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

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

2) Preview rows

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

3) Random sample

Great for spotting weird values.

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

4) Columns and dtypes

df.info()

This tells you:

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

5) Descriptive stats

print(df.describe())

For categorical columns:

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

Core sanity checks

Missing values per column

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

Duplicates

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

Value counts for a category

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

Tip: create an 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

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

🧪 Try It Yourself

Exercise 1 – Check the Shape

Press Run to see what your code prints.

Exercise 2 – Summarize Numeric Columns

Press Run to see what your code prints.

Exercise 3 – Count Missing Values Per Column

Press Run to see what your code prints.

Check your understanding

Next

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