Python pandas.compat.product() Examples
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Example #1
Source File: test_rank.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_rank_2d_tie_methods(self): df = self.df def _check2d(df, expected, method='average', axis=0): exp_df = DataFrame({'A': expected, 'B': expected}) if axis == 1: df = df.T exp_df = exp_df.T result = df.rank(method=method, axis=axis) assert_frame_equal(result, exp_df) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, axis, dtype in product(results, [0, 1], dtypes): if (dtype, method) in disabled: continue frame = df if dtype is None else df.astype(dtype) _check2d(frame, results[method], method=method, axis=axis)
Example #2
Source File: test_rank.py From vnpy_crypto with MIT License | 6 votes |
def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method)
Example #3
Source File: test_pivot.py From recruit with Apache License 2.0 | 6 votes |
def test_margins_dtype(self): # GH 17013 df = self.data.copy() df[['D', 'E', 'F']] = np.arange(len(df) * 3).reshape(len(df), 3) mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')] mi = MultiIndex.from_tuples(mi_val, names=('A', 'B')) expected = DataFrame({'dull': [12, 21, 3, 9, 45], 'shiny': [33, 0, 36, 51, 120]}, index=mi).rename_axis('C', axis=1) expected['All'] = expected['dull'] + expected['shiny'] result = df.pivot_table(values='D', index=['A', 'B'], columns='C', margins=True, aggfunc=np.sum, fill_value=0) tm.assert_frame_equal(expected, result)
Example #4
Source File: test_rank.py From recruit with Apache License 2.0 | 6 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected)
Example #5
Source File: test_pivot.py From vnpy_crypto with MIT License | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #6
Source File: test_resample.py From vnpy_crypto with MIT License | 6 votes |
def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T') ts = Series(np.random.randint(0, n // k, n).astype('int64'), index=np.random.choice(dr, n)) left = ts.resample('30T').nunique() ix = date_range(start=ts.index.min(), end=ts.index.max(), freq='30T') vals = ts.values bins = np.searchsorted(ix.values, ts.index, side='right') sorter = np.lexsort((vals, bins)) vals, bins = vals[sorter], bins[sorter] mask = np.r_[True, vals[1:] != vals[:-1]] mask |= np.r_[True, bins[1:] != bins[:-1]] arr = np.bincount(bins[mask] - 1, minlength=len(ix)).astype('int64', copy=False) right = Series(arr, index=ix) assert_series_equal(left, right)
Example #7
Source File: test_rank.py From vnpy_crypto with MIT License | 6 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected)
Example #8
Source File: test_rank.py From recruit with Apache License 2.0 | 6 votes |
def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = {(object, 'first')} results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method)
Example #9
Source File: test_rank.py From vnpy_crypto with MIT License | 6 votes |
def test_rank_2d_tie_methods(self): df = self.df def _check2d(df, expected, method='average', axis=0): exp_df = DataFrame({'A': expected, 'B': expected}) if axis == 1: df = df.T exp_df = exp_df.T result = df.rank(method=method, axis=axis) assert_frame_equal(result, exp_df) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, axis, dtype in product(results, [0, 1], dtypes): if (dtype, method) in disabled: continue frame = df if dtype is None else df.astype(dtype) _check2d(frame, results[method], method=method, axis=axis)
Example #10
Source File: test_pivot.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #11
Source File: test_pivot.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_margins_dtype(self): # GH 17013 df = self.data.copy() df[['D', 'E', 'F']] = np.arange(len(df) * 3).reshape(len(df), 3) mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')] mi = MultiIndex.from_tuples(mi_val, names=('A', 'B')) expected = DataFrame({'dull': [12, 21, 3, 9, 45], 'shiny': [33, 0, 36, 51, 120]}, index=mi).rename_axis('C', axis=1) expected['All'] = expected['dull'] + expected['shiny'] result = df.pivot_table(values='D', index=['A', 'B'], columns='C', margins=True, aggfunc=np.sum, fill_value=0) tm.assert_frame_equal(expected, result)
Example #12
Source File: test_rank.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = {(object, 'first')} results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method)
Example #13
Source File: test_pivot.py From recruit with Apache License 2.0 | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #14
Source File: test_pivot.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #15
Source File: test_rank.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected)
Example #16
Source File: test_rank.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected)
Example #17
Source File: test_rank.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method)
Example #18
Source File: test_pivot.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #19
Source File: test_resample.py From twitter-stock-recommendation with MIT License | 6 votes |
def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T') ts = Series(np.random.randint(0, n // k, n).astype('int64'), index=np.random.choice(dr, n)) left = ts.resample('30T').nunique() ix = date_range(start=ts.index.min(), end=ts.index.max(), freq='30T') vals = ts.values bins = np.searchsorted(ix.values, ts.index, side='right') sorter = np.lexsort((vals, bins)) vals, bins = vals[sorter], bins[sorter] mask = np.r_[True, vals[1:] != vals[:-1]] mask |= np.r_[True, bins[1:] != bins[:-1]] arr = np.bincount(bins[mask] - 1, minlength=len(ix)).astype('int64', copy=False) right = Series(arr, index=ix) assert_series_equal(left, right)
Example #20
Source File: test_pivot.py From coffeegrindsize with MIT License | 6 votes |
def test_margins_dtype(self): # GH 17013 df = self.data.copy() df[['D', 'E', 'F']] = np.arange(len(df) * 3).reshape(len(df), 3) mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')] mi = MultiIndex.from_tuples(mi_val, names=('A', 'B')) expected = DataFrame({'dull': [12, 21, 3, 9, 45], 'shiny': [33, 0, 36, 51, 120]}, index=mi).rename_axis('C', axis=1) expected['All'] = expected['dull'] + expected['shiny'] result = df.pivot_table(values='D', index=['A', 'B'], columns='C', margins=True, aggfunc=np.sum, fill_value=0) tm.assert_frame_equal(expected, result)
Example #21
Source File: test_pivot.py From coffeegrindsize with MIT License | 6 votes |
def test_pivot_table_dropna(self): df = DataFrame({'amount': {0: 60000, 1: 100000, 2: 50000, 3: 30000}, 'customer': {0: 'A', 1: 'A', 2: 'B', 3: 'C'}, 'month': {0: 201307, 1: 201309, 2: 201308, 3: 201310}, 'product': {0: 'a', 1: 'b', 2: 'c', 3: 'd'}, 'quantity': {0: 2000000, 1: 500000, 2: 1000000, 3: 1000000}}) pv_col = df.pivot_table('quantity', 'month', [ 'customer', 'product'], dropna=False) pv_ind = df.pivot_table( 'quantity', ['customer', 'product'], 'month', dropna=False) m = MultiIndex.from_tuples([('A', 'a'), ('A', 'b'), ('A', 'c'), ('A', 'd'), ('B', 'a'), ('B', 'b'), ('B', 'c'), ('B', 'd'), ('C', 'a'), ('C', 'b'), ('C', 'c'), ('C', 'd')], names=['customer', 'product']) tm.assert_index_equal(pv_col.columns, m) tm.assert_index_equal(pv_ind.index, m)
Example #22
Source File: test_rank.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_rank_2d_tie_methods(self): df = self.df def _check2d(df, expected, method='average', axis=0): exp_df = DataFrame({'A': expected, 'B': expected}) if axis == 1: df = df.T exp_df = exp_df.T result = df.rank(method=method, axis=axis) assert_frame_equal(result, exp_df) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, axis, dtype in product(results, [0, 1], dtypes): if (dtype, method) in disabled: continue frame = df if dtype is None else df.astype(dtype) _check2d(frame, results[method], method=method, axis=axis)
Example #23
Source File: test_rank.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected)
Example #24
Source File: test_rank.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method)
Example #25
Source File: test_resample.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def test_resample_group_info(self): # GH10914 for n, k in product((10000, 100000), (10, 100, 1000)): dr = date_range(start='2015-08-27', periods=n // 10, freq='T') ts = Series(np.random.randint(0, n // k, n).astype('int64'), index=np.random.choice(dr, n)) left = ts.resample('30T').nunique() ix = date_range(start=ts.index.min(), end=ts.index.max(), freq='30T') vals = ts.values bins = np.searchsorted(ix.values, ts.index, side='right') sorter = np.lexsort((vals, bins)) vals, bins = vals[sorter], bins[sorter] mask = np.r_[True, vals[1:] != vals[:-1]] mask |= np.r_[True, bins[1:] != bins[:-1]] arr = np.bincount(bins[mask] - 1, minlength=len(ix)).astype('int64', copy=False) right = Series(arr, index=ix) assert_series_equal(left, right)
Example #26
Source File: test_pivot.py From recruit with Apache License 2.0 | 5 votes |
def test_margins_dtype_len(self): mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')] mi = MultiIndex.from_tuples(mi_val, names=('A', 'B')) expected = DataFrame({'dull': [1, 1, 2, 1, 5], 'shiny': [2, 0, 2, 2, 6]}, index=mi).rename_axis('C', axis=1) expected['All'] = expected['dull'] + expected['shiny'] result = self.data.pivot_table(values='D', index=['A', 'B'], columns='C', margins=True, aggfunc=len, fill_value=0) tm.assert_frame_equal(expected, result)
Example #27
Source File: test_pivot.py From coffeegrindsize with MIT License | 5 votes |
def test_margins_dtype_len(self): mi_val = list(product(['bar', 'foo'], ['one', 'two'])) + [('All', '')] mi = MultiIndex.from_tuples(mi_val, names=('A', 'B')) expected = DataFrame({'dull': [1, 1, 2, 1, 5], 'shiny': [2, 0, 2, 2, 6]}, index=mi).rename_axis('C', axis=1) expected['All'] = expected['dull'] + expected['shiny'] result = self.data.pivot_table(values='D', index=['A', 'B'], columns='C', margins=True, aggfunc=len, fill_value=0) tm.assert_frame_equal(expected, result)
Example #28
Source File: test_function.py From recruit with Apache License 2.0 | 5 votes |
def test_size(df): grouped = df.groupby(['A', 'B']) result = grouped.size() for key, group in grouped: assert result[key] == len(group) grouped = df.groupby('A') result = grouped.size() for key, group in grouped: assert result[key] == len(group) grouped = df.groupby('B') result = grouped.size() for key, group in grouped: assert result[key] == len(group) df = DataFrame(np.random.choice(20, (1000, 3)), columns=list('abc')) for sort, key in cart_product((False, True), ('a', 'b', ['a', 'b'])): left = df.groupby(key, sort=sort).size() right = df.groupby(key, sort=sort)['c'].apply(lambda a: a.shape[0]) tm.assert_series_equal(left, right, check_names=False) # GH11699 df = DataFrame([], columns=['A', 'B']) out = Series([], dtype='int64', index=Index([], name='A')) tm.assert_series_equal(df.groupby('A').size(), out) # pipe # --------------------------------
Example #29
Source File: test_counting.py From recruit with Apache License 2.0 | 5 votes |
def test_ngroup_cumcount_pair(self): # brute force comparison for all small series for p in cart_product(range(3), repeat=4): df = DataFrame({'a': p}) g = df.groupby(['a']) order = sorted(set(p)) ngroupd = [order.index(val) for val in p] cumcounted = [p[:i].count(val) for i, val in enumerate(p)] assert_series_equal(g.ngroup(), Series(ngroupd)) assert_series_equal(g.cumcount(), Series(cumcounted))
Example #30
Source File: test_rank.py From twitter-stock-recommendation with MIT License | 5 votes |
def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: df = self.df.dropna() else: df = self.df.astype(dtype) res = df.rank(ascending=False) expected = (df.max() - df).rank() assert_frame_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (df.max() - df).rank(method=method) if dtype != 'O': res2 = df.rank(method=method, ascending=False, numeric_only=True) assert_frame_equal(res2, expected) res3 = df.rank(method=method, ascending=False, numeric_only=False) assert_frame_equal(res3, expected)