The Tool Desk
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What can you learn from a quick EDA pass?
Exploratory data analysis (EDA) begins by checking what a table contains before drawing conclusions from it. These methods provide three complementary clues:
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info(): How many rows and columns are there, what are the column types, and how many values are non-null?select_dtypes(): Which columns does pandas currently classify as numeric, textual, or categorical?value_counts(): Which individual values or combinations occur most often?
They are diagnostic starting points, not a full data-quality audit. A type can be technically valid but semantically wrong, and a frequent value is not automatically correct.
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DataFrame.info() prints a concise summary of a DataFrame, including its index and columns, non-null counts, dtypes, and memory information. It is a useful first check for questions such as whether a column has missing entries or whether an expected numeric field appears to have a text dtype. See the pandas DataFrame.info() reference.
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df.info()Read the non-null counts alongside the row count to spot columns with missing values. Treat the output as an overview: its exact display can vary with the method’s arguments and pandas display options, and it does not explain why values are missing or whether missingness is acceptable for your analysis.
2. Use select_dtypes() to group columns by type
select_dtypes(include=..., exclude=...) returns a DataFrame containing columns whose dtypes match the types you specify. That lets you focus later checks on numeric columns or on columns commonly used for categorical review. The method reports pandas’ current dtype assignments; it cannot tell whether those assignments match the meaning of the data. See the pandas select_dtypes() reference.
# Count columns by the dtype pandas currently sees
df.dtypes.value_counts()
# Separate columns for focused inspection
numeric = df.select_dtypes(include="number")
textual = df.select_dtypes(include=["object", "string", "category"])For example, a column of digits stored as text will not be included in numeric. That may be intentional—an identifier is often better treated as a label than a quantity—or it may signal that the data needs cleaning or conversion. Check the field’s meaning before changing its type. The pandas user guide also documents counting dtypes with df.dtypes.value_counts(); see pandas’ introductory guide to essential functionality.
3. Use value_counts() to check frequencies
On a Series, value_counts() computes frequencies for the values in that one-dimensional column. It can reveal dominant categories, rare entries, and values that merit checking against expectations. If missing values matter to the review, set dropna=False explicitly.
# Replace "status" with a categorical column in your DataFrame
df["status"].value_counts(dropna=False)For combinations across columns, call DataFrame.value_counts() and name the columns with subset:
# Replace these names with columns in your DataFrame
df.value_counts(subset=["status", "region"], dropna=False)The pandas user guide describes Series value_counts() as computing a histogram of a one-dimensional array and documents the DataFrame method for joint frequencies. See the pandas user guide. A high-cardinality column can produce a long result, and frequency alone cannot explain why a value appears or whether it is erroneous.
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How these methods complement describe()
describe() remains useful for summary statistics. On a mixed-type DataFrame, its default behavior is dtype-dependent: it describes numeric columns, or categorical columns if there are no numeric columns. Its include and exclude arguments let you control which types are summarized. The three methods here address different first-pass questions—table structure, dtype composition, and value frequency—rather than replacing describe(). The behavior is documented in the pandas user guide.
A compact first-pass workflow
Run the checks in sequence, replacing the example column names with ones in your own DataFrame:
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- Check the table’s shape and null counts: run
df.info(). - See the dtype mix: run
df.dtypes.value_counts(), then useselect_dtypes()to isolate columns for focused checks. - Inspect frequencies: run Series
value_counts()on relevant categorical columns, then DataFramevalue_counts(subset=...)when combinations matter.
Use what you find to decide what to validate next: compare types with field meaning, investigate unexpected or missing values, and account for how the data was collected before relying on it downstream.
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