How to Standardise Inconsistent Text Values (e.g. 'Nairobi' vs 'nairobi')

In real-world datasets, text columns are rarely clean. You’ll often see variations like "Kenya", "kenya ", "KENYA", or "Ken-ya". 




Standardising these values is essential for reliable grouping, analysis, and reporting.

Below is a minimal, practical workflow using a CSV file in Python (Pandas).


1. Load the Data

import pandas as pd

df = pd.read_csv("survey_data.csv")

df.head()






2. Inspect the inconsistent column

df['country'].unique()

This helps you identify variations like:

  • extra spaces

  • inconsistent casing

  • spelling differences




3. Standardise text values

Step 1: Lowercase everything

Step 2: Strip spaces

Step 3: Replace known inconsistencies

df['country_clean'] = (
    df['country']
    .str.lower()
    .str.strip()
    .replace({
        'ken-ya': 'kenya',
        'k e n y a': 'kenya'
    })
)



4. Optional: Capitalise properly

If you want clean reporting format:

df['country_clean'] = df['country_clean'].str.title()


5. Validate results

df[['country', 'country_clean']].drop_duplicates()




Key idea

Standardisation is not just cleaning—it is making categories comparable so your analysis doesn’t split the same entity into multiple fake groups.



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