Pandas df.head() Function

Pandas 常用函数Pandas Common Functions

head()is one of the most commonly used functions in Pandas DataFrame and Series, used to quickly view the beginning of a dataset. It returns the first n rows of data, allowing us to understand the basic structure and content of the data without loading the entire dataset.

This function is used very frequently in data analysis work, especially when handling large datasets, where we usually first usehead()to view the first few rows of data, confirm whether the data structure meets expectations, and then proceed with further analysis and processing.

Basic Syntax and Parameters

head()is a member function of DataFrame and Series, called through the dot operator.to invoke. It does not require any mandatory parameters, but accepts an optional parameter to specify the number of rows to return.

Syntax Format

DataFrame.head(n=5)
Series.head(n=5)

Parameter Description

Parameter Type Required Description Default Value
n int Optional Returns the first n rows of data. If n is greater than the total number of rows, returns all data. 5

Return Value Description

  • Return value type: When the caller is a DataFrame, a DataFrame is returned; when the caller is a Series, a Series is returned.
  • Number of rows returned: Returns at most n rows; if the data has fewer than n rows, returns all data.

Examples

Let's fully masterhead()usage through a series of examples.

Example 1: Basic Usage - View the First Few Rows of a DataFrame

First create a simple DataFrame, then usehead()to view the first few rows of data.

Example

import pandas as pd

# Create a sample DataFrame containing student grade data
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank', 'Grace', 'Henry', 'Iris', 'Jack'],
    'age': [18, 19, 17, 18, 20, 19, 18, 17, 19, 18],
    'score': [85, 92, 78, 90, 88, 95, 82, 76, 89, 91],
    'grade': ['A', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A']
}
df = pd.DataFrame(data)

# Default returns the first 5 rows
print("Default first 5 rows:")
print(df.head())

# Specify returning the first 3 rows
print("nFirst 3 rows:")
print(df.head(3))

# Return the first 7 rows
print("nFirst 7 rows:")
print(df.head(7))

Output result:

默认前 5 行:
      name  age  score grade
0    Alice   18     85     A
1      Bob   19     92     A
2  Charlie   17     78     B
3    David   18     90     A
4      Eve   20     88     B

前 3 行:
      name  age  score grade
0    Alice   18     85     A
1      Bob   19     92     A
2  Charlie   17     78     B

前 7 行:
      name  age  score grade grade
0    Alice   18     85     A
1      Bob   19     92     A
2  Charlie   17     78     B
3    David   18     90     A
4      Eve   20     88     B
5    Frank   19     95     A
6    Grace   18     82     B

Code explanation:

  1. Created a DataFrame containing 10 rows of student data.
  2. df.head()Without parameters, it defaults to returning the first 5 rows of data; this is the most common usage.
  3. df.head(3)Returns the first 3 rows; the number of rows to view can be adjusted as needed.
  4. When the specified n value is greater than the total number of data rows, all data is returned without an error.

Example 2: View the First Few Rows of a Series

head()Not only applicable to DataFrames, but also works for Series objects.

Example

import pandas as pd

# Create a Series containing a series of values
s = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])

# View the first 3 elements of the Series
print("First 3 elements:")
print(s.head(3))

# Create a Series with an index
s2 = pd.Series([100, 200, 300, 400, 500], index=['a', 'b', 'c', 'd', 'e'])
print("nFirst 2 elements of the indexed Series:")
print(s2.head(2))

Output result:

前 3 个元素:
0    10
1    20
2    30
dtype: int64

带索引 Series 的前 2 个元素:
a    100
b    200
dtype: int64

Code explanation:

  • Series also supports thehead()method, returning the first n elements.
  • A Series with a custom index can also usehead()normally, and the original index will be preserved.

Example 3: Use in Combination with Other Functions

head()Often used in combination with other DataFrame functions for data exploration and analysis.

Example

import pandas as pd
import numpy as np

# Create a larger DataFrame
np.random.seed(42)  # Set random seed to ensure reproducible results
df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=100),
    'value': np.random.randn(100).round(2),
    'category': np.random.choice(['A', 'B', 'C'], 100)
})

# View data type information
print("Data type information:")
print(df.dtypes)
print()

# View the first 10 rows of data
print("First 10 rows of data:")
print(df.head(10))

# Sort first, then view the first few rows
df_sorted = df.sort_values('value', ascending=False)
print("nFirst 5 rows after sorting by value in descending order:")
print(df_sorted.head())

# View statistical information for the first few rows
print("nStatistical information for the first 5 rows:")
print(df.head().describe())

Output result:

数据类型信息:
date    datetime64[ns]
value            float64
category          object
dtype: object

前 10 行数据:
        date  value category
0 2024-01-01   0.34        A
1 2024-01-02  -0.23        B
2 2024-01-03   0.54        C
3 2024-01-04  -1.58        B
4 2024-01-05  -0.29       后几行
...

按 value 降序排列后的前 5 行:
         date  value category
72 2024-03-13   2.87        C
55 2024-02-25   2.32        A
31 2024-02-01   2.21        B
64 2024-03-05   1.71        C
84 2024-03-25   1.55        A

前 5 行的统计信息:
         age     score
count   5.0    5.000
mean   18.0   87.200
std     1.0    5.403
min    17.0   78.000
50%   18.0   88.000
max   20.0   95.000

Code explanation:

  1. head()Can be used in combination withsort_values()to sort first and then view the first few rows.
  2. head()The returned result is still a DataFrame, so other DataFrame methods can continue to be called.
  3. describe()Statistical summary can be performed onhead()the data returned.

Notes

  • head()Does not modify the original DataFrame or Series; it returns a new object.
  • When n is less than or equal to 0, an empty DataFrame or empty Series is returned.
  • For large datasets, first usinghead()to view the structure is a good practice.
  • head()The returned data retains the original index values and does not renumber them.

Tip: In Jupyter Notebook or JupyterLab environments, directly entering the DataFrame variable name and executing it will by default display thehead()result, which greatly facilitates data exploration work.

Summary

head()is one of the most basic and practical data viewing functions in Pandas. It can quickly preview the beginning of a dataset, helping us understand key information such as data structure, column names, and data types.

In actual data analysis work, we usually: first usehead()to view the basic structure of the data, then usetail()to view the end of the data, and then useinfo()anddescribe()to understand the overall picture of the data. This "four-step data viewing process" is a fundamental skill that every data analyst should master.

Pandas 常用函数Pandas Common Functions

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