def test_pandas_as_index():
# Define Pandas Indexes
pdf_int_index = pd.Int64Index([1, 2, 3, 4, 5])
pdf_float_index = pd.Float64Index([1.0, 2.0, 3.0, 4.0, 5.0])
pdf_datetime_index = pd.DatetimeIndex(
[1000000, 2000000, 3000000, 4000000, 5000000])
pdf_category_index = pd.CategoricalIndex(["a", "b", "c", "b", "a"])
# Define cudf Indexes
gdf_int_index = as_index(pdf_int_index)
gdf_float_index = as_index(pdf_float_index)
gdf_datetime_index = as_index(pdf_datetime_index)
gdf_category_index = as_index(pdf_category_index)
# Check instance types
assert isinstance(gdf_int_index, GenericIndex)
assert isinstance(gdf_float_index, GenericIndex)
assert isinstance(gdf_datetime_index, DatetimeIndex)
assert isinstance(gdf_category_index, CategoricalIndex)
# Check equality
assert_eq(pdf_int_index, gdf_int_index)
assert_eq(pdf_float_index, gdf_float_index)
assert_eq(pdf_datetime_index, gdf_datetime_index)
assert_eq(pdf_category_index, gdf_category_index)
assert_eq(pdf_category_index.codes, gdf_category_index.codes.to_array())
def test_pandas_as_index():
# Define Pandas Indexes
pdf_int_index = pd.Int64Index([1, 2, 3, 4, 5])
pdf_float_index = pd.Float64Index([1., 2., 3., 4., 5.])
pdf_datetime_index = pd.DatetimeIndex(
[1000000, 2000000, 3000000, 4000000, 5000000])
pdf_category_index = pd.CategoricalIndex(['a', 'b', 'c', 'b', 'a'])
# Define cudf Indexes
gdf_int_index = as_index(pdf_int_index)
gdf_float_index = as_index(pdf_float_index)
gdf_datetime_index = as_index(pdf_datetime_index)
gdf_category_index = as_index(pdf_category_index)
# Check instance types
assert isinstance(gdf_int_index, GenericIndex)
assert isinstance(gdf_float_index, GenericIndex)
assert isinstance(gdf_datetime_index, DatetimeIndex)
assert isinstance(gdf_category_index, CategoricalIndex)
# Check equality
assert_eq(pdf_int_index, gdf_int_index)
assert_eq(pdf_float_index, gdf_float_index)
assert_eq(pdf_datetime_index, gdf_datetime_index)
assert_eq(pdf_category_index, gdf_category_index)
def _apply_basic_agg(self, agg_type, sort_results=False):
"""
Parameters
----------
agg_type : str
The aggregation function to run.
"""
result = DataFrame()
add_col_values = True
ctx = ffi.new('gdf_context*')
ctx.flag_sorted = 0
ctx.flag_method = self._method
ctx.flag_distinct = 0
val_columns = self._val_columns
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx,
val_columns,
val_columns_out,
sort_result=sort_results)
# If a Groupby has one index column and one value column
# and as_index is set, return a Series instead of a df
if isinstance(val_columns, (str, Number)) and self._as_index:
result_series = result[val_columns]
idx = index.as_index(result[self._by[0]])
if self.level == 0:
idx.name = self._original_index_name
else:
idx.name = self._by[0]
result_series = result_series.set_index(idx)
return result_series
# TODO: Do MultiIndex here
if (self._as_index):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result.drop_column(idx.name)
if self.level == 0:
idx.name = self._original_index_name
else:
idx.name = self._by[0]
result = result.set_index(idx)
nvtx_range_pop()
return result
def _getitem_tuple_arg(self, arg):
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.index import as_index
columns = self._get_column_selection(arg[1])
df = DataFrame()
for col in columns:
df.add_column(name=col, data=self._df[col].loc[arg[0]])
if df.shape[0] == 1: # we have a single row
if isinstance(arg[0], slice):
df.index = as_index(arg[0].start)
else:
df.index = as_index(arg[0])
return df
def quantile(self,
q,
interpolation='midpoint',
exact=True,
quant_index=True):
"""
Return values at the given quantile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute
interpolation : {’linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points i and j:
columns : list of str
List of column names to include.
exact : boolean
Whether to use approximate or exact quantile algorithm.
quant_index : boolean
Whether to use the list of quantiles as index.
Returns
-------
DataFrame
"""
if not quant_index:
return Series(self._column.quantile(q, interpolation, exact))
else:
return Series(self._column.quantile(q, interpolation, exact),
index=as_index(np.asarray(q)))
def test_reflected_ops_scalar(func, dtype, obj_class):
import pandas as pd
# create random series
np.random.seed(12)
random_series = pd.Series(np.random.sample(100) + 10, dtype=dtype)
# gpu series
gs = Series(random_series)
# class typing
if obj_class == 'Index':
gs = as_index(gs)
gs_result = func(gs)
# class typing
if obj_class == 'Index':
gs = Series(gs)
# pandas
ps_result = func(random_series)
# verify
np.testing.assert_allclose(ps_result, gs_result)
def test_categorical_basic():
cat = pd.Categorical(["a", "a", "b", "c", "a"], categories=["a", "b", "c"])
cudf_cat = as_index(cat)
pdsr = pd.Series(cat)
sr = Series(cat)
np.testing.assert_array_equal(cat.codes, sr.to_array())
assert sr.dtype == pdsr.dtype
# Test attributes
assert tuple(pdsr.cat.categories) == tuple(sr.cat.categories)
assert pdsr.cat.ordered == sr.cat.ordered
np.testing.assert_array_equal(pdsr.cat.codes.values,
sr.cat.codes.to_array())
np.testing.assert_array_equal(pdsr.cat.codes.dtype, sr.cat.codes.dtype)
string = str(sr)
expect_str = """
0 a
1 a
2 b
3 c
4 a
"""
assert all(x == y for x, y in zip(string.split(), expect_str.split()))
assert_eq(cat.codes, cudf_cat.codes.to_array())
def reverse(self):
"""Reverse the Series
"""
data = cudautils.reverse_array(self.to_gpu_array())
index = as_index(cudautils.reverse_array(self.index.gpu_values))
col = self._column.replace(data=Buffer(data))
return self._copy_construct(data=col, index=index)
def _get_column_major(self, df, row_tuple):
from cudf import Series
from cudf import DataFrame
valid_indices = self._get_valid_indices_by_tuple(
df.columns, row_tuple, len(df._cols)
)
result = df._take_columns(valid_indices)
if isinstance(row_tuple, (numbers.Number, slice)):
row_tuple = [row_tuple]
if len(result) == 0 and len(result.columns) == 0:
result_columns = df.columns.copy(deep=False)
clear_codes = DataFrame()
for name in df.columns.names:
clear_codes[name] = Series([])
result_columns._codes = clear_codes
result_columns._source_data = clear_codes
result.columns = result_columns
elif len(row_tuple) < len(self.levels) and (
not slice(None) in row_tuple
and not isinstance(row_tuple[0], slice)
):
columns = self._popn(len(row_tuple))
result.columns = columns.take(valid_indices)
else:
result.columns = self.take(valid_indices)
if len(result.columns.levels) == 1:
columns = []
for code in result.columns.codes[result.columns.codes.columns[0]]:
columns.append(result.columns.levels[0][code])
name = result.columns.names[0]
result.columns = as_index(columns, name=name)
return result
def test_index_rename():
pds = pd.Index([1, 2, 3], name='asdf')
gds = as_index(pds)
expect = pds.rename('new_name')
got = gds.rename('new_name')
assert_eq(expect, got)
def test_series_bitwise_binop(binop, obj_class, lhs_dtype, rhs_dtype):
arr1 = (np.random.random(100) * 100).astype(lhs_dtype)
sr1 = Series(arr1)
arr2 = (np.random.random(100) * 100).astype(rhs_dtype)
sr2 = Series(arr2)
if obj_class == 'Index':
sr1 = as_index(sr1)
sr2 = as_index(sr2)
result = binop(sr1, sr2)
if obj_class == 'Index':
result = Series(result)
np.testing.assert_almost_equal(result.to_array(), binop(arr1, arr2))
def test_series_cmpop_mixed_dtype(cmpop, lhs_dtype, rhs_dtype, obj_class):
nelem = 5
lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
sr1 = Series(lhs)
sr2 = Series(rhs)
if obj_class == 'Index':
sr1 = as_index(sr1)
sr2 = as_index(sr2)
result = cmpop(Series(sr1), Series(sr2))
if obj_class == 'Index':
result = Series(result)
np.testing.assert_array_equal(result.to_array(), cmpop(lhs, rhs))
def test_series_binop_mixed_dtype(binop, lhs_dtype, rhs_dtype, obj_class):
nelem = 10
lhs = (np.random.random(nelem) * nelem).astype(lhs_dtype)
rhs = (np.random.random(nelem) * nelem).astype(rhs_dtype)
sr1 = Series(lhs)
sr2 = Series(rhs)
if obj_class == "Index":
sr1 = as_index(sr1)
sr2 = as_index(sr2)
result = binop(Series(sr1), Series(sr2))
if obj_class == "Index":
result = Series(result)
np.testing.assert_almost_equal(result.to_array(), binop(lhs, rhs))
def set_index(self, index):
"""Returns a new Series with a different index.
Parameters
----------
index : Index, Series-convertible
the new index or values for the new index
"""
index = index if isinstance(index, Index) else as_index(index)
return self._copy_construct(index=index)
def _getitem_tuple_arg(self, arg):
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.index import as_index
from cudf.utils.cudautils import arange
from cudf import MultiIndex
# Step 1: Gather columns
if isinstance(self._df.columns, MultiIndex):
columns_df = self._df.columns._get_column_major(self._df, arg[1])
else:
columns = self._get_column_selection(arg[1])
columns_df = DataFrame()
for col in columns:
columns_df.add_column(name=col, data=self._df[col])
# Step 2: Gather rows
if isinstance(columns_df.index, MultiIndex):
return columns_df.index._get_row_major(columns_df, arg[0])
else:
if isinstance(self._df.columns, MultiIndex):
if isinstance(arg[0], slice):
start, stop, step = arg[0].indices(len(columns_df))
indices = arange(start, stop, step)
df = columns_df.take(indices)
else:
df = columns_df.take(arg[0])
else:
df = DataFrame()
for col in columns_df.columns:
df[col] = columns_df[col].loc[arg[0]]
# Step 3: Gather index
if df.shape[0] == 1: # we have a single row
if isinstance(arg[0], slice):
start = arg[0].start
if start is None:
start = self._df.index[0]
df.index = as_index(start)
else:
df.index = as_index(arg[0])
# Step 4: Downcast
if self._can_downcast_to_series(df, arg):
return self._downcast_to_series(df, arg)
return df
def test_series_binop(binop, obj_class):
nelem = 1000
arr1 = utils.gen_rand('float64', nelem) * 10000
# Keeping a low value because CUDA 'pow' has 2 full range error
arr2 = utils.gen_rand('float64', nelem) * 10
sr1 = Series(arr1)
sr2 = Series(arr2)
if obj_class == 'Index':
sr1 = as_index(sr1)
sr2 = as_index(sr2)
result = binop(sr1, sr2)
expect = binop(pd.Series(arr1), pd.Series(arr2))
if obj_class == 'Index':
result = Series(result)
utils.assert_eq(result, expect)
def test_index_rename():
pds = pd.Index([1, 2, 3], name="asdf")
gds = as_index(pds)
expect = pds.rename("new_name")
got = gds.rename("new_name")
assert_eq(expect, got)
"""
From here on testing recursive creation
and if name is being handles in recursive creation.
"""
pds = pd.Index(expect)
gds = as_index(got)
assert_eq(pds, gds)
pds = pd.Index(pds, name="abc")
gds = as_index(gds, name="abc")
assert_eq(pds, gds)
def test_series_binop(binop, obj_class):
arr = np.random.random(100)
sr = Series(arr)
if obj_class == 'Index':
sr = as_index(sr)
result = binop(sr, sr)
if obj_class == 'Index':
result = Series(result)
np.testing.assert_almost_equal(result.to_array(), binop(arr, arr))
def test_series_compare(cmpop, obj_class, dtype):
arr1 = np.random.randint(0, 100, 100).astype(dtype)
arr2 = np.random.randint(0, 100, 100).astype(dtype)
sr1 = Series(arr1)
sr2 = Series(arr2)
if obj_class == 'Index':
sr1 = as_index(sr1)
sr2 = as_index(sr2)
result1 = cmpop(sr1, sr1)
result2 = cmpop(sr2, sr2)
result3 = cmpop(sr1, sr2)
if obj_class == 'Index':
result1 = Series(result1)
result2 = Series(result2)
result3 = Series(result3)
np.testing.assert_equal(result1.to_array(), cmpop(arr1, arr1))
np.testing.assert_equal(result2.to_array(), cmpop(arr2, arr2))
np.testing.assert_equal(result3.to_array(), cmpop(arr1, arr2))
def test_series_binop_scalar(nelem, binop, obj_class):
arr = np.random.random(nelem)
rhs = np.asscalar(random.choice(arr))
sr = Series(arr)
if obj_class == 'Index':
sr = as_index(sr)
result = binop(sr, rhs)
if obj_class == 'Index':
result = Series(result)
np.testing.assert_almost_equal(result.to_array(), binop(arr, rhs))
def value_counts(self, method='sort', sort=True):
"""Returns unique values of this Series.
"""
if method != 'sort':
msg = 'non sort based value_count() not implemented yet'
raise NotImplementedError(msg)
if self.null_count == len(self):
return Series(np.array([], dtype=np.int64))
vals, cnts = self._column.value_counts(method=method)
res = Series(cnts, index=as_index(vals))
if sort:
return res.sort_values(ascending=False)
return res
def __init__(self, data=None, index=None, name=None, nan_as_null=True,
dtype=None):
if isinstance(data, pd.Series):
name = data.name
index = as_index(data.index)
if isinstance(data, Series):
index = data._index if index is None else index
name = data.name
data = data._column
if data is None:
data = {}
if not isinstance(data, columnops.TypedColumnBase):
data = columnops.as_column(data, nan_as_null=nan_as_null,
dtype=dtype)
if index is not None and not isinstance(index, Index):
index = as_index(index)
assert isinstance(data, columnops.TypedColumnBase)
self._column = data
self._index = RangeIndex(len(data)) if index is None else index
self.name = name
def test_series_compare_scalar(nelem, cmpop, obj_class, dtype):
arr1 = np.random.randint(0, 100, 100).astype(dtype)
sr1 = Series(arr1)
rhs = np.asscalar(random.choice(arr1))
if obj_class == 'Index':
sr1 = as_index(sr1)
result1 = cmpop(sr1, rhs)
result2 = cmpop(rhs, sr1)
if obj_class == 'Index':
result1 = Series(result1)
result2 = Series(result2)
np.testing.assert_equal(result1.to_array(), cmpop(arr1, rhs))
np.testing.assert_equal(result2.to_array(), cmpop(rhs, arr1))
def apply_multiindex_or_single_index(self, result):
if len(result) == 0:
final_result = DataFrame()
for col in result.columns:
if col not in self._by:
final_result[col] = result[col]
if len(self._by) == 1 or len(final_result.columns) == 0:
dtype = 'float64' if len(self._by) == 1 else 'object'
name = self._by[0] if len(self._by) == 1 else None
from cudf.dataframe.index import GenericIndex
index = GenericIndex(Series([], dtype=dtype))
index.name = name
final_result.index = index
else:
mi = MultiIndex(source_data=result[self._by])
mi.names = self._by
final_result.index = mi
if len(final_result.columns) == 1 and hasattr(self, "_gotattr"):
final_series = Series([], name=final_result.columns[0])
final_series.index = final_result.index
return final_series
return final_result
if len(self._by) == 1:
from cudf.dataframe import index
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.drop(idx.name)
if idx.name == self._LEVEL_0_INDEX_NAME:
idx.name = self._original_index_name
result = result.set_index(idx)
return result
else:
multi_index = MultiIndex(source_data=result[self._by])
final_result = DataFrame()
for col in result.columns:
if col not in self._by:
final_result[col] = result[col]
if len(final_result.columns) == 1 and hasattr(self, "_gotattr"):
final_series = Series(final_result[final_result.columns[0]])
final_series.name = final_result.columns[0]
final_series.index = multi_index
return final_series
return final_result.set_index(multi_index)
def __getitem__(self, arg):
rows = []
len_idx = len(self._sr)
if isinstance(arg, tuple):
for idx in arg:
rows.append(idx)
elif isinstance(arg, int):
rows.append(arg)
elif isinstance(arg, slice):
start, stop, step, sln = utils.standard_python_slice(len_idx, arg)
if sln > 0:
for idx in range(start, stop, step):
rows.append(idx)
else:
raise TypeError(type(arg))
# To check whether all the indices are valid.
for idx in rows:
if abs(idx) > len_idx or idx == len_idx:
raise IndexError("positional indexers are out-of-bounds")
for i in range(len(rows)):
if rows[i] < 0:
rows[i] = len_idx + rows[i]
# returns the single elem similar to pandas
if isinstance(arg, int) and len(rows) == 1:
return self._sr[rows[0]]
ret_list = []
for idx in rows:
ret_list.append(self._sr[idx])
col_data = columnops.as_column(ret_list,
dtype=self._sr.dtype,
nan_as_null=True)
return Series(col_data, index=as_index(np.asarray(rows)))
def __init__(self, data=None, index=None, name=None, nan_as_null=True):
if isinstance(data, pd.Series):
name = data.name
index = as_index(data.index)
if isinstance(data, Series):
index = data._index if index is None else index
name = data.name
data = data._column
if data is None:
data = {}
if not isinstance(data, columnops.TypedColumnBase):
data = columnops.as_column(data, nan_as_null=nan_as_null)
if index is not None and not isinstance(index, Index):
raise TypeError('index not a Index type: got {!r}'.format(index))
assert isinstance(data, columnops.TypedColumnBase)
self._column = data
self._index = RangeIndex(len(data)) if index is None else index
self.name = name
def to_pandas(self):
pandas_codes = []
for code in self.codes.columns:
pandas_codes.append(self.codes[code].to_array())
# We do two things here to mimic Pandas behavior:
# 1. as_index() on each level, so DatetimeColumn becomes DatetimeIndex
# 2. convert levels to numpy array so empty levels become Float64Index
levels = np.array(
[as_index(level).to_pandas() for level in self.levels]
)
# Backwards compatibility:
# Construct a dummy MultiIndex and check for the codes attr.
# This indicates that it is pandas >= 0.24
# If no codes attr is present it is pandas <= 0.23
if hasattr(pd.MultiIndex([[]], [[]]), "codes"):
pandas_mi = pd.MultiIndex(levels=levels, codes=pandas_codes)
else:
pandas_mi = pd.MultiIndex(levels=levels, labels=pandas_codes)
if self.names is not None:
pandas_mi.names = self.names
return pandas_mi
def test_reflected_ops_scalar(func, dtype, obj_class):
# create random series
np.random.seed(12)
random_series = utils.gen_rand(dtype, 100, low=10)
# gpu series
gs = Series(random_series)
# class typing
if obj_class == 'Index':
gs = as_index(gs)
gs_result = func(gs)
# class typing
if obj_class == 'Index':
gs = Series(gs)
# pandas
ps_result = func(random_series)
# verify
np.testing.assert_allclose(ps_result, gs_result)
def _getitem_tuple_arg(self, arg):
from cudf import MultiIndex
from cudf.dataframe.dataframe import DataFrame
from cudf.dataframe.dataframe import Series
from cudf.dataframe.index import as_index
# Iloc Step 1:
# Gather the columns specified by the second tuple arg
columns = self._get_column_selection(arg[1])
if isinstance(self._df.columns, MultiIndex):
columns_df = self._df.columns._get_column_major(self._df, arg[1])
if (
len(columns_df) == 0
and len(columns_df.columns) == 0
and not isinstance(arg[0], slice)
):
result = Series([], name=arg[0])
result._index = columns_df.columns.copy(deep=False)
return result
else:
if isinstance(arg[0], slice):
columns_df = DataFrame()
for col in columns:
columns_df.add_column(name=col, data=self._df[col])
columns_df._index = self._df._index
else:
columns_df = self._df._columns_view(columns)
# Iloc Step 2:
# Gather the rows specified by the first tuple arg
if isinstance(columns_df.index, MultiIndex):
df = columns_df.index._get_row_major(columns_df, arg[0])
if (len(df) == 1 and len(columns_df) >= 1) and not (
isinstance(arg[0], slice) or isinstance(arg[1], slice)
):
# Pandas returns a numpy scalar in this case
return df[0]
if self._can_downcast_to_series(df, arg):
return self._downcast_to_series(df, arg)
return df
else:
df = DataFrame()
for key, col in columns_df._cols.items():
df[key] = col.iloc[arg[0]]
df.columns = columns_df.columns
# Iloc Step 3:
# Reindex
if df.shape[0] == 1: # we have a single row without an index
if isinstance(arg[0], slice):
start = arg[0].start
if start is None:
start = 0
df.index = as_index(self._df.index[start])
else:
df.index = as_index(self._df.index[arg[0]])
# Iloc Step 4:
# Downcast
if self._can_downcast_to_series(df, arg):
if isinstance(df.columns, MultiIndex):
if len(df) > 0 and not (
isinstance(arg[0], slice) or isinstance(arg[1], slice)
):
return list(df._cols.values())[0][0]
elif df.shape[1] > 1:
result = self._downcast_to_series(df, arg)
result.index = df.columns
return result
elif not isinstance(arg[0], slice):
result_series = list(df._cols.values())[0]
result_series.index = df.columns
result_series.name = arg[0]
return result_series
else:
return list(df._cols.values())[0]
return self._downcast_to_series(df, arg)
if df.shape[0] == 0 and df.shape[1] == 0:
from cudf.dataframe.index import RangeIndex
slice_len = arg[0].stop or len(self._df)
start, stop, step = arg[0].indices(slice_len)
df._index = RangeIndex(start, stop)
return df
def __getitem__(self, arg):
from cudf.dataframe.index import as_index
return as_index(self.idx.to_series().loc[arg])