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Fill Missing Values in a CSV

Replace gaps with a sensible value, and see how many were filled.

Text case
Missing values
Column
Number column
Method
Common values: 1.5 for IQR, 3 for z-score, 3.5 for modified z.

Before and after

MeasureBeforeAfter
Rows1414
Columns55
Blank cells12
  • 2 whitespace fixed in cells
  • 27 cells converted to numbers or yes/no
  • 1 missing cells filled

Cleaned data (first 14 rows)

NameCityAgeSalaryActive
Alice Smithdhaka3452000true
alice smithDhaka3452000true
Bob KhanCHITTAGONG2948500false
Carla GomezDhaka4161200true
Dev PatelSylhet3445000true
Eve Adamsblank3858300false
Frank WuDhaka36980000true
Gita RaoSylhet3150100blank
Hana SatoChittagong3354700false
Ivan PetrovDhaka3557000true
Jo LeeSylhet3251300false
Kim ParkDhaka3049900true
Lena FoxChittagong24056000false
Bob KhanCHITTAGONG2948500false

Flagged outliers are highlighted. The CSV download holds every row, not only this preview.

  1. Tools
  2. Data Science
  3. Data Cleaner
  4. Fill Missing Values in a CSV

About Fill Missing Values in a CSV

This page opens with NA treated as missing, types fixed and missing values filled with the column median. A number column such as Age gets its median, while a text column is left alone because a median of text means nothing. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.

In the sample, 1 cell was filled, and 2 blank cells remain in text columns. Use "most common value" to fill text columns. Filling hides the fact that a value was missing, so note it in your analysis.

How to use Fill Missing Values in a CSV

  1. 1Open Paste or upload data to load your file, or start with the sample.
  2. 2Switch on the steps you need: whitespace, case, types, missing values, duplicates, find and replace.
  3. 3To look for outliers, choose a number column and a method, and adjust the threshold if you want.
  4. 4Read Before and after for the row counts and what changed, and check the preview below it.
  5. 5Copy the cleaned CSV or download it.

Features

  • Trim and collapse whitespace.
  • Remove duplicate rows, optionally ignoring case.
  • Treat NA, N/A, null, nan and a lone dash as missing, then keep, drop, or fill with a value, the mean, the median, the most common value or the value above.
  • Fix types: currency and thousands separators, bracket negatives and yes/no, while keeping ids with leading zeros.
  • Standardise text to lower, upper or title case.
  • Find and replace in one column or all, with whole-cell, case and regular expression options.
  • Outlier detection with IQR, z-score or modified z-score, highlighted in the preview, with an option to remove them.
  • A before and after summary with counts of every change.

Worked examples

  • Median fill with NA as missing → gives 1 cell filled with the median

    Age is a number column, so its blank gets the median. The N/A cells in text columns stay blank, because a median of text means nothing.

Privacy: how it works

  • Your table is read and cleaned by code running in this browser tab. Nothing is uploaded, and your original file is never modified.

Tips

  • Trim whitespace first. A trailing space is the usual reason two equal-looking values count as different.
  • Turn on "treat NA as missing" before filling, so the placeholders are counted as gaps.
  • Filling with the mean pulls values towards the centre and understates spread. The median is safer when outliers exist.
  • Download a cleaned copy and keep the original. This tool never edits the source file.

Frequently asked questions

Is it really free?

Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.

Do I need an account?

No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.

Does it run in my browser?

Yes. Your data are processed by code running in your browser tab, on your own device.

Is my data uploaded?

No. Your data are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.

Mean or median?

The median is safer when a column has outliers, since the mean is pulled towards them. The mean is fine for data without extreme values.

More Data Cleaner pages

  • Data CleanerTidy a messy table and download it as a clean CSV, with a before and after summary.
  • Remove Duplicates From CSVDelete repeated rows and keep the first of each.
  • Trim Whitespace in a CSVRemove stray spaces that make equal values look different.
  • Change Case in a CSVMake text consistent: Title Case, lower case or UPPER CASE.
  • Fix Data Types in a CSVConvert currency text and thousands separators into plain numbers.
  • Find and Replace in a CSVReplace values across every column or just one.
  • Outlier Detection in a CSVFlag the values that sit far from the rest of a column.
  • IQR Outlier CalculatorFind values outside Q1 - 1.5 x IQR and Q3 + 1.5 x IQR.
  • Z-Score Outlier DetectionFlag values more than a set number of standard deviations from the mean.
  • Modified Z-Score OutliersA robust outlier rule based on the median and the median absolute deviation.

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