title: Initial visualization of datasets in Scikit - head() command
body: <p>In considering the potential equivalence of Python to R for data processing, I am working on the basics. In particular, when loading a database, such as <em>Iris</em> in R, the simple command <code>head()</code> produces a beautiful printout on the screen:</p>
  
  <pre><code>head(iris)
    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  1          5.1         3.5          1.4         0.2  setosa
  2          4.9         3.0          1.4         0.2  setosa
  3          4.7         3.2          1.3         0.2  setosa
  4          4.6         3.1          1.5         0.2  setosa
  5          5.0         3.6          1.4         0.2  setosa
  6          5.4         3.9          1.7         0.4  setosa
  </code></pre>
  
  <p><a href="http://scikit-learn.org/stable/" rel="nofollow noreferrer">Scikit</a> include this dataset, but if I weren't familiar with it, and was trying to just take a peek at what it looks like, these would be the first unfortunate results:</p>
  
  <pre><code>from sklearn.datasets import load_iris
  iris = load_iris()
  iris
  {'DESCR': 'Iris Plants Database\n\nNotes\n-----\nData Set Characteristics:\n    :Number of Instances: 150 (50 in each of three classes)\n    :Number of Attributes: 4 numeric, predictive attributes and the class\n    :Attribute Information:\n        - sepal length in cm\n        - sepal width in cm\n        - petal length in cm\n        - petal width in cm\n        - class:\n                - Iris-Setosa\n                - Iris-Versicolour\n                - Iris-Virginica\n    :Summary Statistics:\n\n    ============== ==== ==== ======= ===== ====================\n                    Min  Max   Mean    SD   Class Correlation\n    ============== ==== ==== ======= ===== ====================\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n    ============== ==== ==== ======= ===== ====================\n\n    :Missing Attribute Values: None\n    :Class Distribution: 33.3% for each of 3 classes.\n    :Creator: R.A. Fisher\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n    :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature.  Fisher\'s paper is a classic in the field and\nis referenced frequently to this day.  (See Duda &amp; Hart, for example.)  The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant.  One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n     Mathematical Statistics" (John Wiley, NY, 1950).\n   - Duda,R.O., &amp; Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n     (Q327.D83) John Wiley &amp; Sons.  ISBN 0-471-22361-1.  See page 218.\n   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n     Structure and Classification Rule for Recognition in Partially Exposed\n     Environments".  IEEE Transactions on Pattern Analysis and Machine\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions\n     on Information Theory, May 1972, 431-433.\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II\n     conceptual clustering system finds 3 classes in the data.\n   - Many, many more ...\n',
   'data': array([[ 5.1,  3.5,  1.4,  0.2],
          [ 4.9,  3. ,  1.4,  0.2],
          [ 4.7,  3.2,  1.3,  0.2],
          [ 4.6,  3.1,  1.5,  0.2],
          [ 5. ,  3.6,  1.4,  0.2],
          [ 5.4,  3.9,  1.7,  0.4],
          [ 4.6,  3.4,  1.4,  0.3],
          [ 5. ,  3.4,  1.5,  0.2],
          [ 4.4,  2.9,  1.4,  0.2],
          [ 4.9,  3.1,  1.5,  0.1],
          [ 5.4,  3.7,  1.5,  0.2],
          [ 4.8,  3.4,  1.6,  0.2],
          [ 4.8,  3. ,  1.4,  0.1],
          [ 4.3,  3. ,  1.1,  0.1],
          [ 5.8,  4. ,  1.2,  0.2],
          [ 5.7,  4.4,  1.5,  0.4],
          [ 5.4,  3.9,  1.3,  0.4],
          [ 5.1,  3.5,  1.4,  0.3],
          [ 5.7,  3.8,  1.7,  0.3],
          [ 5.1,  3.8,  1.5,  0.3],
          [ 5.4,  3.4,  1.7,  0.2],
          [ 5.1,  3.7,  1.5,  0.4],
          [ 4.6,  3.6,  1. ,  0.2],
          [ 5.1,  3.3,  1.7,  0.5],
          [ 4.8,  3.4,  1.9,  0.2],
          [ 5. ,  3. ,  1.6,  0.2],
          [ 5. ,  3.4,  1.6,  0.4],
          [ 5.2,  3.5,  1.5,  0.2],
          [ 5.2,  3.4,  1.4,  0.2],
          [ 4.7,  3.2,  1.6,  0.2],
          [ 4.8,  3.1,  1.6,  0.2],
          [ 5.4,  3.4,  1.5,  0.4],
          [ 5.2,  4.1,  1.5,  0.1],
          [ 5.5,  4.2,  1.4,  0.2],
          [ 4.9,  3.1,  1.5,  0.1],
          [ 5. ,  3.2,  1.2,  0.2],
          [ 5.5,  3.5,  1.3,  0.2],
          [ 4.9,  3.1,  1.5,  0.1],
          [ 4.4,  3. ,  1.3,  0.2],
          [ 5.1,  3.4,  1.5,  0.2],
          [ 5. ,  3.5,  1.3,  0.3],
          [ 4.5,  2.3,  1.3,  0.3],
          [ 4.4,  3.2,  1.3,  0.2],
          [ 5. ,  3.5,  1.6,  0.6],
          [ 5.1,  3.8,  1.9,  0.4],
          [ 4.8,  3. ,  1.4,  0.3],
          [ 5.1,  3.8,  1.6,  0.2],
          [ 4.6,  3.2,  1.4,  0.2],
          [ 5.3,  3.7,  1.5,  0.2],
          [ 5. ,  3.3,  1.4,  0.2],
          [ 7. ,  3.2,  4.7,  1.4],
          [ 6.4,  3.2,  4.5,  1.5],
          [ 6.9,  3.1,  4.9,  1.5],
          [ 5.5,  2.3,  4. ,  1.3],
          [ 6.5,  2.8,  4.6,  1.5],
          [ 5.7,  2.8,  4.5,  1.3],
          [ 6.3,  3.3,  4.7,  1.6],
          [ 4.9,  2.4,  3.3,  1. ],
          [ 6.6,  2.9,  4.6,  1.3],
          [ 5.2,  2.7,  3.9,  1.4],
          [ 5. ,  2. ,  3.5,  1. ],
          [ 5.9,  3. ,  4.2,  1.5],
          [ 6. ,  2.2,  4. ,  1. ],
          [ 6.1,  2.9,  4.7,  1.4],
          [ 5.6,  2.9,  3.6,  1.3],
          [ 6.7,  3.1,  4.4,  1.4],
          [ 5.6,  3. ,  4.5,  1.5],
          [ 5.8,  2.7,  4.1,  1. ],
          [ 6.2,  2.2,  4.5,  1.5],
          [ 5.6,  2.5,  3.9,  1.1],
          [ 5.9,  3.2,  4.8,  1.8],
          [ 6.1,  2.8,  4. ,  1.3],
          [ 6.3,  2.5,  4.9,  1.5],
          [ 6.1,  2.8,  4.7,  1.2],
          [ 6.4,  2.9,  4.3,  1.3],
          [ 6.6,  3. ,  4.4,  1.4],
          [ 6.8,  2.8,  4.8,  1.4],
          [ 6.7,  3. ,  5. ,  1.7],
          [ 6. ,  2.9,  4.5,  1.5],
          [ 5.7,  2.6,  3.5,  1. ],
          [ 5.5,  2.4,  3.8,  1.1],
          [ 5.5,  2.4,  3.7,  1. ],
          [ 5.8,  2.7,  3.9,  1.2],
          [ 6. ,  2.7,  5.1,  1.6],
          [ 5.4,  3. ,  4.5,  1.5],
          [ 6. ,  3.4,  4.5,  1.6],
          [ 6.7,  3.1,  4.7,  1.5],
          [ 6.3,  2.3,  4.4,  1.3],
          [ 5.6,  3. ,  4.1,  1.3],
          [ 5.5,  2.5,  4. ,  1.3],
          [ 5.5,  2.6,  4.4,  1.2],
          [ 6.1,  3. ,  4.6,  1.4],
          [ 5.8,  2.6,  4. ,  1.2],
          [ 5. ,  2.3,  3.3,  1. ],
          [ 5.6,  2.7,  4.2,  1.3],
          [ 5.7,  3. ,  4.2,  1.2],
          [ 5.7,  2.9,  4.2,  1.3],
          [ 6.2,  2.9,  4.3,  1.3],
          [ 5.1,  2.5,  3. ,  1.1],
          [ 5.7,  2.8,  4.1,  1.3],
          [ 6.3,  3.3,  6. ,  2.5],
          [ 5.8,  2.7,  5.1,  1.9],
          [ 7.1,  3. ,  5.9,  2.1],
          [ 6.3,  2.9,  5.6,  1.8],
          [ 6.5,  3. ,  5.8,  2.2],
          [ 7.6,  3. ,  6.6,  2.1],
          [ 4.9,  2.5,  4.5,  1.7],
          [ 7.3,  2.9,  6.3,  1.8],
          [ 6.7,  2.5,  5.8,  1.8],
          [ 7.2,  3.6,  6.1,  2.5],
          [ 6.5,  3.2,  5.1,  2. ],
          [ 6.4,  2.7,  5.3,  1.9],
          [ 6.8,  3. ,  5.5,  2.1],
          [ 5.7,  2.5,  5. ,  2. ],
          [ 5.8,  2.8,  5.1,  2.4],
          [ 6.4,  3.2,  5.3,  2.3],
          [ 6.5,  3. ,  5.5,  1.8],
          [ 7.7,  3.8,  6.7,  2.2],
          [ 7.7,  2.6,  6.9,  2.3],
          [ 6. ,  2.2,  5. ,  1.5],
          [ 6.9,  3.2,  5.7,  2.3],
          [ 5.6,  2.8,  4.9,  2. ],
          [ 7.7,  2.8,  6.7,  2. ],
          [ 6.3,  2.7,  4.9,  1.8],
          [ 6.7,  3.3,  5.7,  2.1],
          [ 7.2,  3.2,  6. ,  1.8],
          [ 6.2,  2.8,  4.8,  1.8],
          [ 6.1,  3. ,  4.9,  1.8],
          [ 6.4,  2.8,  5.6,  2.1],
          [ 7.2,  3. ,  5.8,  1.6],
          [ 7.4,  2.8,  6.1,  1.9],
          [ 7.9,  3.8,  6.4,  2. ],
          [ 6.4,  2.8,  5.6,  2.2],
          [ 6.3,  2.8,  5.1,  1.5],
          [ 6.1,  2.6,  5.6,  1.4],
          [ 7.7,  3. ,  6.1,  2.3],
          [ 6.3,  3.4,  5.6,  2.4],
          [ 6.4,  3.1,  5.5,  1.8],
          [ 6. ,  3. ,  4.8,  1.8],
          [ 6.9,  3.1,  5.4,  2.1],
          [ 6.7,  3.1,  5.6,  2.4],
          [ 6.9,  3.1,  5.1,  2.3],
          [ 5.8,  2.7,  5.1,  1.9],
          [ 6.8,  3.2,  5.9,  2.3],
          [ 6.7,  3.3,  5.7,  2.5],
          [ 6.7,  3. ,  5.2,  2.3],
          [ 6.3,  2.5,  5. ,  1.9],
          [ 6.5,  3. ,  5.2,  2. ],
          [ 6.2,  3.4,  5.4,  2.3],
          [ 5.9,  3. ,  5.1,  1.8]]),
   'feature_names': ['sepal length (cm)',
    'sepal width (cm)',
    'petal length (cm)',
    'petal width (cm)'],
   '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]),
   'target_names': array(['setosa', 'versicolor', 'virginica'], 
         dtype='&lt;U10')}
  </code></pre>
  
  <p>Tough! I could try converting the dataset into a dataframe using pandas...</p>
  
  <pre><code>from pandas import *    
  iris = load_iris()
  df = DataFrame(iris.data, columns=iris.feature_names)
  df.head()
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/Ziuj7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Ziuj7.png" alt="enter image description here"></a></p>
  
  <p>which, aesthetics aside (ugly frame and bold font - subjective), is more similar to R's output, except for missing the <code>Species</code> column.</p>
  
  <p>What am I doing wrong?</p>