aiming-lab/data-validation-first
memory_data/skills/data-validation-first/SKILL.md
Use this skill before any data analysis, transformation, or modeling. Always inspect and validate the data before drawing conclusions or writing transformations.
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SKILL.md
- name:
- data-validation-first
- description:
- Use this skill before any data analysis, transformation, or modeling. Always inspect and validate the data before drawing conclusions or writing transformations.
- category:
- data_analysis
Data Validation First
Before writing any analysis code, understand the data:
# Always run these first
df.shape # rows x columns
df.dtypes # column types
df.isnull().sum() # missing values per column
df.describe() # statistics for numeric columns
df.head() # sample rows
Key questions:
- Are there nulls in columns you'll join or filter on?
- Are numeric columns stored as strings? (parse_dates, astype)
- Are there unexpected duplicates (check primary key uniqueness)?
- Does the row count match your expectation from the source?
Anti-pattern: Running .groupby().sum() without first checking for nulls in the groupby key.
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