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Pandas & NumPy Academy · Lesson

Basic DataFrame Inspection

Use head(), tail(), info(), describe(), and shape to quickly understand the content and structure of any DataFrame.

The First Five Steps with Any DataFrame

Whenever you load a new dataset, a systematic inspection sequence saves hours of debugging. The Pandas toolkit for this includes: head() to see the first rows, tail() for the last rows, info() for column types and nulls, describe() for statistics, and shape for dimensions. Running these five checks takes less than a minute and reveals most data quality issues immediately.

import pandas as pd

# Assume df is loaded from a CSV
print(df.shape)   # (rows, cols)
df.head()         # first 5 rows
df.info()         # dtypes + non-null counts
df.describe()     # statistics

head() and tail()

df.head(n) returns the first n rows (default 5) as a DataFrame. df.tail(n) returns the last n rows. Together they help you verify that the data was parsed correctly — checking that column names are in the header row, that numeric columns contain numbers, and that date strings were not misinterpreted. They are non-destructive and return new DataFrames.

import pandas as pd

df = pd.DataFrame({'a': range(20), 'b': range(20, 40)})
print(df.head(3))
#    a   b
# 0  0  20
# 1  1  21
# 2  2  22
print(df.tail(2))
#     a   b
# 18 18  38
# 19 19  39

All lessons in this course

  1. Creating DataFrames
  2. Selecting Columns and Rows
  3. Adding and Dropping Columns
  4. Basic DataFrame Inspection
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