3 Ways To Make
Data Cleaning
Easier
1
Use tools like Python’s pandas or
SQL to understand your data.
By starting with exploration, you
can create a clear roadmap for
cleaning your data effectively.
It helps you understand the extent
of issues, prioritize tasks, and
avoid unnecessary
transformations.
The more you explore, the more
confident you’ll be in tackling.
2
Handling missing values requires
understanding their context and
extent.
Impute numeric data with the mean,
median, or mode, and use
placeholders or the most frequent
category for categorical data.
For complex cases, predictive models
can fill gaps, while excessive missing
data may warrant dropping rows or
columns.
3
Data cleaning is essential to ensure
that inconsistencies and inaccuracies
are addressed for reliable analysis.
Inconsistencies often arise from
varying formats, spelling errors, or
different naming conventions (e.g.,
"NY" vs. "New York").
Additionally, ensuring data correctness
involves verifying that values are in the
appropriate format (e.g., dates as
YYYY-MM-DD, numbers as integers.
4
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