Dataset

This dataset was created by Kesar_vani

Released under CC0: Public Domain

Contents

In this project, I conducted a comprehensive analysis of retail and warehouse sales data to derive actionable insights. The primary objective was to understand sales trends, evaluate performance across channels, and identify key contributors to overall business success.

To achieve this, I transformed raw data into interactive Excel dashboards that highlight sales performance and channel contributions, providing a clear and concise representation of business metrics.

Key Highlights of the Project:

Created two dashboards: Sales Dashboard and Contribution Dashboard. Answered critical business questions, such as monthly trends, channel performance, and top contributors. Presented actionable insights with professional visuals, making it easy for stakeholders to make data-driven decisions.

Example of how I use MS Excel's VLOOKUP() function to filter my data.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Beginning big data with Power BI and Excel 2013 : big data processing and analysis using Power BI in Excel 2013. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Project Description:

Title: Pandas Data Manipulation and File Conversion

Overview: This project aims to demonstrate the basic functionalities of Pandas, a powerful data manipulation library in Python. In this project, we will create a DataFrame, perform some data manipulation operations using Pandas, and then convert the DataFrame into both Excel and CSV formats.

Key Objectives:

1. DataFrame Creation: Utilize Pandas to create a DataFrame with sample data.
2. Data Manipulation: Perform basic data manipulation tasks such as adding columns, filtering data, and performing calculations.
3. File Conversion: Convert the DataFrame into Excel (.xlsx) and CSV (.csv) file formats.

Tools and Libraries Used:

• Python
• Pandas

Project Implementation:

1. DataFrame Creation:

   • Import the Pandas library.
   • Create a DataFrame using either a dictionary, a list of dictionaries, or by reading data from an external source like a CSV file.
   • Populate the DataFrame with sample data representing various data types (e.g., integer, float, string, datetime).

2. Data Manipulation:

   • Add new columns to the DataFrame representing derived data or computations based on existing columns.
   • Filter the DataFrame to include only specific rows based on certain conditions.
   • Perform basic calculations or transformations on the data, such as aggregation functions or arithmetic operations.

3. File Conversion:

   • Utilize Pandas to convert the DataFrame into an Excel (.xlsx) file using the to_excel() function.
   • Convert the DataFrame into a CSV (.csv) file using the to_csv() function.
   • Save the generated files to the local file system for further analysis or sharing.

Expected Outcome:

Upon completion of this project, you will have gained a fundamental understanding of how to work with Pandas DataFrames, perform basic data manipulation tasks, and convert DataFrames into different file formats. This knowledge will be valuable for data analysis, preprocessing, and data export tasks in various data science and analytics projects.

Conclusion:

The Pandas library offers powerful tools for data manipulation and file conversion in Python. By completing this project, you will have acquired essential skills that are widely applicable in the field of data science and analytics. You can further extend this project by exploring more advanced Pandas functionalities or integrating it into larger data processing pipelines.in this data we add number of data and make that data a data frame.and save in single excel file as different sheet name and then convert that excel file in csv file .

The data set contains all data used to generate the figures included in the publication, Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber surfaces1. The data is arranged by figures and the excel spreadsheet tabs indicate the figure the data is from. All the data presented in the excel file is clearly labeled. Thornton, S.B.; Boggins, S.J.; Peloquin, D.M.; Luxton, T.P. and Clar, J.G. (2020). Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber. Science of the Total Environment 737: 7. This dataset is associated with the following publication: Thorton, S.B., S.J. Boggins, D.M. Peloquin, T.P. Luxton, and J.G. Clar. Release and transformation of nanoparticle additives from surface coatings on pristine & weathered pressure treated lumber. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 737: 139451, (2020).

This references Dirty Excel Data but also includes it extracted ,transformed and loaded quite nicely. There are several sheets:The first sheet is the Dirty Excel Data

Contains the NodeJS code for data extraction, processing, and storage, a dump of the final data in a couchDB 1.6 file, and all excel files including the data used in the paper.See Readme.MD for dataprocessing details.Source code is currently in a private GIT repository, just copied here due to need for anonymization.

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: NM Food Retailers, 2022 - Microsoft Excel VersionItem Type: Microsoft ExcelSummary: Food Retailers by type (mobile, restaurant, etc.), as a Microsoft Excel fileNotes: Prepared by: Link uploaded by EMcRae_NMCDCSource: NM Environment Dept. - sent directlyFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=fdf6b9eeb01d4cd8bbc32d5b7da16f62UID: 7, 8, 38, 70Data Requested: Food trucks, Local cottage industry (commercial kitchens, etc), Food retailers, Grocery Stores - location, size, typeMethod of Acquisition: Contact made with NM Environment Dept. Date Acquired: May of 2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 9, 7, 11, 6Tags: PENDING_ Title New Mexico Food Retailers 2022 - NMFoodRetailers2022

Summary List of licensed food retailers with categories as of April 2022

Notes

Source New Mexico Environment Department

Prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=69d62107fa3d49a18acb87a8a584ca03

Alias Definition

Name Name

License License Number

Status Status

Street1 Street 1

Street2 Street 2

City City

State State

Zip Zip

Retail Food Establishment (Retail)

Mobile Mobile Food Establisment

MobType MobileType

MobSup Mobile Support Unit

ServArea Servicing Area (Commissary)

FullServ Full Service Restaurant

Restrnt Restaurant

Deli Deli

Seafood Seafood Market

Meat Meat Market

ConvStore Convenience Store

Daycare Day Care

SchFood School Food Program

Bar Bar

Coffee Coffee Shop

Catering Catering Operation

Concess Concession Stand/Snack Bar

Snack Institution

Bakery Bakery

Grocery Market (Grocery)

Other Other

Lat Latitude

Long Longitude

AccScore Accuracy Score

AccType Accuracy Type

Number Number

Street Street

UnitType Unit Type

UnitNum Unit Number

GCCity City

GCState State

GCCounty County

GCZip Zip

GCCountry Country

GCSource Source

Flow data processing

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Contains monthly data from the Assuring Transformation dataset. Data is available in Excel or CSV format. Update 23rd July 2021 The CSV file has been updated due to an error in the originally loaded file, which contained AT data for April 2021, rather than May 2021. The May 2021 data tables have been updated due to an error in the commissioner submission breakdown in table 1.1, where April 2021 data had been refreshed. The original data has now been restored (rows 11 to 13, column T). PLEASE NOTE: Some updates to the structure and numbering of the data tables and csv were applied from April 2021. This was primarily to group similar table types and content together. Additionally we have increased the amount of tables that have time series data retrospectively updated each month (green tabs). We welcome any feedback on this updated format.

The Search_2 dataset consists of (1) a set of 44 high-resolution digital color images of different complex natural scenes, (2) the ground truth corresponding to each of these scenes, and (3) the results of psychophysical experiments on each of these images. The images in the Search_2 dataset are a subset of a larger set that was used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. Areport describes the images in detail, and presents the corresponding ground truth and observer data. The image dataset, an Excel file with the ground truth and observer data, and a copy of this report are included in the dataset. The complete dataset can be used to validate (1) digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and (2) models of human visual search and detection.

Types of data processing Claude's Code Interpreter can handle

The dataset was generated from a laboratory experiment based on the dot-matrix integration paradigm, designed to measure death thought accessibility (DTA). The study was conducted under controlled conditions, with participants tested individually in a quiet, dimly lit room. Stimulus presentation and response collection were implemented using PsychoPy (exact version number provided in the supplementary materials), and reaction times were recorded via a standard USB keyboard. Experimental stimuli consisted of five categories of two-character Chinese words rendered in dot-matrix form: death-related words, metaphorical-death words, positive words, neutral words, and meaningless words. Stimuli were centrally displayed on the screen, with presentation durations and inter-stimulus intervals (ISI) precisely controlled at the millisecond level.Data collection took place in spring 2025, with a total of 39 participants contributing approximately 16,699 valid trials. Each trial-level record includes participant ID, priming condition (0 = neutral priming, 1 = mortality salience priming), word type, inter-stimulus interval (in milliseconds), reaction time (in milliseconds), and recognition accuracy (0 = incorrect, 1 = correct). In the dataset, rows correspond to single trials and columns represent experimental variables. Reaction times were measured in milliseconds and later log-transformed for statistical analyses to reduce skewness. Accuracy was coded as a binary variable indicating correct recognition.Data preprocessing included the removal of extreme reaction times (less than 150 ms or greater than 3000 ms). Only trials with valid responses were retained for analysis. Missing data were minimal (<1% of all trials), primarily due to occasional non-responses by participants, and are explicitly marked in the dataset. Potential sources of error include natural individual variability in reaction times and minor recording fluctuations from input devices, which are within the millisecond range and do not affect overall patterns.The data files are stored in Excel format (.xlsx), with each participant’s data saved in a separate file named according to the participant ID. Within each file, the first row contains variable names, and subsequent rows record trial-level observations, allowing for straightforward data access and processing. Excel files are compatible with a wide range of statistical software, including R, Python, SPSS, and Microsoft Excel, and no additional software is required to open them. A supplementary documentation file accompanies the dataset, providing detailed explanations of all variables and data processing steps. A complete codebook of variable definitions is included in the appendix to facilitate data interpretation and ensure reproducibility of the analyses.

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

NaiveBayes_R.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given recidivism (P(x_ij│R)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|R): This tab contains probabilities of feature attributes among recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. Recidivated_Train: This tab contains re-coded features of recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|R) tab. NaiveBayes_NR.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given non-recidivism (P(x_ij│N)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|N): This tab contains probabilities of feature attributes among non-recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. NonRecidivated_Train: This tab contains re-coded features of non-recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given non-recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|N) tab. Training_LnTransformed.xlsx: Figures in each cell are log-transformed ratios of probabilities in NaiveBayes_R.xlsx (P(Xi|R)) to the probabilities in NaiveBayes_NR.xlsx (P(Xi|N)). TestData.xlsx: This Excel file includes the following tabs based on the test data: P(Xi|R), P(Xi|N), NIJ_Recoded, and Test_LnTransformed (log-transformed P(Xi|R)/ P(Xi|N)). Training_LnTransformed.dta: We transform Training_LnTransformed.xlsx to Stata data set. We use Stat/Transfer 13 software package to transfer the file format. StataLog.smcl: This file includes the results of the logistic regression analysis. Both estimated intercept and coefficient estimates in this Stata log correspond to the raw weights and standardized weights in Figure 1. Brier Score_Re-Check.xlsx: This Excel file recalculates Brier scores of Relaxed Naïve Bayes Classifier in Table 3, showing evidence that results displayed in Table 3 are correct. *****Full List***** NaiveBayes_R.xlsx NaiveBayes_NR.xlsx Training_LnTransformed.xlsx TestData.xlsx Training_LnTransformed.dta StataLog.smcl Brier Score_Re-Check.xlsx Data for Weka (Training Set): Bayes_2022_NoID Data for Weka (Test Set): BayesTest_2022_NoID Weka output for machine learning models (Conventional naïve Bayes, AdaBoost, Multilayer Perceptron, Logistic Regression, and Random Forest)

This dataset contains the original data, analysis data, and a results synopsis of 12 slug tests performed in 7 wells completed in unconfined fractured bedrock near the North Shore of Lake Superior in Minnesota. Aquifers tested include extrusive and intrusive volcanic rocks and slate. Estimated hydraulic conductivity range from 10.2 to 2x10-6 feet/day. Mean and median hydraulic conductivity are 3.7 and 1.6, respectively. The highest and lowest hydraulic conductivities were in slate and fractured lava, respectively. Compressed air and traditional displacement-tube methods were employed. Water levels were measured with barometrically compensated (11 tests) and absolute pressure transducers (1 test) and recorded with data loggers. Test data were analyzed with AQTESOLV software using the unconfined KGS (Hyder and others, 1994; 9 tests) and Bower-Rice, 1976 models (3 tests).This dataset contains the original data, analysis data, and a results synopsis of 12 slug tests performed in 7 wells completed in unconfined fractured bedrock near the North Shore of Lake Superior in Minnesota. Aquifers tested include extrusive and intrusive volcanic rocks and slate. Estimated hydraulic conductivity range from 10.2 to 2x10-6 feet/day. Mean and median hydraulic conductivity are 3.7 and 1.6, respectively. The highest and lowest hydraulic conductivities were in slate and fractured lava, respectively. Compressed air and traditional displacement-tube methods were employed. Water levels were measured with barometrically compensated (11 tests) and absolute pressure transducers (1 test) and recorded with data loggers. Test data were analyzed with AQTESOLV software using the unconfined KGS (Hyder and others, 1994; 9 tests) and Bower-Rice, 1976 models (3 tests). Data files include the original recorded data, data files transformed into a form necessary for AQTESLOV, AQTESOLV analysis files and results files, and a compilation of well information and slug-test results. All files are formatted as tab-delimited ASCII except for the AQTESOVE analysis and results files, which are proprietary aqt and PDF files respectively. For convenience, a Microsoft Excel file is included that contains a synopsis of the well data and slug-test results, original recorded, transformed, and plotted slug-test data, data formats, constants and variables used in the data analysis, and notes about each test. Data files include the original recorded data, data files transformed into a form necessary for AQTESLOV, AQTESOLV analysis files and results files, and a compilation of well information and slug-test results. All files are formatted as tab-delimited ASCII except for the AQTESOVE analysis and results files, which are proprietary aqt and PDF files respectively. For convenience, a Microsoft Excel file is included that contains a synopsis of the well data and slug-test results, original recorded, transformed, and plotted slug-test data, data formats, constants and variables used in the data analysis, and notes about each test.

This project focuses on data mapping, integration, and analysis to support the development and enhancement of six UNCDF operational applications: OrgTraveler, Comms Central, Internal Support Hub, Partnership 360, SmartHR, and TimeTrack. These apps streamline workflows for travel claims, internal support, partnership management, and time tracking within UNCDF.Key Features and Tools:Data Mapping for Salesforce CRM Migration: Structured and mapped data flows to ensure compatibility and seamless migration to Salesforce CRM.Python for Data Cleaning and Transformation: Utilized pandas, numpy, and APIs to clean, preprocess, and transform raw datasets into standardized formats.Power BI Dashboards: Designed interactive dashboards to visualize workflows and monitor performance metrics for decision-making.Collaboration Across Platforms: Integrated Google Collab for code collaboration and Microsoft Excel for data validation and analysis.

This data release consists of two directories: DepthToWater and WaterLevelModels. The DepthToWater directory contains five Microsoft Excel workbooks that present depth-to-groundwater data and drawdown analyses from five wells during an aquifer test at Well ER-6-1-2 Main (USGS site identification number 365901115593501). The WaterLevelModels directory contains 11 Microsoft Excel workbooks that present 10 archived SeriesSee (Halford and others, 2012) water-level models that were used to examine drawdown at 9 wells during the same aquifer test. An additional Microsoft Excel workbook (Continuous+TransformedData.xlsx) contains all raw and transformed data series used in the 10 water-level models.