def test_pivot(self):
from pandas.core.reshape import _slow_pivot
df = pivot(np.array([1, 2, 3, 4, 5]), np.array(["a", "b", "c", "d", "e"]), np.array([1, 2, 3, 5, 4.0]))
self.assertEqual(df["a"][1], 1)
self.assertEqual(df["b"][2], 2)
self.assertEqual(df["c"][3], 3)
self.assertEqual(df["d"][4], 5)
self.assertEqual(df["e"][5], 4)
# weird overlap, TODO: test?
a, b, c = (np.array([1, 2, 3, 4, 4]), np.array(["a", "a", "a", "a", "a"]), np.array([1.0, 2.0, 3.0, 4.0, 5.0]))
df = pivot(a, b, c)
expected = _slow_pivot(a, b, c)
assert_frame_equal(df, expected)
# corner case, empty
df = pivot(np.array([]), np.array([]), np.array([]))
def pivot_query(sql, rows, columns, values, con):
"""
Returns DataFrame with columns corresponding to unique Item
entries in the requested SQL query.
Parameters
----------
sql: string
SQL query to be executed
con: SQLConnection
"""
data = frame_query(sql, con)
data = dict([(key.lower(), values) for key, values in data.iteritems()])
pivoted = pivot(data[rows], data[columns], data[values])
return pivoted
def pivot_query(sql, rows, columns, values, con):
"""
Returns DataFrame with columns corresponding to unique Item
entries in the requested SQL query.
Parameters
----------
sql: string
SQL query to be executed
con: SQLConnection
"""
data = frame_query(sql, con)
data = dict([(key.lower(), values) for key, values in data.iteritems()])
pivoted = pivot(data[rows], data[columns], data[values])
return pivoted
