本文整理汇总了Python中pyvttbl.DataFrame.marginals方法的典型用法代码示例。如果您正苦于以下问题：Python DataFrame.marginals方法的具体用法？Python DataFrame.marginals怎么用？Python DataFrame.marginals使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyvttbl.DataFrame的用法示例。
在下文中一共展示了DataFrame.marginals方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test05
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import marginals [as 别名]
def test05(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004]), ('dN', [10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367])], val='WORDS', factors=['AGE', 'CONDITION'], where='AGE == "old"')"""
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS',
factors=['AGE','CONDITION'],
where='AGE == "old"')示例2: test04
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import marginals [as 别名]
def test04(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
==========================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221 """
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = str(df.marginals('WORDS',
factors=['AGE','CONDITION'],
where='AGE == "old"'))
self.assertEqual(D, R)示例3: test02
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import marginals [as 别名]
def test02(self):
R = """\
AGE CONDITION Mean Count Std. 95% CI 95% CI
Error lower upper
============================================================
old adjective 11.000 10 0.789 9.454 12.546
old counting 7.000 10 0.577 5.868 8.132
old imagery 13.400 10 1.424 10.610 16.190
old intention 12.000 10 1.183 9.681 14.319
old rhyming 6.900 10 0.674 5.579 8.221
young adjective 14.800 10 1.104 12.637 16.963
young counting 6.500 10 0.453 5.611 7.389
young imagery 17.600 10 0.819 15.994 19.206
young intention 19.300 10 0.844 17.646 20.954
young rhyming 7.600 10 0.618 6.388 8.812 """
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = df.marginals('WORDS',factors=['AGE','CONDITION'])
self.assertEqual(str(D), R)示例4: test0
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import marginals [as 别名]
def test0(self):
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
x=df.marginals('WORDS',factors=['AGE','CONDITION'])
for d,r in zip(x['dmu'],[11,7,13.4,12,6.9,14.8,6.5,17.6,19.3,7.6]):
self.failUnlessAlmostEqual(d,r)
for d,r in zip(x['dN'],[10,10,10,10,10,10,10,10,10,10]):
self.failUnlessAlmostEqual(d,r)
for d,r in zip(x['dsem'],[0.788810638,
0.577350269,
1.423610434,
1.183215957,
0.674124947,
1.103529690,
0.453382350,
0.819213715,
0.843932593,
0.618241233]):
self.failUnlessAlmostEqual(d,r)示例5: test03
# 需要导入模块: from pyvttbl import DataFrame [as 别名]
# 或者: from pyvttbl.DataFrame import marginals [as 别名]
def test03(self):
R = """Marginals([('factorials', OrderedDict([('AGE', [u'old', u'old', u'old', u'old', u'old', u'young', u'young', u'young', u'young', u'young']), ('CONDITION', [u'adjective', u'counting', u'imagery', u'intention', u'rhyming', u'adjective', u'counting', u'imagery', u'intention', u'rhyming'])])), ('dmu', [11.0, 7.0, 13.4, 12.0, 6.9000000000000004, 14.800000000000001, 6.5, 17.600000000000001, 19.300000000000001, 7.5999999999999996]), ('dN', [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]), ('dsem', [0.78881063774661542, 0.57735026918962573, 1.4236104336041748, 1.1832159566199232, 0.67412494720522276, 1.1035296904831231, 0.4533823502911814, 0.81921371516296715, 0.84393259341147731, 0.61824123303304679]), ('dlower', [9.4539311500166345, 5.868393472388334, 10.609723550135818, 9.6808967250249509, 5.578715103477764, 12.637081806653079, 5.6113705934292843, 15.994341118280586, 17.645892116913505, 6.3882471832552277]), ('dupper', [12.546068849983365, 8.131606527611666, 16.190276449864182, 14.319103274975049, 8.2212848965222367, 16.962918193346923, 7.3886294065707157, 19.205658881719415, 20.954107883086497, 8.8117528167447716])], val='WORDS', factors=['AGE', 'CONDITION'])"""
df=DataFrame()
df.read_tbl('data/words~ageXcondition.csv')
D = repr(df.marginals('WORDS',factors=['AGE','CONDITION']))