Pandas DataFrame to_xarray() Method

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

• The Pandas library enables access to/from a DataFrame.
• The Xarray library works with labeled multi-dimensional arrays and advanced analytics.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install xarray

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to Install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import xarray

DataFrame to_xarray()

The to_xarray() method returns an xarray. Dataset/xarray.DataArray from a DataFrame/Series dependent on the object passed.

💡 Note: If the object is a DataFrame, the data converts to a Dataset.

If a Series, the data converts to a DataArray.

The syntax for this method is as follows:

DataFrame.to_xarray()

This method has no parameters.

For this example, Alice needs to grocery shop for a party. This DataFrame displays the grocery list (including categories, prices, and amounts). This object then converts to a Dataset.

Code – Example 1

df = pd.DataFrame([('Fruits',          'Apples',    3.97,   4),
                   ('Dairy',           'Milk',       2.43,   4),
                   ('Vegetables', 'Carrots',  4.21,  12),
                   ('Meat',           'Steak',    18.57, 4)],
                   columns=['Category', 'Name', 'Price', 'Quantity'])
print(df)

result = df.to_xarray()
print(result)

• Line [1] creates a DataFrame from a list of tuples containing grocery list details and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] converts the DataFrame to a Dataset. The output saves to result.
• Line [4] outputs the result to the terminal.

Output

df

result

Dimensions:   	(index: 4)
Coordinates:
  * index     	(index) int64 0 1 2 3
Data variables:
    Category	(index) object 'Fruits' 'Dairy' 'Vegetables' 'Meat'
    Name      	(index) object 'Apples' 'Milk' 'Carrots' 'Steak'
    Price     		(index) float64 3.97 2.43 4.21 18.57
    Quantity		(index) int64 4 4 12 4

You could also extract one (1) column from a DataFrame.

Code  – Example 2

df = pd.DataFrame([('Fruits', 'Apples', 3.97, 4),
                   ('Dairy', 'Milk', 2.43, 4),
                   ('Vegetables', 'Carrots', 4.21, 12),
                   ('Meat', 'Steak', 18.57, 4)],
                   columns=['Category', 'Name', 'Price', 'Quantity'])

result = df['Name'].to_xarray()
print(result)

• Line [1] creates a DataFrame from a List of Tuples containing grocery list details and saves it to df.
• Line [2] converts a column of the DataFrame to a Dataset. The output saves to result.
• Line [3] outputs the result to the terminal.

Output

<xarray.DataArray 'Name' (index: 4)>
array(['Apples', 'Milk', 'Carrots', 'Steak'], dtype=object)
Coordinates:
  * index    (index) int64 0 1 2 3

This example converts a Series containing the first six (6) months of the year and the days per month to an xarray.

Code – Example 3

my_series = pd.Series(['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'June'])
my_idx    = [31, 28, 31, 30, 31, 30]
my_series.index = my_idx
print(my_series)

result = my_series.to_xarray()
print(result)

• Line [1] creates a Series from a list of months.
• Line [2] creates and sets the indexes as a list of days for each month.
• Line [3] sets the index for the Series.
• Line [4] outputs the Series to the terminal.
• Line [5] converts the Series to an xarray.
• Line [6] outputs the result to the terminal.

Output

my_series

result

dtype: object
<xarray.DataArray (index: 6)>
array(['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'June'], dtype=object)
Coordinates:
  * index    (index) int64 31 28 31 30 31 30

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.