Clean questionaire in csv smoothly

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Aug 6th, 2022
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How to Clean questionaire in csv

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now that we have our survey data in an Excel table lets clean it up by clean up we mean getting rid of the scores that are meaningless either because theres such outliers that they cant be true or maybe because somebody went to the survey really quickly and gave the same answer for every answer its data thats just noise its not data thats not meaningful and then we ran it well get rid of that data so here we have our downloaded survey and we have our variable numbers in the top row of the table this this row up here above is the the question that was in Google Forms and then weve got the their responses in numeric format in each row of the table now before we do this Google Forms freezes the top row let and that means that when we scroll down that top row stays down there now lets when we scroll lets keep the variable names there and also lets keep the participant number over here so we want this row this called be frozen and the first three rows to be frozen so we put the

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There can be 2 things you can do with duplicate data Highlight It or Delete It. Highlight Duplicate Data: Select the data and Go to Home Conditional Formatting Highlight Cells Rules Duplicate Values. Delete Duplicates in Data: Select the data and Go to Data Remove Duplicates.
How to clean data Step 1: Remove duplicate or irrelevant observations. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Step 2: Fix structural errors. Step 3: Filter unwanted outliers. Step 4: Handle missing data. Step 5: Validate and QA.
How to clean data Step 1: Remove duplicate or irrelevant observations. Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Step 2: Fix structural errors. Step 3: Filter unwanted outliers. Step 4: Handle missing data. Step 5: Validate and QA.
The cleaner you survey data, the more solid insights youll have about what they need, allowing you to make business decisions that will make sense to them. Insights from clean data also make it easier to get buy-in from stakeholders. Clean survey data keeps costs down.
You can clean data by identifying errors or corruptions, correcting or deleting them, or manually processing data as needed to prevent the same errors from occurring. Most aspects of data cleaning can be done through the use of software tools, but a portion of it must be done manually.
There can be 2 things you can do with duplicate data Highlight It or Delete It. Highlight Duplicate Data: Select the data and Go to Home Conditional Formatting Highlight Cells Rules Duplicate Values. Delete Duplicates in Data: Select the data and Go to Data Remove Duplicates.
Cleaning Missing Values from Data dropna() Drop all rows that have any NaN values. dropna(how=all) Drop only if ALL columns are NaN. dropna(thresh=2) Drop row if it does not have at least two values that are not NaN. dropna(subset=[1]) Drop only if NaN in specific column.
Steps to Clean Data Remove Duplicate and Incomplete Cases: Remove Oversample: Ensure Answers are Formatted Correctly: Remove Nonsense Answers and Unreadable Data: Identify and Review Outliers: Code Open Ended Data. Check Data Consistency: Perform Final Quality Assurance Checks:

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