DATA ANALYSIS - Mindstorm

ALL October 4, 2022

How to select rows and columns in pandas (Python)

Πως να επιλέγετε γραμμές και στήλες με τη βιβλιοθήκη pandas της γλώσσας προγραμματισμού Python See below the data and the code for the result shown on image . . . How to select rows and columns in pandas # Credit : Trifonas Papadopoulos, Oct 4th, 2022, Version 2.0 # start of code —————————————————————————————– import pandas as pd import numpy as np # data data = [[‘A0001′,”,”,”,0,’variable’,0,0], [‘A0001′,’WHITE’,’S’,’Small’,1,’variation’,0,0], [‘A0001′,’WHITE’,’M’,’Medium’,2,’variation’,0,0], [‘A0001′,’WHITE’,’L’,’Large’,3,’variation’,0,0], [‘A0001′,’WHITE’,’XL’,’Extra Large’,4,’variation’,0,0], [‘A0001′,’BLACK’,’L’,’Large’,5,’variation’,0,0], [‘A0003′,”,”,”,6,’variable’,0,0], [‘A0003′,’BLACK’,’XL’,’Extra Large’,7,’variation’,0,0], [‘A0002′,”,”,”,8,’variable’,0,0], [‘A0002′,’BLACK’,’L’,’Large’,9,’variation’,0,0], [‘A0002′,’BLACK’,’XL’,’Extra Large’,10,’variation’,0,0], [‘A0004′,”,”,”,12,’variable’,0,0], [‘A0004′,’WHITE’,’S’,’Small’,12,’variation’,0,0], [‘A0004′,’RED’,’S’,’Small’,13,’variation’,0,0], [‘A0004′,’RED’,’M’,’Medium’,14,’variation’,0,0], [‘A0001′,’WHITE’,’XXL’,’2 Extra Large’,15,’variation’,0,0], ] # print list of columns print(‘–list of columns———————————————————–‘) cols = list(df.columns.values) print(cols) # dataframe with defined column names df = pd.DataFrame(data, columns=[‘parent’, ‘color’, ‘size’, ‘size_desc’, ‘order’, ‘type’, ‘quantity’,’price’]) print(”) print(‘–dataframe with defined column names—————————————‘) print(df) # end of code —————————————————————————————– If you want to see more ways to select rows and columns in pandas (Python), read my code (Version…

ALL September 23, 2022

Comparison of data values between two excel files and creation of a new excel and csv file with PYTHON

Let’s assume that you have 2 excel files : product_data_excel.xlsx (99 rows x 5 columns, A2: E100) product_data_excel_2.xlsx (107 rows x 5 columns, A2:E108) The 5 columns of the two excel files are : product_id, product_description, quantity, regular_price and category.product_data_excel.xlsx You have changed quantity and price values for existing product_ids : “A0001” and “A0003” (column : “product_id”) and you have added 8 new products in excel file : product_data_excel_2.xlsx. product_data_excel_2.xlsx Now you want to compare product_data_excel.xlsx and product_data_excel_2.xlsx and create a new excel file : product_data_excel_changed_and_new_records.xlsx and csv file : product_data_excel_changed_and_new_data.csv with the 2 existing products you have changed and the 8 new ones you have added. product_data_excel_changed_and_new_records.xlsx Download product_data_excel.xlsx, product_data_excel_2.xlsx, product_data_excel_changed_and_new_records.xlsx, product_data_excel_changed_and_new_records.csv Click comparing_data_values_between_2_excel_files to see the code (html format) . . .

ALL September 17, 2022

Join, merge multiple CSV files to one CSV or EXCEL file, renaming headers and styling multiple EXCEL files with PYTHON

Let’s assume that you got three csv files from the sales department : “_sales_january_2021_d.csv” “_sales_feb_2021_d.csv”“_sales_mar_2021_d.csv” and you want : to join them in one new csv file : “sales_jan_feb_mar_2021_d.csv” to find actual sales sum per month, actual sales sum (quantity) per month and actual sales mean per month to save actual sales by person per month in csv file : “sales_by_person.csv”and in excel file : “sales_by_person.xlsx” to save “sales_by person.csv” as “sales_by_person_modified.csv” with different english headers to save “sales_by person.csv” as “sales_by_person_modified_gr.csv” with different greek headers to find actual sales sum, actual sales mean and actual sales total for salesperson : “Trifon Papadopoulos” to create a graph(bar) for Actual Sales total per Salesperson to create a graph(bar) for Actual Sales total per Month_ref to join “sales_jan_feb_mar_2021_d.csv” with “sales_january_2021_d_products_orders.csv” and save the output to excel file : “output1.xlsx” to improve styling of excel files : “sales_jan_feb_mar_2021_d.xlsx” and “output1.xlsx” Results of…

ALL August 18, 2022

Data Analysis of Bugdet and Actual Sales with Python

Excel file 1 : sales_january_2021_d.xlsx – Download : https://lnkd.in/dyT6zrCX Excel file 2 : sales_feb_2021_d.xlsx – Download : https://lnkd.in/d5aaNWiq Excel file 3 : sales_march_2021_d.xlsx – Download : https://lnkd.in/d-G2hNiM Task Collection of data from the above mentioned three different excel files and creation of a new one : sales_jan_feb_mar_2021_d.xlsx – Download : https://lnkd.in/d-PTEPtv with three different sheets : sales_data month_productivity salesperson_productivity We did a comparison between Budget and Actual Sales. The reason we did this is to know the month productivity and the salesperson productivity for months Jan, Feb and Mar 2021. All sheets, data, graphs and styling in file : sales_jan_feb_mar_2021_d.xlsx have been created automatically with python coding. Tools used : Excel, Python #programming #coding #python #productivity #data_analysis #mindstormGR / www.mindstorm.gr Code for this project from ctypes.wintypes import WORD import pandas as pd import numpy as np import matplotlib.pyplot as plt importopenpyxl from openpyxl.styles import PatternFill, Border, Side, colors, Alignment, Protection,…