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 test_nonmatching_index_setitem(nrows):
np.random.seed(0)
gdf = DataFrame()
gdf['a'] = np.random.randint(2147483647, size=nrows)
gdf['b'] = np.random.randint(2147483647, size=nrows)
gdf = gdf.set_index('b')
test_values = np.random.randint(2147483647, size=nrows)
gdf['c'] = test_values
assert (len(test_values) == len(gdf['c']))
assert (gdf['c'].to_pandas().equals(
Series(test_values).set_index(gdf._index).to_pandas()))
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 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:
if len(self._by) == 1:
dtype = self._df[self._by[0]]
else:
dtype = '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
return final_result
if len(self._by) == 1:
from cudf.dataframe import index
idx = index.as_index(result[self._by[0]])
name = self._by[0]
if isinstance(name, str):
name = self._by[0].split('+')
if name[0] == 'cudfvalcol':
idx.name = name[1]
else:
idx.name = name[0]
result = result.drop(self._by[0])
for col in result.columns:
if isinstance(col, str):
colnames = col.split('+')
if colnames[0] == 'cudfvalcol':
result[colnames[1]] = result[col]
result = result.drop(col)
if idx.name == _LEVEL_0_INDEX_NAME:
idx.name = self._original_index_name
result = result.set_index(idx)
return result
else:
for col in result.columns:
if isinstance(col, str):
colnames = col.split('+')
if colnames[0] == 'cudfvalcol':
result[colnames[1]] = result[col]
result = result.drop(col)
new_by = []
for by in self._by:
if isinstance(col, str):
splitby = by.split('+')
if splitby[0] == 'cudfvalcol':
new_by.append(splitby[1])
else:
new_by.append(splitby[0])
else:
new_by.append(by)
self._by = new_by
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) > 0:
return final_result.set_index(multi_index)
else:
return result.set_index(multi_index)
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:
levels = []
codes = []
names = []
for by in self._by:
levels.append([])
codes.append([])
names.append(by)
mi = MultiIndex(levels, codes)
mi.names = names
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:
levels = []
codes = DataFrame()
names = []
# Note: This is an O(N^2) solution using gpu masking
# to compute new codes for the MultiIndex. There may be
# a faster solution that could be executed on gpu at the same
# time the groupby is calculated.
for by in self._by:
level = result[by].unique()
replaced = result[by].replace(level, range(len(level)))
levels.append(level)
codes[by] = Series(replaced, dtype="int32")
names.append(by)
multi_index = MultiIndex(levels=levels, codes=codes, names=names)
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 read_csv(filepath_or_buffer,
lineterminator='\n',
quotechar='"',
quoting=0,
doublequote=True,
header='infer',
mangle_dupe_cols=True,
usecols=None,
sep=',',
delimiter=None,
delim_whitespace=False,
skipinitialspace=False,
names=None,
dtype=None,
skipfooter=0,
skiprows=0,
dayfirst=False,
compression='infer',
thousands=None,
decimal='.',
true_values=None,
false_values=None,
nrows=None,
byte_range=None,
skip_blank_lines=True,
comment=None,
na_values=None,
keep_default_na=True,
na_filter=True,
prefix=None,
index_col=None):
"""
Load and parse a CSV file into a DataFrame
Parameters
----------
filepath_or_buffer : str
Path of file to be read or a file-like object containing the file.
sep : char, default ','
Delimiter to be used.
delimiter : char, default None
Alternative argument name for sep.
delim_whitespace : bool, default False
Determines whether to use whitespace as delimiter.
lineterminator : char, default '\\n'
Character to indicate end of line.
skipinitialspace : bool, default False
Skip spaces after delimiter.
names : list of str, default None
List of column names to be used.
dtype : list of str or dict of {col: dtype}, default None
List of data types in the same order of the column names
or a dictionary with column_name:dtype (pandas style).
quotechar : char, default '"'
Character to indicate start and end of quote item.
quoting : str or int, default 0
Controls quoting behavior. Set to one of
0 (csv.QUOTE_MINIMAL), 1 (csv.QUOTE_ALL),
2 (csv.QUOTE_NONNUMERIC) or 3 (csv.QUOTE_NONE).
Quoting is enabled with all values except 3.
doublequote : bool, default True
When quoting is enabled, indicates whether to interpret two
consecutive quotechar inside fields as single quotechar
header : int, default 'infer'
Row number to use as the column names. Default behavior is to infer
the column names: if no names are passed, header=0;
if column names are passed explicitly, header=None.
usecols : list of int or str, default None
Returns subset of the columns given in the list. All elements must be
either integer indices (column number) or strings that correspond to
column names
mangle_dupe_cols : boolean, default True
Duplicate columns will be specified as 'X','X.1',...'X.N'.
skiprows : int, default 0
Number of rows to be skipped from the start of file.
skipfooter : int, default 0
Number of rows to be skipped at the bottom of file.
compression : {'infer', 'gzip', 'zip', None}, default 'infer'
For on-the-fly decompression of on-disk data. If ‘infer’, then detect
compression from the following extensions: ‘.gz’,‘.zip’ (otherwise no
decompression). If using ‘zip’, the ZIP file must contain only one
data file to be read in, otherwise the first non-zero-sized file will
be used. Set to None for no decompression.
decimal : char, default '.'
Character used as a decimal point.
thousands : char, default None
Character used as a thousands delimiter.
true_values : list, default None
Values to consider as boolean True
false_values : list, default None
Values to consider as boolean False
nrows : int, default None
If specified, maximum number of rows to read
byte_range : list or tuple, default None
Byte range within the input file to be read. The first number is the
offset in bytes, the second number is the range size in bytes. Set the
size to zero to read all data after the offset location. Reads the row
that starts before or at the end of the range, even if it ends after
the end of the range.
skip_blank_lines : bool, default True
If True, discard and do not parse empty lines
If False, interpret empty lines as NaN values
comment : char, default None
Character used as a comments indicator. If found at the beginning of a
line, the line will be ignored altogether.
na_values : list, default None
Values to consider as invalid
keep_default_na : bool, default True
Whether or not to include the default NA values when parsing the data.
na_filter : bool, default True
Detect missing values (empty strings and the values in na_values).
Passing False can improve performance.
prefix : str, default None
Prefix to add to column numbers when parsing without a header row
index_col : int or string, default None
Column to use as the row labels
Returns
-------
GPU ``DataFrame`` object.
Examples
--------
Create a test csv file
>>> import cudf
>>> filename = 'foo.csv'
>>> lines = [
... "num1,datetime,text",
... "123,2018-11-13T12:00:00,abc",
... "456,2018-11-14T12:35:01,def",
... "789,2018-11-15T18:02:59,ghi"
... ]
>>> with open(filename, 'w') as fp:
... fp.write('\\n'.join(lines)+'\\n')
Read the file with ``cudf.read_csv``
>>> cudf.read_csv(filename)
num1 datetime text
0 123 2018-11-13T12:00:00.000 5451
1 456 2018-11-14T12:35:01.000 5784
2 789 2018-11-15T18:02:59.000 6117
See Also
--------
.read_csv_strings
"""
if delim_whitespace:
if delimiter is not None:
raise ValueError("cannot set both delimiter and delim_whitespace")
if sep != ',':
raise ValueError("cannot set both sep and delim_whitespace")
# Alias sep -> delimiter.
if delimiter is None:
delimiter = sep
if dtype is not None:
if isinstance(dtype, collections.abc.Mapping):
dtype_dict = True
elif isinstance(dtype, collections.abc.Iterable):
dtype_dict = False
else:
msg = '''dtype must be 'list like' or 'dict' '''
raise TypeError(msg)
if names is not None and len(dtype) != len(names):
msg = '''All column dtypes must be specified.'''
raise TypeError(msg)
nvtx_range_push("CUDF_READ_CSV", "purple")
csv_reader = ffi.new('csv_read_arg*')
# Populate csv_reader struct
if is_file_like(filepath_or_buffer):
if compression == 'infer':
compression = None
buffer = filepath_or_buffer.read()
# check if StringIO is used
if hasattr(buffer, 'encode'):
buffer_as_bytes = buffer.encode()
else:
buffer_as_bytes = buffer
buffer_data_holder = ffi.new("char[]", buffer_as_bytes)
csv_reader.input_data_form = libgdf.HOST_BUFFER
csv_reader.filepath_or_buffer = buffer_data_holder
csv_reader.buffer_size = len(buffer_as_bytes)
else:
if (not os.path.isfile(filepath_or_buffer)):
raise (FileNotFoundError)
if (not os.path.exists(filepath_or_buffer)):
raise (FileNotFoundError)
file_path = _wrap_string(filepath_or_buffer)
csv_reader.input_data_form = libgdf.FILE_PATH
csv_reader.filepath_or_buffer = file_path
if header == 'infer':
header = -1
header_infer = header
arr_names = []
arr_dtypes = []
if names is None:
if header is -1:
header_infer = 0
if header is None:
header_infer = -1
csv_reader.names = ffi.NULL
csv_reader.num_cols = 0
else:
if header is None:
header_infer = -1
csv_reader.num_cols = len(names)
for col_name in names:
arr_names.append(_wrap_string(col_name))
if dtype is not None:
if dtype_dict:
arr_dtypes.append(_wrap_string(str(dtype[col_name])))
names_ptr = ffi.new('char*[]', arr_names)
csv_reader.names = names_ptr
if dtype is None:
csv_reader.dtype = ffi.NULL
else:
if not dtype_dict:
for col_dtype in dtype:
arr_dtypes.append(_wrap_string(str(col_dtype)))
dtype_ptr = ffi.new('char*[]', arr_dtypes)
csv_reader.dtype = dtype_ptr
csv_reader.use_cols_int = ffi.NULL
csv_reader.use_cols_int_len = 0
csv_reader.use_cols_char = ffi.NULL
csv_reader.use_cols_char_len = 0
if usecols is not None:
arr_col_names = []
if (all(isinstance(x, int) for x in usecols)):
usecols_ptr = ffi.new('int[]', usecols)
csv_reader.use_cols_int = usecols_ptr
csv_reader.use_cols_int_len = len(usecols)
else:
for col_name in usecols:
arr_col_names.append(_wrap_string(col_name))
col_names_ptr = ffi.new('char*[]', arr_col_names)
csv_reader.use_cols_char = col_names_ptr
csv_reader.use_cols_char_len = len(usecols)
if decimal == delimiter:
raise ValueError("decimal cannot be the same as delimiter")
if thousands == delimiter:
raise ValueError("thousands cannot be the same as delimiter")
if nrows is not None and skipfooter != 0:
raise ValueError("cannot use both nrows and skipfooter parameters")
if byte_range is not None:
if skipfooter != 0 or skiprows != 0 or nrows is not None:
raise ValueError("""cannot manually limit rows to be read when
using the byte range parameter""")
arr_true_values = []
for value in true_values or []:
arr_true_values.append(_wrap_string(str(value)))
arr_true_values_ptr = ffi.new('char*[]', arr_true_values)
csv_reader.true_values = arr_true_values_ptr
csv_reader.num_true_values = len(arr_true_values)
arr_false_values = []
for value in false_values or []:
arr_false_values.append(_wrap_string(str(value)))
false_values_ptr = ffi.new('char*[]', arr_false_values)
csv_reader.false_values = false_values_ptr
csv_reader.num_false_values = len(arr_false_values)
arr_na_values = []
for value in na_values or []:
arr_na_values.append(_wrap_string(str(value)))
arr_na_values_ptr = ffi.new('char*[]', arr_na_values)
csv_reader.na_values = arr_na_values_ptr
csv_reader.num_na_values = len(arr_na_values)
compression_bytes = _wrap_string(compression)
prefix_bytes = _wrap_string(prefix)
csv_reader.delimiter = delimiter.encode()
csv_reader.lineterminator = lineterminator.encode()
csv_reader.quotechar = quotechar.encode()
csv_reader.quoting = _quoting_enum[quoting]
csv_reader.doublequote = doublequote
csv_reader.delim_whitespace = delim_whitespace
csv_reader.skipinitialspace = skipinitialspace
csv_reader.dayfirst = dayfirst
csv_reader.header = header_infer
csv_reader.skiprows = skiprows
csv_reader.skipfooter = skipfooter
csv_reader.mangle_dupe_cols = mangle_dupe_cols
csv_reader.windowslinetermination = False
csv_reader.compression = compression_bytes
csv_reader.decimal = decimal.encode()
csv_reader.thousands = thousands.encode() if thousands else b'\0'
csv_reader.nrows = nrows if nrows is not None else -1
if byte_range is not None:
csv_reader.byte_range_offset = byte_range[0]
csv_reader.byte_range_size = byte_range[1]
else:
csv_reader.byte_range_offset = 0
csv_reader.byte_range_size = 0
csv_reader.skip_blank_lines = skip_blank_lines
csv_reader.comment = comment.encode() if comment else b'\0'
csv_reader.keep_default_na = keep_default_na
csv_reader.na_filter = na_filter
csv_reader.prefix = prefix_bytes
# Call read_csv
libgdf.read_csv(csv_reader)
out = csv_reader.data
if out == ffi.NULL:
raise ValueError("Failed to parse CSV")
# Extract parsed columns
outcols = []
new_names = []
for i in range(csv_reader.num_cols_out):
newcol = Column.from_cffi_view(out[i])
new_names.append(ffi.string(out[i].col_name).decode())
if (newcol.dtype == np.dtype('datetime64[ms]')):
outcols.append(newcol.view(DatetimeColumn, dtype='datetime64[ms]'))
else:
outcols.append(newcol.view(NumericalColumn, dtype=newcol.dtype))
# Build dataframe
df = DataFrame()
# if names is not None and header_infer is -1:
for k, v in zip(new_names, outcols):
df[k] = v
# Set index if the index_col parameter is passed
if index_col is not None and index_col is not False:
if isinstance(index_col, (int)):
df = df.set_index(df.columns[index_col])
else:
df = df.set_index(index_col)
nvtx_range_pop()
return df
def agg(self, args):
""" Invoke aggregation functions on the groups.
Parameters
----------
args : dict, list, str, callable
- str
The aggregate function name.
- list
List of *str* of the aggregate function.
- dict
key-value pairs of source column name and list of
aggregate functions as *str*.
Returns
-------
result : DataFrame
Notes
-----
Since multi-indexes aren't supported aggregation results are returned
in columns using the naming scheme of `aggregation_columnname`.
"""
result = DataFrame()
add_col_values = True
ctx = ffi.new('gdf_context*')
ctx.flag_sorted = 0
ctx.flag_method = self._method
ctx.flag_distinct = 0
sort_result = True
# TODO: Use MultiColumn here instead of use_prefix
# use_prefix enables old functionality - prefixing column
# groupby names since we don't support MultiColumn quite yet
use_prefix = 1 < len(self._val_columns) or 1 < len(args)
if not isinstance(args, str) and isinstance(args,
collections.abc.Sequence):
for agg_type in args:
val_columns_out = [
agg_type + '_' + val for val in self._val_columns
]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx,
self._val_columns,
val_columns_out,
sort_result=sort_result)
add_col_values = False # we only want to add them once
# TODO: Do multindex here
if (self._as_index) and 1 == len(self._by):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.set_index(idx)
result.drop_column(idx.name)
elif isinstance(args, collections.abc.Mapping):
if (len(args.keys()) == 1):
if (len(list(args.values())[0]) == 1):
sort_result = False
for val, agg_type in args.items():
if not isinstance(agg_type, str) and \
isinstance(agg_type, collections.abc.Sequence):
for sub_agg_type in agg_type:
val_columns_out = [sub_agg_type + '_' + val]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(sub_agg_type,
result,
add_col_values,
ctx, [val],
val_columns_out,
sort_result=sort_result)
elif isinstance(agg_type, str):
val_columns_out = [agg_type + '_' + val]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx, [val],
val_columns_out,
sort_result=sort_result)
add_col_values = False # we only want to add them once
# TODO: Do multindex here
if (self._as_index) and 1 == len(self._by):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.set_index(idx)
result.drop_column(idx.name)
else:
result = self.agg([args])
nvtx_range_pop()
return result