def create_data():
""" create the pickle/msgpack data """
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.]
}
scalars = dict(timestamp=Timestamp('20130101'))
if LooseVersion(pandas.__version__) >= '0.17.0':
scalars['period'] = Period('2012','M')
index = dict(int=Index(np.arange(10)),
date=date_range('20130101', periods=10),
period=period_range('2013-01-01', freq='M', periods=10))
mi = dict(reg2=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
names=['first', 'second']))
series = dict(float=Series(data['A']),
int=Series(data['B']),
mixed=Series(data['E']),
ts=Series(np.arange(10).astype(np.int64), index=date_range('20130101',periods=10)),
mi=Series(np.arange(5).astype(np.float64),
index=MultiIndex.from_tuples(tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])),
names=['one', 'two'])),
dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']),
<<<<<<< HEAD
<<<<<<< HEAD
cat=Series(Categorical(['foo', 'bar', 'baz'])))
if LooseVersion(pandas.__version__) >= '0.17.0':
series['period'] = Series([Period('2000Q1')] * 5)
def test_pivot_datetime_tz(self):
dates1 = ['2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00',
'2011-07-19 07:00:00', '2011-07-19 08:00:00', '2011-07-19 09:00:00']
dates2 = ['2013-01-01 15:00:00', '2013-01-01 15:00:00', '2013-01-01 15:00:00',
'2013-02-01 15:00:00', '2013-02-01 15:00:00', '2013-02-01 15:00:00']
df = DataFrame({'label': ['a', 'a', 'a', 'b', 'b', 'b'],
'dt1': dates1, 'dt2': dates2,
'value1': range(6), 'value2': [1, 2] * 3})
df['dt1'] = df['dt1'].apply(lambda d: pd.Timestamp(d, tz='US/Pacific'))
df['dt2'] = df['dt2'].apply(lambda d: pd.Timestamp(d, tz='Asia/Tokyo'))
exp_idx = pd.DatetimeIndex(['2011-07-19 07:00:00', '2011-07-19 08:00:00',
'2011-07-19 09:00:00'], tz='US/Pacific', name='dt1')
exp_col1 = Index(['value1', 'value1'])
exp_col2 = Index(['a', 'b'], name='label')
exp_col = MultiIndex.from_arrays([exp_col1, exp_col2])
expected = DataFrame([[0, 3], [1, 4], [2, 5]],
index=exp_idx, columns=exp_col)
result = pivot_table(df, index=['dt1'], columns=['label'], values=['value1'])
tm.assert_frame_equal(result, expected)
exp_col1 = Index(['sum', 'sum', 'sum', 'sum', 'mean', 'mean', 'mean', 'mean'])
exp_col2 = Index(['value1', 'value1', 'value2', 'value2'] * 2)
exp_col3 = pd.DatetimeIndex(['2013-01-01 15:00:00', '2013-02-01 15:00:00'] * 4,
tz='Asia/Tokyo', name='dt2')
exp_col = MultiIndex.from_arrays([exp_col1, exp_col2, exp_col3])
expected = DataFrame(np.array([[0, 3, 1, 2, 0, 3, 1, 2], [1, 4, 2, 1, 1, 4, 2, 1],
[2, 5, 1, 2, 2, 5, 1, 2]]), index=exp_idx, columns=exp_col)
result = pivot_table(df, index=['dt1'], columns=['dt2'], values=['value1', 'value2'],
aggfunc=[np.sum, np.mean])
tm.assert_frame_equal(result, expected)
def test_format_sparse_display():
index = MultiIndex(levels=[[0, 1], [0, 1], [0, 1], [0]],
labels=[[0, 0, 0, 1, 1, 1], [0, 0, 1, 0, 0, 1],
[0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0]])
result = index.format()
assert result[3] == '1 0 0 0'
def test_multiindex_objects():
mi = MultiIndex(levels=[["b", "d", "a"], [1, 2, 3]],
codes=[[0, 1, 0, 2], [2, 0, 0, 1]],
names=["col1", "col2"])
recons = mi._sort_levels_monotonic()
# These are equal.
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
# _hashed_values and hash_pandas_object(..., index=False) equivalency.
expected = hash_pandas_object(mi, index=False).values
result = mi._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = hash_pandas_object(recons, index=False).values
result = recons._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = mi._hashed_values
result = recons._hashed_values
# Values should match, but in different order.
tm.assert_numpy_array_equal(np.sort(result), np.sort(expected))
def test_na_value_dict(self):
data = """A,B,C
foo,bar,NA
bar,foo,foo
foo,bar,NA
bar,foo,foo"""
df = read_csv(StringIO(data),
na_values={'A': ['foo'], 'B': ['bar']})
expected = DataFrame({'A': [np.nan, 'bar', np.nan, 'bar'],
'B': [np.nan, 'foo', np.nan, 'foo'],
'C': [np.nan, 'foo', np.nan, 'foo']})
assert_frame_equal(df, expected)
data = """\
a,b,c,d
0,NA,1,5
"""
xp = DataFrame({'b': [np.nan], 'c': [1], 'd': [5]}, index=[0])
xp.index.name = 'a'
df = read_csv(StringIO(data), na_values={}, index_col=0)
assert_frame_equal(df, xp)
xp = DataFrame({'b': [np.nan], 'd': [5]},
MultiIndex.from_tuples([(0, 1)]))
df = read_csv(StringIO(data), na_values={}, index_col=[0, 2])
assert_frame_equal(df, xp)
xp = DataFrame({'b': [np.nan], 'd': [5]},
MultiIndex.from_tuples([(0, 1)]))
df = read_csv(StringIO(data), na_values={}, index_col=['a', 'c'])
assert_frame_equal(df, xp)
def test_from_product_empty():
# 0 levels
with tm.assert_raises_regex(
ValueError, "Must pass non-zero number of levels/labels"):
MultiIndex.from_product([])
# 1 level
result = MultiIndex.from_product([[]], names=['A'])
expected = pd.Index([], name='A')
tm.assert_index_equal(result.levels[0], expected)
# 2 levels
l1 = [[], ['foo', 'bar', 'baz'], []]
l2 = [[], [], ['a', 'b', 'c']]
names = ['A', 'B']
for first, second in zip(l1, l2):
result = MultiIndex.from_product([first, second], names=names)
expected = MultiIndex(levels=[first, second],
labels=[[], []], names=names)
tm.assert_index_equal(result, expected)
# GH12258
names = ['A', 'B', 'C']
for N in range(4):
lvl2 = lrange(N)
result = MultiIndex.from_product([[], lvl2, []], names=names)
expected = MultiIndex(levels=[[], lvl2, []],
labels=[[], [], []], names=names)
tm.assert_index_equal(result, expected)
def test_multiindex_objects(self):
mi = MultiIndex(levels=[['b', 'd', 'a'], [1, 2, 3]],
labels=[[0, 1, 0, 2], [2, 0, 0, 1]],
names=['col1', 'col2'])
recons = mi._sort_levels_monotonic()
# these are equal
assert mi.equals(recons)
assert Index(mi.values).equals(Index(recons.values))
# _hashed_values and hash_pandas_object(..., index=False)
# equivalency
expected = hash_pandas_object(
mi, index=False).values
result = mi._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = hash_pandas_object(
recons, index=False).values
result = recons._hashed_values
tm.assert_numpy_array_equal(result, expected)
expected = mi._hashed_values
result = recons._hashed_values
# values should match, but in different order
tm.assert_numpy_array_equal(np.sort(result),
np.sort(expected))
def test_loc_getitem_array(self):
# GH15434
# passing an array as a key with a MultiIndex
index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
x = Series(index=index, data=range(9), dtype=np.float64)
y = np.array([1, 3])
expected = Series(
data=[0, 1, 2, 6, 7, 8],
index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
dtype=np.float64)
result = x.loc[y]
tm.assert_series_equal(result, expected)
# empty array:
empty = np.array([])
expected = Series([], index=MultiIndex(
levels=index.levels, labels=[[], []], dtype=np.float64))
result = x.loc[empty]
tm.assert_series_equal(result, expected)
# 0-dim array (scalar):
scalar = np.int64(1)
expected = Series(
data=[0, 1, 2],
index=['A', 'B', 'C'],
dtype=np.float64)
result = x.loc[scalar]
tm.assert_series_equal(result, expected)
def test_loc_multiindex_incomplete(self):
# GH 7399
# incomplete indexers
s = Series(np.arange(15, dtype='int64'),
MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
expected = s.loc[:, 'a':'c']
result = s.loc[0:4, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
result = s.loc[:4, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
result = s.loc[0:, 'a':'c']
tm.assert_series_equal(result, expected)
tm.assert_series_equal(result, expected)
# GH 7400
# multiindexer gettitem with list of indexers skips wrong element
s = Series(np.arange(15, dtype='int64'),
MultiIndex.from_product([range(5), ['a', 'b', 'c']]))
expected = s.iloc[[6, 7, 8, 12, 13, 14]]
result = s.loc[2:4:2, 'a':'c']
tm.assert_series_equal(result, expected)
def test_unstack_fill_frame(self):
# From a dataframe
rows = [[1, 2], [3, 4], [5, 6], [7, 8]]
df = DataFrame(rows, columns=list('AB'), dtype=np.int32)
df.index = MultiIndex.from_tuples(
[('x', 'a'), ('x', 'b'), ('y', 'b'), ('z', 'a')])
result = df.unstack(fill_value=-1)
rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]]
expected = DataFrame(rows, index=list('xyz'), dtype=np.int32)
expected.columns = MultiIndex.from_tuples(
[('A', 'a'), ('A', 'b'), ('B', 'a'), ('B', 'b')])
assert_frame_equal(result, expected)
# From a mixed type dataframe
df['A'] = df['A'].astype(np.int16)
df['B'] = df['B'].astype(np.float64)
result = df.unstack(fill_value=-1)
expected['A'] = expected['A'].astype(np.int16)
expected['B'] = expected['B'].astype(np.float64)
assert_frame_equal(result, expected)
# From a dataframe with incorrect data type for fill_value
result = df.unstack(fill_value=0.5)
rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]]
expected = DataFrame(rows, index=list('xyz'), dtype=np.float)
expected.columns = MultiIndex.from_tuples(
[('A', 'a'), ('A', 'b'), ('B', 'a'), ('B', 'b')])
assert_frame_equal(result, expected)
def test_loc_getitem_series(self):
# GH14730
# passing a series as a key with a MultiIndex
index = MultiIndex.from_product([[1, 2, 3], ['A', 'B', 'C']])
x = Series(index=index, data=range(9), dtype=np.float64)
y = Series([1, 3])
expected = Series(
data=[0, 1, 2, 6, 7, 8],
index=MultiIndex.from_product([[1, 3], ['A', 'B', 'C']]),
dtype=np.float64)
result = x.loc[y]
tm.assert_series_equal(result, expected)
result = x.loc[[1, 3]]
tm.assert_series_equal(result, expected)
# GH15424
y1 = Series([1, 3], index=[1, 2])
result = x.loc[y1]
tm.assert_series_equal(result, expected)
empty = Series(data=[], dtype=np.float64)
expected = Series([], index=MultiIndex(
levels=index.levels, labels=[[], []], dtype=np.float64))
result = x.loc[empty]
tm.assert_series_equal(result, expected)
def test_loc_getitem_duplicates_multiindex_missing_indexers(indexer, is_level1,
expected_error):
# GH 7866
# multi-index slicing with missing indexers
idx = MultiIndex.from_product([['A', 'B', 'C'],
['foo', 'bar', 'baz']],
names=['one', 'two'])
s = Series(np.arange(9, dtype='int64'), index=idx).sort_index()
if indexer == []:
expected = s.iloc[[]]
elif is_level1:
expected = Series([0, 3, 6], index=MultiIndex.from_product(
[['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
else:
exp_idx = MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
names=['one', 'two'])
expected = Series(np.arange(3, dtype='int64'),
index=exp_idx).sort_index()
if expected_error is not None:
with pytest.raises(KeyError, match=expected_error):
s.loc[indexer]
else:
result = s.loc[indexer]
tm.assert_series_equal(result, expected)
def test_reset_index(self):
df = tm.makeDataFrame()[:5]
ser = df.stack()
ser.index.names = ['hash', 'category']
ser.name = 'value'
df = ser.reset_index()
assert 'value' in df
df = ser.reset_index(name='value2')
assert 'value2' in df
# check inplace
s = ser.reset_index(drop=True)
s2 = ser
s2.reset_index(drop=True, inplace=True)
tm.assert_series_equal(s, s2)
# level
index = MultiIndex(levels=[['bar'], ['one', 'two', 'three'], [0, 1]],
codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2],
[0, 1, 0, 1, 0, 1]])
s = Series(np.random.randn(6), index=index)
rs = s.reset_index(level=1)
assert len(rs.columns) == 2
rs = s.reset_index(level=[0, 2], drop=True)
tm.assert_index_equal(rs.index, Index(index.get_level_values(1)))
assert isinstance(rs, Series)
def test_loc_getitem_int_slice(self):
# GH 3053
# loc should treat integer slices like label slices
index = MultiIndex.from_tuples([t for t in itertools.product(
[6, 7, 8], ['a', 'b'])])
df = DataFrame(np.random.randn(6, 6), index, index)
result = df.loc[6:8, :]
expected = df
tm.assert_frame_equal(result, expected)
index = MultiIndex.from_tuples([t
for t in itertools.product(
[10, 20, 30], ['a', 'b'])])
df = DataFrame(np.random.randn(6, 6), index, index)
result = df.loc[20:30, :]
expected = df.iloc[2:]
tm.assert_frame_equal(result, expected)
# doc examples
result = df.loc[10, :]
expected = df.iloc[0:2]
expected.index = ['a', 'b']
tm.assert_frame_equal(result, expected)
result = df.loc[:, 10]
expected = df[10]
tm.assert_frame_equal(result, expected)
def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2):
# GH #19686
# .loc should work with nested indexers which can be
# any list-like objects (see `pandas.api.types.is_list_like`) or slices
def convert_nested_indexer(indexer_type, keys):
if indexer_type == np.ndarray:
return np.array(keys)
if indexer_type == slice:
return slice(*keys)
return indexer_type(keys)
a = [10, 20, 30]
b = [1, 2, 3]
index = MultiIndex.from_product([a, b])
df = DataFrame(
np.arange(len(index), dtype='int64'),
index=index, columns=['Data'])
keys = ([10, 20], [2, 3])
types = (indexer_type_1, indexer_type_2)
# check indexers with all the combinations of nested objects
# of all the valid types
indexer = tuple(
convert_nested_indexer(indexer_type, k)
for indexer_type, k in zip(types, keys))
result = df.loc[indexer, 'Data']
expected = Series(
[1, 2, 4, 5], name='Data',
index=MultiIndex.from_product(keys))
tm.assert_series_equal(result, expected)
def test_concat_with_group_keys(self):
df = DataFrame(np.random.randn(4, 3))
df2 = DataFrame(np.random.randn(4, 4))
# axis=0
df = DataFrame(np.random.randn(3, 4))
df2 = DataFrame(np.random.randn(4, 4))
result = concat([df, df2], keys=[0, 1])
exp_index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1, 1],
[0, 1, 2, 0, 1, 2, 3]])
expected = DataFrame(np.r_[df.values, df2.values],
index=exp_index)
tm.assert_frame_equal(result, expected)
result = concat([df, df], keys=[0, 1])
exp_index2 = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1],
[0, 1, 2, 0, 1, 2]])
expected = DataFrame(np.r_[df.values, df.values],
index=exp_index2)
tm.assert_frame_equal(result, expected)
# axis=1
df = DataFrame(np.random.randn(4, 3))
df2 = DataFrame(np.random.randn(4, 4))
result = concat([df, df2], keys=[0, 1], axis=1)
expected = DataFrame(np.c_[df.values, df2.values],
columns=exp_index)
tm.assert_frame_equal(result, expected)
result = concat([df, df], keys=[0, 1], axis=1)
expected = DataFrame(np.c_[df.values, df.values],
columns=exp_index2)
tm.assert_frame_equal(result, expected)
def test_append_mixed_dtypes():
# GH 13660
dti = date_range('2011-01-01', freq='M', periods=3, )
dti_tz = date_range('2011-01-01', freq='M', periods=3, tz='US/Eastern')
pi = period_range('2011-01', freq='M', periods=3)
mi = MultiIndex.from_arrays([[1, 2, 3],
[1.1, np.nan, 3.3],
['a', 'b', 'c'],
dti, dti_tz, pi])
assert mi.nlevels == 6
res = mi.append(mi)
exp = MultiIndex.from_arrays([[1, 2, 3, 1, 2, 3],
[1.1, np.nan, 3.3, 1.1, np.nan, 3.3],
['a', 'b', 'c', 'a', 'b', 'c'],
dti.append(dti),
dti_tz.append(dti_tz),
pi.append(pi)])
tm.assert_index_equal(res, exp)
other = MultiIndex.from_arrays([['x', 'y', 'z'], ['x', 'y', 'z'],
['x', 'y', 'z'], ['x', 'y', 'z'],
['x', 'y', 'z'], ['x', 'y', 'z']])
res = mi.append(other)
exp = MultiIndex.from_arrays([[1, 2, 3, 'x', 'y', 'z'],
[1.1, np.nan, 3.3, 'x', 'y', 'z'],
['a', 'b', 'c', 'x', 'y', 'z'],
dti.append(pd.Index(['x', 'y', 'z'])),
dti_tz.append(pd.Index(['x', 'y', 'z'])),
pi.append(pd.Index(['x', 'y', 'z']))])
tm.assert_index_equal(res, exp)
def test_apply_categorical_data(self):
# GH 10138
for ordered in [True, False]:
dense = Categorical(list('abc'), ordered=ordered)
# 'b' is in the categories but not in the list
missing = Categorical(
list('aaa'), categories=['a', 'b'], ordered=ordered)
values = np.arange(len(dense))
df = DataFrame({'missing': missing,
'dense': dense,
'values': values})
grouped = df.groupby(['missing', 'dense'])
# missing category 'b' should still exist in the output index
idx = MultiIndex.from_product(
[Categorical(['a', 'b'], ordered=ordered),
Categorical(['a', 'b', 'c'], ordered=ordered)],
names=['missing', 'dense'])
expected = DataFrame([0, 1, 2, np.nan, np.nan, np.nan],
index=idx,
columns=['values'])
assert_frame_equal(grouped.apply(lambda x: np.mean(x)), expected)
assert_frame_equal(grouped.mean(), expected)
assert_frame_equal(grouped.agg(np.mean), expected)
# but for transform we should still get back the original index
idx = MultiIndex.from_product([['a'], ['a', 'b', 'c']],
names=['missing', 'dense'])
expected = Series(1, index=idx)
assert_series_equal(grouped.apply(lambda x: 1), expected)
def test_set_index_names(self):
df = pd.util.testing.makeDataFrame()
df.index.name = 'name'
assert df.set_index(df.index).index.names == ['name']
mi = MultiIndex.from_arrays(df[['A', 'B']].T.values, names=['A', 'B'])
mi2 = MultiIndex.from_arrays(df[['A', 'B', 'A', 'B']].T.values,
names=['A', 'B', 'C', 'D'])
df = df.set_index(['A', 'B'])
assert df.set_index(df.index).index.names == ['A', 'B']
# Check that set_index isn't converting a MultiIndex into an Index
assert isinstance(df.set_index(df.index).index, MultiIndex)
# Check actual equality
tm.assert_index_equal(df.set_index(df.index).index, mi)
idx2 = df.index.rename(['C', 'D'])
# Check that [MultiIndex, MultiIndex] yields a MultiIndex rather
# than a pair of tuples
assert isinstance(df.set_index([df.index, idx2]).index, MultiIndex)
# Check equality
tm.assert_index_equal(df.set_index([df.index, idx2]).index, mi2)
def test_get_info_after_update(chunkstore_lib):
df = DataFrame(data={'data': [1.1, 2.1, 3.1]},
index=MultiIndex.from_tuples([(dt(2016, 1, 1), 1),
(dt(2016, 1, 2), 1),
(dt(2016, 1, 3), 1)],
names=['date', 'id'])
)
chunkstore_lib.write('test_df', df, 'D')
df2 = DataFrame(data={'data': [1.1, 1.1, 1.1]},
index=MultiIndex.from_tuples([(dt(2016, 1, 1), 2),
(dt(2016, 1, 2), 2),
(dt(2016, 1, 4), 1)],
names=['date', 'id'])
)
chunkstore_lib.update('test_df', df2)
assert_frame_equal(chunkstore_lib.read('test_df'), pd.concat([df, df2]).sort())
info = {'rows': 6,
'dtype': [('date', '<M8[ns]'), ('id', '<i8'), ('data', '<f8')],
'chunk_count': 4,
'col_names': {u'index': [u'date', u'id'], u'index_tz': [None, None], u'columns': [u'data']},
'type': u'df',
'size': 144}
assert(chunkstore_lib.get_info('test_df') == info)
def test_mangles_multi_index(self):
# See GH 18062
data = """A,A,A,B\none,one,one,two\n0,40,34,0.1"""
df = self.read_csv(StringIO(data), header=[0, 1])
expected = DataFrame([[0, 40, 34, 0.1]],
columns=MultiIndex.from_tuples(
[('A', 'one'), ('A', 'one.1'),
('A', 'one.2'), ('B', 'two')]))
tm.assert_frame_equal(df, expected)
data = """A,A,A,B\none,one,one.1,two\n0,40,34,0.1"""
df = self.read_csv(StringIO(data), header=[0, 1])
expected = DataFrame([[0, 40, 34, 0.1]],
columns=MultiIndex.from_tuples(
[('A', 'one'), ('A', 'one.1'),
('A', 'one.1.1'), ('B', 'two')]))
tm.assert_frame_equal(df, expected)
data = """A,A,A,B,B\none,one,one.1,two,two\n0,40,34,0.1,0.1"""
df = self.read_csv(StringIO(data), header=[0, 1])
expected = DataFrame([[0, 40, 34, 0.1, 0.1]],
columns=MultiIndex.from_tuples(
[('A', 'one'), ('A', 'one.1'),
('A', 'one.1.1'), ('B', 'two'),
('B', 'two.1')]))
tm.assert_frame_equal(df, expected)
def test_join_multiindex(self):
index1 = MultiIndex.from_arrays([['a', 'a', 'a', 'b', 'b', 'b'],
[1, 2, 3, 1, 2, 3]],
names=['first', 'second'])
index2 = MultiIndex.from_arrays([['b', 'b', 'b', 'c', 'c', 'c'],
[1, 2, 3, 1, 2, 3]],
names=['first', 'second'])
df1 = DataFrame(data=np.random.randn(6), index=index1,
columns=['var X'])
df2 = DataFrame(data=np.random.randn(6), index=index2,
columns=['var Y'])
df1 = df1.sort_index(level=0)
df2 = df2.sort_index(level=0)
joined = df1.join(df2, how='outer')
ex_index = Index(index1.values).union(Index(index2.values))
expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
expected.index.names = index1.names
assert_frame_equal(joined, expected)
assert joined.index.names == index1.names
df1 = df1.sort_index(level=1)
df2 = df2.sort_index(level=1)
joined = df1.join(df2, how='outer').sort_index(level=0)
ex_index = Index(index1.values).union(Index(index2.values))
expected = df1.reindex(ex_index).join(df2.reindex(ex_index))
expected.index.names = index1.names
assert_frame_equal(joined, expected)
assert joined.index.names == index1.names
def test_groupby_as_index_apply(df):
# GH #4648 and #3417
df = DataFrame({'item_id': ['b', 'b', 'a', 'c', 'a', 'b'],
'user_id': [1, 2, 1, 1, 3, 1],
'time': range(6)})
g_as = df.groupby('user_id', as_index=True)
g_not_as = df.groupby('user_id', as_index=False)
res_as = g_as.head(2).index
res_not_as = g_not_as.head(2).index
exp = Index([0, 1, 2, 4])
tm.assert_index_equal(res_as, exp)
tm.assert_index_equal(res_not_as, exp)
res_as_apply = g_as.apply(lambda x: x.head(2)).index
res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index
# apply doesn't maintain the original ordering
# changed in GH5610 as the as_index=False returns a MI here
exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (
2, 4)])
tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
exp_as_apply = MultiIndex.from_tuples(tp, names=['user_id', None])
tm.assert_index_equal(res_as_apply, exp_as_apply)
tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)
ind = Index(list('abcde'))
df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
res = df.groupby(0, as_index=False).apply(lambda x: x).index
tm.assert_index_equal(res, ind)
def test_set_index_names(self):
df = pd.util.testing.makeDataFrame()
df.index.name = 'name'
self.assertEqual(df.set_index(df.index).index.names, ['name'])
mi = MultiIndex.from_arrays(df[['A', 'B']].T.values, names=['A', 'B'])
mi2 = MultiIndex.from_arrays(df[['A', 'B', 'A', 'B']].T.values,
names=['A', 'B', 'A', 'B'])
df = df.set_index(['A', 'B'])
self.assertEqual(df.set_index(df.index).index.names, ['A', 'B'])
# Check that set_index isn't converting a MultiIndex into an Index
self.assertTrue(isinstance(df.set_index(df.index).index, MultiIndex))
# Check actual equality
tm.assert_index_equal(df.set_index(df.index).index, mi)
# Check that [MultiIndex, MultiIndex] yields a MultiIndex rather
# than a pair of tuples
self.assertTrue(isinstance(df.set_index(
[df.index, df.index]).index, MultiIndex))
# Check equality
tm.assert_index_equal(df.set_index([df.index, df.index]).index, mi2)
def test_hash_pandas_object(self):
for obj in [Series([1, 2, 3]),
Series([1.0, 1.5, 3.2]),
Series([1.0, 1.5, np.nan]),
Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]),
Series(['a', 'b', 'c']),
Series(['a', np.nan, 'c']),
Series(['a', None, 'c']),
Series([True, False, True]),
Series(),
Index([1, 2, 3]),
Index([True, False, True]),
DataFrame({'x': ['a', 'b', 'c'], 'y': [1, 2, 3]}),
DataFrame(),
tm.makeMissingDataframe(),
tm.makeMixedDataFrame(),
tm.makeTimeDataFrame(),
tm.makeTimeSeries(),
tm.makeTimedeltaIndex(),
tm.makePeriodIndex(),
Series(tm.makePeriodIndex()),
Series(pd.date_range('20130101',
periods=3, tz='US/Eastern')),
MultiIndex.from_product(
[range(5),
['foo', 'bar', 'baz'],
pd.date_range('20130101', periods=2)]),
MultiIndex.from_product(
[pd.CategoricalIndex(list('aabc')),
range(3)])]:
self.check_equal(obj)
self.check_not_equal_with_index(obj)
def create_data():
""" create the pickle/msgpack data """
data = {
'A': [0., 1., 2., 3., np.nan],
'B': [0, 1, 0, 1, 0],
'C': ['foo1', 'foo2', 'foo3', 'foo4', 'foo5'],
'D': date_range('1/1/2009', periods=5),
'E': [0., 1, Timestamp('20100101'), 'foo', 2.]
}
index = dict(int=Index(np.arange(10)),
date=date_range('20130101', periods=10),
period=period_range('2013-01-01', freq='M', periods=10))
mi = dict(reg2=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']])),
names=['first', 'second']))
series = dict(float=Series(data['A']),
int=Series(data['B']),
mixed=Series(data['E']),
ts=TimeSeries(np.arange(10).astype(np.int64), index=date_range('20130101',periods=10)),
mi=Series(np.arange(5).astype(np.float64),
index=MultiIndex.from_tuples(tuple(zip(*[[1, 1, 2, 2, 2], [3, 4, 3, 4, 5]])),
names=['one', 'two'])),
dup=Series(np.arange(5).astype(np.float64), index=['A', 'B', 'C', 'D', 'A']),
cat=Series(Categorical(['foo', 'bar', 'baz'])),
per=Series([Period('2000Q1')] * 5))
mixed_dup_df = DataFrame(data)
mixed_dup_df.columns = list("ABCDA")
frame = dict(float=DataFrame(dict(A=series['float'], B=series['float'] + 1)),
int=DataFrame(dict(A=series['int'], B=series['int'] + 1)),
mixed=DataFrame(dict([(k, data[k]) for k in ['A', 'B', 'C', 'D']])),
mi=DataFrame(dict(A=np.arange(5).astype(np.float64), B=np.arange(5).astype(np.int64)),
index=MultiIndex.from_tuples(tuple(zip(*[['bar', 'bar', 'baz', 'baz', 'baz'],
['one', 'two', 'one', 'two', 'three']])),
names=['first', 'second'])),
dup=DataFrame(np.arange(15).reshape(5, 3).astype(np.float64),
columns=['A', 'B', 'A']),
cat_onecol=DataFrame(dict(A=Categorical(['foo', 'bar']))),
cat_and_float=DataFrame(dict(A=Categorical(['foo', 'bar', 'baz']),
B=np.arange(3).astype(np.int64))),
mixed_dup=mixed_dup_df)
mixed_dup_panel = Panel(dict(ItemA=frame['float'], ItemB=frame['int']))
mixed_dup_panel.items = ['ItemA', 'ItemA']
panel = dict(float=Panel(dict(ItemA=frame['float'], ItemB=frame['float'] + 1)),
dup=Panel(np.arange(30).reshape(3, 5, 2).astype(np.float64),
items=['A', 'B', 'A']),
mixed_dup=mixed_dup_panel)
return dict(series=series,
frame=frame,
panel=panel,
index=index,
mi=mi,
sp_series=dict(float=_create_sp_series(),
ts=_create_sp_tsseries()),
sp_frame=dict(float=_create_sp_frame()))
def setup_method(self, method):
self.series_ints = Series(np.random.rand(4), index=lrange(0, 8, 2))
self.frame_ints = DataFrame(np.random.randn(4, 4),
index=lrange(0, 8, 2),
columns=lrange(0, 12, 3))
self.series_uints = Series(np.random.rand(4),
index=UInt64Index(lrange(0, 8, 2)))
self.frame_uints = DataFrame(np.random.randn(4, 4),
index=UInt64Index(lrange(0, 8, 2)),
columns=UInt64Index(lrange(0, 12, 3)))
self.series_floats = Series(np.random.rand(4),
index=Float64Index(range(0, 8, 2)))
self.frame_floats = DataFrame(np.random.randn(4, 4),
index=Float64Index(range(0, 8, 2)),
columns=Float64Index(range(0, 12, 3)))
m_idces = [MultiIndex.from_product([[1, 2], [3, 4]]),
MultiIndex.from_product([[5, 6], [7, 8]]),
MultiIndex.from_product([[9, 10], [11, 12]])]
self.series_multi = Series(np.random.rand(4),
index=m_idces[0])
self.frame_multi = DataFrame(np.random.randn(4, 4),
index=m_idces[0],
columns=m_idces[1])
self.series_labels = Series(np.random.randn(4), index=list('abcd'))
self.frame_labels = DataFrame(np.random.randn(4, 4),
index=list('abcd'), columns=list('ABCD'))
self.series_mixed = Series(np.random.randn(4), index=[2, 4, 'null', 8])
self.frame_mixed = DataFrame(np.random.randn(4, 4),
index=[2, 4, 'null', 8])
self.series_ts = Series(np.random.randn(4),
index=date_range('20130101', periods=4))
self.frame_ts = DataFrame(np.random.randn(4, 4),
index=date_range('20130101', periods=4))
dates_rev = (date_range('20130101', periods=4)
.sort_values(ascending=False))
self.series_ts_rev = Series(np.random.randn(4),
index=dates_rev)
self.frame_ts_rev = DataFrame(np.random.randn(4, 4),
index=dates_rev)
self.frame_empty = DataFrame()
self.series_empty = Series()
# form agglomerates
for o in self._objs:
d = dict()
for t in self._typs:
d[t] = getattr(self, '%s_%s' % (o, t), None)
setattr(self, o, d)
def test_sort_index_multiindex(self, level):
# GH13496
# sort rows by specified level of multi-index
mi = MultiIndex.from_tuples([
[2, 1, 3], [2, 1, 2], [1, 1, 1]], names=list('ABC'))
df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mi)
expected_mi = MultiIndex.from_tuples([
[1, 1, 1],
[2, 1, 2],
[2, 1, 3]], names=list('ABC'))
expected = pd.DataFrame([
[5, 6],
[3, 4],
[1, 2]], index=expected_mi)
result = df.sort_index(level=level)
assert_frame_equal(result, expected)
# sort_remaining=False
expected_mi = MultiIndex.from_tuples([
[1, 1, 1],
[2, 1, 3],
[2, 1, 2]], names=list('ABC'))
expected = pd.DataFrame([
[5, 6],
[1, 2],
[3, 4]], index=expected_mi)
result = df.sort_index(level=level, sort_remaining=False)
assert_frame_equal(result, expected)
def __init__(self, node_name, parents=None, node_domain=None):
super(CPT, self).__init__(node_name)
if node_domain is None or node_domain.__len__() == 0:
self._domain = ['T', 'F']
else:
self._domain = node_domain[:]
self.m = 1
self.n = self._domain.__len__()
if parents is None or parents.__len__() == 0:
self.rows = [self._name]
self.cols = MultiIndex.from_product([self._domain])
else:
parents_names = []
parents_domains = []
for parent in parents:
parents_names.append(parent.name)
parents_domains.append(parent.domain)
self.m = self.m * parent.domain.__len__()
self.cols = MultiIndex.from_product([self._domain], names=[self._name])
self.rows = MultiIndex.from_product(parents_domains, names=parents_names)
self._values = np.zeros((self.m, self.n))
self._table = DataFrame(self._values, index=self.rows, columns=self.cols)
def test_join_multi_to_multi(self, join_type):
# GH 20475
leftindex = MultiIndex.from_product([list('abc'), list('xy'), [1, 2]],
names=['abc', 'xy', 'num'])
left = DataFrame({'v1': range(12)}, index=leftindex)
rightindex = MultiIndex.from_product([list('abc'), list('xy')],
names=['abc', 'xy'])
right = DataFrame({'v2': [100 * i for i in range(1, 7)]},
index=rightindex)
result = left.join(right, on=['abc', 'xy'], how=join_type)
expected = (left.reset_index()
.merge(right.reset_index(),
on=['abc', 'xy'], how=join_type)
.set_index(['abc', 'xy', 'num'])
)
assert_frame_equal(expected, result)
msg = (r'len\(left_on\) must equal the number of levels in the index'
' of "right"')
with pytest.raises(ValueError, match=msg):
left.join(right, on='xy', how=join_type)
with pytest.raises(ValueError, match=msg):
right.join(left, on=['abc', 'xy'], how=join_type)
def test_get_indexer_with_missing_value(index_arr, labels, expected):
# issue 19132
idx = MultiIndex.from_arrays(index_arr)
result = idx.get_indexer(labels)
tm.assert_numpy_array_equal(result, expected)
def decode(obj):
"""
Decoder for deserializing numpy data types.
"""
typ = obj.get(u'typ')
if typ is None:
return obj
elif typ == u'timestamp':
freq = obj[u'freq'] if 'freq' in obj else obj[u'offset']
return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq)
elif typ == u'nat':
return NaT
elif typ == u'period':
return Period(ordinal=obj[u'ordinal'], freq=obj[u'freq'])
elif typ == u'index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype,
obj.get(u'compress'))
return Index(data, dtype=dtype, name=obj[u'name'])
elif typ == u'range_index':
return RangeIndex(obj[u'start'],
obj[u'stop'],
obj[u'step'],
name=obj[u'name'])
elif typ == u'multi_index':
dtype = dtype_for(obj[u'dtype'])
data = unconvert(obj[u'data'], dtype,
obj.get(u'compress'))
data = [tuple(x) for x in data]
return MultiIndex.from_tuples(data, names=obj[u'names'])
elif typ == u'period_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'])
freq = d.pop('freq', None)
return PeriodIndex(PeriodArray(data, freq), **d)
elif typ == u'datetime_index':
data = unconvert(obj[u'data'], np.int64, obj.get(u'compress'))
d = dict(name=obj[u'name'], freq=obj[u'freq'])
result = DatetimeIndex(data, **d)
tz = obj[u'tz']
# reverse tz conversion
if tz is not None:
result = result.tz_localize('UTC').tz_convert(tz)
return result
elif typ in (u'interval_index', 'interval_array'):
return globals()[obj[u'klass']].from_arrays(obj[u'left'],
obj[u'right'],
obj[u'closed'],
name=obj[u'name'])
elif typ == u'category':
from_codes = globals()[obj[u'klass']].from_codes
return from_codes(codes=obj[u'codes'],
categories=obj[u'categories'],
ordered=obj[u'ordered'])
elif typ == u'interval':
return Interval(obj[u'left'], obj[u'right'], obj[u'closed'])
elif typ == u'series':
dtype = dtype_for(obj[u'dtype'])
pd_dtype = pandas_dtype(dtype)
index = obj[u'index']
result = Series(unconvert(obj[u'data'], dtype, obj[u'compress']),
index=index,
dtype=pd_dtype,
name=obj[u'name'])
return result
elif typ == u'block_manager':
axes = obj[u'axes']
def create_block(b):
values = _safe_reshape(unconvert(
b[u'values'], dtype_for(b[u'dtype']),
b[u'compress']), b[u'shape'])
# locs handles duplicate column names, and should be used instead
# of items; see GH 9618
if u'locs' in b:
placement = b[u'locs']
else:
placement = axes[0].get_indexer(b[u'items'])
if is_datetime64tz_dtype(b[u'dtype']):
assert isinstance(values, np.ndarray), type(values)
assert values.dtype == 'M8[ns]', values.dtype
values = DatetimeArray(values, dtype=b[u'dtype'])
return make_block(values=values,
klass=getattr(internals, b[u'klass']),
placement=placement,
dtype=b[u'dtype'])
blocks = [create_block(b) for b in obj[u'blocks']]
return globals()[obj[u'klass']](BlockManager(blocks, axes))
elif typ == u'datetime':
return parse(obj[u'data'])
elif typ == u'datetime64':
return np.datetime64(parse(obj[u'data']))
elif typ == u'date':
return parse(obj[u'data']).date()
elif typ == u'timedelta':
return timedelta(*obj[u'data'])
elif typ == u'timedelta64':
return np.timedelta64(int(obj[u'data']))
# elif typ == 'sparse_series':
# dtype = dtype_for(obj['dtype'])
# return SparseSeries(
# unconvert(obj['sp_values'], dtype, obj['compress']),
# sparse_index=obj['sp_index'], index=obj['index'],
# fill_value=obj['fill_value'], kind=obj['kind'], name=obj['name'])
# elif typ == 'sparse_dataframe':
# return SparseDataFrame(
# obj['data'], columns=obj['columns'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind']
# )
# elif typ == 'sparse_panel':
# return SparsePanel(
# obj['data'], items=obj['items'],
# default_fill_value=obj['default_fill_value'],
# default_kind=obj['default_kind'])
elif typ == u'block_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'blocs'],
obj[u'blengths'])
elif typ == u'int_index':
return globals()[obj[u'klass']](obj[u'length'], obj[u'indices'])
elif typ == u'ndarray':
return unconvert(obj[u'data'], np.typeDict[obj[u'dtype']],
obj.get(u'compress')).reshape(obj[u'shape'])
elif typ == u'np_scalar':
if obj.get(u'sub_typ') == u'np_complex':
return c2f(obj[u'real'], obj[u'imag'], obj[u'dtype'])
else:
dtype = dtype_for(obj[u'dtype'])
try:
return dtype(obj[u'data'])
except (ValueError, TypeError):
return dtype.type(obj[u'data'])
elif typ == u'np_complex':
return complex(obj[u'real'] + u'+' + obj[u'imag'] + u'j')
elif isinstance(obj, (dict, list, set)):
return obj
else:
return obj
def test_join_inner_multiindex(self):
key1 = ["bar", "bar", "bar", "foo", "foo", "baz", "baz", "qux", "qux", "snap"]
key2 = [
"two",
"one",
"three",
"one",
"two",
"one",
"two",
"two",
"three",
"one",
]
data = np.random.randn(len(key1))
data = DataFrame({"key1": key1, "key2": key2, "data": data})
index = MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=["first", "second"],
)
to_join = DataFrame(
np.random.randn(10, 3), index=index, columns=["j_one", "j_two", "j_three"]
)
joined = data.join(to_join, on=["key1", "key2"], how="inner")
expected = merge(
data,
to_join.reset_index(),
left_on=["key1", "key2"],
right_on=["first", "second"],
how="inner",
sort=False,
)
expected2 = merge(
to_join,
data,
right_on=["key1", "key2"],
left_index=True,
how="inner",
sort=False,
)
tm.assert_frame_equal(joined, expected2.reindex_like(joined))
expected2 = merge(
to_join,
data,
right_on=["key1", "key2"],
left_index=True,
how="inner",
sort=False,
)
expected = expected.drop(["first", "second"], axis=1)
expected.index = joined.index
assert joined.index.is_monotonic
tm.assert_frame_equal(joined, expected)
def time_multiindex_from_iterables(self):
MultiIndex.from_product(self.iterables)
def test_constructor_dict_of_tuples(self):
data = {(1, 2): 3, (None, 5): 6}
result = Series(data).sort_values()
expected = Series([3, 6],
index=MultiIndex.from_tuples([(1, 2), (None, 5)]))
tm.assert_series_equal(result, expected)
def test_slice_locs_with_missing_value(index_arr, expected, start_idx,
end_idx):
# issue 19132
idx = MultiIndex.from_arrays(index_arr)
result = idx.slice_locs(start=start_idx, end=end_idx)
assert result == expected
def test_get_slice_bound_with_missing_value(index_arr, expected, target, algo):
# issue 19132
idx = MultiIndex.from_arrays(index_arr)
result = idx.get_slice_bound(target, side=algo, kind="loc")
assert result == expected
expected = DataFrame(
[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
columns=MultiIndex.from_tuples([("a", ), ("b", ), ("c", )],
names=[("A", "a")]),
index=pd.Index([1, 2, 3], name=("B", "b")),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"unstack_idx, expected_values, expected_index, expected_columns",
[
(
("A", "a"),
[[1, 1], [1, 1], [1, 1], [1, 1]],
MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)],
names=["B", "C"]),
MultiIndex.from_tuples([("a", ), ("b", )], names=[("A", "a")]),
),
(
(("A", "a"), "B"),
[[1, 1, 1, 1], [1, 1, 1, 1]],
pd.Index([3, 4], name="C"),
MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)],
names=[("A", "a"), "B"]),
),
],
)
def test_unstack_mixed_type_name_in_multiindex(unstack_idx, expected_values,
expected_index,
expected_columns):
# GH 19966
def create_fip(temporary_store=None, year=None):
assert temporary_store is not None
assert year is not None
# fip : fichier d'imposition des personnes
"""
Creates a 'fipDat' table containing all these 'fip individuals'
"""
# Some individuals are declared as 'personne à charge' (pac) on 'tax forms'
# but are not present in the erf or eec tables.
# We add them to ensure consistency between concepts.
year_specific_by_generic = year_specific_by_generic_data_frame_name(year)
erfs_survey_collection = SurveyCollection.load(
collection='erfs', config_files_directory=config_files_directory)
survey = erfs_survey_collection.get_survey('erfs_{}'.format(year))
log.info(u"Démarrage de 03_fip")
# anaisenf is a string containing letter code of pac (F,G,H,I,J,N,R) and year of birth (example: 'F1990H1992')
# when a child is invalid, he appears twice in anaisenf (example: F1900G1900 is a single invalid child born in 1990)
erfFoyVar = ['declar', 'anaisenf']
foyer = survey.get_values(table=year_specific_by_generic["foyer"],
variables=erfFoyVar)
foyer.replace({'anaisenf': {'NA': np.nan}}, inplace=True)
log.info(u"Etape 1 : on récupere les personnes à charge des foyers")
log.info(u" 1.1 : Création des codes des enfants")
foyer['anaisenf'] = foyer['anaisenf'].astype('string')
nb_pac_max = len(max(foyer['anaisenf'], key=len)) / 5
log.info(u"il ya a au maximum {} pac par foyer".format(nb_pac_max))
# Separating the string coding the pac of each "déclaration".
# Creating a list containing the new variables.
# Creating the multi_index for the columns
multi_index_columns = []
assert int(
nb_pac_max
) == nb_pac_max, "nb_pac_max = {} which is not an integer".format(
nb_pac_max)
nb_pac_max = int(nb_pac_max)
for i in range(1, nb_pac_max + 1):
pac_tuples_list = [(i, 'declaration'), (i, 'type_pac'), (i, 'naia')]
multi_index_columns += pac_tuples_list
columns = MultiIndex.from_tuples(multi_index_columns,
names=['pac_number', 'variable'])
fip = DataFrame(np.random.randn(len(foyer), 3 * nb_pac_max),
columns=columns)
for i in range(1, nb_pac_max +
1): # TODO: using values to deal with mismatching indexes
fip[(i, 'declaration')] = foyer['declar'].values
fip[(i, 'type_pac')] = foyer['anaisenf'].str[5 * (i - 1)].values
fip[(i, 'naia')] = foyer['anaisenf'].str[5 * (i - 1) + 1:5 * i].values
fip = fip.stack("pac_number")
fip.reset_index(inplace=True)
fip.drop(['level_0'], axis=1, inplace=True)
log.info(u" 1.2 : elimination des foyers fiscaux sans pac")
# Clearing missing values and changing data format
fip = fip[(fip.type_pac.notnull()) & (fip.naia != 'an') &
(fip.naia != '')].copy()
fip = fip.sort(columns=['declaration', 'naia', 'type_pac'])
fip.set_index(["declaration", "pac_number"], inplace=True)
fip = fip.reset_index()
fip.drop(['pac_number'], axis=1, inplace=True)
assert fip.type_pac.isin(["F", "G", "H", "I", "J", "N", "R"]).all(), \
"Certains types de PAC ne sont pas des cases connues"
# control(fip, debug=True, verbose=True, verbose_columns=['naia'])
log.info(
u" 1.3 : on enlève les individus F pour lesquels il existe un individu G"
)
type_FG = fip[fip.type_pac.isin(
['F', 'G'])].copy() # Filtre pour ne travailler que sur F & G
type_FG['same_pair'] = type_FG.duplicated(subset=['declaration', 'naia'],
take_last=True)
type_FG['is_twin'] = type_FG.duplicated(
subset=['declaration', 'naia', 'type_pac'])
type_FG['to_keep'] = ~(type_FG['same_pair']) | type_FG['is_twin']
# Note : On conserve ceux qui ont des couples déclar/naia différents et les jumeaux
# puis on retire les autres (à la fois F et G)
fip['to_keep'] = np.nan
fip.update(type_FG)
log.info(u" 1.4 : on enlève les H pour lesquels il y a un I")
type_HI = fip[fip.type_pac.isin(['H', 'I'])].copy()
type_HI['same_pair'] = type_HI.duplicated(subset=['declaration', 'naia'],
take_last=True)
type_HI['is_twin'] = type_HI.duplicated(
subset=['declaration', 'naia', 'type_pac'])
type_HI['to_keep'] = (~(type_HI['same_pair']) |
(type_HI['is_twin'])).values
fip.update(type_HI)
fip['to_keep'] = fip['to_keep'].fillna(True)
log.info(
u"{} F, G, H or I non redundant pac kept over {} potential candidates".
format(fip['to_keep'].sum(), len(fip)))
indivifip = fip[fip['to_keep']].copy()
del indivifip['to_keep'], fip, type_FG, type_HI
#
# control(indivifip, debug=True)
log.info(u"Step 2 : matching indivifip with eec file")
indivi = temporary_store['indivim_{}'.format(year)]
pac = indivi[(indivi.persfip.notnull()) & (indivi.persfip == 'pac')].copy()
assert indivifip.naia.notnull().all(
), "Il y a des valeurs manquantes de la variable naia"
# For safety enforce pac.naia and indivifip.naia dtypes
pac['naia'] = pac.naia.astype('int32')
indivifip['naia'] = indivifip.naia.astype('int32')
pac['key1'] = zip(pac.naia, pac['declar1'].str[:29])
pac['key2'] = zip(pac.naia, pac['declar2'].str[:29])
indivifip['key'] = zip(indivifip.naia.values,
indivifip['declaration'].str[:29].values)
assert pac.naia.dtype == indivifip.naia.dtype, \
"Les dtypes de pac.naia {} et indvifip.naia {} sont différents".format(pac.naia.dtype, indivifip.naia.dtype)
fip = indivifip[~(indivifip.key.isin(pac.key1.values))].copy()
fip = fip[~(fip.key.isin(pac.key2.values))].copy()
log.info(u" 2.1 new fip created")
# We build a dataframe to link the pac to their type and noindiv
tmp_pac1 = pac[['noindiv', 'key1']].copy()
tmp_pac2 = pac[['noindiv', 'key2']].copy()
tmp_indivifip = indivifip[['key', 'type_pac', 'naia']].copy()
pac_ind1 = tmp_pac1.merge(tmp_indivifip,
left_on='key1',
right_on='key',
how='inner')
log.info(u"{} pac dans les 1ères déclarations".format(len(pac_ind1)))
pac_ind2 = tmp_pac2.merge(tmp_indivifip,
left_on='key2',
right_on='key',
how='inner')
log.info(u"{} pac dans les 2èms déclarations".format(len(pac_ind2)))
log.info("{} duplicated pac_ind1".format(pac_ind1.duplicated().sum()))
log.info("{} duplicated pac_ind2".format(pac_ind2.duplicated().sum()))
del pac_ind1['key1'], pac_ind2['key2']
if len(pac_ind1.index) == 0:
if len(pac_ind2.index) == 0:
log.info(
u"Warning : no link between pac and noindiv for both pacInd1&2"
)
else:
log.info(u"Warning : pacInd1 is an empty data frame")
pacInd = pac_ind2
elif len(pac_ind2.index) == 0:
log.info(u"Warning : pacInd2 is an empty data frame")
pacInd = pac_ind1
else:
pacInd = concat([pac_ind2, pac_ind1])
assert len(pac_ind1) + len(pac_ind2) == len(pacInd)
log.info("{} null pac_ind2.type_pac".format(
pac_ind2.type_pac.isnull().sum()))
log.info("pacInd.type_pac.value_counts()) \n {}".format(
pacInd.type_pac.value_counts(dropna=False)))
log.info(u" 2.2 : pacInd created")
log.info(u"doublons noindiv, type_pac {}".format(
pacInd.duplicated(['noindiv', 'type_pac']).sum()))
log.info(u"doublons noindiv seulement {}".format(
pacInd.duplicated('noindiv').sum()))
log.info(u"nb de NaN {}".format(pacInd.type_pac.isnull().sum()))
del pacInd["key"]
pacIndiv = pacInd[~(pacInd.duplicated('noindiv'))].copy()
# pacIndiv.reset_index(inplace=True)
log.info("{}".format(pacIndiv.columns))
temporary_store['pacIndiv_{}'.format(year)] = pacIndiv
log.info("{}".format(pacIndiv.type_pac.value_counts()))
gc.collect()
# We keep the fip in the menage of their parents because it is used in to
# build the famille. We should build an individual ident (ménage) for the fip that are
# older than 18 since they are not in their parents' menage according to the eec
log.info("{}".format(indivi['declar1'].str[0:2].value_counts()))
log.info("{}".format(indivi['declar1'].str[0:2].describe()))
log.info("{}".format(indivi['declar1'].str[0:2].notnull().all()))
log.info("{}".format(indivi.info()))
selection = indivi['declar1'].str[0:2] != ""
indivi['noidec'] = indivi.declar1[selection].str[0:2].astype(
'int32') # To be used later to set idfoy
individec1 = indivi[(indivi.declar1.isin(fip.declaration.values))
& (indivi.persfip == "vous")]
individec1 = individec1[[
"declar1", "noidec", "ident", "rga", "ztsai", "ztsao"
]].copy()
individec1 = individec1.rename(columns={'declar1': 'declaration'})
fip1 = fip.merge(individec1, on='declaration')
log.info(u" 2.3 : fip1 created")
individec2 = indivi.loc[
(indivi.declar2.isin(fip.declaration.values)) &
(indivi['persfip'] == "vous"),
["declar2", "noidec", "ident", "rga", "ztsai", "ztsao"]].copy()
individec2.rename(columns={'declar2': 'declaration'}, inplace=True)
fip2 = fip.merge(individec2)
log.info(u" 2.4 : fip2 created")
fip1.duplicated().value_counts()
fip2.duplicated().value_counts()
fip = concat([fip1, fip2])
fip['persfip'] = 'pac'
fip['year'] = year
fip['year'] = fip['year'].astype(
'float') # BUG; pas de colonne année dans la DF
fip['noi'] = 99
fip['noicon'] = None
fip['noindiv'] = fip['declaration'].copy()
fip['noiper'] = None
fip['noimer'] = None
fip['declar1'] = fip['declaration'].copy()
fip['naim'] = 99
fip['lien'] = None
fip['quelfic'] = 'FIP'
fip['acteu'] = None
fip['agepf'] = fip['year'] - fip.naia.astype('float')
fip['lpr'] = (fip['agepf'] <= 20) * 3 + (fip['agepf'] > 20) * 4
fip['stc'] = None
fip['contra'] = None
fip['titc'] = None
fip['mrec'] = None
fip['forter'] = None
fip['rstg'] = None
fip['retrai'] = None
fip['cohab'] = None
fip['sexe'] = None
fip['persfip'] = "pac"
fip['agepr'] = None
fip['actrec'] = (fip['agepf'] <= 15) * 9 + (fip['agepf'] > 15) * 5
# TODO: probleme actrec des enfants fip entre 16 et 20 ans : on ne sait pas s'ils sont étudiants ou salariés */
# TODO problème avec les mois des enfants FIP : voir si on ne peut pas remonter à ces valeurs: Alexis: clairement non
# Reassigning noi for fip children if they are more than one per foyer fiscal
fip["noi"] = fip["noi"].astype("int64")
fip["ident"] = fip["ident"].astype("int64")
fip_tmp = fip[['noi', 'ident']]
while any(fip.duplicated(subset=['noi', 'ident'])):
fip_tmp = fip.loc[:, ['noi', 'ident']]
dup = fip_tmp.duplicated()
tmp = fip.loc[dup, 'noi']
log.info("{}".format(len(tmp)))
fip.loc[dup, 'noi'] = tmp.astype('int64') - 1
fip['idfoy'] = 100 * fip['ident'] + fip['noidec']
fip['noindiv'] = 100 * fip['ident'] + fip['noi']
fip['type_pac'] = 0
fip['key'] = 0
log.info("Number of duplicated fip: {}".format(
fip.duplicated('noindiv').value_counts()))
temporary_store['fipDat_{}'.format(year)] = fip
del fip, fip1, individec1, indivifip, indivi, pac
log.info(u"fip sauvegardé")
def test_get_loc_level():
index = MultiIndex(
levels=[Index(np.arange(4)),
Index(np.arange(4)),
Index(np.arange(4))],
codes=[
np.array([0, 0, 1, 2, 2, 2, 3, 3]),
np.array([0, 1, 0, 0, 0, 1, 0, 1]),
np.array([1, 0, 1, 1, 0, 0, 1, 0]),
],
)
loc, new_index = index.get_loc_level((0, 1))
expected = slice(1, 2)
exp_index = index[expected].droplevel(0).droplevel(0)
assert loc == expected
assert new_index.equals(exp_index)
loc, new_index = index.get_loc_level((0, 1, 0))
expected = 1
assert loc == expected
assert new_index is None
with pytest.raises(KeyError, match=r"^\(2, 2\)$"):
index.get_loc_level((2, 2))
# GH 22221: unused label
with pytest.raises(KeyError, match=r"^2$"):
index.drop(2).get_loc_level(2)
# Unused label on unsorted level:
with pytest.raises(KeyError, match=r"^2$"):
index.drop(1, level=2).get_loc_level(2, level=2)
index = MultiIndex(
levels=[[2000], list(range(4))],
codes=[np.array([0, 0, 0, 0]),
np.array([0, 1, 2, 3])],
)
result, new_index = index.get_loc_level((2000, slice(None, None)))
expected = slice(None, None)
assert result == expected
assert new_index.equals(index.droplevel(0))
def test_sort_index_nan_multiindex(self):
# GH#14784
# incorrect sorting w.r.t. nans
tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(np.arange(16).reshape(4, 4),
index=mi,
columns=list("ABCD"))
s = Series(np.arange(4), index=mi)
df2 = DataFrame({
"date":
pd.DatetimeIndex([
"20121002",
"20121007",
"20130130",
"20130202",
"20130305",
"20121002",
"20121207",
"20130130",
"20130202",
"20130305",
"20130202",
"20130305",
]),
"user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
"whole_cost": [
1790,
np.nan,
280,
259,
np.nan,
623,
90,
312,
np.nan,
301,
359,
801,
],
"cost": [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12],
}).set_index(["date", "user_id"])
# sorting frame, default nan position is last
result = df.sort_index()
expected = df.iloc[[3, 0, 2, 1], :]
tm.assert_frame_equal(result, expected)
# sorting frame, nan position last
result = df.sort_index(na_position="last")
expected = df.iloc[[3, 0, 2, 1], :]
tm.assert_frame_equal(result, expected)
# sorting frame, nan position first
result = df.sort_index(na_position="first")
expected = df.iloc[[1, 2, 3, 0], :]
tm.assert_frame_equal(result, expected)
# sorting frame with removed rows
result = df2.dropna().sort_index()
expected = df2.sort_index().dropna()
tm.assert_frame_equal(result, expected)
# sorting series, default nan position is last
result = s.sort_index()
expected = s.iloc[[3, 0, 2, 1]]
tm.assert_series_equal(result, expected)
# sorting series, nan position last
result = s.sort_index(na_position="last")
expected = s.iloc[[3, 0, 2, 1]]
tm.assert_series_equal(result, expected)
# sorting series, nan position first
result = s.sort_index(na_position="first")
expected = s.iloc[[1, 2, 3, 0]]
tm.assert_series_equal(result, expected)
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"arr, idx",
[
([1, 2, 3, 4], [0, 2, 1, 3]),
([1, np.nan, 3, np.nan], [0, 2, 1, 3]),
(
[1, np.nan, 3, np.nan],
MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c"), (3, "c")]),
),
],
)
def test_equals(arr, idx):
s1 = Series(arr, index=idx)
s2 = s1.copy()
assert s1.equals(s2)
s1[1] = 9
assert not s1.equals(s2)
@pytest.mark.parametrize(
"val",
[1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {
class TestDataFrameSortIndex:
def test_sort_index_and_reconstruction_doc_example(self):
# doc example
df = DataFrame(
{"value": [1, 2, 3, 4]},
index=MultiIndex(levels=[["a", "b"], ["bb", "aa"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1]]),
)
assert df.index.is_lexsorted()
assert not df.index.is_monotonic
# sort it
expected = DataFrame(
{"value": [2, 1, 4, 3]},
index=MultiIndex(levels=[["a", "b"], ["aa", "bb"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1]]),
)
result = df.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
# reconstruct
result = df.sort_index().copy()
result.index = result.index._sort_levels_monotonic()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
def test_sort_index_non_existent_label_multiindex(self):
# GH#12261
df = DataFrame(0, columns=[], index=MultiIndex.from_product([[], []]))
df.loc["b", "2"] = 1
df.loc["a", "3"] = 1
result = df.sort_index().index.is_monotonic
assert result is True
def test_sort_index_reorder_on_ops(self):
# GH#15687
df = DataFrame(
np.random.randn(8, 2),
index=MultiIndex.from_product(
[["a", "b"], ["big", "small"], ["red", "blu"]],
names=["letter", "size", "color"],
),
columns=["near", "far"],
)
df = df.sort_index()
def my_func(group):
group.index = ["newz", "newa"]
return group
result = df.groupby(
level=["letter", "size"]).apply(my_func).sort_index()
expected = MultiIndex.from_product(
[["a", "b"], ["big", "small"], ["newa", "newz"]],
names=["letter", "size", None],
)
tm.assert_index_equal(result.index, expected)
def test_sort_index_nan_multiindex(self):
# GH#14784
# incorrect sorting w.r.t. nans
tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]]
mi = MultiIndex.from_tuples(tuples)
df = DataFrame(np.arange(16).reshape(4, 4),
index=mi,
columns=list("ABCD"))
s = Series(np.arange(4), index=mi)
df2 = DataFrame({
"date":
pd.DatetimeIndex([
"20121002",
"20121007",
"20130130",
"20130202",
"20130305",
"20121002",
"20121207",
"20130130",
"20130202",
"20130305",
"20130202",
"20130305",
]),
"user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5],
"whole_cost": [
1790,
np.nan,
280,
259,
np.nan,
623,
90,
312,
np.nan,
301,
359,
801,
],
"cost": [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12],
}).set_index(["date", "user_id"])
# sorting frame, default nan position is last
result = df.sort_index()
expected = df.iloc[[3, 0, 2, 1], :]
tm.assert_frame_equal(result, expected)
# sorting frame, nan position last
result = df.sort_index(na_position="last")
expected = df.iloc[[3, 0, 2, 1], :]
tm.assert_frame_equal(result, expected)
# sorting frame, nan position first
result = df.sort_index(na_position="first")
expected = df.iloc[[1, 2, 3, 0], :]
tm.assert_frame_equal(result, expected)
# sorting frame with removed rows
result = df2.dropna().sort_index()
expected = df2.sort_index().dropna()
tm.assert_frame_equal(result, expected)
# sorting series, default nan position is last
result = s.sort_index()
expected = s.iloc[[3, 0, 2, 1]]
tm.assert_series_equal(result, expected)
# sorting series, nan position last
result = s.sort_index(na_position="last")
expected = s.iloc[[3, 0, 2, 1]]
tm.assert_series_equal(result, expected)
# sorting series, nan position first
result = s.sort_index(na_position="first")
expected = s.iloc[[1, 2, 3, 0]]
tm.assert_series_equal(result, expected)
def test_sort_index_nan(self):
# GH#3917
# Test DataFrame with nan label
df = DataFrame(
{
"A": [1, 2, np.nan, 1, 6, 8, 4],
"B": [9, np.nan, 5, 2, 5, 4, 5]
},
index=[1, 2, 3, 4, 5, 6, np.nan],
)
# NaN label, ascending=True, na_position='last'
sorted_df = df.sort_index(kind="quicksort",
ascending=True,
na_position="last")
expected = DataFrame(
{
"A": [1, 2, np.nan, 1, 6, 8, 4],
"B": [9, np.nan, 5, 2, 5, 4, 5]
},
index=[1, 2, 3, 4, 5, 6, np.nan],
)
tm.assert_frame_equal(sorted_df, expected)
# NaN label, ascending=True, na_position='first'
sorted_df = df.sort_index(na_position="first")
expected = DataFrame(
{
"A": [4, 1, 2, np.nan, 1, 6, 8],
"B": [5, 9, np.nan, 5, 2, 5, 4]
},
index=[np.nan, 1, 2, 3, 4, 5, 6],
)
tm.assert_frame_equal(sorted_df, expected)
# NaN label, ascending=False, na_position='last'
sorted_df = df.sort_index(kind="quicksort", ascending=False)
expected = DataFrame(
{
"A": [8, 6, 1, np.nan, 2, 1, 4],
"B": [4, 5, 2, 5, np.nan, 9, 5]
},
index=[6, 5, 4, 3, 2, 1, np.nan],
)
tm.assert_frame_equal(sorted_df, expected)
# NaN label, ascending=False, na_position='first'
sorted_df = df.sort_index(kind="quicksort",
ascending=False,
na_position="first")
expected = DataFrame(
{
"A": [4, 8, 6, 1, np.nan, 2, 1],
"B": [5, 4, 5, 2, 5, np.nan, 9]
},
index=[np.nan, 6, 5, 4, 3, 2, 1],
)
tm.assert_frame_equal(sorted_df, expected)
def test_sort_index_multi_index(self):
# GH#25775, testing that sorting by index works with a multi-index.
df = DataFrame({
"a": [3, 1, 2],
"b": [0, 0, 0],
"c": [0, 1, 2],
"d": list("abc")
})
result = df.set_index(list("abc")).sort_index(level=list("ba"))
expected = DataFrame({
"a": [1, 2, 3],
"b": [0, 0, 0],
"c": [1, 2, 0],
"d": list("bca")
})
expected = expected.set_index(list("abc"))
tm.assert_frame_equal(result, expected)
def test_sort_index_inplace(self):
frame = DataFrame(np.random.randn(4, 4),
index=[1, 2, 3, 4],
columns=["A", "B", "C", "D"])
# axis=0
unordered = frame.loc[[3, 2, 4, 1]]
a_id = id(unordered["A"])
df = unordered.copy()
return_value = df.sort_index(inplace=True)
assert return_value is None
expected = frame
tm.assert_frame_equal(df, expected)
assert a_id != id(df["A"])
df = unordered.copy()
return_value = df.sort_index(ascending=False, inplace=True)
assert return_value is None
expected = frame[::-1]
tm.assert_frame_equal(df, expected)
# axis=1
unordered = frame.loc[:, ["D", "B", "C", "A"]]
df = unordered.copy()
return_value = df.sort_index(axis=1, inplace=True)
assert return_value is None
expected = frame
tm.assert_frame_equal(df, expected)
df = unordered.copy()
return_value = df.sort_index(axis=1, ascending=False, inplace=True)
assert return_value is None
expected = frame.iloc[:, ::-1]
tm.assert_frame_equal(df, expected)
def test_sort_index_different_sortorder(self):
A = np.arange(20).repeat(5)
B = np.tile(np.arange(5), 20)
indexer = np.random.permutation(100)
A = A.take(indexer)
B = B.take(indexer)
df = DataFrame({"A": A, "B": B, "C": np.random.randn(100)})
ex_indexer = np.lexsort((df.B.max() - df.B, df.A))
expected = df.take(ex_indexer)
# test with multiindex, too
idf = df.set_index(["A", "B"])
result = idf.sort_index(ascending=[1, 0])
expected = idf.take(ex_indexer)
tm.assert_frame_equal(result, expected)
# also, Series!
result = idf["C"].sort_index(ascending=[1, 0])
tm.assert_series_equal(result, expected["C"])
def test_sort_index_level(self):
mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC"))
df = DataFrame([[1, 2], [3, 4]], mi)
result = df.sort_index(level="A", sort_remaining=False)
expected = df
tm.assert_frame_equal(result, expected)
result = df.sort_index(level=["A", "B"], sort_remaining=False)
expected = df
tm.assert_frame_equal(result, expected)
# Error thrown by sort_index when
# first index is sorted last (GH#26053)
result = df.sort_index(level=["C", "B", "A"])
expected = df.iloc[[1, 0]]
tm.assert_frame_equal(result, expected)
result = df.sort_index(level=["B", "C", "A"])
expected = df.iloc[[1, 0]]
tm.assert_frame_equal(result, expected)
result = df.sort_index(level=["C", "A"])
expected = df.iloc[[1, 0]]
tm.assert_frame_equal(result, expected)
def test_sort_index_categorical_index(self):
df = DataFrame({
"A":
np.arange(6, dtype="int64"),
"B":
Series(list("aabbca")).astype(CategoricalDtype(list("cab"))),
}).set_index("B")
result = df.sort_index()
expected = df.iloc[[4, 0, 1, 5, 2, 3]]
tm.assert_frame_equal(result, expected)
result = df.sort_index(ascending=False)
expected = df.iloc[[2, 3, 0, 1, 5, 4]]
tm.assert_frame_equal(result, expected)
def test_sort_index(self):
# GH#13496
frame = DataFrame(
np.arange(16).reshape(4, 4),
index=[1, 2, 3, 4],
columns=["A", "B", "C", "D"],
)
# axis=0 : sort rows by index labels
unordered = frame.loc[[3, 2, 4, 1]]
result = unordered.sort_index(axis=0)
expected = frame
tm.assert_frame_equal(result, expected)
result = unordered.sort_index(ascending=False)
expected = frame[::-1]
tm.assert_frame_equal(result, expected)
# axis=1 : sort columns by column names
unordered = frame.iloc[:, [2, 1, 3, 0]]
result = unordered.sort_index(axis=1)
tm.assert_frame_equal(result, frame)
result = unordered.sort_index(axis=1, ascending=False)
expected = frame.iloc[:, ::-1]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("level", ["A", 0]) # GH#21052
def test_sort_index_multiindex(self, level):
# GH#13496
# sort rows by specified level of multi-index
mi = MultiIndex.from_tuples([[2, 1, 3], [2, 1, 2], [1, 1, 1]],
names=list("ABC"))
df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mi)
expected_mi = MultiIndex.from_tuples([[1, 1, 1], [2, 1, 2], [2, 1, 3]],
names=list("ABC"))
expected = DataFrame([[5, 6], [3, 4], [1, 2]], index=expected_mi)
result = df.sort_index(level=level)
tm.assert_frame_equal(result, expected)
# sort_remaining=False
expected_mi = MultiIndex.from_tuples([[1, 1, 1], [2, 1, 3], [2, 1, 2]],
names=list("ABC"))
expected = DataFrame([[5, 6], [1, 2], [3, 4]], index=expected_mi)
result = df.sort_index(level=level, sort_remaining=False)
tm.assert_frame_equal(result, expected)
def test_sort_index_intervalindex(self):
# this is a de-facto sort via unstack
# confirming that we sort in the order of the bins
y = Series(np.random.randn(100))
x1 = Series(np.sign(np.random.randn(100)))
x2 = pd.cut(Series(np.random.randn(100)), bins=[-3, -0.5, 0, 0.5, 3])
model = pd.concat([y, x1, x2], axis=1, keys=["Y", "X1", "X2"])
result = model.groupby(["X1", "X2"], observed=True).mean().unstack()
expected = IntervalIndex.from_tuples([(-3.0, -0.5), (-0.5, 0.0),
(0.0, 0.5), (0.5, 3.0)],
closed="right")
result = result.columns.levels[1].categories
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.parametrize(
"original_dict, sorted_dict, ascending, ignore_index, output_index",
[
({
"A": [1, 2, 3]
}, {
"A": [2, 3, 1]
}, False, True, [0, 1, 2]),
({
"A": [1, 2, 3]
}, {
"A": [1, 3, 2]
}, True, True, [0, 1, 2]),
({
"A": [1, 2, 3]
}, {
"A": [2, 3, 1]
}, False, False, [5, 3, 2]),
({
"A": [1, 2, 3]
}, {
"A": [1, 3, 2]
}, True, False, [2, 3, 5]),
],
)
def test_sort_index_ignore_index(self, inplace, original_dict, sorted_dict,
ascending, ignore_index, output_index):
# GH 30114
original_index = [2, 5, 3]
df = DataFrame(original_dict, index=original_index)
expected_df = DataFrame(sorted_dict, index=output_index)
kwargs = {
"ascending": ascending,
"ignore_index": ignore_index,
"inplace": inplace,
}
if inplace:
result_df = df.copy()
result_df.sort_index(**kwargs)
else:
result_df = df.sort_index(**kwargs)
tm.assert_frame_equal(result_df, expected_df)
tm.assert_frame_equal(df, DataFrame(original_dict,
index=original_index))
@pytest.mark.parametrize("inplace", [True, False])
@pytest.mark.parametrize(
"original_dict, sorted_dict, ascending, ignore_index, output_index",
[
(
{
"M1": [1, 2],
"M2": [3, 4]
},
{
"M1": [1, 2],
"M2": [3, 4]
},
True,
True,
[0, 1],
),
(
{
"M1": [1, 2],
"M2": [3, 4]
},
{
"M1": [2, 1],
"M2": [4, 3]
},
False,
True,
[0, 1],
),
(
{
"M1": [1, 2],
"M2": [3, 4]
},
{
"M1": [1, 2],
"M2": [3, 4]
},
True,
False,
MultiIndex.from_tuples([[2, 1], [3, 4]], names=list("AB")),
),
(
{
"M1": [1, 2],
"M2": [3, 4]
},
{
"M1": [2, 1],
"M2": [4, 3]
},
False,
False,
MultiIndex.from_tuples([[3, 4], [2, 1]], names=list("AB")),
),
],
)
def test_sort_index_ignore_index_multi_index(self, inplace, original_dict,
sorted_dict, ascending,
ignore_index, output_index):
# GH 30114, this is to test ignore_index on MulitIndex of index
mi = MultiIndex.from_tuples([[2, 1], [3, 4]], names=list("AB"))
df = DataFrame(original_dict, index=mi)
expected_df = DataFrame(sorted_dict, index=output_index)
kwargs = {
"ascending": ascending,
"ignore_index": ignore_index,
"inplace": inplace,
}
if inplace:
result_df = df.copy()
result_df.sort_index(**kwargs)
else:
result_df = df.sort_index(**kwargs)
tm.assert_frame_equal(result_df, expected_df)
tm.assert_frame_equal(df, DataFrame(original_dict, index=mi))
def test_sort_index_categorical_multiindex(self):
# GH#15058
df = DataFrame({
"a":
range(6),
"l1":
pd.Categorical(
["a", "a", "b", "b", "c", "c"],
categories=["c", "a", "b"],
ordered=True,
),
"l2": [0, 1, 0, 1, 0, 1],
})
result = df.set_index(["l1", "l2"]).sort_index()
expected = DataFrame(
[4, 5, 0, 1, 2, 3],
columns=["a"],
index=MultiIndex(
levels=[
pd.CategoricalIndex(
["c", "a", "b"],
categories=["c", "a", "b"],
ordered=True,
name="l1",
dtype="category",
),
[0, 1],
],
codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
names=["l1", "l2"],
),
)
tm.assert_frame_equal(result, expected)
def test_sort_index_and_reconstruction(self):
# GH#15622
# lexsortedness should be identical
# across MultiIndex construction methods
df = DataFrame([[1, 1], [2, 2]], index=list("ab"))
expected = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_tuples([(0.5, "a"), (0.5, "b"), (0.8, "a"),
(0.8, "b")]),
)
assert expected.index.is_lexsorted()
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_product([[0.5, 0.8], list("ab")]),
)
result = result.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex(levels=[[0.5, 0.8], ["a", "b"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1]]),
)
result = result.sort_index()
assert result.index.is_lexsorted()
tm.assert_frame_equal(result, expected)
concatted = pd.concat([df, df], keys=[0.8, 0.5])
result = concatted.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
# GH#14015
df = DataFrame(
[[1, 2], [6, 7]],
columns=MultiIndex.from_tuples(
[(0, "20160811 12:00:00"), (0, "20160809 12:00:00")],
names=["l1", "Date"],
),
)
df.columns = df.columns.set_levels(pd.to_datetime(
df.columns.levels[1]),
level=1)
assert not df.columns.is_lexsorted()
assert not df.columns.is_monotonic
result = df.sort_index(axis=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
result = df.sort_index(axis=1, level=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
# TODO: better name, de-duplicate with test_sort_index_level above
def test_sort_index_level2(self):
mi = MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=["first", "second"],
)
frame = DataFrame(
np.random.randn(10, 3),
index=mi,
columns=Index(["A", "B", "C"], name="exp"),
)
df = frame.copy()
df.index = np.arange(len(df))
# axis=1
# series
a_sorted = frame["A"].sort_index(level=0)
# preserve names
assert a_sorted.index.names == frame.index.names
# inplace
rs = frame.copy()
return_value = rs.sort_index(level=0, inplace=True)
assert return_value is None
tm.assert_frame_equal(rs, frame.sort_index(level=0))
def test_sort_index_level_large_cardinality(self):
# GH#2684 (int64)
index = MultiIndex.from_arrays([np.arange(4000)] * 3)
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int64)
# it works!
result = df.sort_index(level=0)
assert result.index.lexsort_depth == 3
# GH#2684 (int32)
index = MultiIndex.from_arrays([np.arange(4000)] * 3)
df = DataFrame(np.random.randn(4000), index=index, dtype=np.int32)
# it works!
result = df.sort_index(level=0)
assert (result.dtypes.values == df.dtypes.values).all()
assert result.index.lexsort_depth == 3
def test_sort_index_level_by_name(self):
mi = MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=["first", "second"],
)
frame = DataFrame(
np.random.randn(10, 3),
index=mi,
columns=Index(["A", "B", "C"], name="exp"),
)
frame.index.names = ["first", "second"]
result = frame.sort_index(level="second")
expected = frame.sort_index(level=1)
tm.assert_frame_equal(result, expected)
def test_sort_index_level_mixed(self):
mi = MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3],
[0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
names=["first", "second"],
)
frame = DataFrame(
np.random.randn(10, 3),
index=mi,
columns=Index(["A", "B", "C"], name="exp"),
)
sorted_before = frame.sort_index(level=1)
df = frame.copy()
df["foo"] = "bar"
sorted_after = df.sort_index(level=1)
tm.assert_frame_equal(sorted_before, sorted_after.drop(["foo"],
axis=1))
dft = frame.T
sorted_before = dft.sort_index(level=1, axis=1)
dft["foo", "three"] = "bar"
sorted_after = dft.sort_index(level=1, axis=1)
tm.assert_frame_equal(
sorted_before.drop([("foo", "three")], axis=1),
sorted_after.drop([("foo", "three")], axis=1),
)
def test_sort_index_preserve_levels(self,
multiindex_dataframe_random_data):
frame = multiindex_dataframe_random_data
result = frame.sort_index()
assert result.index.names == frame.index.names
@pytest.mark.parametrize(
"gen,extra",
[
([1.0, 3.0, 2.0, 5.0], 4.0),
([1, 3, 2, 5], 4),
(
[
Timestamp("20130101"),
Timestamp("20130103"),
Timestamp("20130102"),
Timestamp("20130105"),
],
Timestamp("20130104"),
),
(["1one", "3one", "2one", "5one"], "4one"),
],
)
def test_sort_index_multilevel_repr_8017(self, gen, extra):
np.random.seed(0)
data = np.random.randn(3, 4)
columns = MultiIndex.from_tuples([("red", i) for i in gen])
df = DataFrame(data, index=list("def"), columns=columns)
df2 = pd.concat(
[
df,
DataFrame(
"world",
index=list("def"),
columns=MultiIndex.from_tuples([("red", extra)]),
),
],
axis=1,
)
# check that the repr is good
# make sure that we have a correct sparsified repr
# e.g. only 1 header of read
assert str(df2).splitlines()[0].split() == ["red"]
# GH 8017
# sorting fails after columns added
# construct single-dtype then sort
result = df.copy().sort_index(axis=1)
expected = df.iloc[:, [0, 2, 1, 3]]
tm.assert_frame_equal(result, expected)
result = df2.sort_index(axis=1)
expected = df2.iloc[:, [0, 2, 1, 4, 3]]
tm.assert_frame_equal(result, expected)
# setitem then sort
result = df.copy()
result[("red", extra)] = "world"
result = result.sort_index(axis=1)
tm.assert_frame_equal(result, expected)
def test_binary_ops_align(self):
# test aligning binary ops
# GH 6681
index = MultiIndex.from_product(
[list('abc'), ['one', 'two', 'three'], [1, 2, 3]],
names=['first', 'second', 'third'])
df = DataFrame(np.arange(27 * 3).reshape(27, 3),
index=index,
columns=['value1', 'value2', 'value3']).sort_index()
idx = pd.IndexSlice
for op in ['add', 'sub', 'mul', 'div', 'truediv']:
opa = getattr(operator, op, None)
if opa is None:
continue
x = Series([1.0, 10.0, 100.0], [1, 2, 3])
result = getattr(df, op)(x, level='third', axis=0)
expected = pd.concat([
opa(df.loc[idx[:, :, i], :], v) for i, v in x.iteritems()
]).sort_index()
assert_frame_equal(result, expected)
x = Series([1.0, 10.0], ['two', 'three'])
result = getattr(df, op)(x, level='second', axis=0)
expected = (pd.concat([
opa(df.loc[idx[:, i], :], v) for i, v in x.iteritems()
]).reindex_like(df).sort_index())
assert_frame_equal(result, expected)
# GH9463 (alignment level of dataframe with series)
midx = MultiIndex.from_product([['A', 'B'], ['a', 'b']])
df = DataFrame(np.ones((2, 4), dtype='int64'), columns=midx)
s = pd.Series({'a': 1, 'b': 2})
df2 = df.copy()
df2.columns.names = ['lvl0', 'lvl1']
s2 = s.copy()
s2.index.name = 'lvl1'
# different cases of integer/string level names:
res1 = df.mul(s, axis=1, level=1)
res2 = df.mul(s2, axis=1, level=1)
res3 = df2.mul(s, axis=1, level=1)
res4 = df2.mul(s2, axis=1, level=1)
res5 = df2.mul(s, axis=1, level='lvl1')
res6 = df2.mul(s2, axis=1, level='lvl1')
exp = DataFrame(np.array([[1, 2, 1, 2], [1, 2, 1, 2]], dtype='int64'),
columns=midx)
for res in [res1, res2]:
assert_frame_equal(res, exp)
exp.columns.names = ['lvl0', 'lvl1']
for res in [res3, res4, res5, res6]:
assert_frame_equal(res, exp)
def test_tz_convert_and_localize(self, fn):
l0 = date_range('20140701', periods=5, freq='D')
l1 = date_range('20140701', periods=5, freq='D')
int_idx = Index(range(5))
if fn == 'tz_convert':
l0 = l0.tz_localize('UTC')
l1 = l1.tz_localize('UTC')
for idx in [l0, l1]:
l0_expected = getattr(idx, fn)('US/Pacific')
l1_expected = getattr(idx, fn)('US/Pacific')
df1 = DataFrame(np.ones(5), index=l0)
df1 = getattr(df1, fn)('US/Pacific')
assert_index_equal(df1.index, l0_expected)
# MultiIndex
# GH7846
df2 = DataFrame(np.ones(5), MultiIndex.from_arrays([l0, l1]))
df3 = getattr(df2, fn)('US/Pacific', level=0)
assert not df3.index.levels[0].equals(l0)
assert_index_equal(df3.index.levels[0], l0_expected)
assert_index_equal(df3.index.levels[1], l1)
assert not df3.index.levels[1].equals(l1_expected)
df3 = getattr(df2, fn)('US/Pacific', level=1)
assert_index_equal(df3.index.levels[0], l0)
assert not df3.index.levels[0].equals(l0_expected)
assert_index_equal(df3.index.levels[1], l1_expected)
assert not df3.index.levels[1].equals(l1)
df4 = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0]))
# TODO: untested
df5 = getattr(df4, fn)('US/Pacific', level=1) # noqa
assert_index_equal(df3.index.levels[0], l0)
assert not df3.index.levels[0].equals(l0_expected)
assert_index_equal(df3.index.levels[1], l1_expected)
assert not df3.index.levels[1].equals(l1)
# Bad Inputs
# Not DatetimeIndex / PeriodIndex
with pytest.raises(TypeError, match='DatetimeIndex'):
df = DataFrame(index=int_idx)
df = getattr(df, fn)('US/Pacific')
# Not DatetimeIndex / PeriodIndex
with pytest.raises(TypeError, match='DatetimeIndex'):
df = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0]))
df = getattr(df, fn)('US/Pacific', level=0)
# Invalid level
with pytest.raises(ValueError, match='not valid'):
df = DataFrame(index=l0)
df = getattr(df, fn)('US/Pacific', level=1)
def test_sort_index_and_reconstruction(self):
# GH#15622
# lexsortedness should be identical
# across MultiIndex construction methods
df = DataFrame([[1, 1], [2, 2]], index=list("ab"))
expected = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_tuples([(0.5, "a"), (0.5, "b"), (0.8, "a"),
(0.8, "b")]),
)
assert expected.index.is_lexsorted()
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex.from_product([[0.5, 0.8], list("ab")]),
)
result = result.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
result = DataFrame(
[[1, 1], [2, 2], [1, 1], [2, 2]],
index=MultiIndex(levels=[[0.5, 0.8], ["a", "b"]],
codes=[[0, 0, 1, 1], [0, 1, 0, 1]]),
)
result = result.sort_index()
assert result.index.is_lexsorted()
tm.assert_frame_equal(result, expected)
concatted = pd.concat([df, df], keys=[0.8, 0.5])
result = concatted.sort_index()
assert result.index.is_lexsorted()
assert result.index.is_monotonic
tm.assert_frame_equal(result, expected)
# GH#14015
df = DataFrame(
[[1, 2], [6, 7]],
columns=MultiIndex.from_tuples(
[(0, "20160811 12:00:00"), (0, "20160809 12:00:00")],
names=["l1", "Date"],
),
)
df.columns = df.columns.set_levels(pd.to_datetime(
df.columns.levels[1]),
level=1)
assert not df.columns.is_lexsorted()
assert not df.columns.is_monotonic
result = df.sort_index(axis=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
result = df.sort_index(axis=1, level=1)
assert result.columns.is_lexsorted()
assert result.columns.is_monotonic
def test_stack_partial_multiIndex(self):
# GH 8844
def _test_stack_with_multiindex(multiindex):
df = DataFrame(
np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
columns=multiindex,
)
for level in (-1, 0, 1, [0, 1], [1, 0]):
result = df.stack(level=level, dropna=False)
if isinstance(level, int):
# Stacking a single level should not make any all-NaN rows,
# so df.stack(level=level, dropna=False) should be the same
# as df.stack(level=level, dropna=True).
expected = df.stack(level=level, dropna=True)
if isinstance(expected, Series):
tm.assert_series_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
df.columns = MultiIndex.from_tuples(df.columns.to_numpy(),
names=df.columns.names)
expected = df.stack(level=level, dropna=False)
if isinstance(expected, Series):
tm.assert_series_equal(result, expected)
else:
tm.assert_frame_equal(result, expected)
full_multiindex = MultiIndex.from_tuples(
[("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
names=["Upper", "Lower"],
)
for multiindex_columns in (
[0, 1, 2, 3, 4],
[0, 1, 2, 3],
[0, 1, 2, 4],
[0, 1, 2],
[1, 2, 3],
[2, 3, 4],
[0, 1],
[0, 2],
[0, 3],
[0],
[2],
[4],
):
_test_stack_with_multiindex(full_multiindex[multiindex_columns])
if len(multiindex_columns) > 1:
multiindex_columns.reverse()
_test_stack_with_multiindex(
full_multiindex[multiindex_columns])
df = DataFrame(np.arange(6).reshape(2, 3),
columns=full_multiindex[[0, 1, 3]])
result = df.stack(dropna=False)
expected = DataFrame(
[[0, 2], [1, np.nan], [3, 5], [4, np.nan]],
index=MultiIndex(
levels=[[0, 1], ["u", "x", "y", "z"]],
codes=[[0, 0, 1, 1], [1, 3, 1, 3]],
names=[None, "Lower"],
),
columns=Index(["B", "C"], name="Upper"),
dtype=df.dtypes[0],
)
tm.assert_frame_equal(result, expected)
def test_construction_list_tuples_nan(self, na_value, vtype):
# GH#18505 : valid tuples containing NaN
values = [(1, "two"), (3.0, na_value)]
result = Index(vtype(values))
expected = MultiIndex.from_tuples(values)
tm.assert_index_equal(result, expected)
def frame_random_data_integer_multi_index():
levels = [[0, 1], [0, 1, 2]]
codes = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]]
index = MultiIndex(levels=levels, codes=codes)
return DataFrame(np.random.randn(6, 2), index=index)
def test_unstack_nan_index(self): # GH7466
def cast(val):
val_str = "" if val != val else val
return f"{val_str:1}"
def verify(df):
mk_list = lambda a: list(a) if isinstance(a, tuple) else [a]
rows, cols = df.notna().values.nonzero()
for i, j in zip(rows, cols):
left = sorted(df.iloc[i, j].split("."))
right = mk_list(df.index[i]) + mk_list(df.columns[j])
right = sorted(map(cast, right))
assert left == right
df = DataFrame({
"jim": ["a", "b", np.nan, "d"],
"joe": ["w", "x", "y", "z"],
"jolie": ["a.w", "b.x", " .y", "d.z"],
})
left = df.set_index(["jim", "joe"]).unstack()["jolie"]
right = df.set_index(["joe", "jim"]).unstack()["jolie"].T
tm.assert_frame_equal(left, right)
for idx in itertools.permutations(df.columns[:2]):
mi = df.set_index(list(idx))
for lev in range(2):
udf = mi.unstack(level=lev)
assert udf.notna().values.sum() == len(df)
verify(udf["jolie"])
df = DataFrame({
"1st": ["d"] * 3 + [np.nan] * 5 + ["a"] * 2 + ["c"] * 3 +
["e"] * 2 + ["b"] * 5,
"2nd": ["y"] * 2 + ["w"] * 3 + [np.nan] * 3 + ["z"] * 4 +
[np.nan] * 3 + ["x"] * 3 + [np.nan] * 2,
"3rd": [
67,
39,
53,
72,
57,
80,
31,
18,
11,
30,
59,
50,
62,
59,
76,
52,
14,
53,
60,
51,
],
})
df["4th"], df["5th"] = (
df.apply(lambda r: ".".join(map(cast, r)), axis=1),
df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1),
)
for idx in itertools.permutations(["1st", "2nd", "3rd"]):
mi = df.set_index(list(idx))
for lev in range(3):
udf = mi.unstack(level=lev)
assert udf.notna().values.sum() == 2 * len(df)
for col in ["4th", "5th"]:
verify(udf[col])
# GH7403
df = pd.DataFrame({
"A": list("aaaabbbb"),
"B": range(8),
"C": range(8)
})
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [
[3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan],
[np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7],
]
vals = list(map(list, zip(*vals)))
idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B")
cols = MultiIndex(levels=[["C"], ["a", "b"]],
codes=[[0, 0], [0, 1]],
names=[None, "A"])
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
df = DataFrame({
"A": list("aaaabbbb"),
"B": list(range(4)) * 2,
"C": range(8)
})
df.iloc[2, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]]
cols = MultiIndex(levels=[["C"], ["a", "b"]],
codes=[[0, 0], [0, 1]],
names=[None, "A"])
idx = Index([np.nan, 0, 1, 2, 3], name="B")
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
df = pd.DataFrame({
"A": list("aaaabbbb"),
"B": list(range(4)) * 2,
"C": range(8)
})
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack(0)
vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]]
cols = MultiIndex(levels=[["C"], ["a", "b"]],
codes=[[0, 0], [0, 1]],
names=[None, "A"])
idx = Index([np.nan, 0, 1, 2, 3], name="B")
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
# GH7401
df = pd.DataFrame({
"A":
list("aaaaabbbbb"),
"B": (date_range("2012-01-01", periods=5).tolist() * 2),
"C":
np.arange(10),
})
df.iloc[3, 1] = np.NaN
left = df.set_index(["A", "B"]).unstack()
vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]])
idx = Index(["a", "b"], name="A")
cols = MultiIndex(
levels=[["C"], date_range("2012-01-01", periods=5)],
codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]],
names=[None, "B"],
)
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
# GH4862
vals = [
["Hg", np.nan, np.nan, 680585148],
["U", 0.0, np.nan, 680585148],
["Pb", 7.07e-06, np.nan, 680585148],
["Sn", 2.3614e-05, 0.0133, 680607017],
["Ag", 0.0, 0.0133, 680607017],
["Hg", -0.00015, 0.0133, 680607017],
]
df = DataFrame(
vals,
columns=["agent", "change", "dosage", "s_id"],
index=[17263, 17264, 17265, 17266, 17267, 17268],
)
left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack()
vals = [
[np.nan, np.nan, 7.07e-06, np.nan, 0.0],
[0.0, -0.00015, np.nan, 2.3614e-05, np.nan],
]
idx = MultiIndex(
levels=[[680585148, 680607017], [0.0133]],
codes=[[0, 1], [-1, 0]],
names=["s_id", "dosage"],
)
cols = MultiIndex(
levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]],
codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]],
names=[None, "agent"],
)
right = DataFrame(vals, columns=cols, index=idx)
tm.assert_frame_equal(left, right)
left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"])
tm.assert_frame_equal(left.unstack(), right)
# GH9497 - multiple unstack with nulls
df = DataFrame({
"1st": [1, 2, 1, 2, 1, 2],
"2nd": pd.date_range("2014-02-01", periods=6, freq="D"),
"jim": 100 + np.arange(6),
"joe": (np.random.randn(6) * 10).round(2),
})
df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02")
df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan
df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan
left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"])
assert left.notna().values.sum() == 2 * len(df)
for col in ["jim", "joe"]:
for _, r in df.iterrows():
key = r["1st"], (col, r["2nd"], r["3rd"])
assert r[col] == left.loc[key]
def test_loader_given_multiple_columns(self):
class Loader1DataSet1(DataSet):
col1 = Column(float)
col2 = Column(float32)
class Loader1DataSet2(DataSet):
col1 = Column(float32)
col2 = Column(float32)
class Loader2DataSet(DataSet):
col1 = Column(float32)
col2 = Column(float32)
constants1 = {Loader1DataSet1.col1: 1,
Loader1DataSet1.col2: 2,
Loader1DataSet2.col1: 3,
Loader1DataSet2.col2: 4}
loader1 = RecordingPrecomputedLoader(constants=constants1,
dates=self.dates,
sids=self.assets)
constants2 = {Loader2DataSet.col1: 5,
Loader2DataSet.col2: 6}
loader2 = RecordingPrecomputedLoader(constants=constants2,
dates=self.dates,
sids=self.assets)
engine = SimplePipelineEngine(
lambda column:
loader2 if column.dataset == Loader2DataSet else loader1,
self.dates, self.asset_finder,
)
pipe_col1 = RollingSumSum(inputs=[Loader1DataSet1.col1,
Loader1DataSet2.col1,
Loader2DataSet.col1],
window_length=2)
pipe_col2 = RollingSumSum(inputs=[Loader1DataSet1.col2,
Loader1DataSet2.col2,
Loader2DataSet.col2],
window_length=3)
pipe_col3 = RollingSumSum(inputs=[Loader2DataSet.col1],
window_length=3)
columns = OrderedDict([
('pipe_col1', pipe_col1),
('pipe_col2', pipe_col2),
('pipe_col3', pipe_col3),
])
result = engine.run_pipeline(
Pipeline(columns=columns),
self.dates[2], # index is >= the largest window length - 1
self.dates[-1]
)
min_window = min(pip_col.window_length
for pip_col in itervalues(columns))
col_to_val = ChainMap(constants1, constants2)
vals = {name: (sum(col_to_val[col] for col in pipe_col.inputs)
* pipe_col.window_length)
for name, pipe_col in iteritems(columns)}
index = MultiIndex.from_product([self.dates[2:], self.assets])
def expected_for_col(col):
val = vals[col]
offset = columns[col].window_length - min_window
return concatenate(
[
full(offset * index.levshape[1], nan),
full(
(index.levshape[0] - offset) * index.levshape[1],
val,
float,
)
],
)
expected = DataFrame(
data={col: expected_for_col(col) for col in vals},
index=index,
columns=columns,
)
assert_frame_equal(result, expected)
self.assertEqual(set(loader1.load_calls),
{ColumnArgs.sorted_by_ds(Loader1DataSet1.col1,
Loader1DataSet2.col1),
ColumnArgs.sorted_by_ds(Loader1DataSet1.col2,
Loader1DataSet2.col2)})
self.assertEqual(set(loader2.load_calls),
{ColumnArgs.sorted_by_ds(Loader2DataSet.col1,
Loader2DataSet.col2)})
def test_missing_key_raises_keyerror2(self):
# GH#21168 KeyError, not "IndexingError: Too many indexers"
ser = Series(-1, index=MultiIndex.from_product([[0, 1]] * 2))
with pytest.raises(KeyError, match=r"\(0, 3\)"):
ser.loc[0, 3]
def test_per_axis_per_level_getitem(self):
# GH6134
# example test case
ix = MultiIndex.from_product(
[_mklbl("A", 5),
_mklbl("B", 7),
_mklbl("C", 4),
_mklbl("D", 2)])
df = DataFrame(np.arange(len(ix.to_numpy())), index=ix)
result = df.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :]
expected = df.loc[[
tuple([a, b, c, d]) for a, b, c, d in df.index.values
if (a == "A1" or a == "A2" or a == "A3") and (
c == "C1" or c == "C3")
]]
tm.assert_frame_equal(result, expected)
expected = df.loc[[
tuple([a, b, c, d]) for a, b, c, d in df.index.values
if (a == "A1" or a == "A2" or a == "A3") and (
c == "C1" or c == "C2" or c == "C3")
]]
result = df.loc[(slice("A1", "A3"), slice(None), slice("C1", "C3")), :]
tm.assert_frame_equal(result, expected)
# test multi-index slicing with per axis and per index controls
index = MultiIndex.from_tuples([("A", 1), ("A", 2), ("A", 3),
("B", 1)],
names=["one", "two"])
columns = MultiIndex.from_tuples(
[("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")],
names=["lvl0", "lvl1"],
)
df = DataFrame(np.arange(16, dtype="int64").reshape(4, 4),
index=index,
columns=columns)
df = df.sort_index(axis=0).sort_index(axis=1)
# identity
result = df.loc[(slice(None), slice(None)), :]
tm.assert_frame_equal(result, df)
result = df.loc[(slice(None), slice(None)), (slice(None), slice(None))]
tm.assert_frame_equal(result, df)
result = df.loc[:, (slice(None), slice(None))]
tm.assert_frame_equal(result, df)
# index
result = df.loc[(slice(None), [1]), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), 1), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
# columns
result = df.loc[:, (slice(None), ["foo"])]
expected = df.iloc[:, [1, 3]]
tm.assert_frame_equal(result, expected)
# both
result = df.loc[(slice(None), 1), (slice(None), ["foo"])]
expected = df.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(result, expected)
result = df.loc["A", "a"]
expected = DataFrame(
dict(bar=[1, 5, 9], foo=[0, 4, 8]),
index=Index([1, 2, 3], name="two"),
columns=Index(["bar", "foo"], name="lvl1"),
)
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), [1, 2]), :]
expected = df.iloc[[0, 1, 3]]
tm.assert_frame_equal(result, expected)
# multi-level series
s = Series(np.arange(len(ix.to_numpy())), index=ix)
result = s.loc["A1":"A3", :, ["C1", "C3"]]
expected = s.loc[[
tuple([a, b, c, d]) for a, b, c, d in s.index.values
if (a == "A1" or a == "A2" or a == "A3") and (
c == "C1" or c == "C3")
]]
tm.assert_series_equal(result, expected)
# boolean indexers
result = df.loc[(slice(None), df.loc[:, ("a", "bar")] > 5), :]
expected = df.iloc[[2, 3]]
tm.assert_frame_equal(result, expected)
with pytest.raises(ValueError):
df.loc[(slice(None), np.array([True, False])), :]
# ambiguous notation
# this is interpreted as slicing on both axes (GH #16396)
result = df.loc[slice(None), [1]]
expected = df.iloc[:, []]
tm.assert_frame_equal(result, expected)
result = df.loc[(slice(None), [1]), :]
expected = df.iloc[[0, 3]]
tm.assert_frame_equal(result, expected)
# not lexsorted
assert df.index.lexsort_depth == 2
df = df.sort_index(level=1, axis=0)
assert df.index.lexsort_depth == 0
msg = ("MultiIndex slicing requires the index to be "
r"lexsorted: slicing on levels \[1\], lexsort depth 0")
with pytest.raises(UnsortedIndexError, match=msg):
df.loc[(slice(None), slice("bar")), :]
# GH 16734: not sorted, but no real slicing
result = df.loc[(slice(None), df.loc[:, ("a", "bar")] > 5), :]
tm.assert_frame_equal(result, df.iloc[[1, 3], :])
def single_level_multiindex():
"""single level MultiIndex"""
return MultiIndex(levels=[["foo", "bar", "baz", "qux"]],
codes=[[0, 1, 2, 3]],
names=["first"])
"a\n04.15.2016",
dict(parse_dates=True, index_col=0),
DataFrame(index=DatetimeIndex(["2016-04-15"], name="a")),
),
(
"a,b\n04.15.2016,09.16.2013",
dict(parse_dates=["a", "b"]),
DataFrame([[datetime(2016, 4, 15),
datetime(2013, 9, 16)]],
columns=["a", "b"]),
),
(
"a,b\n04.15.2016,09.16.2013",
dict(parse_dates=True, index_col=[0, 1]),
DataFrame(index=MultiIndex.from_tuples([(datetime(2016, 4, 15),
datetime(2013, 9, 16))],
names=["a", "b"])),
),
],
)
def test_parse_dates_no_convert_thousands(all_parsers, data, kwargs, expected):
# see gh-14066
parser = all_parsers
result = parser.read_csv(StringIO(data), thousands=".", **kwargs)
tm.assert_frame_equal(result, expected)
def test_parse_date_time_multi_level_column_name(all_parsers):
data = """\
D,T,A,B
def test_per_axis_per_level_setitem(self):
# test index maker
idx = pd.IndexSlice
# test multi-index slicing with per axis and per index controls
index = MultiIndex.from_tuples([("A", 1), ("A", 2), ("A", 3),
("B", 1)],
names=["one", "two"])
columns = MultiIndex.from_tuples(
[("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")],
names=["lvl0", "lvl1"],
)
df_orig = DataFrame(np.arange(16, dtype="int64").reshape(4, 4),
index=index,
columns=columns)
df_orig = df_orig.sort_index(axis=0).sort_index(axis=1)
# identity
df = df_orig.copy()
df.loc[(slice(None), slice(None)), :] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc(axis=0)[:, :] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), slice(None)), (slice(None), slice(None))] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[:, (slice(None), slice(None))] = 100
expected = df_orig.copy()
expected.iloc[:, :] = 100
tm.assert_frame_equal(df, expected)
# index
df = df_orig.copy()
df.loc[(slice(None), [1]), :] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), 1), :] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc(axis=0)[:, 1] = 100
expected = df_orig.copy()
expected.iloc[[0, 3]] = 100
tm.assert_frame_equal(df, expected)
# columns
df = df_orig.copy()
df.loc[:, (slice(None), ["foo"])] = 100
expected = df_orig.copy()
expected.iloc[:, [1, 3]] = 100
tm.assert_frame_equal(df, expected)
# both
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ["foo"])] = 100
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[idx[:, 1], idx[:, ["foo"]]] = 100
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc["A", "a"] = 100
expected = df_orig.copy()
expected.iloc[0:3, 0:2] = 100
tm.assert_frame_equal(df, expected)
# setting with a list-like
df = df_orig.copy()
df.loc[(slice(None), 1),
(slice(None), ["foo"])] = np.array([[100, 100], [100, 100]],
dtype="int64")
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = 100
tm.assert_frame_equal(df, expected)
# not enough values
df = df_orig.copy()
with pytest.raises(ValueError):
df.loc[(slice(None), 1),
(slice(None), ["foo"])] = np.array([[100], [100, 100]],
dtype="int64")
with pytest.raises(ValueError):
df.loc[(slice(None), 1),
(slice(None), ["foo"])] = np.array([100, 100, 100, 100],
dtype="int64")
# with an alignable rhs
df = df_orig.copy()
df.loc[(slice(None), 1),
(slice(None), ["foo"])] = (df.loc[(slice(None), 1),
(slice(None), ["foo"])] * 5)
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] = expected.iloc[[0, 3], [1, 3]] * 5
tm.assert_frame_equal(df, expected)
df = df_orig.copy()
df.loc[(slice(None), 1),
(slice(None), ["foo"])] *= df.loc[(slice(None), 1),
(slice(None), ["foo"])]
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(df, expected)
rhs = df_orig.loc[(slice(None), 1), (slice(None), ["foo"])].copy()
rhs.loc[:, ("c", "bah")] = 10
df = df_orig.copy()
df.loc[(slice(None), 1), (slice(None), ["foo"])] *= rhs
expected = df_orig.copy()
expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]]
tm.assert_frame_equal(df, expected)
)
result = data.groupby("color").apply(lambda g: g.iloc[0]).dtypes
expected = Series(
[np.dtype("datetime64[ns]"), object, object, np.int64, object],
index=["observation", "color", "mood", "intensity", "score"],
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
pd.CategoricalIndex(list("abc")),
pd.interval_range(0, 3),
pd.period_range("2020", periods=3, freq="D"),
MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
],
)
def test_apply_index_has_complex_internals(index):
# GH 31248
df = DataFrame({"group": [1, 1, 2], "value": [0, 1, 0]}, index=index)
result = df.groupby("group").apply(lambda x: x)
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize(
"function, expected_values",
[
(lambda x: x.index.to_list(), [[0, 1], [2, 3]]),
(lambda x: set(x.index.to_list()), [{0, 1}, {2, 3}]),
(lambda x: tuple(x.index.to_list()), [(0, 1), (2, 3)]),
def test_multiindex_slicers_non_unique(self):
# GH 7106
# non-unique mi index support
df = (DataFrame(
dict(
A=["foo", "foo", "foo", "foo"],
B=["a", "a", "a", "a"],
C=[1, 2, 1, 3],
D=[1, 2, 3, 4],
)).set_index(["A", "B", "C"]).sort_index())
assert not df.index.is_unique
expected = (DataFrame(
dict(A=["foo", "foo"], B=["a", "a"], C=[1, 1],
D=[1, 3])).set_index(["A", "B", "C"]).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
tm.assert_frame_equal(result, expected)
# this is equivalent of an xs expression
result = df.xs(1, level=2, drop_level=False)
tm.assert_frame_equal(result, expected)
df = (DataFrame(
dict(
A=["foo", "foo", "foo", "foo"],
B=["a", "a", "a", "a"],
C=[1, 2, 1, 2],
D=[1, 2, 3, 4],
)).set_index(["A", "B", "C"]).sort_index())
assert not df.index.is_unique
expected = (DataFrame(
dict(A=["foo", "foo"], B=["a", "a"], C=[1, 1],
D=[1, 3])).set_index(["A", "B", "C"]).sort_index())
result = df.loc[(slice(None), slice(None), 1), :]
assert not result.index.is_unique
tm.assert_frame_equal(result, expected)
# GH12896
# numpy-implementation dependent bug
ints = [
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
12,
13,
14,
14,
16,
17,
18,
19,
200000,
200000,
]
n = len(ints)
idx = MultiIndex.from_arrays([["a"] * n, ints])
result = Series([1] * n, index=idx)
result = result.sort_index()
result = result.loc[(slice(None), slice(100000))]
expected = Series([1] * (n - 2), index=idx[:-2]).sort_index()
tm.assert_series_equal(result, expected)
def test_from_product_invalid_input(invalid_input):
msg = (r"Input must be a list / sequence of iterables|"
"Input must be list-like")
with pytest.raises(TypeError, match=msg):
MultiIndex.from_product(iterables=invalid_input)
def test_column_dups_operations(self):
def check(result, expected=None):
if expected is not None:
assert_frame_equal(result, expected)
result.dtypes
str(result)
# assignment
# GH 3687
arr = np.random.randn(3, 2)
idx = lrange(2)
df = DataFrame(arr, columns=['A', 'A'])
df.columns = idx
expected = DataFrame(arr, columns=idx)
check(df, expected)
idx = date_range('20130101', periods=4, freq='Q-NOV')
df = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]],
columns=['a', 'a', 'a', 'a'])
df.columns = idx
expected = DataFrame(
[[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=idx)
check(df, expected)
# insert
df = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]],
columns=['foo', 'bar', 'foo', 'hello'])
df['string'] = 'bah'
expected = DataFrame([[1, 1, 1, 5, 'bah'], [1, 1, 2, 5, 'bah'],
[2, 1, 3, 5, 'bah']],
columns=['foo', 'bar', 'foo', 'hello', 'string'])
check(df, expected)
with tm.assert_raises_regex(ValueError, 'Length of value'):
df.insert(0, 'AnotherColumn', range(len(df.index) - 1))
# insert same dtype
df['foo2'] = 3
expected = DataFrame([[1, 1, 1, 5, 'bah', 3], [1, 1, 2, 5, 'bah', 3],
[2, 1, 3, 5, 'bah', 3]],
columns=['foo', 'bar', 'foo', 'hello',
'string', 'foo2'])
check(df, expected)
# set (non-dup)
df['foo2'] = 4
expected = DataFrame([[1, 1, 1, 5, 'bah', 4], [1, 1, 2, 5, 'bah', 4],
[2, 1, 3, 5, 'bah', 4]],
columns=['foo', 'bar', 'foo', 'hello',
'string', 'foo2'])
check(df, expected)
df['foo2'] = 3
# delete (non dup)
del df['bar']
expected = DataFrame([[1, 1, 5, 'bah', 3], [1, 2, 5, 'bah', 3],
[2, 3, 5, 'bah', 3]],
columns=['foo', 'foo', 'hello', 'string', 'foo2'])
check(df, expected)
# try to delete again (its not consolidated)
del df['hello']
expected = DataFrame([[1, 1, 'bah', 3], [1, 2, 'bah', 3],
[2, 3, 'bah', 3]],
columns=['foo', 'foo', 'string', 'foo2'])
check(df, expected)
# consolidate
df = df._consolidate()
expected = DataFrame([[1, 1, 'bah', 3], [1, 2, 'bah', 3],
[2, 3, 'bah', 3]],
columns=['foo', 'foo', 'string', 'foo2'])
check(df, expected)
# insert
df.insert(2, 'new_col', 5.)
expected = DataFrame([[1, 1, 5., 'bah', 3], [1, 2, 5., 'bah', 3],
[2, 3, 5., 'bah', 3]],
columns=['foo', 'foo', 'new_col', 'string',
'foo2'])
check(df, expected)
# insert a dup
tm.assert_raises_regex(ValueError, 'cannot insert',
df.insert, 2, 'new_col', 4.)
df.insert(2, 'new_col', 4., allow_duplicates=True)
expected = DataFrame([[1, 1, 4., 5., 'bah', 3],
[1, 2, 4., 5., 'bah', 3],
[2, 3, 4., 5., 'bah', 3]],
columns=['foo', 'foo', 'new_col',
'new_col', 'string', 'foo2'])
check(df, expected)
# delete (dup)
del df['foo']
expected = DataFrame([[4., 5., 'bah', 3], [4., 5., 'bah', 3],
[4., 5., 'bah', 3]],
columns=['new_col', 'new_col', 'string', 'foo2'])
assert_frame_equal(df, expected)
# dup across dtypes
df = DataFrame([[1, 1, 1., 5], [1, 1, 2., 5], [2, 1, 3., 5]],
columns=['foo', 'bar', 'foo', 'hello'])
check(df)
df['foo2'] = 7.
expected = DataFrame([[1, 1, 1., 5, 7.], [1, 1, 2., 5, 7.],
[2, 1, 3., 5, 7.]],
columns=['foo', 'bar', 'foo', 'hello', 'foo2'])
check(df, expected)
result = df['foo']
expected = DataFrame([[1, 1.], [1, 2.], [2, 3.]],
columns=['foo', 'foo'])
check(result, expected)
# multiple replacements
df['foo'] = 'string'
expected = DataFrame([['string', 1, 'string', 5, 7.],
['string', 1, 'string', 5, 7.],
['string', 1, 'string', 5, 7.]],
columns=['foo', 'bar', 'foo', 'hello', 'foo2'])
check(df, expected)
del df['foo']
expected = DataFrame([[1, 5, 7.], [1, 5, 7.], [1, 5, 7.]], columns=[
'bar', 'hello', 'foo2'])
check(df, expected)
# values
df = DataFrame([[1, 2.5], [3, 4.5]], index=[1, 2], columns=['x', 'x'])
result = df.values
expected = np.array([[1, 2.5], [3, 4.5]])
assert (result == expected).all().all()
# rename, GH 4403
df4 = DataFrame(
{'RT': [0.0454],
'TClose': [22.02],
'TExg': [0.0422]},
index=MultiIndex.from_tuples([(600809, 20130331)],
names=['STK_ID', 'RPT_Date']))
df5 = DataFrame({'RPT_Date': [20120930, 20121231, 20130331],
'STK_ID': [600809] * 3,
'STK_Name': [u('饡驦'), u('饡驦'), u('饡驦')],
'TClose': [38.05, 41.66, 30.01]},
index=MultiIndex.from_tuples(
[(600809, 20120930),
(600809, 20121231),
(600809, 20130331)],
names=['STK_ID', 'RPT_Date']))
k = pd.merge(df4, df5, how='inner', left_index=True, right_index=True)
result = k.rename(
columns={'TClose_x': 'TClose', 'TClose_y': 'QT_Close'})
str(result)
result.dtypes
expected = (DataFrame([[0.0454, 22.02, 0.0422, 20130331, 600809,
u('饡驦'), 30.01]],
columns=['RT', 'TClose', 'TExg',
'RPT_Date', 'STK_ID', 'STK_Name',
'QT_Close'])
.set_index(['STK_ID', 'RPT_Date'], drop=False))
assert_frame_equal(result, expected)
# reindex is invalid!
df = DataFrame([[1, 5, 7.], [1, 5, 7.], [1, 5, 7.]],
columns=['bar', 'a', 'a'])
pytest.raises(ValueError, df.reindex, columns=['bar'])
pytest.raises(ValueError, df.reindex, columns=['bar', 'foo'])
# drop
df = DataFrame([[1, 5, 7.], [1, 5, 7.], [1, 5, 7.]],
columns=['bar', 'a', 'a'])
result = df.drop(['a'], axis=1)
expected = DataFrame([[1], [1], [1]], columns=['bar'])
check(result, expected)
result = df.drop('a', axis=1)
check(result, expected)
# describe
df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]],
columns=['bar', 'a', 'a'], dtype='float64')
result = df.describe()
s = df.iloc[:, 0].describe()
expected = pd.concat([s, s, s], keys=df.columns, axis=1)
check(result, expected)
# check column dups with index equal and not equal to df's index
df = DataFrame(np.random.randn(5, 3), index=['a', 'b', 'c', 'd', 'e'],
columns=['A', 'B', 'A'])
for index in [df.index, pd.Index(list('edcba'))]:
this_df = df.copy()
expected_ser = pd.Series(index.values, index=this_df.index)
expected_df = DataFrame({'A': expected_ser,
'B': this_df['B'],
'A': expected_ser},
columns=['A', 'B', 'A'])
this_df['A'] = index
check(this_df, expected_df)
# operations
for op in ['__add__', '__mul__', '__sub__', '__truediv__']:
df = DataFrame(dict(A=np.arange(10), B=np.random.rand(10)))
expected = getattr(df, op)(df)
expected.columns = ['A', 'A']
df.columns = ['A', 'A']
result = getattr(df, op)(df)
check(result, expected)
# multiple assignments that change dtypes
# the location indexer is a slice
# GH 6120
df = DataFrame(np.random.randn(5, 2), columns=['that', 'that'])
expected = DataFrame(1.0, index=range(5), columns=['that', 'that'])
df['that'] = 1.0
check(df, expected)
df = DataFrame(np.random.rand(5, 2), columns=['that', 'that'])
expected = DataFrame(1, index=range(5), columns=['that', 'that'])
df['that'] = 1
check(df, expected)