calculate your sell price for amazon
# #amazon has sales commission to pay
# #you might want to account for monthly subscription fee (based on how much you sell in a month and how much subscription fee you pay)# In[50]:# possible income tax
AG2 = price_calculation_df[‘NA-1’] = round(W2-R2+L2*0.2,2)AI2 = price_calculation_df[‘amazon_profit’] = round(L2*amazon_profit, 2)
AJ2 = price_calculation_df[‘amazon_our_cost’] = round(L2+S2+P2+R2+AI2*income_tax,2)
AH2 = price_calculation_df[‘Amazon Final Value Fee’] = round( (AI2 + AJ2) * amazon_final_fee_rate, 2)
AK2 = price_calculation_df[‘amazon_list_price’] = round(AJ2+AI2+AH2, 2)
AL2 = price_calculation_df[‘amazon_special_price’] = round(L2*amazon_special_profit+AJ2+(L2*amazon_special_profit+AJ2)*income_tax+L2*amazon_special_profit*income_tax-L2*amazon_profit*income_tax, 2)
AM2 = price_calculation_df[‘Amazon Final Value Fee USD’] = round(AH2*cad_to_usd, 2)
AN2 = price_calculation_df[‘amazon_profit_usd’] = round(AI2*cad_to_usd, 2)
AO2 = price_calculation_df[‘amazon_our_cost_usd’] = round(AJ2*cad_to_usd, 2)
AP2 = price_calculation_df[‘amazon_list_price_usd’] = round(AK2*cad_to_usd, 2)
AQ2 = price_calculation_df[‘amazon_special_price_usd’] = round(AL2*cad_to_usd, 2)# In[51]:AE2[:2], AF2[:2] , AG2[:2] , AH2[:2] , AI2[:2] , AJ2[:2] , AK2[:2] , 
AL2[:2] , AM2[:2] , AN2[:2] , AO2[:2] , AP2[:2] , AQ2[:2]# In[52]:AR2 = price_calculation_df[‘amazon_list_price_gbp’] = round(AK2*cad_to_gbp, 2)
AS2 = price_calculation_df[‘amazon_special_price_gbp’] = round(AL2*cad_to_gbp, 2)
AT2 = price_calculation_df[‘amazon_list_price_eu’] = round(AK2*cad_to_euro,2)
AU2 = price_calculation_df[‘amazon_special_price_eu’] = round(AL2*cad_to_euro, 2)
AV2 = price_calculation_df[‘amazon_list_price_mxn’] = round(AK2*cad_to_mxn, 2)
AW2 = price_calculation_df[‘amazon_special_price_mxn’] = round(AL2*cad_to_mxn, 2)AR2[:2], AS2[:2], AT2[:2], AU2[:2], AV2[:2], AW2[:2]# In[53]:#D2 = 1
BD2 = raw_df[‘Weight Kg’] 
AX2 = price_calculation_df[‘ama_safe_refund_amount’] = round(AL2-H2-I2-K2-S2-P2-R2-AH2-D2–18, 2)
AY2 = price_calculation_df[‘Weight’] = round(BD2*2.2, 2)
BJ2 = price_calculation_df[‘original in stock’] = raw_df[‘Stock status’]
AZ2 = price_calculation_df[‘Stock status’] = (BJ2==”In Stock”).astype(int)
#AZ2 = AZ2.astype(int)
AX2[:2], AY2[:2], AZ2[:2], BJ2[:2], BD2[:2]# In[55]:AY2[:5]# In[56]:min_raw_price_to_show_on_amazon = 10
BA2 = price_calculation_df[‘categories’] = raw_df[‘Category Name’] + ‘/’ + raw_df[‘Subcategory Name’]
BB2 = price_calculation_df[‘_root_category’] = ‘All’
BC2 = price_calculation_df[‘visibility’] = 4vis = []
for x in D2:
 if x < min_raw_price_to_show_on_amazon:
 #vis.append(1)
 # temporary 
 vis.append(‘Catalog, Search’)
 else:
 vis.append(‘Catalog, Search’) 
BC2 = price_calculation_df[‘visibility’] = vis
# temporary
# BC2 = price_calculation_df[‘visibility’] = 4# In[57]:BC2[:5]# In[61]:# take width height from supplier to push to Amazon# In[62]:BE2 = price_calculation_df[‘Height mm’] = raw_df[‘Height mm’]
BF2 = price_calculation_df[‘Width mm’] = raw_df[‘Width mm’]
BG2 = price_calculation_df[‘Depth mm’] = raw_df[‘Depth mm’]# In[64]:# We do not want to sell products that has weight > 40 lbs
# status = 2 = disabled on Amazon
# based on ‘weight right’ products will be disabled or enabled# In[65]:# default value
BH2 = price_calculation_df[‘status’] = 2
BN2 = price_calculation_df[‘weight right’] = 0vis = []
for x in AY2:
 if x <= 20:
 vis.append(1)
 else:
 vis.append(0)BN2 = price_calculation_df[‘weight right’] = vis
BN2[:5]# In[66]:# based on shipping cost, you might not want to sell products. 
# we will not sell if the shipping cost is more than $40# In[67]:vis = []
for x in F2.astype(float):
 if x >= 40:
 vis.append(0)
 else:
 vis.append(1)
 
BO2 = price_calculation_df[‘Ship Right’] = vis
#if (F2 >= 40) and (D2/F2 <= 2):
 #BO2 = price_calculation_df[‘Ship Right’] = 0
 
BO2[:5]# In[60]:# F2[2531]# In[68]:# if the product will be discontinued soon, we are not selling.
# will set status = 2. initially will set Is Normal = No = 0# In[69]:BM2 = price_calculation_df[‘Soon Discontinued’] = raw_df[‘Soon Discontinued’] 
BP2 = price_calculation_df[‘Is Normal’] = 0vis = []
for x in BM2:
 if x == ‘Soon Discontinued’:
 vis.append(0)
 else:
 vis.append(1)
 
BP2 = price_calculation_df[‘Is Normal’] = vis 
BP2[10603:10610]# In[70]:‘’’
BH2 = [1] * len(BN2)
for index in range(0, len(BN2) ):
 if (BN2[index]==1):
 if (BO2[index]==1):
 if (BP2[index]==1):
 if (AZ2[index]==1):
 BH2[index] = 1
 price_calculation_df[‘status’][index] = 1‘’’# In[71]:# set status for some or until now
# status = enabled if all conditions are right i.e. BN2 and BO2 and BP2 and AZ2
# i.e. price right, ship right, weight right, not discontinued
BH2 = BN2 and BO2 and BP2 and AZ2
BH2.replace(0, 2, inplace=True)
#sorted(BH2, reverse=True)
price_calculation_df[‘status’] = BH2# In[72]:# keep track of supplier retail price (recommended retail) and retail+shipping price
BI2 = price_calculation_df[‘Retail Price’] = raw_df[‘Retail Price’]# In[73]:BH2[1] , raw_df[1:2][‘Model Code’]# In[74]:price_calculation_df[‘Retail Price’] = price_calculation_df[‘Retail Price’].astype(str)# In[75]:#list(price_calculation_df[‘Retail Price’].unique()[0:len(price_calculation_df[‘Retail Price’])+1])
BI2 = price_calculation_df[‘Retail Price’] = price_calculation_df[‘Retail Price’].str.replace(‘,’,’’)#max(sorted(price_calculation_df[‘Retail Price’].astype(str)))
BK2 = price_calculation_df[‘R+shipp’] = BI2.astype(float) + F2.astype(float) #price_calculation_df[‘Retail Price’] + price_calculation_df[‘cheapest_courier_shipping’]# In[76]:BK2[:5]# In[77]:#F2
# compare our retail shop = magento price with supplier recommended retail price
BL2 = price_calculation_df[‘Shop vs retail’] = Y2 — BK2 #round(W2+L2*0.15,22) #- price_calculation_df[‘Retail Price’] #- price_calculation_df[‘cheapest_courier_shipping’]# In[78]:# calculate prices for Amazon# In[79]:BQ2 = price_calculation_df[‘amazon_ca_minimum_price’] = round(AJ2-H2-K2-I2-P2-S2,2)
usd_rate = 0.761242
BR2 = price_calculation_df[‘amazon_usd_minimum_price’] = round(BQ2 * cad_to_usd, 2)
BL2[:5], BQ2[:5], BR2[:5]# In[80]:BS2 = price_calculation_df[‘amazon_gbp_minimum_price’] = round(BQ2 * cad_to_gbp, 2)
BT2 = price_calculation_df[‘amazon_eu_minimum_price’] = round(BQ2 * cad_to_euro, 2)
BU2 = price_calculation_df[‘amazon_mxn_minimum’] = round(BQ2 * cad_to_mxn, 2)“””
E2 = [500] * len(raw_df)
min_e2_f2 = E2
if F2.any() < min_e2_f2.any():
 min_e2_f2 = F2
“””#BV2 = price_calculation_df[‘amazon_ca_abs_minimum’] = round(BQ2-R2+Q2-F2+ F2 )
BW2 = price_calculation_df[‘expedited_cost’] = 45
BU2[1]#[:5], BT2[:5], BU2[:5], BW2[:5]# In[81]:BV2 = price_calculation_df[‘amazon_ca_abs_minimum’] = round ( BQ2 — R2 + Q2, 2) 
BV2[:5]# In[ ]:# output Price Calculation# In[82]:output_folder = ‘./data-supplier-2020–04–14/output/’# In[83]:price_calculation_df.to_csv( output_folder + ‘calculated_price_’ + input_file_name + today + ‘.csv’)# In[84]:df_calculated = pd.read_csv(output_folder + ‘calculated_price_’ + input_file_name + today + ‘.csv’)
df_calculated.head(2)# In[85]:price_calculation_df[[‘product_id’, ‘sku’, ‘Name’]][:5]

Medium: https://medium.com/@SayedAhmedCanada

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Sayed Ahmed

BSc. Eng. in Comp. Sc. & Eng. (BUET)

MSc. in Comp. Sc. (U of Manitoba, Canada)

MSc. in Data Science and Analytics (Ryerson University, Canada)

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://Bangla.SaLearningSchool.com, http://SitesTree.com

Training Courses: http://Training.SitesTree.com

8112223 Canada Inc/Justetc: http://JustEtc.net

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