8-Steps To Follow For Data Cleaning Process

Dirty data can ruin even the most sophisticated analysis. 80% of a data professional's time is spent cleaning and preparing data before any meaningful insights can be extracted.

In this edition, I’ll walk you through a structured 8-step process to clean and refine your data efficiently. Whether you're a Data Scientist, Analyst, or Engineer, mastering these steps will save time and improve accuracy in your projects.

From handling missing values to transforming data for analysis, these techniques will help you create cleaner, more reliable datasets.

𝟭. 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗩𝗮𝗹𝘂𝗲:

- Identify and address missing data.
- Choose an appropriate method for
- managing gaps, such as imputation.

df.dropna(inplace=True)
df.fillna(value, inplace=True)
df.fillna(df.mean(), inplace=True)

𝟮. 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀:

- Identify and eliminate redundant records.
- Confirm the elimination of all identical values.

df.drop_duplicates(inplace=True)


𝟯. 𝗘𝗻𝘀𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗼𝗿𝗺𝗮𝘁:

- Validate that all data is in the correct format.
- Convert data into the appropriate format (e.g. standardize Dates).
- Address inconsistent data formats.

df['date_column'] = pd.to_datetime
(df['date_column'])


𝟰. 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗢𝘂𝘁𝗹𝗶𝗲𝗿𝘀:

- Identify outliers or unusual values.
- Choose a method for handling extreme values (deletion, transformation, or imputation).
- Implement the chosen method.

df['column'] = np.log1p(df['column'])


𝟱. 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆:

- Ensure uniformity across all records and variables.
- Address inconsistencies in values or data types.
- Rectify any discrepancies in the data

df['category'] =
df['category'].str.lower().str.replace('_', ' ')


𝟲. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆:

- Verify data validity and consistency.
- Rectify errors such as typos or incorrect values.
- Ensure adherence to appropriate rules and constraints.

df['column'].unique()


𝟳. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗗𝗮𝘁𝗮:

- Standardize variable names and values for consistency.
- Bring categorical variables to a uniform format (e.g., uppercase to lowercase, unit standardization)

df['category'] = df['category'].str.lower().str.strip()


𝟴. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗗𝗮𝘁𝗮 𝗙𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀:

Prepare data for analysis through transformations like normalization or aggregation.

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
df[['column']] = scaler.fit_transform(df[['column']])

Data cleaning is not just a one-time task—it’s a critical skill that defines the quality of your analysis and models. By following these 8 essential steps, you ensure your data is accurate, consistent, and ready for powerful insights.

Remember, better data leads to better decisions! Stay consistent with your cleaning process, and you’ll spend less time debugging and more time uncovering valuable patterns.

Got any favourite data cleaning tricks? Reply and share your thoughts!

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