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fix #574 : added argument 'wide' to read_csv, read_excel, from_lists and from_strings functions + and updated df_aslarray so as to be able to load arrays stored in narrow format
1 parent 37151a9 commit 7964815

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‎doc/source/changes/version_0_28.rst.inc

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@@ -289,6 +289,17 @@ Miscellaneous improvements
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Argument `transpose` has a different purpose than `wide` and is mainly useful to allow multiple axes as header
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when exporting arrays with more than 2 dimensions. Closes :issue:`575` and :issue:`371`.
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* added argument `wide` to `read_csv` and `read_excel` functions. If False, the array to be loaded is assumed to
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be stored in "narrow" format:
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>>> # assuming the array was saved using command: arr.to_excel('my_file.xlsx', wide=False)
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>>> read_excel('my_file.xlsx', wide=False)
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a\b b0 b1 b2
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a0 0 1 2
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a1 3 4 5
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Closes :issue:`574`.
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* added argument `name` to `to_series` method allowing to set a name to the Pandas Series returned by the method.
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* added argument `value_name` to `to_csv` and `to_excel` allowing to change the default name ('value') to

‎larray/inout/array.py

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‎larray/inout/excel.py

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@@ -418,9 +418,9 @@ def __setattr__(self, key, value):
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setattr(self.xw_sheet, key, value)
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def load(self, header=True, convert_float=True, nb_index=None, index_col=None, fill_value=np.nan,
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sort_rows=False, sort_columns=False):
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sort_rows=False, sort_columns=False, wide=True):
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return self[:].load(header=header, convert_float=convert_float, nb_index=nb_index, index_col=index_col,
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sort_rows=sort_rows, sort_columns=sort_columns, fill_value=fill_value)
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fill_value=fill_value, sort_rows=sort_rows, sort_columns=sort_columns, wide=wide)
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# TODO: generalize to more than 2 dimensions or scrap it
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def array(self, data, row_labels=None, column_labels=None, names=None):
@@ -547,15 +547,15 @@ def __str__(self):
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__repr__ = __str__
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def load(self, header=True, convert_float=True, nb_index=None, index_col=None, fill_value=np.nan,
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sort_rows=False, sort_columns=False):
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sort_rows=False, sort_columns=False, wide=True):
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if not self.ndim:
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return LArray([])
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list_data = self._converted_value(convert_float=convert_float)
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if header:
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return from_lists(list_data, nb_index=nb_index, index_col=index_col, fill_value=fill_value,
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sort_rows=sort_rows, sort_columns=sort_columns)
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sort_rows=sort_rows, sort_columns=sort_columns, wide=wide)
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else:
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return LArray(list_data)
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‎larray/tests/data/test1d_narrow.csv

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a,value
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a0,0
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a1,1
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a2,2

‎larray/tests/data/test2d_narrow.csv

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a,b,value
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1,b0,0
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1,b1,1
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2,b0,2
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2,b1,3
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3,b0,4
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3,b1,5

‎larray/tests/data/test3d_narrow.csv

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a,b,c,value
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1,b0,c0,0
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1,b0,c1,1
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1,b0,c2,2
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1,b1,c0,3
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1,b1,c1,4
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1,b1,c2,5
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2,b0,c0,6
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2,b0,c1,7
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2,b0,c2,8
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2,b1,c0,9
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2,b1,c1,10
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2,b1,c2,11
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3,b0,c0,12
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3,b0,c1,13
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3,b0,c2,14
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3,b1,c0,15
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3,b1,c1,16
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3,b1,c2,17

‎larray/tests/data/test_narrow.xlsx

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‎larray/tests/data/testmissing_values_narrow.csv

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a,b,c,value
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1,b0,c0,0
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1,b0,c1,1
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1,b0,c2,2
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1,b1,c0,3
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1,b1,c1,4
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1,b1,c2,5
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2,b1,c0,9
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2,b1,c2,11
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3,b0,c0,12
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3,b0,c1,13
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3,b0,c2,14

‎larray/tests/data/testunsorted_narrow.csv

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a,b,c,value
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3,b1,c2,0
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3,b1,c1,1
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3,b1,c0,2
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3,b0,c2,3
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3,b0,c1,4
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3,b0,c0,5
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2,b1,c2,6
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2,b1,c1,7
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2,b1,c0,8
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2,b0,c2,9
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2,b0,c1,10
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2,b0,c0,11
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1,b1,c2,12
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1,b1,c1,13
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1,b1,c0,14
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1,b0,c2,15
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1,b0,c1,16
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1,b0,c0,17

‎larray/tests/test_array.py

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@@ -96,6 +96,8 @@ def setUp(self):
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self.io_missing_values = ndtest("a=1..3; b=b0,b1; c=c0..c2", dtype=float)
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self.io_missing_values[2, 'b0'] = np.nan
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self.io_missing_values[3, 'b1'] = np.nan
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self.io_narrow_missing_values = self.io_missing_values.copy()
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self.io_narrow_missing_values[2, 'b1', 'c1'] = np.nan
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@pytest.fixture(autouse=True)
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def setup(self, tmpdir):
@@ -2637,6 +2639,26 @@ def test_read_csv(self):
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res = read_csv(StringIO('a,a2,a0,a1\n,2,0,1\n'), sort_columns=True)
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assert_array_equal(res, ndtest(3))
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#################
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# narrow format #
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#################
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res = read_csv(inputpath('test1d_narrow.csv'), wide=False)
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assert_array_equal(res, self.io_1d)
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res = read_csv(inputpath('test2d_narrow.csv'), wide=False)
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assert_array_equal(res, self.io_2d)
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res = read_csv(inputpath('test3d_narrow.csv'), wide=False)
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assert_array_equal(res, self.io_3d)
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# missing values
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res = read_csv(inputpath('testmissing_values_narrow.csv'), wide=False)
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assert_array_nan_equal(res, self.io_narrow_missing_values)
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# unsorted values
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res = read_csv(inputpath('testunsorted_narrow.csv'), wide=False)
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assert_array_equal(res, self.io_unsorted)
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def test_read_eurostat(self):
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la = read_eurostat(inputpath('test5d_eurostat.csv'))
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self.assertEqual(la.ndim, 5)
@@ -2667,19 +2689,48 @@ def test_read_excel_xlwings(self):
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axis = Axis('dim=1d,2d,3d,5d')
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arr = read_excel(inputpath('test.xlsx'), axis['1d'])
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assert_array_equal(arr, ndtest(3))
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assert_array_equal(arr, self.io_1d)
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# missing rows + fill_value argument
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arr = read_excel(inputpath('test.xlsx'), 'missing_values', fill_value=42)
26742696
expected = self.io_missing_values.copy()
26752697
expected[isnan(expected)] = 42
26762698
assert_array_equal(arr, expected)
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2678-
# invalid keyword argument
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#################
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# narrow format #
2702+
#################
2703+
arr = read_excel(inputpath('test_narrow.xlsx'), '1d', wide=False)
2704+
assert_array_equal(arr, self.io_1d)
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2706+
arr = read_excel(inputpath('test_narrow.xlsx'), '2d', wide=False)
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assert_array_equal(arr, self.io_2d)
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2709+
arr = read_excel(inputpath('test_narrow.xlsx'), '3d', wide=False)
2710+
assert_array_equal(arr, self.io_3d)
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2712+
# missing rows + fill_value argument
2713+
arr = read_excel(inputpath('test_narrow.xlsx'), 'missing_values', fill_value=42, wide=False)
2714+
expected = self.io_narrow_missing_values.copy()
2715+
expected[isnan(expected)] = 42
2716+
assert_array_equal(arr, expected)
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2718+
# unsorted values
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arr = read_excel(inputpath('test_narrow.xlsx'), 'unsorted', wide=False)
2720+
assert_array_equal(arr, self.io_unsorted)
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##############################
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# invalid keyword argument #
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##############################
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26792726
with self.assertRaisesRegexp(TypeError, "'dtype' is an invalid keyword argument for this function when using "
26802727
"the xlwings backend"):
26812728
read_excel(inputpath('test.xlsx'), engine='xlwings', dtype=float)
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2730+
#################
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# blank cells #
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#################
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# Excel sheet with blank cells on right/bottom border of the array to read
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fpath = inputpath('test_blank_cells.xlsx')
26852736
good = read_excel(fpath, 'good')
@@ -2722,14 +2773,41 @@ def test_read_excel_pandas(self):
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axis = Axis('dim=1d,2d,3d,5d')
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27242775
arr = read_excel(inputpath('test.xlsx'), axis['1d'], engine='xlrd')
2725-
assert_array_equal(arr, ndtest(3))
2776+
assert_array_equal(arr, self.io_1d)
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27272778
# missing rows + fill_value argument
27282779
arr = read_excel(inputpath('test.xlsx'), 'missing_values', fill_value=42, engine='xlrd')
27292780
expected = self.io_missing_values.copy()
27302781
expected[isnan(expected)] = 42
27312782
assert_array_equal(arr, expected)
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2784+
#################
2785+
# narrow format #
2786+
#################
2787+
arr = read_excel(inputpath('test_narrow.xlsx'), '1d', wide=False, engine='xlrd')
2788+
assert_array_equal(arr, self.io_1d)
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2790+
arr = read_excel(inputpath('test_narrow.xlsx'), '2d', wide=False, engine='xlrd')
2791+
assert_array_equal(arr, self.io_2d)
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2793+
arr = read_excel(inputpath('test_narrow.xlsx'), '3d', wide=False, engine='xlrd')
2794+
assert_array_equal(arr, self.io_3d)
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2796+
# missing rows + fill_value argument
2797+
arr = read_excel(inputpath('test_narrow.xlsx'), 'missing_values',
2798+
fill_value=42, wide=False, engine='xlrd')
2799+
expected = self.io_narrow_missing_values
2800+
expected[isnan(expected)] = 42
2801+
assert_array_equal(arr, expected)
2802+
2803+
# unsorted values
2804+
arr = read_excel(inputpath('test_narrow.xlsx'), 'unsorted', wide=False, engine='xlrd')
2805+
assert_array_equal(arr, self.io_unsorted)
2806+
2807+
#################
2808+
# blank cells #
2809+
#################
2810+
27332811
# Excel sheet with blank cells on right/bottom border of the array to read
27342812
fpath = inputpath('test_blank_cells.xlsx')
27352813
good1 = read_excel(fpath, 'good', engine='xlrd')

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