In this tutorial, we will learn how to get a pandas dataframe row as a numpy array. A comfort level with Python is recommended but not required.

## How to get a dataframe’s row as a numpy array?

To obtain a pandas dataframe’s row as a numpy array, we can fetch the row, then convert it into a numpy array. The syntax is –

#Get the row using it's integral index row = df.iloc[row_index] #Convert the row to numpy array row_as_array = row.to_numpy()

Here,

`df`

— The pandas dataframe.`df.iloc[row_index]`

— Pandas dataframe property to access rows and columns by their integer indices.`row.to_numpy()`

— Pandas built-in method to convert dataframe or series objects to numpy array.

In the above code, we fetch the required row using its index and the `iloc`

slicing method of the pandas dataframe. The row thus obtained is a Pandas Series object. To convert series objects to a numpy array, the pandas module comes with a built-in `to_numpy()`

method.

Alternatively, pandas series objects can be converted to numpy arrays using built-in Numpy functions. The syntax will be-

#Get the row using it's integral index row = df.iloc[row_index] #Convert the row to numpy array row_as_array = np.array(row)

Here, the `df.iloc`

slicing method, as discussed earlier, returns the required row as a pandas series object. This is then converted to a numpy array using the `np.array()`

function.

## Examples

Let’s understand the above syntax with some code examples.

For our examples, let’s create a dataframe containing the marks of some children in a class. To keep it simple, we will have only three subjects and three students.

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import pandas as pd import numpy as np #Making a dictionary to pass it to dataframe d = {'Mary': [47, 25, 36], 'Alfred': [81, 85, 76], 'Jean': [93, 87, 71]} #DataFrame object is created with index of choice. df = pd.DataFrame(d, index=['Maths', 'English', 'Science']) #Display the dataframe df

Output:

### Example 1: Get the first row of a dataframe as a numpy array

To get the first row of a dataframe as a numpy array, we will extract the row using `df.iloc[0]`

and then convert it into a numpy array using the pandas `to_numpy()`

function.

#Get the first row first_row = df.iloc[0] #Convert the row to numpy array first_row_as_np_array = first_row.to_numpy() print("First row:", first_row_as_np_array)

Output:

First row: [47 81 93]

As we can see, we have the first row of the dataframe as a numpy array.

To check if the above variable is a numpy array, we can simply print its type.

print("Type of variable 'first_row_as_np_array': ", type(first_row_as_np_array))

Output:

Type of variable 'first_row_as_np_array': <class 'numpy.ndarray'>

As we see in the output, the variable is a numpy array object.

### Example 2: Get the last row of a dataframe as a numpy array

To get the last row of the dataframe as a numpy array, we simply replace 0 with -1 in `df.iloc[0]`

thereby indicating that we want to access the last row of the dataframe.

#Get last row of dataframe last_row = df.iloc[-1] #Get last row of dataframe as numpy array last_row_as_np_array = last_row.to_numpy() print("Last row:", last_row_as_np_array)

Output:

Last row: [36 76 71]

The above code returns the last row of the dataframe, which represents the marks of students in the Science subject.

### Example 3: Get any row of dataframe as a numpy array

To obtain any row in the dataframe, we can pass the index of the required row, let’s say `i`

in `df.iloc[i]`

#Get middle row of dataframe row_number = 1 middle_row = df.iloc[row_number] #Get middle row of dataframe as numpy array middle_row_as_np_array = middle_row.to_numpy() print("Middle Row:", middle_row_as_np_array)

Output:

Middle Row: [25 85 87]

Thus we see that the middle row is returned as an array, which represents the marks of students in the English language in our dataframe.

### Example 4: Using Numpy’s built-in function to convert Pandas DataFrame row to Numpy array

Alternatively, Numpy supports converting pandas dataframe rows (which are pandas series objects) to numpy arrays using the numpy.array() function.

#Get middle row of dataframe row_number = 1 middle_row = df.iloc[row_number] #Get middle row of dataframe as numpy array using np.array() function. middle_row_as_np_array = np.array(middle_row) print("Middle Row:", middle_row_as_np_array)

Output:

Middle Row: [25 85 87]

We see that the above code gives the same output as that seen in previous examples. There is no difference in converting pandas rows to numpy arrays using either of these methods.

## Summary

From this tutorial, we looked at how to:

- Use
`iloc`

to access a dataframe row using its index. - Use the pandas
`to_numpy()`

method of the dataframe row to convert it to a numpy array. - Use the numpy’s built-in function,
`numpy.array()`

to convert a pandas row to a numpy array.

You might also be interested in –

- Average for each row in Pandas Dataframe
- Pandas – Delete rows based on column values
- Pandas DataFrame – Get Row Count
- Pandas – Select first n rows of a DataFrame
- Pandas – Random Sample of Rows

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