pythonpandasdataframedatasetiris-dataset

Iris dataset not showing "Species" column

I am working with numpy and pandas on Python to learn how to work on dataframes.

I'm coding on Collaboratory and I have loaded the Iris dataset but for some reason, there is no "Species" column in my dataframe. Maybe I've loaded it in an incorrect fashion? I'd appreciate help on the matter.

I added an image, if the code is still needed then this is what I have:

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

df = pd.DataFrame(load_iris().data, columns=load_iris().feature_names)

enter image description here

Solution

  • Try:

    import numpy as np
import pandas as pd 
from sklearn.datasets import load_iris

iris = load_iris()

df = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
                  columns= iris['feature_names'] + ['target']).astype({'target': int}) \
       .assign(species=lambda x: x['target'].map(dict(enumerate(iris['target_names']))))

    Output:

    >>> df
     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target    species
0                  5.1               3.5                1.4               0.2       0     setosa
1                  4.9               3.0                1.4               0.2       0     setosa
2                  4.7               3.2                1.3               0.2       0     setosa
3                  4.6               3.1                1.5               0.2       0     setosa
4                  5.0               3.6                1.4               0.2       0     setosa
..                 ...               ...                ...               ...     ...        ...
145                6.7               3.0                5.2               2.3       2  virginica
146                6.3               2.5                5.0               1.9       2  virginica
147                6.5               3.0                5.2               2.0       2  virginica
148                6.2               3.4                5.4               2.3       2  virginica
149                5.9               3.0                5.1               1.8       2  virginica

[150 rows x 6 columns]

    How to create the species column from target and target_names columns?

    >>> iris['target_names']
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
# index 0: setosa
# index 1: versicolor
# index 2: virginica

>>> iris['target']
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

    You just need a dict mapping to replace 0 by 'setosa', 1 by 'versicolor' and 2 by 'virginica'. Use enumerate to create a list of tuples [(0, 'setosa'), (1, 'versicolor), (2, 'virginica')] then dict` to convert as a dictionary:

    >>> dict(enumerate(iris['target_names']))
{0: 'setosa', 1: 'versicolor', 2: 'virginica'}

    Now Series.map will map the corresponding values:

    >>> df['target'].map(dict(enumerate(iris['target_names'])))
0         setosa
1         setosa
2         setosa
3         setosa
4         setosa
         ...    
145    virginica
146    virginica
147    virginica
148    virginica
149    virginica
Name: target, Length: 150, dtype: object