【Vol.4】 head() in pandas – Show DataFrame’s First Rows

In pandas, the Python data analysis library, the DataFrame is a table-like data structure. In this article, we’ll show you how to create a DataFrame and use the handy head() function to preview your data.

▶️ For reference, see the official head documentation:
pandas DataFrame head Documentation

Importing pandas
Creating a DataFrame
Using head()
Comparing with tail()
Summary

Importing pandas

import pandas as pd

Creating a DataFrame

data = {
  "Name": ["Taro", "Hanako", "Jiro", "Mika", "Kenichi", "Keiko", "Sho", "Akane", "Takashi", "Aoi"],
  "Age": [23, 29, 35, 42, 18, 33, 27, 24, 31, 30],
  "Occupation": ["Engineer", "Designer", "Teacher", "Doctor", "Student", "Nurse", "Programmer", "Sales", "Lawyer", "Researcher"],
  "Annual Income (¥)": [4500000, 5500000, 4900000, 7300000, 0, 4000000, 6000000, 3200000, 8000000, 5800000],
  "Location": ["Tokyo", "Osaka", "Nagoya", "Sapporo", "Fukuoka", "Tokyo", "Kobe", "Sendai", "Yokohama", "Chiba"],
  "Years Employed": [2, 4, 10, 15, 1, 5, 3, 1, 12, 8]
}
df = pd.DataFrame(data)

[Failure Story] The first time I created a DataFrame, my column lengths didn’t match and I got a ValueError. Since then, I define each column as a dictionary entry and ensure all lists are the same length.

df

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed
0	Taro	23	Engineer	4500000	Tokyo	2
1	Hanako	29	Designer	5500000	Osaka	4
2	Jiro	35	Teacher	4900000	Nagoya	10
3	Mika	42	Doctor	7300000	Sapporo	15
4	Kenichi	18	Student	0	Fukuoka	1
5	Keiko	33	Nurse	4000000	Tokyo	5
6	Sho	27	Programmer	6000000	Kobe	3
7	Akane	24	Sales	3200000	Sendai	1
8	Takashi	31	Lawyer	8000000	Yokohama	12
9	Aoi	30	Researcher	5800000	Chiba	8

Using head()

Display the First 5 Rows

df.head()

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed
0	Taro	23	Engineer	4500000	Tokyo	2
1	Hanako	29	Designer	5500000	Osaka	4
2	Jiro	35	Teacher	4900000	Nagoya	10
3	Mika	42	Doctor	7300000	Sapporo	15
4	Kenichi	18	Student	0	Fukuoka	1

Display a Custom Number of Rows

df.head(3)  # First 3 rows

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed
0	Taro	23	Engineer	4500000	Tokyo	2
1	Hanako	29	Designer	5500000	Osaka	4
2	Jiro	35	Teacher	4900000	Nagoya	10

View Only Column Names

df.head(0)

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed

Note: Using df.head(0) shows no data rows but displays only the column names. It’s useful for checking your DataFrame’s structure and column order.

[Personal Experience] The first time I used head(0), I thought it was an error because nothing appeared. Later I learned it’s a valid way to see just the headers, and now I always start with it to verify columns.

Combining with print()

print(df.head(3))

This prints rows in a plain text format, which can be hard to read. In Jupyter or Colab, it’s better to rely on df.head() for a nicely formatted table.


 Name   Age Occupation Annual Income (¥) Location Years Employed
0  Taro   23   Engineer            4500000    Tokyo             2
1 Hanako   29   Designer           5500000    Osaka             4
2   Jiro   35   Teacher            4900000    Nagoya           10

Saving the Result as a Variable

first_rows = df.head(3)

[Practical Tip] Saving your head() output to a variable makes it easy to pass directly into plotting or CSV-export steps.

Comparing with tail()

df.tail()   # Last 5 rows

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed
5	Keiko	33	Nurse	4000000	Tokyo	5
6	Sho	27	Programmer	6000000	Kobe	3
7	Akane	24	Sales	3200000	Sendai	1
8	Takashi	31	Lawyer	8000000	Yokohama	12
9	Aoi	30	Researcher	5800000	Chiba	8

df.tail(2)     # Last 2 rows

	Name	Age	Occupation	Annual Income (¥)	Location	Years Employed
8	Takashi	31	Lawyer	8000000	Yokohama	12
9	Aoi	30	Researcher	5800000	Chiba	8

[Note] Use tail() to check for anomalies or missing values at the end of your dataset. I once used a CSV with a trailing NaN row and skewed my stats—now I always verify with tail().

▶️ For reference, see the official tail documentation:
pandas DataFrame tail Documentation

Summary

Syntax	Description
`df.head()`	Display first 5 rows
`df.head(n)`	Display first n rows
`df.head(0)`	Display only column names (same for `df.tail(0)`)
`df.tail()`	Display last 5 rows
`df.tail(n)`	Display last n rows

The head() method is invaluable for quickly inspecting large datasets. Next time, we’ll cover info() and describe() to understand your data’s structure and summary statistics!

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