Pandas Series: to_xarray() function
Series - to_xarray() function
The to_xarray() function is used to get an xarray object from the pandas object.
Syntax:
Series.to_xarray(self)
Returns: xarray.DataArray or xarray.Dataset
Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.
Example:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([('eagle', 'bird', 320.0, 2),
('sparrow', 'bird', 30.0, 2),
('tiger', 'mammal', 90.5, 4),
('fox', 'mammal', np.nan, 4)],
columns=['name', 'class', 'max_speed',
'num_legs'])
df
Output:
name class max_speed num_legs 0 eagle bird 320.0 2 1 sparrow bird 30.0 2 2 tiger mammal 90.5 4 3 fox mammal NaN 4
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([('eagle', 'bird', 320.0, 2),
('sparrow', 'bird', 30.0, 2),
('tiger', 'mammal', 90.5, 4),
('fox', 'mammal', np.nan, 4)],
columns=['name', 'class', 'max_speed',
'num_legs'])
df.to_xarray()
Output:
<xarray.Dataset> Dimensions: (index: 4) Coordinates: * index (index) int64 0 1 2 3 Data variables: name (index) object 'eagle' 'sparrow' 'tiger' 'fox' class (index) object 'bird' 'bird' 'mammal' 'mammal' max_speed (index) float64 320.0 30.0 90.5 nan num_legs (index) int64 2 2 4 4
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame([('eagle', 'bird', 320.0, 2),
('sparrow', 'bird', 30.0, 2),
('tiger', 'mammal', 90.5, 4),
('fox', 'mammal', np.nan, 4)],
columns=['name', 'class', 'max_speed',
'num_legs'])
df['max_speed'].to_xarray()
Output:
<xarray.DataArray 'max_speed' (index: 4)> array([320. , 30. , 90.5, nan]) Coordinates: * index (index) int64 0 1 2 3
Python-Pandas Code:
import numpy as np
import pandas as pd
dates = pd.to_datetime(['2019-01-01', '2019-01-01',
'2019-01-02', '2019-01-02'])
df_multiindex = pd.DataFrame({'date': dates,
'animal': ['eagle', 'sparrow', 'eagle',
'sparrow'],
'speed': [320, 30, 330, 25]}).set_index(['date',
'animal'])
dates = pd.to_datetime(['2019-01-01', '2019-01-01',
'2019-01-02', '2019-01-02'])
df_multiindex = pd.DataFrame({'date': dates,
'animal': ['eagle', 'sparrow', 'eagle',
'sparrow'],
'speed': [320, 30, 330, 25]}).set_index(['date',
'animal'])
df_multiindex
Output:
speed date animal 2019-01-01 eagle 320 sparrow 30 2019-01-02 eagle 330 sparrow 25
Python-Pandas Code:
import numpy as np
import pandas as pd
dates = pd.to_datetime(['2019-01-01', '2019-01-01',
'2019-01-02', '2019-01-02'])
df_multiindex = pd.DataFrame({'date': dates,
'animal': ['eagle', 'sparrow', 'eagle',
'sparrow'],
'speed': [320, 30, 330, 25]}).set_index(['date',
'animal'])
dates = pd.to_datetime(['2019-01-01', '2019-01-01',
'2019-01-02', '2019-01-02'])
df_multiindex = pd.DataFrame({'date': dates,
'animal': ['eagle', 'sparrow', 'eagle',
'sparrow'],
'speed': [320, 30, 330, 25]}).set_index(['date',
'animal'])
df_multiindex.to_xarray()
Output:
<xarray.Dataset> Dimensions: (animal: 2, date: 2) Coordinates: * date (date) datetime64[ns] 2019-01-01 2019-01-02 * animal (animal) object 'eagle' 'sparrow' Data variables: speed (date, animal) int64 320 30 330 25
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