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Experienced data analysts follow a systematic profiling checklist whenever they encounter a new dataset. Rather than diving straight into analysis, they first answer a set of diagnostic questions: How large is the data? What types are the columns? How many values are missing? Are there obvious anomalies? This structured approach prevents hours of wasted work caused by misunderstood data types or hidden nulls corrupting calculations.

The first three checks are always shape (rows × columns), column names (do they match expectations?), and dtypes (are numeric columns actually numeric?). A common surprise is that numeric columns were loaded as object dtype because they contain text entries like 'unknown' or mixed comma-formatted numbers. Catching this at the profiling stage saves you from aggregations that silently return wrong results.

Call df.isna().sum() to count missing values per column, then divide by len(df) to get the percentage. Columns with >50% missing are usually candidates for removal; 1-20% missing typically warrant imputation; 0% missing needs a sanity check — perfect completeness can itself be suspicious if real data rarely comes that clean. Sort the output descending to see the worst-affected columns first.

df.describe() returns count, mean, std, min, quartiles, and max for every numeric column in one call. Always scan the min and max for impossible values (e.g. age = -3 or age = 999), the mean vs. median for skewness (large gap suggests outliers), and the count (if lower than total rows, there are nulls). Add include='all' to include statistics for object columns too (unique count, top, freq).

For categorical columns, check the number of unique values with df[col].nunique() and inspect the most common values with value_counts(). Watch for columns that appear categorical but have hundreds of unique values (possibly a free-text field or an ID), and columns that should be boolean but have three values (True, False, and NaN). Also check for inconsistent capitalisation: 'Male' and 'male' treated as separate categories.

df.head(10) and df.tail(10) show the first and last rows respectively. Examining both is important because datasets often have clean header rows and messy trailing rows from export artefacts (e.g. a 'Total' summary row appended to a CSV export). Also use df.sample(10) to see a random selection of rows, which avoids any bias toward the beginning of the dataset.

Call df.duplicated().sum() to count fully duplicated rows (where every column matches). A dataset claiming to contain unique customer records with even one duplicate is a red flag for data quality issues. Use df[df.duplicated(keep=False)] to inspect the actual duplicate rows. When a table should have unique keys (like a user ID), also check key-level uniqueness with df['id'].duplicated().sum().

If the dataset contains date or timestamp columns, check that Pandas loaded them as datetime64 (not object). Convert with pd.to_datetime(df['col']). Then inspect the date range: df['date'].min() and df['date'].max(). Look for timestamps far in the future (year 2099) or past (epoch zero: 1970-01-01) which indicate sentinel values used to represent missing dates.

Wrapping the profiling steps into a reusable function ensures the same checks run consistently on every dataset. The function should print a structured report showing shape, missing values, dtypes, duplicate count, and basic stats. You can extend it with visualisations or save it as an HTML report. Functions like this are a staple of professional data science teams where multiple analysts work on the same pipeline.

The ydata-profiling library (formerly pandas-profiling) generates a comprehensive HTML report from a DataFrame with a single line: ProfileReport(df).to_file('report.html'). The report includes histograms, correlation matrices, missing value heatmaps, duplicate detection, and interaction plots. It is the fastest way to share a full dataset profile with a stakeholder who needs to understand the data without writing code.

The complete profiling checklist for any new dataset contains seven steps: 1) shape and columns, 2) dtypes and suspicious type assignments, 3) missing value count and percentage per column, 4) descriptive statistics (min, max, mean, median), 5) unique values and value counts for categoricals, 6) duplicate row detection, and 7) date range validation. Completing this checklist before any analysis prevents silent errors that corrupt results downstream.

Test your understanding of the dataset profiling checklist from this lesson.

In this lesson you learned: the 7-step profiling checklist (shape, dtypes, missing values, stats, unique counts, duplicates, dates), wrapping checks into a reusable profile function, and using ydata-profiling for automated HTML reports. Next up we dive into univariate analysis — studying each column independently to spot outliers, skewness, and unusual distributions.