def get_result(self):
if self.float_format is None:
float_format = print_config.float_format
if float_format is None:
fmt_str = '%% .%dg' % print_config.precision
float_format = lambda x: fmt_str % x
else:
float_format = self.float_format
formatter = _stringify if self.formatter is None else self.formatter
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
return self.na_rep
else:
# object dtype
return '%s' % formatter(x)
vals = self.values
is_float = lib.map_infer(vals, com.is_float) & notnull(vals)
leading_space = is_float.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float[i] and leading_space:
fmt_values.append(' %s' % _format(v))
elif is_float[i]:
fmt_values.append(float_format(v))
else:
fmt_values.append(' %s' % _format(v))
return _make_fixed_width(fmt_values, self.justify)
def get_result(self):
if self.float_format is None:
float_format = print_config.float_format
if float_format is None:
fmt_str = '%% .%dg' % print_config.precision
float_format = lambda x: fmt_str % x
else:
float_format = self.float_format
formatter = _stringify if self.formatter is None else self.formatter
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
return self.na_rep
else:
# object dtype
return '%s' % formatter(x)
vals = self.values
is_float = lib.map_infer(vals, com.is_float) & notnull(vals)
leading_space = is_float.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float[i] and leading_space:
fmt_values.append(' %s' % _format(v))
elif is_float[i]:
fmt_values.append(float_format(v))
else:
fmt_values.append(' %s' % _format(v))
return _make_fixed_width(fmt_values, self.justify)
def _dt_box_array(arr, offset=None, tz=None):
if arr is None:
return arr
if not isinstance(arr, np.ndarray):
return arr
boxfunc = lambda x: Timestamp(x, offset=offset, tz=tz)
return lib.map_infer(arr, boxfunc)
def auto_map(arr, f, otherargs, n_results=1, required='all'):
if all(np.isscalar(a) for a in otherargs):
res = lib.map_infer(arr, lambda v: f(v, *otherargs))
return Series(res, index=arr.index, copy=False)
n_otherargs = len(otherargs)
if required == 'all':
required = list(range(n_otherargs))
res = map_iter_args(arr, f, azip(*otherargs), n_otherargs, required, n_results)
res = [Series(col, index=arr.index, copy=False) for col in res]
if n_results == 1:
return res[0]
return res
def auto_map(arr, f, otherargs, n_results=1, required='all'):
if all(np.isscalar(a) for a in otherargs):
res = lib.map_infer(arr, lambda v: f(v, *otherargs))
return Series(res, index=arr.index, copy=False)
n_otherargs = len(otherargs)
if required == 'all':
required = list(range(n_otherargs))
res = map_iter_args(arr, f, azip(*otherargs), n_otherargs, required,
n_results)
res = [Series(col, index=arr.index, copy=False) for col in res]
if n_results == 1:
return res[0]
return res
def _format_strings(self, use_unicode=False):
if self.float_format is None:
float_format = print_config.float_format
if float_format is None:
fmt_str = "%% .%dg" % print_config.precision
float_format = lambda x: fmt_str % x
else:
float_format = self.float_format
if use_unicode:
formatter = _stringify if self.formatter is None else self.formatter
else:
formatter = str if self.formatter is None else self.formatter
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return "None"
return self.na_rep
else:
# object dtype
return "%s" % formatter(x)
vals = self.values
is_float = lib.map_infer(vals, com.is_float) & notnull(vals)
leading_space = is_float.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float[i] and leading_space:
fmt_values.append(" %s" % _format(v))
elif is_float[i]:
fmt_values.append(float_format(v))
else:
fmt_values.append(" %s" % _format(v))
return fmt_values
def get_chunk(self, rows=None):
if rows is not None and self.skip_footer:
raise ValueError('skip_footer not supported for iteration')
try:
content = self._get_lines(rows)
except StopIteration:
if self._first_chunk:
content = []
else:
raise
# done with first read, next time raise StopIteration
self._first_chunk = False
if len(content) == 0: # pragma: no cover
if self.index_col is not None:
if np.isscalar(self.index_col):
index = Index([], name=self.index_name)
else:
index = MultiIndex.from_arrays([[]] * len(self.index_col),
names=self.index_name)
else:
index = Index([])
return DataFrame(index=index, columns=self.columns)
zipped_content = list(lib.to_object_array(content).T)
# no index column specified, so infer that's what is wanted
if self.index_col is not None:
if np.isscalar(self.index_col):
index = zipped_content.pop(self.index_col)
else: # given a list of index
index = []
for idx in self.index_col:
index.append(zipped_content[idx])
# remove index items from content and columns, don't pop in loop
for i in reversed(sorted(self.index_col)):
zipped_content.pop(i)
if np.isscalar(self.index_col):
if self.parse_dates:
index = lib.try_parse_dates(index, parser=self.date_parser)
index, na_count = _convert_types(index, self.na_values)
index = Index(index, name=self.index_name)
if self.verbose and na_count:
print 'Found %d NA values in the index' % na_count
else:
arrays = []
for arr in index:
if self.parse_dates:
arr = lib.try_parse_dates(arr, parser=self.date_parser)
arr, _ = _convert_types(arr, self.na_values)
arrays.append(arr)
index = MultiIndex.from_arrays(arrays, names=self.index_name)
else:
index = Index(np.arange(len(content)))
if not index._verify_integrity():
dups = index.get_duplicates()
idx_str = 'Index' if not self.implicit_idx else 'Implicit index'
err_msg = ('%s (columns %s) have duplicate values %s' %
(idx_str, self.index_col, str(dups)))
raise Exception(err_msg)
if len(self.columns) != len(zipped_content):
raise Exception('wrong number of columns')
data = dict((k, v) for k, v in izip(self.columns, zipped_content))
# apply converters
for col, f in self.converters.iteritems():
if isinstance(col, int) and col not in self.columns:
col = self.columns[col]
data[col] = lib.map_infer(data[col], f)
data = _convert_to_ndarrays(data, self.na_values, self.verbose)
return DataFrame(data=data, columns=self.columns, index=index)
def _have_unicode(self):
mask = lib.map_infer(self.values, lambda x: isinstance(x, unicode))
return mask.any()
def get_chunk(self, rows=None):
if rows is not None and self.skip_footer:
raise ValueError('skip_footer not supported for iteration')
try:
content = self._get_lines(rows)
except StopIteration:
if self._first_chunk:
content = []
else:
raise
# done with first read, next time raise StopIteration
self._first_chunk = False
if len(content) == 0: # pragma: no cover
if self.index_col is not None:
if np.isscalar(self.index_col):
index = Index([], name=self.index_name)
else:
index = MultiIndex.from_arrays([[]] * len(self.index_col),
names=self.index_name)
else:
index = Index([])
return DataFrame(index=index, columns=self.columns)
zipped_content = list(lib.to_object_array(content).T)
# no index column specified, so infer that's what is wanted
if self.index_col is not None:
if np.isscalar(self.index_col):
index = zipped_content.pop(self.index_col)
else: # given a list of index
index = []
for idx in self.index_col:
index.append(zipped_content[idx])
# remove index items from content and columns, don't pop in loop
for i in reversed(sorted(self.index_col)):
zipped_content.pop(i)
if np.isscalar(self.index_col):
if self.parse_dates:
index = lib.try_parse_dates(index, parser=self.date_parser)
index, na_count = _convert_types(index, self.na_values)
index = Index(index, name=self.index_name)
if self.verbose and na_count:
print 'Found %d NA values in the index' % na_count
else:
arrays = []
for arr in index:
if self.parse_dates:
arr = lib.try_parse_dates(arr, parser=self.date_parser)
arr, _ = _convert_types(arr, self.na_values)
arrays.append(arr)
index = MultiIndex.from_arrays(arrays, names=self.index_name)
else:
index = Index(np.arange(len(content)))
if not index._verify_integrity():
dups = index.get_duplicates()
idx_str = 'Index' if not self.implicit_idx else 'Implicit index'
err_msg = ('%s (columns %s) have duplicate values %s'
% (idx_str, self.index_col, str(dups)))
raise Exception(err_msg)
if len(self.columns) != len(zipped_content):
raise Exception('wrong number of columns')
data = dict((k, v) for k, v in izip(self.columns, zipped_content))
# apply converters
for col, f in self.converters.iteritems():
if isinstance(col, int) and col not in self.columns:
col = self.columns[col]
data[col] = lib.map_infer(data[col], f)
data = _convert_to_ndarrays(data, self.na_values, self.verbose)
return DataFrame(data=data, columns=self.columns, index=index)
def get_chunk(self, rows=None):
if rows is not None and self.skip_footer:
raise ValueError("skip_footer not supported for iteration")
try:
content = self._get_lines(rows)
except StopIteration:
if self._first_chunk:
content = []
else:
raise
# done with first read, next time raise StopIteration
self._first_chunk = False
if len(content) == 0: # pragma: no cover
if self.index_col is not None:
if np.isscalar(self.index_col):
index = Index([], name=self.index_name)
else:
index = MultiIndex.from_arrays([[]] * len(self.index_col), names=self.index_name)
else:
index = Index([])
return DataFrame(index=index, columns=self.columns)
zipped_content = list(lib.to_object_array(content).T)
if not self._has_complex_date_col and self.index_col is not None:
index = self._get_index(zipped_content)
else:
index = Index(np.arange(len(content)))
col_len, zip_len = len(self.columns), len(zipped_content)
if col_len != zip_len:
row_num = -1
for (i, l) in enumerate(content):
if len(l) != col_len:
break
footers = 0
if self.skip_footer:
footers = self.skip_footer
row_num = self.pos - (len(content) - i + footers)
msg = "Expecting %d columns, got %d in row %d" % (col_len, zip_len, row_num)
raise ValueError(msg)
data = dict((k, v) for k, v in izip(self.columns, zipped_content))
# apply converters
for col, f in self.converters.iteritems():
if isinstance(col, int) and col not in self.columns:
col = self.columns[col]
data[col] = lib.map_infer(data[col], f)
columns = self.columns
if self.parse_dates is not None:
data, columns = self._process_date_conversion(data)
data = _convert_to_ndarrays(data, self.na_values, self.verbose)
df = DataFrame(data=data, columns=columns, index=index)
if self._has_complex_date_col and self.index_col is not None:
if not self._name_processed:
self.index_name = self._get_index_name()
self._name_processed = True
data = dict(((k, v) for k, v in df.iteritems()))
columns = list(columns)
index = self._get_index(data, col_order=columns, parse_dates=False)
data = dict(((k, v.values) for k, v in data.iteritems()))
df = DataFrame(data=data, columns=columns, index=index)
if self.squeeze and len(df.columns) == 1:
return df[df.columns[0]]
return df
def get_chunk(self, rows=None):
if rows is not None and self.skip_footer:
raise ValueError('skip_footer not supported for iteration')
try:
content = self._get_lines(rows)
except StopIteration:
if self._first_chunk:
content = []
else:
raise
# done with first read, next time raise StopIteration
self._first_chunk = False
if len(content) == 0: # pragma: no cover
if self.index_col is not None:
if np.isscalar(self.index_col):
index = Index([], name=self.index_name)
else:
index = MultiIndex.from_arrays([[]] * len(self.index_col),
names=self.index_name)
else:
index = Index([])
return DataFrame(index=index, columns=self.columns)
zipped_content = list(lib.to_object_array(content).T)
if self.index_col is not None:
if np.isscalar(self.index_col):
index = zipped_content.pop(self.index_col)
else: # given a list of index
index = []
for idx in self.index_col:
index.append(zipped_content[idx])
# remove index items from content and columns, don't pop in
# loop
for i in reversed(sorted(self.index_col)):
zipped_content.pop(i)
if np.isscalar(self.index_col):
if self._should_parse_dates(self.index_col):
index = self._conv_date(index)
index, na_count = _convert_types(index, self.na_values)
index = Index(index, name=self.index_name)
if self.verbose and na_count:
print 'Found %d NA values in the index' % na_count
else:
arrays = []
for i, arr in enumerate(index):
if self._should_parse_dates(self.index_col[i]):
arr = self._conv_date(arr)
arr, _ = _convert_types(arr, self.na_values)
arrays.append(arr)
index = MultiIndex.from_arrays(arrays, names=self.index_name)
else:
index = Index(np.arange(len(content)))
# if not index.is_unique:
# dups = index.get_duplicates()
# idx_str = 'Index' if not self._implicit_index else 'Implicit index'
# err_msg = ('%s (columns %s) have duplicate values %s'
# % (idx_str, self.index_col, str(dups)))
# raise Exception(err_msg)
col_len, zip_len = len(self.columns), len(zipped_content)
if col_len != zip_len:
row_num = -1
for (i, l) in enumerate(content):
if len(l) != col_len:
break
footers = 0
if self.skip_footer:
footers = self.skip_footer
row_num = self.pos - (len(content) - i + footers)
msg = ('Expecting %d columns, got %d in row %d' %
(col_len, zip_len, row_num))
raise ValueError(msg)
data = dict((k, v) for k, v in izip(self.columns, zipped_content))
# apply converters
for col, f in self.converters.iteritems():
if isinstance(col, int) and col not in self.columns:
col = self.columns[col]
data[col] = lib.map_infer(data[col], f)
columns = self.columns
if self.parse_dates is not None:
data, columns = self._process_date_conversion(data)
data = _convert_to_ndarrays(data, self.na_values, self.verbose)
return DataFrame(data=data, columns=columns, index=index)
def _have_unicode(self):
mask = lib.map_infer(self.values, lambda x: isinstance(x, unicode))
return mask.any()
