`pandas.head`

() function is used to access the first n rows of a dataframe or series. It returns a smaller version of the caller object with the first few entries.

In this article, you will learn how to use the python head function , customizing the number of entries and two more functions that do the same job differently.

## pandas.head

**Syntax:**`pandas.head(n=5)`

**Purpose:**Return the first n rows. This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.**Parameters:****n:***int (default 5)*Number of rows to select.

**Returns**same type as caller- The first n rows of the caller object.

```
# Import packages
import pandas as pd
```

## Pandas or Python Head Function

Head function returns the dataframe or series with the first few rows (by default 5). To perform this function, chain `.head()`

function to the dataframe or series.

### 1. Head function on Series

When the `head `

function is applied to a series object, the result is also returned in the form of series.

```
# Create a Series
seriesA = pd.Series(list(range(1,100)))
# Apply head function
seriesA.head()
```

```
0 1
1 2
2 3
3 4
4 5
dtype: int64
```

### 2. Head function on DataFrame

On applying the `head `

function to a dataframe, the result is also returned as a dataframe with fewer rows.

**Length of the dataframe**

```
# Create a dataframe
df = pd.DataFrame({
'Subject_1_Marks': list(range(1,100)),
'Subject_2_Marks': list(range(1,100)),
'Subject_3_Marks': list(range(1,100)),
}
)
# check the length of the dataframe
len(df) # or df.shape[0]
```

`99`

**Applying Head function**

`df.head()`

## How to control the number of rows in the output?

By default, the `head`

function returns only the first 5 rows of the dataset. To control this behavior, you can use the `n`

parameter. It takes in the number of rows you want to display.

```
# Applying head function with n=10
df.head(n=10)
```

### What if n is negative?

If a negative value is passed in the number of rows parameter, `n`

, then the function returns all the rows except the last `n`

rows. It is similar to using the `df[:-n]`

assignment.

```
# Head function with n=-10
df.head(n=-10)
```

## Other Functions

The head function returns the rows from the beginning of the dataset. You can get the rows from the end using the `tail`

function. Also, the `sample`

function returns a random row from the whole dataset. Let’s implement them separately.

### Tail function

It works in the same way as the head function but returns the last few rows.. It can also optionally take the number of rows to be displayed.

```
# tail function with n=7
df.tail(n=7)
```

### Sample function

The sample function returns a random row from the whole dataset. By default, it will return one random row but you can specify the number of rows to be returned using the `n`

parameter.

*Note: n should be less than or equal to the length of the dataset in case of replace=False (default case) of sample function*

```
# sample function with n=2
df.sample(n=3)
```

## Practical Tips

- It is a good practice to look at some rows of the dataset irrespective of the position. You can check for first rows, last rows, or any random row.
- Head function is useful for quickly testing if the dataset contains the right type of data.

## Test your knowledge

**Q1:** Head function can take negative values. True or False?

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**Answer:** True. The function returns all the rows except the last the `n`

rows.

**Q2:** What is the difference between head and tail function?

**Answer:** Tail function returns rows from the end of the dataset while head function returns rows from the beginning.

The article was contributed by Kaustubh G and Shri Varsheni