def test_histogram(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
hist = tbl.histogram(lambda v: (v["x"], 1), 10)
assert len(hist) == 11
ma = max([h[1] for h in hist.values])
mi = min([h[1] for h in hist.values])
assert mi < ma
assert ma < 0.5
su = sum([h[1] for h in hist.values])
assert abs(su - 1) < 1e-5
tbl.values.append([-1., -1.])
tbl.values.append([2., 2.])
hist2 = tbl.histogram(lambda v: (v["x"], 1), 10)
assert hist2[0, 0] > 0
assert hist2[-1, 0] < 1
hist = tbl.values_to_float().histograms(["x", "y"], 10)
fil = hist.filter(lambda v: v["histKey"] is None)
assert len(fil) == 0
fil = hist.filter(lambda v: v["x"] is None)
assert len(fil) > 0
def test_matrix_array2(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
cen = tbl.center_reduce()
self.assertEqual(cen.size, tbl.size)
self.assertNotEqual(cen[0, 0], tbl[0, 0])
def test_pandas_matrix(self):
fold = os.path.split(__file__)[0]
file = os.path.join(fold, "data", "BNP.PA.txt")
df = pandas.read_csv(file, sep=",")
assert "Date" in df.columns
assert "High" in df.columns
assert len(df) == 2344
mat = TableFormula(df)
assert len(mat) == 2344
if not isinstance(mat.header, list):
raise Exception("expecting type: " + str(type(mat.header)))
assert mat.header == [
'index', 'Date', 'Open', 'High', 'Low', 'Close', 'Volume',
'Adj Close'
]
df = pandas.read_csv(file, sep=",")
df.set_index("Date")
mat = TableFormula(df)
assert len(mat) == 2344
assert mat.header == [
'index', 'Date', 'Open', 'High', 'Low', 'Close', 'Volume',
'Adj Close'
]
df = mat.dataframe
assert len(df) == 2344
def test_union_columns(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
union = tbl.union_columns(["x", "y"])
assert union.size == (200, 1)
def test_union_columns(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
union = tbl.union_columns(["x", "y"])
assert union.size == (200, 1)
def test_matrix_array2(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
cen = tbl.center_reduce()
self.assertEqual(cen.size, tbl.size)
self.assertNotEqual(cen[0, 0], tbl[0, 0])
def test_matrix_operator(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
d2 = tbl + tbl
dm = tbl * -1
tt = d2 + dm
self.assertEqual(tbl, tt)
rep = tbl.replicate(2)
self.assertEqual(len(rep), len(tbl) * 2)
def test_matrix_operator(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
d2 = tbl + tbl
dm = tbl * -1
tt = d2 + dm
self.assertEqual(tbl, tt)
rep = tbl.replicate(2)
self.assertEqual(len(rep), len(tbl) * 2)
def test_mu_sigma(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 1000)]
values = [[random.gauss(-1, 3)] for x in values]
tbl = TableFormula(["x"], values)
mu, si = tbl.mu_sigma(lambda v: v["x"])
self.assertTrue(abs(mu + 1) < 0.5)
self.assertTrue(abs(si - 3) < 0.5)
mu, si = tbl.mu_sigma(lambda v: v["x"], removeExtreme=0.01)
self.assertTrue(abs(mu + 1) < 0.5)
self.assertTrue(abs(si - 3) < 0.5)
alls = tbl.mu_sigma_each_column(removeExtreme=0.01)
self.assertEqual(alls.size, (2, 1))
def test_TableFormulaCore_Excel(self):
fold = os.path.split(__file__)[0]
assert TableFormula.delta is not None
file = os.path.join(fold, "data", "BNP.PA.txt")
table = TableFormula(file, sep=",")
table.sort(lambda v: v["Date"])
assert len(table) > 0
tempfold = os.path.join(fold, "temp_store")
if not os.path.exists(tempfold):
os.mkdir(tempfold)
tempexc = os.path.join(tempfold, "temp_excel_table.xls")
if os.path.exists(tempexc):
os.remove(tempexc)
assert not os.path.exists(tempexc)
table.save_as_excel(tempexc)
assert os.path.exists(tempexc)
tempexc = os.path.join(tempfold, "temp_excel_table.xlsx")
if os.path.exists(tempexc):
os.remove(tempexc)
assert not os.path.exists(tempexc)
table.save_as_excel(tempexc)
assert os.path.exists(tempexc)
def test_correlation(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
cov = tbl.covariance()
assert len(cov.values) == 2
assert len(cov.header) == 3
assert cov[1, 1] == cov[0, 2]
cor = tbl.correlation()
assert len(cor.values) == 2
assert len(cor.header) == 3
assert cov[1, 1] == cov[0, 2]
assert abs(cor[0, 1] - cor[1, 2]) < 1e-5
assert abs(1 - cor[1, 2]) < 1e-5
def test_mu_sigma(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 1000)]
values = [[random.gauss(-1, 3)] for x in values]
tbl = TableFormula(["x"], values)
mu, si = tbl.mu_sigma(lambda v: v["x"])
self.assertTrue(abs(mu + 1) < 0.5)
self.assertTrue(abs(si - 3) < 0.5)
mu, si = tbl.mu_sigma(lambda v: v["x"], removeExtreme=0.01)
self.assertTrue(abs(mu + 1) < 0.5)
self.assertTrue(abs(si - 3) < 0.5)
alls = tbl.mu_sigma_each_column(removeExtreme=0.01)
self.assertEqual(alls.size, (2, 1))
def test_split_files(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
fold = os.path.split(__file__)[0]
file = os.path.join(fold, "data", "BNP.PA.txt")
tempf = os.path.join(fold, "temp_split")
assert os.path.exists(file)
if not os.path.exists(tempf):
os.mkdir(tempf)
f_ = os.path.join(tempf, "temp_split")
f1 = f_ + ".0000.txt"
f2 = f_ + ".0001.txt"
for f in [f1, f2]:
if os.path.exists(f):
os.remove(f)
split = TableFormula.random_split_file(file, f_, 2, logFunction=fLOG)
assert split
for f in [f1, f2]:
fLOG(f)
assert os.path.exists(f)
with open(file, "r") as f:
lines = f.readlines()
with open(f1, "r") as f:
lines1 = f.readlines()
with open(f2, "r") as f:
lines2 = f.readlines()
assert len(lines) == len(lines1) + len(lines2) - 1
def test_split_files(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
fold = os.path.split(__file__)[0]
file = os.path.join(fold, "data", "BNP.PA.txt")
tempf = os.path.join(fold, "temp_split")
assert os.path.exists(file)
if not os.path.exists(tempf):
os.mkdir(tempf)
f_ = os.path.join(tempf, "temp_split")
f1 = f_ + ".0000.txt"
f2 = f_ + ".0001.txt"
for f in [f1, f2]:
if os.path.exists(f):
os.remove(f)
split = TableFormula.random_split_file(file, f_, 2, logFunction=fLOG)
assert split
for f in [f1, f2]:
fLOG(f)
assert os.path.exists(f)
with open(file, "r") as f:
lines = f.readlines()
with open(f1, "r") as f:
lines1 = f.readlines()
with open(f2, "r") as f:
lines2 = f.readlines()
assert len(lines) == len(lines1) + len(lines2) - 1
def test_TableFormulaCore_with_dict(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = [{"one": 1, "two": 2}, {"two": 2.1, "three": 3}]
table = TableFormula(data)
for row in table.values:
assert len(row) == 3
def test_correlation(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
cov = tbl.covariance()
assert len(cov.values) == 2
assert len(cov.header) == 3
assert cov[1, 1] == cov[0, 2]
cor = tbl.correlation()
assert len(cor.values) == 2
assert len(cor.header) == 3
assert cov[1, 1] == cov[0, 2]
assert abs(cor[0, 1] - cor[1, 2]) < 1e-5
assert abs(1 - cor[1, 2]) < 1e-5
def test_TableFormulaCore_Excel(self):
fold = os.path.split(__file__)[0]
assert TableFormula.delta is not None
file = os.path.join(fold, "data", "BNP.PA.txt")
table = TableFormula(file, sep=",")
table.sort(lambda v: v["Date"])
assert len(table) > 0
tempfold = os.path.join(fold, "temp_store")
if not os.path.exists(tempfold):
os.mkdir(tempfold)
tempexc = os.path.join(tempfold, "temp_excel_table.xls")
if os.path.exists(tempexc):
os.remove(tempexc)
assert not os.path.exists(tempexc)
table.save_as_excel(tempexc)
assert os.path.exists(tempexc)
tempexc = os.path.join(tempfold, "temp_excel_table.xlsx")
if os.path.exists(tempexc):
os.remove(tempexc)
assert not os.path.exists(tempexc)
table.save_as_excel(tempexc)
assert os.path.exists(tempexc)
def test_pandas_matrix_index(self):
fold = os.path.split(__file__)[0]
file = os.path.join(fold, "data", "BNP.PA.txt")
df = pandas.read_csv(file, sep=",", index_col=["Date"])
mat = TableFormula(df)
assert len(mat) == 2344
assert mat.header == [
'index', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'
]
def test_matrix_array(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
mat = tbl.np_matrix
self.assertTrue(isinstance(mat, numpy.matrix))
tblm = TableFormula(tbl.header, mat)
self.assertTrue(isinstance(tblm[0, 0], float))
self.assertTrue("[[" not in str(tblm))
if tblm != tbl:
delta = tbl.delta(tblm)
for d in delta:
fLOG(d)
raise AssertionError("should not be")
arr = tbl.np_array
self.assertTrue(isinstance(arr, numpy.ndarray))
tbla = TableFormula(tbl.header, arr)
self.assertEqual(tbla, tbl)
def test_json(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
jso = tbl.json
self.assertTrue(len(jso) > 0)
self.assertTrue(isinstance(jso, str))
def test_correlation(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
cov = tbl.covariance()
assert len(cov.values) == 2
assert len(cov.header) == 3
assert cov[1, 1] == cov[0, 2]
cor = tbl.correlation()
assert len(cor.values) == 2
assert len(cor.header) == 3
assert cov[1, 1] == cov[0, 2]
assert abs(cor[0, 1] - cor[1, 2]) < 1e-5
assert abs(1 - cor[1, 2]) < 1e-5
assert tbl.shape is not None
tbl[0, 0] = 0.
tbl[0, 1] = 0.
row = tbl[0]
assert row is not None
assert tbl[0, 0] == 0
row = tbl[:1]
assert row is not None
row = tbl[[0]]
assert row is not None
tbl2 = None
assert tbl != tbl2
tbl2 = tbl
assert tbl == tbl2
res = tbl.avg_std(lambda r: r['x'])
self.assertIsInstance(res, tuple)
cor = tbl.correlation_col('x', 'y')
self.assertIsInstance(cor, float)
cor = tbl.correlation_row(0, 1)
self.assertIsInstance(cor, float)
cor = tbl.covariance_row(0, 1)
self.assertIsInstance(cor, float)
cor = tbl.correlation()
self.assertEqual(cor.shape, (2, 3))
cor = tbl.correlation(useBootstrap=True)
self.assertEqual(cor.shape, (2, 3))
def test_matrix_array(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
mat = tbl.np_matrix
self.assertTrue(isinstance(mat, numpy.matrix))
tblm = TableFormula(tbl.header, mat)
self.assertTrue(isinstance(tblm[0, 0], float))
self.assertTrue("[[" not in str(tblm))
if tblm != tbl:
delta = tbl.delta(tblm)
for d in delta:
fLOG(d)
raise AssertionError("should not be")
arr = tbl.np_array
self.assertTrue(isinstance(arr, numpy.ndarray))
tbla = TableFormula(tbl.header, arr)
self.assertEqual(tbla, tbl)
def test_td9_json(self):
fold = os.path.split(__file__)[0]
data = os.path.join(fold, "data", "td9_by_hours.txt")
tbl = TableFormula(data)
tbl = tbl.values_to_float(True)
jso = []
for row in tbl:
r = copy.copy(row)
r["name"] = r["last_update"]
jso.append(r)
assert len(jso) > 0
outf = os.path.join(fold, "out_json_paris_velib.json")
if os.path.exists(outf):
os.remove(outf)
with open(outf, "w") as f:
f.write("[\n")
f.write("\n".join([str(_) for _ in jso]))
f.write("\n]\n")
assert os.path.exists(outf)
def test_td9_json(self):
fold = os.path.split(__file__)[0]
data = os.path.join(fold, "data", "td9_by_hours.txt")
tbl = TableFormula(data)
tbl = tbl.values_to_float(True)
jso = []
for row in tbl:
r = copy.copy(row)
r["name"] = r["last_update"]
jso.append(r)
assert len(jso) > 0
outf = os.path.join(fold, "out_json_paris_velib.json")
if os.path.exists(outf):
os.remove(outf)
with open(outf, "w") as f:
f.write("[\n")
f.write("\n".join([str(_) for _ in jso]))
f.write("\n]\n")
assert os.path.exists(outf)
def test_addc(self):
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
tbl.addc("a", lambda v: 0, 0)
assert tbl.header == ["a", "x", "y"]
tbl.addc(("aa", "bb"), [lambda v: 4, lambda v: 5], 0)
assert tbl.header == ["aa", "bb", "a", "x", "y"]
tbl.addc(("aaa", "bbb"), lambda v: (7, 8), 0)
assert tbl.header == ["aaa", "bbb", "aa", "bb", "a", "x", "y"]
assert tbl[0, 0] == 7
assert tbl[0, 1] == 8
tbl.addc(("aaaa", "bbba"), lambda v: (8, 9))
assert tbl.header == [
"aaa", "bbb", "aa", "bb", "a", "x", "y", "aaaa", "bbba"
]
assert tbl[0, -2] == 8
assert tbl[0, -1] == 9
def test_iter(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
values = [[random.random(), random.random()] for i in range(0, 10)]
tbl = TableFormula(["x", "y"], values)
nb = 0
for row in tbl:
self.assertTrue(isinstance(row, dict))
self.assertTrue("x" in row)
self.assertTrue("y" in row)
nb += 1
assert nb > 0
def test_multiply_implicit(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
text = """city\tdate
A\tjan
A\tfeb
B\tfeb""".replace(" ", "")
table = TableFormula(text)
assert len(table) == 3
mul = table.multiply_column_by_row_instance(
lambda v: v["date"],
lambda v: v["city"])
exp = """KEY\tA|city\tA|date\tB|city\tB|date
feb\tA\tfeb\tB\tfeb
jan\tA\tjan\tNone\tNone""".replace(" ", "")
exp = TableFormula(exp)
exp.sort(lambda v: v["KEY"])
mul.sort(lambda v: v["KEY"])
delta = mul.delta(exp)
if len(delta) > 0:
for _ in delta:
fLOG(_)
assert False
def test_histogram(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
hist = tbl.histogram(lambda v: (v["x"], 1), 10)
assert len(hist) == 11
ma = max([h[1] for h in hist.values])
mi = min([h[1] for h in hist.values])
assert mi < ma
assert ma < 0.5
su = sum([h[1] for h in hist.values])
assert abs(su - 1) < 1e-5
tbl.values.append([-1., -1.])
tbl.values.append([2., 2.])
hist2 = tbl.histogram(lambda v: (v["x"], 1), 10)
assert hist2[0, 0] > 0
assert hist2[-1, 0] < 1
hist = tbl.values_to_float().histograms(["x", "y"], 10)
fil = hist.filter(lambda v: v["histKey"] is None)
assert len(fil) == 0
fil = hist.filter(lambda v: v["x"] is None)
assert len(fil) > 0
def test_addc(self):
values = [random.random() for i in range(0, 100)]
values = [[x, x + random.random() / 2] for x in values]
tbl = TableFormula(["x", "y"], values)
tbl.addc("a", lambda v: 0, 0)
assert tbl.header == ["a", "x", "y"]
tbl.addc(("aa", "bb"), [lambda v: 4, lambda v: 5], 0)
assert tbl.header == ["aa", "bb", "a", "x", "y"]
tbl.addc(("aaa", "bbb"), lambda v: (7, 8), 0)
assert tbl.header == ["aaa", "bbb", "aa", "bb", "a", "x", "y"]
assert tbl[0, 0] == 7
assert tbl[0, 1] == 8
tbl.addc(("aaaa", "bbba"), lambda v: (8, 9))
assert tbl.header == ["aaa", "bbb", "aa",
"bb", "a", "x", "y", "aaaa", "bbba"]
assert tbl[0, -2] == 8
assert tbl[0, -1] == 9
def test_TableFormulaStat(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
table = TableFormula("sum_y#1#1#1#1#1#1#1#1#1#1#1".replace(
" ", "\t").replace("#", "\n"))
gini = table.Gini(lambda v: v["sum_y"])
assert gini == 0.
table = TableFormula("sum_y#1#1#1#1#1#1#1#1#1#1#1#5#10".replace(
" ", "\t").replace("#", "\n"))
gini = table.Gini(lambda v: v["sum_y"])
assert 0 < gini < 1
def test_multiply_implicit(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
text = """city\tdate
A\tjan
A\tfeb
B\tfeb""".replace(" ", "")
table = TableFormula(text)
assert len(table) == 3
mul = table.multiply_column_by_row_instance(lambda v: v["date"],
lambda v: v["city"])
exp = """KEY\tA|city\tA|date\tB|city\tB|date
feb\tA\tfeb\tB\tfeb
jan\tA\tjan\tNone\tNone""".replace(" ", "")
exp = TableFormula(exp)
exp.sort(lambda v: v["KEY"])
mul.sort(lambda v: v["KEY"])
delta = mul.delta(exp)
if len(delta) > 0:
for _ in delta:
fLOG(_)
assert False
def test_empty_table(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
tbl = TableFormula([["x", "y"]])
self.assertEqual(tbl.size, (0, 2))
def test_TableFormulaCore(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
fold = os.path.split(__file__)[0]
assert TableFormula.delta is not None
file = os.path.join(fold, "data", "BNP.PA.txt")
table = TableFormula(file, sep=",")
table.sort(lambda v: v["Date"])
assert len(table) > 0
table = TableFormula(
"name d_a d_b d_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5".replace(
" ", "\t").replace("#", "\n"))
assert "d_a\td_b\td_c" in str(table)
dist = table.get_distinct_values("name")
assert len(dist) > 0
table.add_column("has_A", lambda v: 1. if "A" in v["name"] else 0.)
assert len(table) > 0
x = 1. / 3
table.add_column_smooth("has_A_smooth", lambda v: v["has_A"],
[-1, 0, 1], [x, x, x])
assert len(table) > 0
fil = table.filter(lambda v: v["d_b"] == 2)
assert len(table) > 0
rnd = table.random(5)
assert len(rnd) > 0
rnd = table.random(1, True)
assert len(rnd) > 0
fil = table.filter_quantile(lambda v: v["d_b"], 0, 0.4)
assert len(fil) > 0
total = table.aggregate(lambda v: v["d_c"])
assert total > 0
table.sort(lambda v: v["d_b"] + v["d_c"])
assert len(table) > 0
union = table.union(table)
assert len(union) > len(table)
group = table.groupby(lambda v: v["name"],
[lambda v: v["d_a"], lambda v: v["d_b"]],
["name", "sum_d_a", "sum_d_b"])
assert len(group) > 0
groupmax = table.groupby(lambda v: v["name"],
[lambda v: v["d_a"], lambda v: v["d_b"]],
["name", "max_d_a", "max_d_b"], [max, max])
assert len(groupmax) > 0
group = table.groupby(lambda v: v["name"], [lambda v: v["d_a"]],
["name", "weight", "sum_d_a"],
[lambda vec, w: sum(vec) / w],
lambda v: v["d_b"])
innerjoin = table.innerjoin(group, lambda v: v["name"],
lambda v: v["name"], "group")
assert len(innerjoin) > 0
ext = table.extract_columns(["name", "d_a"])
assert len(ext) > 0
ext = table.remove_columns(["d_a"])
assert len(ext) > 0
d = table.todict(lambda v: v["name"], lambda v: v["d_b"], True)
assert len(d) > 0
d = table.select(lambda v: (v["name"], v["d_b"]))
assert len(list(d)) > 0
table.create_index(lambda v: (v["name"], v["d_a"]))
row = table.get(('A', 1.1))
assert row
value = table.get(('A', 1.1), 2)
assert value
table = TableFormula(
"name d_a d_b d_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5".replace(
" ", "\t").replace("#", "\n"))
table.add_column("key_add", lambda v: "unique")
mul = table.multiply_column_by_row_instance(lambda v: v["key_add"],
lambda v: v["name"])
assert len(mul) > 0
table = TableFormula(
"key_name sum_a len_b avg_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5".replace(
" ", "\t").replace("#", "\n"))
gr = table.groupby_implicit(lambda v: v["key_name"])
assert len(gr) > 0
def test_TableFormulaCore(self):
fLOG(__file__, self._testMethodName, OutputPrint=__name__ == "__main__")
fold = os.path.split(__file__)[0]
assert TableFormula.delta is not None
file = os.path.join(fold, "data", "BNP.PA.txt")
table = TableFormula(file, sep=",")
table.sort(lambda v: v["Date"])
assert len(table) > 0
table = TableFormula("name d_a d_b d_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5"
.replace(" ", "\t").replace("#", "\n"))
assert "d_a\td_b\td_c" in str(table)
dist = table.get_distinct_values("name")
assert len(dist) > 0
table.add_column("has_A", lambda v: 1. if "A" in v["name"] else 0.)
assert len(table) > 0
x = 1. / 3
table.add_column_smooth("has_A_smooth", lambda v: v[
"has_A"], [-1, 0, 1], [x, x, x])
assert len(table) > 0
fil = table.filter(lambda v: v["d_b"] == 2)
assert len(table) > 0
rnd = table.random(5)
assert len(rnd) > 0
rnd = table.random(1, True)
assert len(rnd) > 0
fil = table.filter_quantile(lambda v: v["d_b"], 0, 0.4)
assert len(fil) > 0
total = table.aggregate(lambda v: v["d_c"])
assert total > 0
table.sort(lambda v: v["d_b"] + v["d_c"])
assert len(table) > 0
union = table.union(table)
assert len(union) > len(table)
group = table.groupby(lambda v: v["name"],
[lambda v: v["d_a"],
lambda v: v["d_b"]],
["name", "sum_d_a", "sum_d_b"])
assert len(group) > 0
groupmax = table.groupby(lambda v: v["name"],
[lambda v: v["d_a"],
lambda v: v["d_b"]],
["name", "max_d_a", "max_d_b"],
[max, max])
assert len(groupmax) > 0
group = table.groupby(lambda v: v["name"],
[lambda v: v["d_a"]],
["name", "weight", "sum_d_a"],
[lambda vec, w: sum(vec) / w],
lambda v: v["d_b"])
innerjoin = table.innerjoin(group, lambda v: v["name"],
lambda v: v["name"], "group")
assert len(innerjoin) > 0
ext = table.extract_columns(["name", "d_a"])
assert len(ext) > 0
ext = table.remove_columns(["d_a"])
assert len(ext) > 0
d = table.todict(lambda v: v["name"], lambda v: v["d_b"], True)
assert len(d) > 0
d = table.select(lambda v: (v["name"], v["d_b"]))
assert len(list(d)) > 0
table.create_index(lambda v: (v["name"], v["d_a"]))
row = table.get(('A', 1.1))
assert row
value = table.get(('A', 1.1), 2)
assert value
table = TableFormula("name d_a d_b d_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5"
.replace(" ", "\t").replace("#", "\n"))
table.add_column("key_add", lambda v: "unique")
mul = table.multiply_column_by_row_instance(
lambda v: v["key_add"],
lambda v: v["name"])
assert len(mul) > 0
table = TableFormula("key_name sum_a len_b avg_c#A 1 2 3#A 1.1 2.1 3.1#B 3 4 5"
.replace(" ", "\t").replace("#", "\n"))
gr = table.groupby_implicit(lambda v: v["key_name"])
assert len(gr) > 0