/usr/share/pyshared/pandas/stats/tests/common.py is in python-pandas 0.7.0-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | # pylint: disable-msg=W0611,W0402
from datetime import datetime
import string
import unittest
import nose
import numpy as np
from pandas.core.api import DataFrame, DateRange
from pandas.util.testing import assert_almost_equal # imported in other tests
N = 100
K = 4
start = datetime(2007, 1, 1)
DATE_RANGE = DateRange(start, periods=N)
COLS = ['Col' + c for c in string.ascii_uppercase[:K]]
def makeDataFrame():
data = DataFrame(np.random.randn(N, K),
columns=COLS,
index=DATE_RANGE)
return data
def getBasicDatasets():
A = makeDataFrame()
B = makeDataFrame()
C = makeDataFrame()
return A, B, C
def check_for_scipy():
try:
import scipy
except ImportError:
raise nose.SkipTest('no scipy')
def check_for_statsmodels():
try:
import scikits.statsmodels as sm
except Exception:
raise nose.SkipTest('no statsmodels')
class BaseTest(unittest.TestCase):
def setUp(self):
check_for_scipy()
check_for_statsmodels()
self.A, self.B, self.C = getBasicDatasets()
self.createData1()
self.createData2()
self.createData3()
def createData1(self):
date = datetime(2007, 1, 1)
date2 = datetime(2007, 1, 15)
date3 = datetime(2007, 1, 22)
A = self.A.copy()
B = self.B.copy()
C = self.C.copy()
A['ColA'][date] = np.NaN
B['ColA'][date] = np.NaN
C['ColA'][date] = np.NaN
C['ColA'][date2] = np.NaN
# truncate data to save time
A = A[:30]
B = B[:30]
C = C[:30]
self.panel_y = A
self.panel_x = {'B' : B, 'C' : C}
self.series_panel_y = A.filter(['ColA'])
self.series_panel_x = {'B' : B.filter(['ColA']),
'C' : C.filter(['ColA'])}
self.series_y = A['ColA']
self.series_x = {'B' : B['ColA'],
'C' : C['ColA']}
def createData2(self):
y_data = [[1, np.NaN],
[2, 3],
[4, 5]]
y_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2),
datetime(2000, 1, 3)]
y_cols = ['A', 'B']
self.panel_y2 = DataFrame(np.array(y_data), index=y_index,
columns=y_cols)
x1_data = [[6, np.NaN],
[7, 8],
[9, 30],
[11, 12]]
x1_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2),
datetime(2000, 1, 3),
datetime(2000, 1, 4)]
x1_cols = ['A', 'B']
x1 = DataFrame(np.array(x1_data), index=x1_index,
columns=x1_cols)
x2_data = [[13, 14, np.NaN],
[15, np.NaN, np.NaN],
[16, 17, 48],
[19, 20, 21],
[22, 23, 24]]
x2_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2),
datetime(2000, 1, 3),
datetime(2000, 1, 4),
datetime(2000, 1, 5)]
x2_cols = ['C', 'A', 'B']
x2 = DataFrame(np.array(x2_data), index=x2_index,
columns=x2_cols)
self.panel_x2 = {'x1' : x1, 'x2' : x2}
def createData3(self):
y_data = [[1, 2],
[3, 4]]
y_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2)]
y_cols = ['A', 'B']
self.panel_y3 = DataFrame(np.array(y_data), index=y_index,
columns=y_cols)
x1_data = [['A', 'B'],
['C', 'A']]
x1_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2)]
x1_cols = ['A', 'B']
x1 = DataFrame(np.array(x1_data), index=x1_index,
columns=x1_cols)
x2_data = [['foo', 'bar'],
['baz', 'foo']]
x2_index = [datetime(2000, 1, 1),
datetime(2000, 1, 2)]
x2_cols = ['A', 'B']
x2 = DataFrame(np.array(x2_data), index=x2_index,
columns=x2_cols)
self.panel_x3 = {'x1' : x1, 'x2' : x2}
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