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| import streamlit as st | |
| import pandas as pd | |
| uploaded_file = st.file_uploader("Choose product file", type="csv") | |
| if uploaded_file: | |
| #df = pd.read_excel(uploaded_file) | |
| df = pd.read_csv(uploaded_file, encoding='utf8') | |
| #st.dataframe(df) | |
| uploaded_file2 = st.file_uploader("Choose inventory file", type="csv") | |
| if uploaded_file2: | |
| #df2 = pd.read_excel(uploaded_file2) | |
| df2 = pd.read_csv(uploaded_file2, encoding='utf8') | |
| #st.dataframe(df2) | |
| #st.table(df2) | |
| def ConvertCitrus(df,df2): | |
| # Load pandas | |
| import re as re | |
| import RemoveHTMLtags as RHT | |
| #INPUT FILE | |
| #df = pd.read_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1 21-10-22.csv', encoding='utf8') | |
| #df2 = pd.read_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/inventory_export_1 21-10-22.csv', encoding='utf8') | |
| df.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1.xlsx',index=False) | |
| df2.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/inventory_export_1.xlsx',index=False) | |
| tagsp=str('<style type=')+str('"')+str('"')+str('text/css')+str('"')+str('"')+str('><!--') | |
| tags_list = ['<p class=','"p1"', 'data-mce-fragment="1">,','<b data-mce-fragment="1">','<i data-mce-fragment="1">','<p>' ,'</p>' , '<p*>', | |
| '<ul>','</ul>', | |
| '</i>','</b>','</p>','</br>', | |
| '<li>','</li>', | |
| '<br>', | |
| '<strong>','</strong>', | |
| '<span*>','</span>', '"utf-8"','UTF-8', | |
| '<a href*>','</a>','<meta charset=utf-8>',';;', | |
| '<em>','</em>','"','<meta charset=','utf-8>','<p>','<p','data-mce-fragment=1',';','<style type=','<style type=','><!--','text/css','<style type=\"\"text/css\"\"><!--','--></style>','td {border: 1px solid #ccc','}br {mso-data-placement:same-cell','}','>'] | |
| def remove_html_tags(text): | |
| """Remove html tags from a string""" | |
| import re | |
| clean = re.compile('<.*?>') | |
| return re.sub(clean, '', text) | |
| #for tag in tags_list: | |
| ## df['overview_copy'] = df['overview_copy'].str.replace(tag, '') | |
| # df.replace(to_replace=tag, value='', regex=True, inplace=True) | |
| for index, row in df.iterrows(): | |
| df.iloc[index,2]=RHT.remove_tags(str(df.iloc[index,2])) | |
| print(df.iloc[:,2]) | |
| df.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/products_export_1-nohtml.xlsx') | |
| #df.fillna('', inplace=True) | |
| df.iloc[:,2] = pd.Series(df.iloc[:,2],dtype="string") | |
| print(df.iloc[:,2].dtype) | |
| #s = pd.Series(['a', 'b', 'c'], dtype="string") | |
| #s.dtype | |
| #CONVERT FORMATS | |
| #Column A(0) – Ignore | |
| #Column B(1) “Title” > Column B(1) “Product Name” | |
| #Column C(2) – Ignore | |
| #Column D(3) “Vendor” > Column K(10) “Brand” | |
| #Column F(5) “Custom Product Type” > Column AF(31) “Short Description” | |
| #Column J(9) “Option1 Value” > Column I(8) “Size 1” | |
| #Column L(11) “Option2 Value” > Column H(7) > Colour | |
| #Column M(12) - Ignore | |
| #Column N(13) “Option 3 Value” > Column A(0) “Style Number” | |
| #1. Problems in Column N. Some codes do not stay as a number when the Citrus Lime csv is re-opened (8.05652E+12 instead of 8056516179091) The saved csv keeps turning this column back to “general’ format column when I re-open it, even after I save it as number column. The upload must keep this as a number formatted column. | |
| #Column O(14) - Ignore | |
| #Column P(15) “Variant Grams” > Column AE (30) “Weight (grams)” | |
| #Column R(17) “Variant Inventory Qty” > Column R (17) “Stock Count”. THIS IS THE KEY TO THE DAILY UPLOAD | |
| #Column U(20) “Variant Price” > Column F (5) “Unit MSRP” | |
| #Column Y > C&D | |
| ################################################################################################# | |
| temp_cols=df.columns.tolist() | |
| new_cols=temp_cols.copy() | |
| new_cols[1]=temp_cols[1] | |
| new_cols[17]=temp_cols[17] | |
| ################################################################################################# | |
| #THERE IS NO EXISTING COLUMN ON THE SHOPIFY EXPORT TO DIRECTLY PROVIDE DATA FOR COLUMN E ON THE CITRUS LIME CSV (which is the wholesale price ex VAT to the retailer). However – Column U “ Variant Price” can provide the information for Column E with the following formula: | |
| #((Column U/1.2)/1.6)*0.96 | |
| #Column Y “Variant Barcode” > Column C “Vendor SKU” (2) (and D "UPC/EAN" (3)??) | |
| #There are 2 problems with converting Column Y to Column C. | |
| #2. Shopify exports the UPC data and adds an apostrophe. This fails the SIM process. We need to get data without the apostrophe. | |
| #3. Vendor SKU. The CSV file keeps switching the data to a non-number eg 8056516178308 shows as 8.05652E+12. The saved csv keeps turning this column to “general’ format column when I re-open it, even after I save it as number column. The upload must keep this as a number formatted column. | |
| #This is where it gets complicated… | |
| #Shopify exports the image file as https:// links in an odd way. Instead of attributing image 1, image 2, and image 3 etc in dedicated and separate columns, it spreads them across the sizes for the related product in the same column (Column Z “Image Src”). Column AA in the Shopify export csv just shows the image position instead. We need to find a solution. We need to be able to provide https// image links in separate columns for each product and size. For example, if a product has 3 images, these need to be converted into Citrus Lime CSV columns Column Z “Image 1”, Column AA “Image 2”, Column AB “Image 3”, Column AC “Image 4” etc. | |
| #new_cols[4]=((temp_cols[20]/1.2)/1.96)*0.96 | |
| #Column C “Body (HTML)” > Column AG “Long Description” (32) | |
| df_copy=df[new_cols].copy(deep=True) | |
| print("SKU") | |
| print(df.iloc[:,24]) | |
| local_df = df.copy(deep=True) | |
| df_copy.iloc[:,0]=local_df.iloc[:,13].copy(deep=True) | |
| df_copy.iloc[:,5]=local_df.iloc[:,20].copy(deep=True) | |
| df_copy.iloc[:,7]=local_df.iloc[:,11].copy(deep=True) | |
| #24 is variant Bar code | |
| df_copy.iloc[:,2]=local_df.iloc[:,24].copy(deep=True) | |
| df_copy.iloc[:,8]=local_df.iloc[:,9].copy(deep=True) | |
| df_copy.iloc[:,10]=local_df.iloc[:,3].copy(deep=True) | |
| df_copy.rename(columns={df_copy.columns[10]: 'Brand'},inplace=True) | |
| df_copy.columns.values[10] = 'Brand' | |
| df_copy.iloc[:,30]=local_df.iloc[:,15].copy(deep=True) | |
| df_copy.iloc[:,31]=local_df.iloc[:,5].copy(deep=True) | |
| df_copy.iloc[:,32]=local_df.iloc[:,2].copy(deep=True) | |
| df_copy.rename(columns={df_copy.columns[8]: 'Size 1'},inplace=True) | |
| print(list(df_copy.columns.values)) | |
| #WE CONVERT COLUMN 20 to numeric (in case it's read as string) | |
| df_copy.iloc[:,20] = df_copy.iloc[:,20].astype(float) | |
| df_copy.iloc[:,4]=(((df_copy.iloc[:,20]/1.2)/1.96)*0.96) | |
| from babel.numbers import format_currency | |
| df_copy.iloc[:,4] = df_copy.iloc[:,4].apply(lambda x: format_currency(x, currency="GBP", locale="en_GB")) | |
| df_copy.iloc[:,5] = df_copy.iloc[:,5].apply(lambda x: format_currency(x, currency="GBP", locale="en_GB")) | |
| print(((df_copy.iloc[:,20]/1.2)/1.96)*0.96) | |
| #df_copy.iloc[:,2]=df_copy.iloc[:,2].str.replace("'","") | |
| df_copy.iloc[:,2] = df_copy.iloc[:,2].astype(str).str.replace("'","") | |
| #df_copy.iloc[:,24]=df_copy.iloc[:,24].str.replace("'","") | |
| df_copy.iloc[:,24] = df_copy.iloc[:,24].astype(str).str.replace("'","") | |
| print("SKU") | |
| print(df_copy.iloc[:,2]) | |
| #rename specific column names | |
| #df_copy.rename(columns = {'Variant Inventory Qty':'Stock Count','Variant Grams' : 'Weight (grams)'}, inplace = True) | |
| #df_copy.rename(columns = {'Option2 Value':'Colour','Option1 Value' : 'Size 1'}, inplace = True) | |
| #df_copy.rename(columns = {'Vendor':'Brand','Title' : 'Product Name'}, inplace = True) | |
| #df_copy.rename(columns = {'Body (HTML)':'Long Description'}, inplace = True) | |
| #df_copy.rename(columns={df_copy.columns[4]: 'Unit Cost'},inplace=True) | |
| print(list(df_copy.columns.values)) | |
| #df_copy.rename(columns={df_copy.columns[31]: 'Short Description'},inplace=True) | |
| #df_copy.rename(columns={df_copy.columns[2]: 'Vendor SKU'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[6]: 'Colour Code (Simple Colour)'},inplace=True) | |
| ##IN COLUMN H (6), WE HAVE SOME TAGS AND WE WANT TO GET THE TAG "MEN, WOMEN, LADY OR BOTH (UNISEX)" | |
| #WE ARE GETTING THAT INFO BEFORE REMOVING DATA FROM 6 | |
| for index, row in df_copy.iterrows(): | |
| if index==0: | |
| print(row['Colour Code (Simple Colour)']) | |
| if " mens" in str(row['Colour Code (Simple Colour)']): | |
| if " womens" in str(row['Colour Code (Simple Colour)']): | |
| df_copy.iloc[index,12]="Unisex" | |
| else: | |
| df_copy.iloc[index,12]="Mens" | |
| if " womens" in str(row['Colour Code (Simple Colour)']): | |
| if " mens" in str(row['Colour Code (Simple Colour)']): | |
| df_copy.iloc[index,12]="Unisex" | |
| else: | |
| df_copy.iloc[index,12]="Womens" | |
| if " ladys" in str(row['Colour Code (Simple Colour)']): | |
| df_copy.iloc[index,12]="Ladys" | |
| if index==0: | |
| print(row[12]) | |
| print(df_copy.iloc[:,12]) | |
| df_copy.iloc[:,6] = "" | |
| #Style Number Product Name Vendor SKU UPC/EAN Unit Cost Unit MSRP Colour Code (Simple Colour) Colour | |
| df_copy.rename(columns={df_copy.columns[0]: 'Style Number'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[1]: 'Product Name'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[2]: 'Vendor SKU'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[3]: 'UPC/EAN'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[4]: 'Unit Cost'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[5]: 'Unit MSRP'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[6]: 'Colour Code (Simple Colour)'},inplace=True) | |
| print(df_copy.columns[6]) | |
| df_copy.rename(columns={df_copy.columns[7]: 'Colour'},inplace=True) | |
| #Size 1 Size 2 Brand Year or Season Gender Manufacturer Part Code Other Barcode VAT Pack Qty | |
| df_copy.rename(columns={df_copy.columns[8]: 'Size 1'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[9]: 'Size 2'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[10]: 'Brand'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[11]: 'Year of Season'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[12]: 'Gender'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[13]: 'Manufacturer Part Code'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[14]: 'Other Bar Code'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[15]: 'VAT'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[16]: 'Pack Qty'},inplace=True) | |
| #Stock Count Price Band 1 Price Band 2 IE VAT Unit Cost in Euros MSRP in Euros | |
| df_copy.rename(columns={df_copy.columns[17]: 'Stock Count'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[18]: 'Price Band 1'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[19]: 'Price Band 2'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[20]: 'IE VAT'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[21]: 'Unit Cost in Euros'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[22]: 'MSRP in Euros'},inplace=True) | |
| #Commodity Codes Country of Origin Image (multiple images can be added in separate columns if available) | |
| df_copy.rename(columns={df_copy.columns[23]: 'Commodity Codes'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[24]: 'Country of Origin'},inplace=True) | |
| #Weight Short Description Long Description Video Link | |
| df_copy.rename(columns={df_copy.columns[30]: 'Weight'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[31]: 'Short Description'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[32]: 'Long Description'},inplace=True) | |
| df_copy.rename(columns={df_copy.columns[33]: 'Video Link'},inplace=True) | |
| df_copy.iloc[:,9] = "" | |
| df_copy.iloc[:,13] = "" | |
| df_copy.iloc[:,14] = "" | |
| df_copy.iloc[:,16] = "" | |
| df_copy.iloc[:,18] = "" | |
| df_copy.iloc[:,19] = "" | |
| df_copy.iloc[:,20] = "" | |
| df_copy.iloc[:,21] = "" | |
| df_copy.iloc[:,22] = "" | |
| #df_copy.rename(columns={df_copy.columns[26]: 'Weight (Grams)'},inplace=True) | |
| #df_copy.iloc[:,26] = "" | |
| df_copy.iloc[:,33] = "" | |
| #df_copy.iloc[:,5] = " " | |
| df_copy.iloc[:,15] = "20" | |
| print(list(df_copy.columns.values)) | |
| #Column Y in the export and this code should go into both Columns C and D in the conversion with the titles “Vendor SKU” and “UPC/EAN” It is replicated for a complicated reason that I won’t explain here, but Column Y in the export should go into both Column C and D in the conversion | |
| df_copy.iloc[:,3] = df_copy.iloc[:,2] | |
| df_copy.columns.values[10] = 'Brand' | |
| df_copy.iloc[:,11] = "" | |
| df_copy.iloc[:,22] = "" | |
| #df_copy.rename(columns={df_copy.columns[30]: 'Weight (Grams)'},inplace=True) | |
| print("SKU") | |
| print(df_copy.iloc[:,2]) | |
| #DATA COMING FROM THE OTHER CSV FILE | |
| df_copy.iloc[:,23] = "" | |
| df_copy.iloc[:,24] = "" | |
| #WARNING: HEADER IS IN SECOND ROW. WE DONT HAVE INTO ACCOUNT FIRST ROW | |
| #df2 = pd.read_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/inventory_export_12.xlsx',engine="openpyxl", header=1) | |
| #WE HAVE TO REORDER COLUMNS COO and HS Code in df2 in order to match the index order of df | |
| #list1=df_copy.set_index('Vendor SKU').T.to_dict('list') | |
| #print(list1) | |
| new_index=df['Variant SKU'] | |
| boolean = df['Variant SKU'].duplicated().any() | |
| #print(boolean) | |
| boolean = df2['SKU'].duplicated().any() | |
| #print(boolean) | |
| duplicateRows2 = df2[df2.duplicated(['SKU'],keep = False)] | |
| #print(duplicateRows2['SKU']) | |
| duplicateRows = df[df.duplicated(['Variant SKU'],keep = False)] | |
| #print(duplicateRows) | |
| #print(duplicateRows['Variant SKU']) | |
| #print(new_index) | |
| df2=df2.set_index('SKU') | |
| #print(df2) | |
| #i=df2.index | |
| #for x in i: | |
| # print(x) | |
| df2.reindex(new_index) | |
| #i=df2.index | |
| #for x in i: | |
| # print(x) | |
| #print(df2) | |
| #print(df2.index) | |
| #df3 = pd.DataFrame(students, index=['a', 'b', 'c', 'd', 'e']) | |
| #print("Original DataFrame: ") | |
| #print(df) | |
| print("TERMINE") | |
| df_copy.iloc[:,24] = df2.loc[:,'COO'] | |
| df_copy.iloc[:,23] = df2.loc[:,'HS Code'] | |
| df_copy['Commodity Codes']=df2['HS Code'].values | |
| df_copy['Country of Origin']=df2['COO'].values | |
| #print(df2.loc[:,'COO']) | |
| #print(df2.loc[:,'HS Code']) | |
| #print(df_copy.iloc[:,24]) | |
| #print(df_copy.iloc[:,23]) | |
| print("SKU") | |
| print(df_copy.iloc[:,2]) | |
| #WE COMPLETE THE DATAFRMAE WITH DUMMY COLUMNS TILL THE MAXIMUM DESIRED NUMBER | |
| header_list=[] | |
| for i in range(49,58): | |
| #df.insert(i, "Dummy", [], True) | |
| header_list.append(str(i)) | |
| df_copy[str(i)]='' | |
| column_indices=[] | |
| for i in range(0,24): | |
| column_indices.append(34+i) | |
| #Tech Specs Size Chart Geometry Chart Frame Rear Shock Fork | |
| #Headset Stem Handlebar Bar Tape / Grip Brakes Levers Brake Calipers Tyres Wheels Front Derailleur | |
| #Rear Derailleur Shift Levers Chain Cassette Chainset Bottom Bracket Pedals Saddle Seatpost | |
| old_names = df_copy.columns[column_indices] | |
| new_names = ['Tech Specs','Size Chart','Geometry Chart','Frame', 'Rear Shock', 'Fork', 'Headset', 'Stem', 'Handlebar', 'Bar Tape / Grip', 'Brakes Levers', 'Brake Calipers', 'Tyres', 'Wheels', 'Front Derailleur', 'Rear Derailleur', 'Shift Levers' ,'Chain' ,'Cassette' ,'Chainset' ,'Bottom Bracket', 'Pedals', 'Saddle', 'Seatpost'] | |
| old_names = df_copy.columns[column_indices] | |
| df_copy.rename(columns=dict(zip(old_names, new_names)), inplace=True) | |
| df_copy.iloc[:,34:58]='' | |
| print("SKUf") | |
| print(df_copy.iloc[:,2]) | |
| #print(df_copy.iloc[:,3]) | |
| ## Rename all columns with list | |
| #cols = ['Courses','Courses_Fee','Courses_Duration'] | |
| #df_copy.columns = cols | |
| #print(df.columns) | |
| ################### | |
| #PUT IMAGES IN A SIGNLE ROW: WE LOOK FOR IMAGES COMING FROM COMMON NAMES | |
| #Shopify exports the image file as https:// links in an odd way. Instead of attributing image 1, image 2, and image 3 etc in dedicated | |
| #and separate columns, it spreads them across the sizes for the related product in the same column (Column Z “Image Src”). | |
| #Column AA in the Shopify export csv just shows the image position instead. We need to find a solution. | |
| #We need to be able to provide https// image links in separate columns for each product and size. For example, if a product has 3 images, | |
| #these need to be converted into Citrus Lime CSV columns Column Z “Image 1”, Column AA “Image 2”, Column AB “Image 3”, Column AC “Image 4” | |
| #etc | |
| #################### | |
| #region imagesRow2Column | |
| #We get the list of rows with NAN data in Product Name column (same product name but different sizes (XS, XL...). Each of these rows has a image scr link | |
| list_col=df_copy.loc[pd.isna(df_copy.loc[:,'Product Name']), :].index | |
| images=df_copy.loc[list_col,'Image Src'] | |
| list_end=[] | |
| for row in df_copy.index: | |
| #NotNA gets rows where Product Name column has a name in it (first image and row where we should add the images) | |
| if pd.notna(df_copy.loc[row,'Product Name']): | |
| #print(df_copy.loc[row,'Product Name']) | |
| rowNotNa=row | |
| i=1 | |
| #j=1 | |
| list_img=[] | |
| #WE INCLUDE IN THE LIST THE FIRST IMAGE | |
| list_img.append(df_copy.loc[row,'Image Src']) | |
| while pd.isna(df_copy.loc[row+i,'Product Name']) and row+i<len(df_copy.index)-1: | |
| #WE ADD THE REST OF THE IMAGES (FOLLOWING ROWS) | |
| if "http" in str(df_copy.loc[row+i,'Image Src']): | |
| list_img.append(df_copy.loc[row+i,'Image Src']) | |
| i=i+1 | |
| list_end.append(list_img) | |
| #IN list_end WE HAVE ALL OF THE IMAGES FOR EACH PRODUCT NAME | |
| index_nonnan=df_copy.loc[pd.notna(df_copy.loc[:,'Product Name']), :].index | |
| max=0 | |
| for i in range(len(list_end)): | |
| if max<len(list_end[i]): | |
| max=len(list_end[i]) | |
| print("SKUf") | |
| print(df_copy.iloc[:,2]) | |
| #WE CHANGE THE COLUMN NAME OF THE COLUMNS WHERE THERE ARE IMAGES: EACH COLUMN IS CALLED "Image x" | |
| #We first delete old values in the Image columns | |
| for j in range(max): | |
| df_copy.iloc[:,25+j]='' | |
| counter=0 | |
| for index in index_nonnan: | |
| for j in range(len(list_end[counter])): | |
| if list_end[counter][j]!='nan': | |
| df_copy.iloc[index,25+j]=list_end[counter][j] | |
| df_copy.rename(columns={df_copy.columns[25+j]: 'Image'+str(j+1)},inplace=True) | |
| counter=counter+1 | |
| print("SKUf") | |
| print(df_copy.iloc[:,2]) | |
| #WE HAVE TO FILL NAN ROWS (SAME PRODUCT BUT DIFFERENT SIZES) WITH THE SAME IMAGES THAT IN NON NAN ROWS (MAIN PRODUCT-SIZE) | |
| listImages=[None] * max | |
| list1=[None] * max | |
| list2=[None] * max | |
| list3=[None] * max | |
| list4=[None] * max | |
| list5=[None] * max | |
| for index, row in df_copy.iterrows(): | |
| #NotNA gets rows where Product Name column has a name in it (first image and row where we should add the images) | |
| #print(df_copy.iloc[index,1]) | |
| if pd.notna(df_copy.iloc[index,1]): | |
| for j in range(0,max): | |
| listImages[j]=str((df_copy.iloc[index,25+j])) | |
| #list1[j]=str((df_copy.iloc[index,1+j])) | |
| #list2[j]=str((df_copy.iloc[index,10+j])) | |
| #list3[j]=str((df_copy.iloc[index,12+j])) | |
| #list4[j]=str((df_copy.iloc[index,31+j])) | |
| #list5[j]=str((df_copy.iloc[index,32+j])) | |
| list1[j]=str((df_copy.iloc[index,1])) | |
| list2[j]=str((df_copy.iloc[index,10])) | |
| list3[j]=str((df_copy.iloc[index,12])) | |
| list4[j]=str((df_copy.iloc[index,31])) | |
| list5[j]=str((df_copy.iloc[index,32])) | |
| else: | |
| for j in range(0,max): | |
| df_copy.iloc[index,25+j]=listImages[j] | |
| #df_copy.iloc[index,1+j]=list1[j] | |
| #df_copy.iloc[index,10+j]=list2[j] | |
| #df_copy.iloc[index,12+j]=list3[j] | |
| #df_copy.iloc[index,31+j]=list4[j] | |
| #df_copy.iloc[index,32+j]=list5[j] | |
| df_copy.iloc[index,1]=list1[j] | |
| df_copy.iloc[index,10]=list2[j] | |
| df_copy.iloc[index,12]=list3[j] | |
| df_copy.iloc[index,31]=list4[j] | |
| df_copy.iloc[index,32]=list5[j] | |
| #endregion | |
| print("SKUf") | |
| print(df_copy.iloc[:,2]) | |
| #print(df_copy.iloc[:,3]) | |
| ################################################################################### | |
| df_copy.to_excel('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File_st.xlsx',index=False) | |
| #df_copy.to_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File.csv',index=False, encoding='utf-8') | |
| df_copy.to_csv('C:/Users/15572890/Desktop/I+D/MarksCsvConversion/Validation2/OCCHIO-Cycle-Data-File_st.csv',index=False, encoding='utf_8_sig') | |
| if uploaded_file and uploaded_file2: | |
| ConvertCitrus(df,df2) | |