Data Inspection (head, tail, info, describe)

The first 60 seconds with a new dataset

Whenever you load a new dataset, do this first.

1) Check shape

Shape

import pandas as pd
 
df = pd.read_csv("data/sales.csv")
print(df.shape)  # (rows, columns)

2) Preview rows

Head / Tail

print(df.head())
print(df.tail())

3) Random sample

Great for spotting weird values.

Sample rows

print(df.sample(5, random_state=42))

4) Columns and dtypes

Info

df.info()

This tells you:

Column names
Non-null counts
Dtypes
Memory usage (helpful when data grows)

5) Descriptive stats

Describe

print(df.describe())

For categorical columns:

Describe object columns

print(df.describe(include=["object"]))

Core sanity checks

Missing values per column

Missing values

missing = df.isna().sum().sort_values(ascending=False)
print(missing)

Duplicates

Duplicate rows

print("duplicate rows:", df.duplicated().sum())

Value counts for a category

Value counts

print(df["city"].value_counts(dropna=False).head(10))

Tip: create an inspection helper

Quick inspection helper

import pandas as pd
 
def inspect(df: pd.DataFrame, n: int = 5) -> None:
    print("shape:", df.shape)
    print("columns:", list(df.columns))
    print("\nhead:")
    print(df.head(n))
    print("\nmissing:")
    print(df.isna().sum())
 
# inspect(df)

Visualize it

diagram The 60-second inspection routine mermaid

  flowchart LR
  A["df.shape"] --> B["df.head() / df.tail()"]
  B --> C["df.info()"]
  C --> D["df.describe()"]
  D --> E["df.isna().sum()"]

A repeatable sequence for getting oriented with any new dataset.

🧪 Try It Yourself

Exercise 1 – Check the Shape

Exercise 2 – Summarize Numeric Columns

Exercise 3 – Count Missing Values Per Column

Once you know what’s in your data, learn how to slice into it precisely with Indexing and Selecting Data (loc, iloc).

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