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# tail

Get bottom rows of table, timetable, or tall array

## Syntax

``B = tail(A)``
``B = tail(A,k)``

## Description

example

````B = tail(A)` returns the last eight rows of table or timetable `A`.```

example

````B = tail(A,k)` returns the last `k` rows of `A`.```

## Examples

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Create a table that contains 100 rows and five variables.

```load patients T = table(LastName,Gender,Age,Height,Weight); size(T)```
```ans = 100 5 ```

Preview the last eight rows.

`T2 = tail(T)`
```T2=8x5 table null LastName Gender Age Height Weight ___________ ________ ___ ______ ______ 'Foster' 'Female' 30 70 124 'Gonzales' 'Male' 48 71 174 'Bryant' 'Female' 48 66 134 'Alexander' 'Male' 25 69 171 'Russell' 'Male' 44 69 188 'Griffin' 'Male' 49 70 186 'Diaz' 'Male' 45 68 172 'Hayes' 'Male' 48 66 177 ```

Create a tall table and preview the bottom few rows of data.

Create a tall table for the `airlinesmall.csv` data set. Select a subset of the variables to work with. Use `tail` to extract the last few rows of data.

```varnames = {'Year','Month','ArrDelay','DepDelay','UniqueCarrier'}; ds = datastore('airlinesmall.csv','TreatAsMissing','NA',... 'SelectedVariableNames',varnames); T = tall(ds)```
```T = Mx5 tall table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ 1987 10 8 12 'PS' 1987 10 8 1 'PS' 1987 10 21 20 'PS' 1987 10 13 12 'PS' 1987 10 4 -1 'PS' 1987 10 59 63 'PS' 1987 10 3 -2 'PS' 1987 10 11 -1 'PS' : : : : : : : : : : ```
`tt = tail(T)`
```tt = Mx5 tall table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? : : : : : : : : : : ```

Collect the results into memory to view the data.

`last_rows = gather(tt)`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 1: Completed in 1 sec Evaluation completed in 1 sec ```
```last_rows=8x5 table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ 2008 12 14 1 'DL' 2008 12 -8 -1 'DL' 2008 12 1 9 'DL' 2008 12 -8 -4 'DL' 2008 12 15 -2 'DL' 2008 12 -15 -1 'DL' 2008 12 -12 1 'DL' 2008 12 -1 11 'DL' ```

Preview the last 20 rows of data in a tall table.

Create a tall table for the `airlinesmall.csv` data set. Select a subset of the variables to work with, and treat `'NA'` values as missing data so that `datastore` replaces them with `NaN` values. Use `tail` to view the last 20 rows of data.

```varnames = {'Year','Month','ArrDelay','DepDelay','UniqueCarrier'}; ds = datastore('airlinesmall.csv','TreatAsMissing','NA',... 'SelectedVariableNames',varnames); T = tall(ds)```
```T = Mx5 tall table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ 1987 10 8 12 'PS' 1987 10 8 1 'PS' 1987 10 21 20 'PS' 1987 10 13 12 'PS' 1987 10 4 -1 'PS' 1987 10 59 63 'PS' 1987 10 3 -2 'PS' 1987 10 11 -1 'PS' : : : : : : : : : : ```
`tt = tail(T,20)`
```tt = Mx5 tall table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? : : : : : : : : : : ```

Collect the results into memory to view the data.

`b20 = gather(tt)`
```Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 1: Completed in 1 sec Evaluation completed in 1 sec ```
```b20=20x5 table Year Month ArrDelay DepDelay UniqueCarrier ____ _____ ________ ________ _____________ 2008 12 0 -4 'CO' 2008 12 -16 13 'CO' 2008 12 17 -3 'CO' 2008 12 3 -5 'CO' 2008 12 2 6 'DL' 2008 12 6 -2 'DL' 2008 12 37 35 'DL' 2008 12 -1 -6 'DL' 2008 12 39 12 'DL' 2008 12 -3 -6 'DL' 2008 12 -6 -1 'DL' 2008 12 -2 1 'DL' 2008 12 14 1 'DL' 2008 12 -8 -1 'DL' 2008 12 1 9 'DL' 2008 12 -8 -4 'DL' ```

## Input Arguments

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Input array, specified as a table or timetable.

Data Types: `table` | `timetable`

Number of rows to extract, specified as a positive scalar integer. If `A` has fewer than `k` rows, then `tail` returns all of `A`.

## Output Arguments

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Requested rows, returned as a table or timetable. The data type of `B` is the same as `A`.

## See Also

### Topics

#### Introduced in R2016b

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#### The Manager's Guide to Solving the Big Data Conundrum

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