Dataset

This dataset was created by Kesar_vani

Released under CC0: Public Domain

Contents

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 .

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

BackgroundMicrosoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue.ResultsHere, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/).ConclusionsEscape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

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.

1. 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).

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.

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.

Ziemann’s supplementary file. Tab-separated, plain text version of the Ziemann et al. [2] supplementary file. (TSV 148 kb)

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

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.

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.

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

The zip file includes one command file, one instruction, and data for the manuscript “Green Innovation Transformation, Economic Sustainability and Energy Consumption during China's New Normal Stage”. The Data file consists of two parts: the micro and macro empirical data. Both of them are uploaded by two types, dta format (for STATA software) and excel format. The Command.do file is uesed for the STATA software. The instruction describe how to use data and command files in steps.

Flow data processing

This is a full set of experimental data derived from biodegradation experiments that were conducted with COREXIT 9500 dispersant in seawater, with the objectives of defining the rates and transformation products of degradation for each set of surfactants in the commercial COREXIT 9500 series. In addition, the data set contains detailed mass spectral characterization results for the surfactants in COREXIT 9500 dispersants. Data files include raw mass spectral files (in mzML format), peak lists processed through mass spectral data analysis software (in Excel format), custom data processing routines (written in the R language), and figures generated from the raw data.

This data set is a collection of historic data of wind turbine installations in the whole of Denmark from the Danish Energy Agency (Energistyrelsen). The data was used in a power system flexibility study by Karen Olsen in 2018-19 leading to a paper that is to appear in the proceedings of the ICAE19 conference and is entitled: "Data-driven flexibility requirements for current and future scenarios with high penetration of renewables". A journal paper has also been submitted using the same data.The data has been extracted from the website of Energistyrelsen at the following link where historic data is publicly available (called "Stamdataregister"):http://ens.dk/service/statistik-data-noegletal-og-kort/data-oversigt-over-energisektoren The present version was extracted in September 2019 and contains installation and production data until and including June 2019. The data is in the originally downloaded excel file, ready to be parsed by the python code written by Karen Olsen (see reference to Fanfare code).Data used for analysis:- turbine ID number (column: "Turbine identifier (GSRN)" in the excel spreadsheet)- date of installation (column: "Date of original connection to grid" in the excel spreadsheet)- turbine capacity (column: "Capacity (kW)" in the excel spreadsheet)- turbine location commune (column: "Local authority no" in the excel spreadsheet)- turbine placing sea/land (column: "Type of location" in the excel spreadsheet)- yearly production (columns starting at: "Historic production figures (kWh):" in the excel spreadsheet)Further information and code for analysis can be found under:https://kpolsen.github.io/FANFARE/

Hurricane Sandy, the largest storm of historical record in the Atlantic basin, severely impacted southern Long Island, New York in October 2012. In 2014, the U.S. Geological Survey (USGS), in cooperation with the U.S. Army Corps of Engineers (USACE), conducted a high-resolution multibeam echosounder survey with Alpine Ocean Seismic Survey, Inc., offshore of Fire Island and western Long Island, New York to document the post-storm conditions of the inner continental shelf. The objectives of the survey were to determine the impact of Hurricane Sandy on the inner continental shelf morphology and modern sediment distribution, and provide additional geospatial data for sediment transport studies and coastal change model development. For more information about the WHCMSC Field Activity, see https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-072-FA.

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.

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.

Excel files contain measurements from a dedicated swimming pool test facility for the study of a selective transmission cover. The Excel files contain the data for the complete period between the 15/6/28 and 3/07/18. The metadata is provided is the first worksheet. The Origin file contains the details of a chemical trial for a comparison of a transparent and selective transmission cover. Details of the study can be found in the notes included with the Origin project file.