A reference table of popular Excel add-ins for consolidating, managing, and analyzing customer data.

Original file: https://www.kaggle.com/datasets/redlineracer/nfl-combine-performance-data-2009-2019

Using NFL Combine data from 2009-2019, the information was cleaned and adjusted to conform to standard measurements in Excel. PivotTables were utilized to analyze the relationship between variables such as BMI, Draft Round, Teams, Schools, Players, Positions, and more. Additionally, a dashboard was created to present the findings in a clear and concise manner.

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 .

This document explain how data were generated and how to interpret them.

LICENSE: CC0 But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license.
Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.

HOW DATA WERE COLLECTED The 21 recorded sessions took place between February 2013 and December 2016.
Data were collected using Turning Technologies' remote controls (called clickers) and TurningPoint software.

The 4 versions of the quiz used during these 4 years are provided in the 'quizzes' folder for information purpose (in PDF and Powerpoint formats).

Turning Technologies records data in a closed format (.tpzx) that can be exported and converted them into 3 formats provided here (these 3 files contain the same data):

• Excel (.xslx)
• Comma-spearated values (.csv)
• SQLite (.sqlite)

The first one was directly exported from TurningPoint and is provided for Excel users who can't read CSV correctly.
CSV was converted from Excel and is provided for non-Excel users.
Finally, SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).

CODEBOOK Here is the name, the meaning and the possible values of the columns (name - meaning [possible values]). If students didn't answer the question, the value is '-'.

Session - session number (chronological) [1 to 21] AcademicYear - academic year [12-13, 13-14, 14-15, 15-16, 16-17] Year - calendar year [2013, 2014, 2015, 2016] Month - month (number) [1 to 12] Day - day (number) [1 to 31] Section - section abbreviation [CH, ESC, GM, IF, SIE, SV] Level - students' level [BA2, BA3, MA] Language - course's language [FR or EN] DeviceID - clicker's ID [(unique ID within a session)] Q1 - answers to question 1 [A, B, C, D, E] Q2 - answers to question 2 [A, B, C, D] Q3 - answers to question 3 [A or B] Q4 - answers to question 4 [A or B] Q5 - answers to question 5 [A or B] Q6 - answers to question 6 [A or B] Q7 - answers to question 7 [A or B] Q8 - answers to question 8 [A or B] Q9 - answers to question 9 [A or B] Q8-9 - answers to the question 8-9 (merge) [A or B] Q10 - answers to question 10 [1, 2] Q11 - answers to question 11 [A or B] Q12 - answers to question 12 [A, B]

Section abbreviation meaning * CH: chemistry * ESC: school of criminal justice (Unil) * GM: mechanical engineering * IF: financial engineering * SIE: environmental engineering * SV: life sciences

Level meaning
* BA2: 2nd year of Bachelor * BA3: 3rd year of Bachelor * MA: Master level

Question types For some questions, multiple answers were allowed: Q1, Q2, Q10 & Q12.
Half of the questions have only one correct answer, true or false: Q3, Q5, Q6, Q7, Q8, Q9 & Q8-9.
Finally, for 2 questions only one answer was accepted, but there is not only one correct answer: Q4 & Q11.

INFORMATION ABOUT THE SESSIONS Except otherwise stated below, all sessions were conducted like the original one: Q1 to Q12 (no Q8-9). The original French version of the quiz has been translated into English for a few sessions with Master students. For sessions 14 and 20, Q5 was removed and Q8 & Q9 were merged in Q8-9.
Session 18 was a short one with only 7 sevens questions: Q1, Q2, Q3, Q4, Q6, Q7 & Q9.

CONTACT INFORMATION If you have any question about these data, contact formations.bib@epfl.ch.

The tables specify connectivity of the nodes in the network as well as the numerical parameters governing each reaction in the network. (XLSX)

Excel spreadsheet containing, in separate sheets, underlying numerical data used to generate the indicated figure panels.

In preparation for some deuterium effects on E. coli and S. cerevisiae, I grew a starter culture and diluted it in 3 different concentrations. 1:10, 1:5, and 1:2. These dilutions were then grown at 37C for 4 hours and an absorption measurement was taken every hour. This fileset contains the raw data and some played with data, along with some figures made in Excel from the data. The file labeled "arb-ecoli-growth.png" is a figure made from manipulated data. I tried to combine the three data sets into one graph to see if I could extract some sort of growth information. I'm pretty sure I didn't do it right, but I included the image here nonetheless. In the 1:10 dilution sample, the cells would double in slightly less than one hour, every hour. In the 1:2 dilution, the growth rate was much slower, and the growth rate seemed to peak rather early in the trial. The 1:5 dilution is an overlap of growths between both the 1:10 and 1:2 dilutions. I don't know what to make of that. Also included in the fileset is an image of the absorbance spectrum from the nanodrop for every sample (including blanks taken every hour).

Context

This dataset includes the results of the 2019 NFL Combine.

Content

The data was extracted from nflcombineresults.com with Excel

Acknowledgements

nflcombineresults.com

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

**Introduction ** This case study will be based on Cyclistic, a bike sharing company in Chicago. I will perform tasks of a junior data analyst to answer business questions. I will do this by following a process that includes the following phases: ask, prepare, process, analyze, share and act.

Background Cyclistic is a bike sharing company that operates 5828 bikes within 692 docking stations. The company has been around since 2016 and separates itself from the competition due to the fact that they offer a variety of bike services including assistive options. Lily Moreno is the director of the marketing team and will be the person to receive these insights from this analysis.

Case Study and business task Lily Morenos perspective on how to generate more income by marketing Cyclistics services correctly includes converting casual riders (one day passes and/or pay per ride customers) into annual riders with a membership. Annual riders are more profitable than casual riders according to the finance analysts. She would rather see a campaign targeting casual riders into annual riders, instead of launching campaigns targeting new costumers. So her strategy as the manager of the marketing team is simply to maximize the amount of annual riders by converting casual riders.

In order to make a data driven decision, Moreno needs the following insights:

A better understanding of how casual riders and annual riders differ Why would a casual rider become an annual one How digital media can affect the marketing tactics Moreno has directed me to the first question - how do casual riders and annual riders differ?

Stakeholders Lily Moreno, manager of the marketing team Cyclistic Marketing team Executive team

Data sources and organization Data used in this report is made available and is licensed by Motivate International Inc. Personal data is hidden to protect personal information. Data used is from the past 12 months (03/2022 – 02/2023) of bike share dataset.

By merging all 12 monthly bike share data provided, an extensive amount of data with 5,785,180 rows were returned and included in this analysis.

Data security and limitations: Personal information is secured and hidden to prevent unlawful use. Original files are backed up in folders and subfolders.

Tools and documentation of cleaning process The tools used for data verification and data cleaning are Microsoft Excel. The original files made accessible by Motivate International Inc. are backed up in their original format and in separate files.

Microsoft Excel is used to generally look through the dataset and get a overview of the content. I performed simple checks of the data by filtering, sorting, formatting and standardizing the data to make it easily mergeable.. In Excel, I also changed data type to have the right format, removed unnecessary data if its incomplete or incorrect, created new columns to subtract and reformat existing columns and deleting empty cells. These tasks are easily done in spreadsheets and provides an initial cleaning process of the data.

Limitations Microsoft Excel has a limitation of 1,048,576 rows while the data of the 12 months combined are over 5,785,180 rows. When combining the 12 months of data into one table/sheet, Excel is no longer efficient and I switched over to R programming.

Context

2008 Population & demographic census data for Israel, at the level of settlements and lower .

Content

Data provided at the sub-settlement level (i.e neighborhoods). Variable names (in Hebrew and English) and data dictionary provided in XLS files. 2008 statistical area names provided (along with top roads/neighborhoods per settlement). Excel data needs cleaning/merging from multiple sub-pages.

Ideas:

• Combine with voting datasets
• Correlate population or economic growth over time with demographics
• Geospatial analysis
• Merge and clean the data from the sub tables.

Acknowledgements

Data from Israel Central Bureau of Statistics (CBS): http://www.cbs.gov.il/census/census/pnimi_page.html?id_topic=12

Photo by Me (Dan Ofer).

In this project, I analysed the employees of an organization located in two distinct countries using Excel. This project covers:

1) How to approach a data analysis project 2) How to systematically clean data 3) Doing EDA with Excel formulas & tables 4) How to use Power Query to combine two datasets 5) Statistical Analysis of data 6) Using formulas like COUNTIFS, SUMIFS, XLOOKUP 7) Making an information finder with your data 8) Male vs. Female Analysis with Pivot tables 9) Calculating Bonuses based on business rules 10) Visual analytics of data with 4 topics 11) Analysing the salary spread (Histograms & Box plots) 12) Relationship between Salary & Rating 13) Staff growth over time - trend analysis 14) Regional Scorecard to compare NZ with India

Including various Excel features such as: 1) Using Tables 2) Working with Power Query 3) Formulas 4) Pivot Tables 5) Conditional formatting 6) Charts 7) Data Validation 8) Keyboard Shortcuts & tricks 9) Dashboard Design

What's in the Data?

This dataset tracks food insecurity across different demographics starting 4/23/2020 to 8/23/2021. It contains fields such as Race, Education, Sex, State, Income, etc. If you're looking for a dataset to examine Covid-19's impact on food insecurity for different demographics, then here you are!

Data Source

This data is from the United States Census Bureau's Pulse Survey. The Pulse Survey is a frequently updating survey designed to collect data on how people's lives have been impacted by the coronavirus. Specifically, this dataset is a cleaned up version of the ' Food Sufficiency for Households, in the Last 7 Days, by Select Characteristics" tables.

The original form of this data can be found at: https://www.census.gov/programs-surveys/household-pulse-survey/data.html

What was done to this data?

The original form of this data was split into 36 excel files containing ~67 sheets each. The data was in a non-tidy format, and questions were also not entirely standard. This dataset is my attempt to combine all these different files, tidy the data up, and combine slightly different questions together.

Why are there so many NA's?

The large amount of NA's are a consequence of how awful the data was originally/ forcing the data into a tidy format. Just filter the NA's out for the question you want to analyze and you'll be fine.

A Capstone project involving Cyclitic Bike Company a fictional bike company that rents out traditional and electric bikes to the public and to their members. I used Excel to the analysis and combine the data.This was done for the year 2022 as it was the most recent year to pick from.

This excel file includes all the data used within the statistical analyses. (XLSX)