In pandas, the head() method is used to return the first n rows of a DataFrame. By default, it returns the first 5 rows, but you can specify a different number of rows by passing an integer as an argument.
In this article, I will explain the Pandas DataFrame head() method by using its syntax, parameters, usage, and how to return a DataFrame or Series containing the initial n rows from the original object.
Key Points –
- The
head()method is used to quickly preview the first n rows of a DataFrame or Series. - By default,
head()returns the first 5 rows of the DataFrame if no parameter is specified. - The method accepts a single optional parameter,
n, which specifies the number of rows to return from the top of the DataFrame. - You can specify the number of rows to return by passing an integer as an argument to the method.
- It returns a new DataFrame containing the first n rows, preserving the original DataFrame's structure and data types.
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Pandas DataFrame head() Introduction
Let’s know the syntax of the head() method.
# Syntax of Pandas DataFrame head()
DataFrame.head(n=5)
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Parameters of the DataFrame head()
It allows only one parameter.
n(int, optional)- An integer specifying the number of rows to return. The default value is 5.
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Return Value
It returns the first n rows of the DataFrame.
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Usage of Pandas DataFrame head() Method
The Pandas DataFrame head() method is used to quickly inspect the beginning of a DataFrame or Series.
To run some examples of the Pandas DataFrame head() function, let’s create a Pandas DataFrame using data from a dictionary.
import pandas as pd
technologies= {
'Courses':["Spark", "PySpark", "Hadoop", "Python", "Pandas", "PySpark", "Java"],
'Fee' :[22000, 25000, 23000, 24000, 26000, 30000, 35000],
'Discount':[1000, 2300, 1000, 1200, 2500, 2000, 2200],
'Duration':['35days', '35days', '40days', '30days', '25days', '50days', '45days']
}
df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)
Yields below output.
The default usage of the head() method in Pandas is to return the first 5 rows of a DataFrame when no argument is specified.
# Using the default usage of the head() method
print("First 5 rows of the DataFrame (default usage):\n", df.head())
Yields below output.
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Specifying Number of Rows
Alternatively, you can specify the number of rows to return with the head() method by passing an integer argument. This allows you to preview a custom number of rows from the beginning of a DataFrame.
# Specifying the number of rows to return
print("First 3 rows of the DataFrame:\n", df.head(3))
# Output:
# First 3 rows of the DataFrame:
# Courses Fee Discount Duration
# 0 Spark 22000 1000 35days
# 1 PySpark 25000 2300 35days
# 2 Hadoop 23000 1000 40days
In the above example, By passing an integer argument to head(), you can specify the number of rows to return.
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Displaying More Than 5 Rows
To display more than the default 5 rows using the head() method in Pandas, you can specify the desired number of rows by passing an integer argument to the method. This allows you to preview a larger portion of your DataFrame.
# Displaying the first 6 rows of the DataFrame
print("First 6 rows of the DataFrame:\n", df.head(6))
# Output:
# First 6 rows of the DataFrame:
# Courses Fee Discount Duration
# 0 Spark 22000 1000 35days
# 1 PySpark 25000 2300 35days
# 2 Hadoop 23000 1000 40days
# 3 Python 24000 1200 30days
# 4 Pandas 26000 2500 25days
# 5 PySpark 30000 2000 50days
By specifying an integer argument greater than 5, you can view more rows from the beginning of your DataFrame.
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Using head() on a DataFrame with DateTime Index
Similarly, using the head() method on a DataFrame with a DateTime index works the same way as with any other DataFrame. This method will return the first n rows of the DataFrame, which is especially useful for time series data where you often need to inspect the initial entries.
import pandas as pd
# Creating a date range
dates = pd.date_range('2024-06-01', periods=7)
# Creating the DataFrame with DateTime index
data = {
'Temperature': [22, 21, 19, 23, 20, 18, 21],
'Humidity': [55, 60, 58, 57, 59, 61, 62]
}
df = pd.DataFrame(data, index=dates)
print("Create DataFrame with DateTime index:\n", df)
# Using head() method to display the first 4 rows
print("First 4 rows of the DataFrame:\n", df.head(4))
# Output:
# Create DataFrame with DateTime index:
# Temperature Humidity
# 2024-06-01 22 55
# 2024-06-02 21 60
# 2024-06-03 19 58
# 2024-06-04 23 57
# 2024-06-05 20 59
# 2024-06-06 18 61
# 2024-06-07 21 62
# First 4 rows of the DataFrame:
# Temperature Humidity
# 2024-06-01 22 55
# 2024-06-02 21 60
# 2024-06-03 19 58
# 2024-06-04 23 57
Using head() on a DataFrame with a DateTime index returns the first n rows while maintaining the DateTime index.
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Using head() on a DataFrame with a MultiIndex
Finally, using the head() method on a DataFrame with a MultiIndex is similar to using it on a regular DataFrame. The head() method will return the first n rows of the DataFrame, including all levels of the MultiIndex.
import pandas as pd
# Creating a MultiIndex
technologies= [
["Spark", "Spark", "PySpark", "PySpark", "Python", "Pandas", "Pandas"],
[22000, 25000, 23000, 24000, 26000, 30000, 35000],
]
index = pd.MultiIndex.from_arrays(technologies, names=('Courses', 'Fee'))
# Creating the DataFrame with MultiIndex
data = {
'Discount':[1000, 2300, 1000, 1200, 2500, 2000, 2200],
}
df = pd.DataFrame(data, index=index)
print("Create DataFrame with MultiIndex:\n", df)
# Using head() method to display the first 4 rows
print("First 4 rows of the DataFrame:\n", df.head(4))
# Output:
# Create DataFrame with MultiIndex:
# Discount
# Courses Fee
# Spark 22000 1000
# 25000 2300
# PySpark 23000 1000
# 24000 1200
# Python 26000 2500
# Pandas 30000 2000
# 35000 2200
# First 4 rows of the DataFrame:
# Discount
# Courses Fee
# Spark 22000 1000
# 25000 2300
# PySpark 23000 1000
# 24000 1200
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FAQ on Pandas DataFrame head() Function
The head() function in Pandas is used to return the first n rows of a DataFrame or Series. By default, it returns the first 5 rows if no argument is specified.
The default number of rows returned by the head() method in Pandas is 5. This means that when you call head() on a DataFrame or Series without specifying the number of rows, it will by default display the first 5 rows of the object.
The head() function returns a DataFrame or Series that contains the first n rows of the original object.
You can use the head() function on a DataFrame with a DateTime index. The function will return the first n rows, preserving the DateTime index.
The head() function can be used on a DataFrame with a MultiIndex. It will return the first n rows, including all levels of the MultiIndex.
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Conclusion
In this article, you have learned the Pandas DataFrame head() function by using its syntax, parameters, usage, and how we can return the first n rows of the DataFrame with examples.
Happy Learning!!
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