Pandas Data Exploration: Essential Sanity Checks

1. Initial Setup Checks

Before analyzing data, ensure that the dataset is properly loaded and structured.

• Check basic structure: Use df.head(), df.tail(), and df.info() to inspect the first and last few rows, data types, and overall shape.
• Check df.sample() to see a random sample.
• Inspect Column Names (.columns)
  • Check for unwanted characters such as #, %, @, etc.
  • Identify and remove whitespaces for easier column access.
  • Standardize case formatting (lowercase is generally easier to handle).
  • If necessary, rename columns systematically.
• Check index (df.index):
  • Ensure uniqueness (df.index.is_unique).
  • Identify index data type (string, numeric, timestamp) to check for consistency.
  • Reset the index if necessary (df.reset_index(drop=True)).
  • Set an appropriate column as an index if required (df.set_index("column_name")).

2. Data Quality Checks

Identify and fix common data issues to ensure accuracy.

• Duplicates: Count duplicate rows (df.duplicated().sum()) and remove if needed (df.drop_duplicates()).
• Missing values: Detect nulls (df.isnull().sum()) and assess impact.
• Invalid values: Look for impossible or inconsistent entries (e.g., negative prices, incorrect dates).
• String formatting: Standardize capitalization, trim whitespace.
• Data types: Ensure numbers are stored as numeric types (df.dtypes).

3. Data Distribution & Summary Statistics

Understand overall patterns and variations in the data.

• Basic stats: Use df.describe() for summary statistics like mean, median, min, and max. This is good to have some sense, we will see the pitfalls of relying on these later.

• Histograms: Visualize distributions (df.hist()). (Normal, Skewed?)

4. Outlier & Anomaly Detection

Find unusual values that could distort analysis:

• Box plots: Identify outliers visually (sns.boxplot(x=df["column_name"])).
• IQR method: Identify values outside [Q1 - 1.5*IQR, Q3 + 1.5*IQR] (interquartile range(IQR) = Q3 - Q1)
• Domain checks: Use subject knowledge to validate anomalies (e.g., a person’s height should not be 10 feet).