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19,900 | pandas-dev/pandas | pandas/io/parsers.py | _validate_names | def _validate_names(names):
"""
Check if the `names` parameter contains duplicates.
If duplicates are found, we issue a warning before returning.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Returns
---... | python | def _validate_names(names):
"""
Check if the `names` parameter contains duplicates.
If duplicates are found, we issue a warning before returning.
Parameters
----------
names : array-like or None
An array containing a list of the names used for the output DataFrame.
Returns
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19,901 | pandas-dev/pandas | pandas/io/parsers.py | _read | def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
"""Generic reader of line files."""
encoding = kwds.get('encoding', None)
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encoding = re.sub('_', '-', encoding).lower()
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compression = kwds.get('compression', 'infer')
compress... | python | def _read(filepath_or_buffer: FilePathOrBuffer, kwds):
"""Generic reader of line files."""
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compression = kwds.get('compression', 'infer')
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19,902 | pandas-dev/pandas | pandas/io/parsers.py | read_fwf | def read_fwf(filepath_or_buffer: FilePathOrBuffer,
colspecs='infer',
widths=None,
infer_nrows=100,
**kwds):
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
... | python | def read_fwf(filepath_or_buffer: FilePathOrBuffer,
colspecs='infer',
widths=None,
infer_nrows=100,
**kwds):
r"""
Read a table of fixed-width formatted lines into DataFrame.
Also supports optionally iterating or breaking of the file
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19,903 | pandas-dev/pandas | pandas/io/parsers.py | _is_potential_multi_index | def _is_potential_multi_index(columns):
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.
Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
Returns
-------
boolean : Whether or not co... | python | def _is_potential_multi_index(columns):
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.
Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
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19,904 | pandas-dev/pandas | pandas/io/parsers.py | _evaluate_usecols | def _evaluate_usecols(usecols, names):
"""
Check whether or not the 'usecols' parameter
is a callable. If so, enumerates the 'names'
parameter and returns a set of indices for
each entry in 'names' that evaluates to True.
If not a callable, returns 'usecols'.
"""
if callable(usecols):
... | python | def _evaluate_usecols(usecols, names):
"""
Check whether or not the 'usecols' parameter
is a callable. If so, enumerates the 'names'
parameter and returns a set of indices for
each entry in 'names' that evaluates to True.
If not a callable, returns 'usecols'.
"""
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19,905 | pandas-dev/pandas | pandas/io/parsers.py | _validate_usecols_names | def _validate_usecols_names(usecols, names):
"""
Validates that all usecols are present in a given
list of names. If not, raise a ValueError that
shows what usecols are missing.
Parameters
----------
usecols : iterable of usecols
The columns to validate are present in names.
nam... | python | def _validate_usecols_names(usecols, names):
"""
Validates that all usecols are present in a given
list of names. If not, raise a ValueError that
shows what usecols are missing.
Parameters
----------
usecols : iterable of usecols
The columns to validate are present in names.
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19,906 | pandas-dev/pandas | pandas/io/parsers.py | _validate_usecols_arg | def _validate_usecols_arg(usecols):
"""
Validate the 'usecols' parameter.
Checks whether or not the 'usecols' parameter contains all integers
(column selection by index), strings (column by name) or is a callable.
Raises a ValueError if that is not the case.
Parameters
----------
useco... | python | def _validate_usecols_arg(usecols):
"""
Validate the 'usecols' parameter.
Checks whether or not the 'usecols' parameter contains all integers
(column selection by index), strings (column by name) or is a callable.
Raises a ValueError if that is not the case.
Parameters
----------
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19,907 | pandas-dev/pandas | pandas/io/parsers.py | _validate_parse_dates_arg | def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case.
"""
msg = ("Only booleans, lists, and "
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"for the 'parse_dates' parameter")
if... | python | def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
is a non-boolean scalar. Raises a ValueError if
that is the case.
"""
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19,908 | pandas-dev/pandas | pandas/io/parsers.py | _stringify_na_values | def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
if v == int(v):
v = int(... | python | def _stringify_na_values(na_values):
""" return a stringified and numeric for these values """
result = []
for x in na_values:
result.append(str(x))
result.append(x)
try:
v = float(x)
# we are like 999 here
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v = int(... | [
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19,909 | pandas-dev/pandas | pandas/io/parsers.py | _get_na_values | def _get_na_values(col, na_values, na_fvalues, keep_default_na):
"""
Get the NaN values for a given column.
Parameters
----------
col : str
The name of the column.
na_values : array-like, dict
The object listing the NaN values as strings.
na_fvalues : array-like, dict
... | python | def _get_na_values(col, na_values, na_fvalues, keep_default_na):
"""
Get the NaN values for a given column.
Parameters
----------
col : str
The name of the column.
na_values : array-like, dict
The object listing the NaN values as strings.
na_fvalues : array-like, dict
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19,910 | pandas-dev/pandas | pandas/io/parsers.py | ParserBase._extract_multi_indexer_columns | def _extract_multi_indexer_columns(self, header, index_names, col_names,
passed_names=False):
""" extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers """
if len(header) < 2:
return header[... | python | def _extract_multi_indexer_columns(self, header, index_names, col_names,
passed_names=False):
""" extract and return the names, index_names, col_names
header is a list-of-lists returned from the parsers """
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19,911 | pandas-dev/pandas | pandas/io/parsers.py | ParserBase._infer_types | def _infer_types(self, values, na_values, try_num_bool=True):
"""
Infer types of values, possibly casting
Parameters
----------
values : ndarray
na_values : set
try_num_bool : bool, default try
try to cast values to numeric (first preference) or boolea... | python | def _infer_types(self, values, na_values, try_num_bool=True):
"""
Infer types of values, possibly casting
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values : ndarray
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19,912 | pandas-dev/pandas | pandas/io/parsers.py | ParserBase._cast_types | def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
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"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
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19,913 | pandas-dev/pandas | pandas/io/parsers.py | CParserWrapper._set_noconvert_columns | def _set_noconvert_columns(self):
"""
Set the columns that should not undergo dtype conversions.
Currently, any column that is involved with date parsing will not
undergo such conversions.
"""
names = self.orig_names
if self.usecols_dtype == 'integer':
... | python | def _set_noconvert_columns(self):
"""
Set the columns that should not undergo dtype conversions.
Currently, any column that is involved with date parsing will not
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"""
names = self.orig_names
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19,914 | pandas-dev/pandas | pandas/io/parsers.py | PythonParser._handle_usecols | def _handle_usecols(self, columns, usecols_key):
"""
Sets self._col_indices
usecols_key is used if there are string usecols.
"""
if self.usecols is not None:
if callable(self.usecols):
col_indices = _evaluate_usecols(self.usecols, usecols_key)
... | python | def _handle_usecols(self, columns, usecols_key):
"""
Sets self._col_indices
usecols_key is used if there are string usecols.
"""
if self.usecols is not None:
if callable(self.usecols):
col_indices = _evaluate_usecols(self.usecols, usecols_key)
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19,915 | pandas-dev/pandas | pandas/io/parsers.py | PythonParser._check_for_bom | def _check_for_bom(self, first_row):
"""
Checks whether the file begins with the BOM character.
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Checks whether the file begins with the BOM character.
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19,916 | pandas-dev/pandas | pandas/io/parsers.py | PythonParser._alert_malformed | def _alert_malformed(self, msg, row_num):
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Alert a user about a malformed row.
If `self.error_bad_lines` is True, the alert will be `ParserError`.
If `self.warn_bad_lines` is True, the alert will be printed out.
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msg : The error message t... | python | def _alert_malformed(self, msg, row_num):
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Alert a user about a malformed row.
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19,917 | pandas-dev/pandas | pandas/io/parsers.py | PythonParser._remove_empty_lines | def _remove_empty_lines(self, lines):
"""
Iterate through the lines and remove any that are
either empty or contain only one whitespace value
Parameters
----------
lines : array-like
The array of lines that we are to filter.
Returns
-------
... | python | def _remove_empty_lines(self, lines):
"""
Iterate through the lines and remove any that are
either empty or contain only one whitespace value
Parameters
----------
lines : array-like
The array of lines that we are to filter.
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-------
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19,918 | pandas-dev/pandas | pandas/io/parsers.py | FixedWidthReader.get_rows | def get_rows(self, infer_nrows, skiprows=None):
"""
Read rows from self.f, skipping as specified.
We distinguish buffer_rows (the first <= infer_nrows
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because it's simpler to leave the other locations
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"""
Read rows from self.f, skipping as specified.
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19,919 | pandas-dev/pandas | pandas/io/msgpack/__init__.py | pack | def pack(o, stream, **kwargs):
"""
Pack object `o` and write it to `stream`
See :class:`Packer` for options.
"""
packer = Packer(**kwargs)
stream.write(packer.pack(o)) | python | def pack(o, stream, **kwargs):
"""
Pack object `o` and write it to `stream`
See :class:`Packer` for options.
"""
packer = Packer(**kwargs)
stream.write(packer.pack(o)) | [
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19,920 | pandas-dev/pandas | pandas/core/internals/concat.py | get_mgr_concatenation_plan | def get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
"""
# Calculat... | python | def get_mgr_concatenation_plan(mgr, indexers):
"""
Construct concatenation plan for given block manager and indexers.
Parameters
----------
mgr : BlockManager
indexers : dict of {axis: indexer}
Returns
-------
plan : list of (BlockPlacement, JoinUnit) tuples
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19,921 | pandas-dev/pandas | pandas/core/internals/concat.py | concatenate_join_units | def concatenate_join_units(join_units, concat_axis, copy):
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"""
if concat_axis == 0 and len(join_units) > 1:
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"""
Concatenate values from several join units along selected axis.
"""
if concat_axis == 0 and len(join_units) > 1:
# Concatenating join units along ax0 is handled in _merge_blocks.
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19,922 | pandas-dev/pandas | pandas/core/internals/concat.py | trim_join_unit | def trim_join_unit(join_unit, length):
"""
Reduce join_unit's shape along item axis to length.
Extra items that didn't fit are returned as a separate block.
"""
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extra_indexers = join_unit.indexers
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extra_block ... | python | def trim_join_unit(join_unit, length):
"""
Reduce join_unit's shape along item axis to length.
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"""
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19,923 | pandas-dev/pandas | pandas/core/internals/concat.py | combine_concat_plans | def combine_concat_plans(plans, concat_axis):
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"""
if len(plans) == 1:
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... | python | def combine_concat_plans(plans, concat_axis):
"""
Combine multiple concatenation plans into one.
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19,924 | pandas-dev/pandas | pandas/plotting/_style.py | _Options.use | def use(self, key, value):
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Temporarily set a parameter value using the with statement.
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try:
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Temporarily set a parameter value using the with statement.
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19,925 | pandas-dev/pandas | pandas/io/stata.py | _dtype_to_stata_type | def _dtype_to_stata_type(dtype, column):
"""
Convert dtype types to stata types. Returns the byte of the given ordinal.
See TYPE_MAP and comments for an explanation. This is also explained in
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19,926 | pandas-dev/pandas | pandas/io/stata.py | _dtype_to_default_stata_fmt | def _dtype_to_default_stata_fmt(dtype, column, dta_version=114,
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"""
Map numpy dtype to stata's default format for this type. Not terribly
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19,927 | pandas-dev/pandas | pandas/io/stata.py | _pad_bytes_new | def _pad_bytes_new(name, length):
"""
Takes a bytes instance and pads it with null bytes until it's length chars.
"""
if isinstance(name, str):
name = bytes(name, 'utf-8')
return name + b'\x00' * (length - len(name)) | python | def _pad_bytes_new(name, length):
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19,928 | pandas-dev/pandas | pandas/io/stata.py | StataReader._setup_dtype | def _setup_dtype(self):
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return self._dtype
dtype = [] # Convert struct data types to numpy data type
for i, typ in enumerate(self.typlist):
if typ in self.NUMPY_TYPE_MAP:
dtype.... | python | def _setup_dtype(self):
"""Map between numpy and state dtypes"""
if self._dtype is not None:
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19,929 | pandas-dev/pandas | pandas/io/stata.py | StataWriter._write | def _write(self, to_write):
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19,930 | pandas-dev/pandas | pandas/io/stata.py | StataWriter._prepare_categoricals | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
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is_cat = [is_categorical_dtype(data[col]) for col in data]
self._is_col_cat = is_cat
self._value_labels = []
if n... | python | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int"""
is_cat = [is_categorical_dtype(data[col]) for col in data]
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19,931 | pandas-dev/pandas | pandas/io/stata.py | StataWriter._close | def _close(self):
"""
Close the file if it was created by the writer.
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then leave this file open for the caller to close. In either case,
attempt to flush the file contents to ensure they are written to disk
... | python | def _close(self):
"""
Close the file if it was created by the writer.
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then leave this file open for the caller to close. In either case,
attempt to flush the file contents to ensure they are written to disk
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19,932 | pandas-dev/pandas | pandas/io/stata.py | StataStrLWriter.generate_table | def generate_table(self):
"""
Generates the GSO lookup table for the DataFRame
Returns
-------
gso_table : OrderedDict
Ordered dictionary using the string found as keys
and their lookup position (v,o) as values
gso_df : DataFrame
DataF... | python | def generate_table(self):
"""
Generates the GSO lookup table for the DataFRame
Returns
-------
gso_table : OrderedDict
Ordered dictionary using the string found as keys
and their lookup position (v,o) as values
gso_df : DataFrame
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19,933 | pandas-dev/pandas | pandas/io/stata.py | StataStrLWriter.generate_blob | def generate_blob(self, gso_table):
"""
Generates the binary blob of GSOs that is written to the dta file.
Parameters
----------
gso_table : OrderedDict
Ordered dictionary (str, vo)
Returns
-------
gso : bytes
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"""
Generates the binary blob of GSOs that is written to the dta file.
Parameters
----------
gso_table : OrderedDict
Ordered dictionary (str, vo)
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19,934 | pandas-dev/pandas | pandas/io/stata.py | StataWriter117._write_header | def _write_header(self, data_label=None, time_stamp=None):
"""Write the file header"""
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self._file.write(bytes('<stata_dta>', 'utf-8'))
bio = BytesIO()
# ds_format - 117
bio.write(self._tag(bytes('117', 'utf-8'), 'release'))
# byteorder
... | python | def _write_header(self, data_label=None, time_stamp=None):
"""Write the file header"""
byteorder = self._byteorder
self._file.write(bytes('<stata_dta>', 'utf-8'))
bio = BytesIO()
# ds_format - 117
bio.write(self._tag(bytes('117', 'utf-8'), 'release'))
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19,935 | pandas-dev/pandas | pandas/io/stata.py | StataWriter117._write_map | def _write_map(self):
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if self._map is None:
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19,936 | pandas-dev/pandas | pandas/io/stata.py | StataWriter117._update_strl_names | def _update_strl_names(self):
"""Update column names for conversion to strl if they might have been
changed to comply with Stata naming rules"""
# Update convert_strl if names changed
for orig, new in self._converted_names.items():
if orig in self._convert_strl:
... | python | def _update_strl_names(self):
"""Update column names for conversion to strl if they might have been
changed to comply with Stata naming rules"""
# Update convert_strl if names changed
for orig, new in self._converted_names.items():
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19,937 | pandas-dev/pandas | pandas/io/stata.py | StataWriter117._convert_strls | def _convert_strls(self, data):
"""Convert columns to StrLs if either very large or in the
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convert_cols = [
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ssw = Sta... | python | def _convert_strls(self, data):
"""Convert columns to StrLs if either very large or in the
convert_strl variable"""
convert_cols = [
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19,938 | pandas-dev/pandas | pandas/plotting/_converter.py | register | def register(explicit=True):
"""
Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
... | python | def register(explicit=True):
"""
Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
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19,939 | pandas-dev/pandas | pandas/plotting/_converter.py | deregister | def deregister():
"""
Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
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pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed... | python | def deregister():
"""
Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
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19,940 | pandas-dev/pandas | pandas/plotting/_converter.py | _get_default_annual_spacing | def _get_default_annual_spacing(nyears):
"""
Returns a default spacing between consecutive ticks for annual data.
"""
if nyears < 11:
(min_spacing, maj_spacing) = (1, 1)
elif nyears < 20:
(min_spacing, maj_spacing) = (1, 2)
elif nyears < 50:
(min_spacing, maj_spacing) = (... | python | def _get_default_annual_spacing(nyears):
"""
Returns a default spacing between consecutive ticks for annual data.
"""
if nyears < 11:
(min_spacing, maj_spacing) = (1, 1)
elif nyears < 20:
(min_spacing, maj_spacing) = (1, 2)
elif nyears < 50:
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19,941 | pandas-dev/pandas | pandas/plotting/_converter.py | period_break | def period_break(dates, period):
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
Name of the period to monitor.
"""
current = getattr(dates, period)
previous = getattr(da... | python | def period_break(dates, period):
"""
Returns the indices where the given period changes.
Parameters
----------
dates : PeriodIndex
Array of intervals to monitor.
period : string
Name of the period to monitor.
"""
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19,942 | pandas-dev/pandas | pandas/plotting/_converter.py | has_level_label | def has_level_label(label_flags, vmin):
"""
Returns true if the ``label_flags`` indicate there is at least one label
for this level.
if the minimum view limit is not an exact integer, then the first tick
label won't be shown, so we must adjust for that.
"""
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"""
Returns true if the ``label_flags`` indicate there is at least one label
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if the minimum view limit is not an exact integer, then the first tick
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19,943 | pandas-dev/pandas | pandas/plotting/_converter.py | PandasAutoDateLocator.get_locator | def get_locator(self, dmin, dmax):
'Pick the best locator based on a distance.'
_check_implicitly_registered()
delta = relativedelta(dmax, dmin)
num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
num_sec = (delta.hours * 60.0 + delta.minutes) * 60.0 + delta.secon... | python | def get_locator(self, dmin, dmax):
'Pick the best locator based on a distance.'
_check_implicitly_registered()
delta = relativedelta(dmax, dmin)
num_days = (delta.years * 12.0 + delta.months) * 31.0 + delta.days
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19,944 | pandas-dev/pandas | pandas/plotting/_converter.py | MilliSecondLocator.autoscale | def autoscale(self):
"""
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"""
Set the view limits to include the data range.
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19,945 | pandas-dev/pandas | pandas/plotting/_converter.py | TimeSeries_DateLocator._get_default_locs | def _get_default_locs(self, vmin, vmax):
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"Returns the default locations of ticks."
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19,946 | pandas-dev/pandas | pandas/plotting/_converter.py | TimeSeries_DateLocator.autoscale | def autoscale(self):
"""
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19,947 | pandas-dev/pandas | pandas/plotting/_converter.py | TimeSeries_DateFormatter._set_default_format | def _set_default_format(self, vmin, vmax):
"Returns the default ticks spacing."
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19,948 | pandas-dev/pandas | pandas/plotting/_converter.py | TimeSeries_DateFormatter.set_locs | def set_locs(self, locs):
'Sets the locations of the ticks'
# don't actually use the locs. This is just needed to work with
# matplotlib. Force to use vmin, vmax
_check_implicitly_registered()
self.locs = locs
(vmin, vmax) = vi = tuple(self.axis.get_view_interval())
... | python | def set_locs(self, locs):
'Sets the locations of the ticks'
# don't actually use the locs. This is just needed to work with
# matplotlib. Force to use vmin, vmax
_check_implicitly_registered()
self.locs = locs
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19,949 | pandas-dev/pandas | pandas/io/json/table_schema.py | build_table_schema | def build_table_schema(data, index=True, primary_key=None, version=True):
"""
Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
... | python | def build_table_schema(data, index=True, primary_key=None, version=True):
"""
Create a Table schema from ``data``.
Parameters
----------
data : Series, DataFrame
index : bool, default True
Whether to include ``data.index`` in the schema.
primary_key : bool or None, default True
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19,950 | pandas-dev/pandas | pandas/io/json/table_schema.py | parse_table_schema | def parse_table_schema(json, precise_float):
"""
Builds a DataFrame from a given schema
Parameters
----------
json :
A JSON table schema
precise_float : boolean
Flag controlling precision when decoding string to double values, as
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Returns
... | python | def parse_table_schema(json, precise_float):
"""
Builds a DataFrame from a given schema
Parameters
----------
json :
A JSON table schema
precise_float : boolean
Flag controlling precision when decoding string to double values, as
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19,951 | pandas-dev/pandas | pandas/core/ops.py | get_op_result_name | def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
should always be either an Index or a Series.
Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
""... | python | def get_op_result_name(left, right):
"""
Find the appropriate name to pin to an operation result. This result
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Parameters
----------
left : {Series, Index}
right : object
Returns
-------
name : object
Usually a string
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19,952 | pandas-dev/pandas | pandas/core/ops.py | _maybe_match_name | def _maybe_match_name(a, b):
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----------
a : o... | python | def _maybe_match_name(a, b):
"""
Try to find a name to attach to the result of an operation between
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19,953 | pandas-dev/pandas | pandas/core/ops.py | maybe_upcast_for_op | def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
and comparison operations.
Parameters
----------
obj: object
Returns
-------
out : object
Notes
-----
Be careful to call this *after* determining the `name` attrib... | python | def maybe_upcast_for_op(obj):
"""
Cast non-pandas objects to pandas types to unify behavior of arithmetic
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----------
obj: object
Returns
-------
out : object
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19,954 | pandas-dev/pandas | pandas/core/ops.py | make_invalid_op | def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
raise TypeError("cannot perform {name} with this index type: "
... | python | def make_invalid_op(name):
"""
Return a binary method that always raises a TypeError.
Parameters
----------
name : str
Returns
-------
invalid_op : function
"""
def invalid_op(self, other=None):
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19,955 | pandas-dev/pandas | pandas/core/ops.py | _gen_eval_kwargs | def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
{'r... | python | def _gen_eval_kwargs(name):
"""
Find the keyword arguments to pass to numexpr for the given operation.
Parameters
----------
name : str
Returns
-------
eval_kwargs : dict
Examples
--------
>>> _gen_eval_kwargs("__add__")
{}
>>> _gen_eval_kwargs("rtruediv")
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19,956 | pandas-dev/pandas | pandas/core/ops.py | _get_opstr | def _get_opstr(op, cls):
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Find the operation string, if any, to pass to numexpr for this
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Parameters
----------
op : binary operator
cls : class
Returns
-------
op_str : string or None
"""
# numexpr is available for non-sparse classes
subtyp = getattr(c... | python | def _get_opstr(op, cls):
"""
Find the operation string, if any, to pass to numexpr for this
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op : binary operator
cls : class
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op_str : string or None
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19,957 | pandas-dev/pandas | pandas/core/ops.py | _get_op_name | def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
if spe... | python | def _get_op_name(op, special):
"""
Find the name to attach to this method according to conventions
for special and non-special methods.
Parameters
----------
op : binary operator
special : bool
Returns
-------
op_name : str
"""
opname = op.__name__.strip('_')
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19,958 | pandas-dev/pandas | pandas/core/ops.py | _make_flex_doc | def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
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to attach to a generated method.
Parameters
----------
op_name : str {'__add__', '__sub__', ... '__eq__', '_... | python | def _make_flex_doc(op_name, typ):
"""
Make the appropriate substitutions for the given operation and class-typ
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19,959 | pandas-dev/pandas | pandas/core/ops.py | mask_cmp_op | def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
#... | python | def mask_cmp_op(x, y, op, allowed_types):
"""
Apply the function `op` to only non-null points in x and y.
Parameters
----------
x : array-like
y : array-like
op : binary operation
allowed_types : class or tuple of classes
Returns
-------
result : ndarray[bool]
"""
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19,960 | pandas-dev/pandas | pandas/core/ops.py | should_series_dispatch | def should_series_dispatch(left, right, op):
"""
Identify cases where a DataFrame operation should dispatch to its
Series counterpart.
Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
-------
override : bool
"""
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"""
Identify cases where a DataFrame operation should dispatch to its
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Parameters
----------
left : DataFrame
right : DataFrame
op : binary operator
Returns
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override : bool
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19,961 | pandas-dev/pandas | pandas/core/ops.py | dispatch_to_index_op | def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
to the index implementation. DatetimeIndex and TimedeltaIndex perform
type checking, timezone handling, overflow checks, etc.
Parameters
----------
op : binary... | python | def dispatch_to_index_op(op, left, right, index_class):
"""
Wrap Series left in the given index_class to delegate the operation op
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type checking, timezone handling, overflow checks, etc.
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19,962 | pandas-dev/pandas | pandas/core/ops.py | dispatch_to_extension_op | def dispatch_to_extension_op(op, left, right):
"""
Assume that left or right is a Series backed by an ExtensionArray,
apply the operator defined by op.
"""
# The op calls will raise TypeError if the op is not defined
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"""
Assume that left or right is a Series backed by an ExtensionArray,
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# The op calls will raise TypeError if the op is not defined
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19,963 | pandas-dev/pandas | pandas/core/ops.py | _align_method_SERIES | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
# avoid repeate... | python | def _align_method_SERIES(left, right, align_asobject=False):
""" align lhs and rhs Series """
# ToDo: Different from _align_method_FRAME, list, tuple and ndarray
# are not coerced here
# because Series has inconsistencies described in #13637
if isinstance(right, ABCSeries):
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19,964 | pandas-dev/pandas | pandas/core/ops.py | _construct_divmod_result | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
dtype=dtype),
_construct_result(left, result[1],... | python | def _construct_divmod_result(left, result, index, name, dtype=None):
"""divmod returns a tuple of like indexed series instead of a single series.
"""
return (
_construct_result(left, result[0], index=index, name=name,
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19,965 | pandas-dev/pandas | pandas/core/ops.py | _combine_series_frame | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
o... | python | def _combine_series_frame(self, other, func, fill_value=None, axis=None,
level=None):
"""
Apply binary operator `func` to self, other using alignment and fill
conventions determined by the fill_value, axis, and level kwargs.
Parameters
----------
self : DataFrame
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19,966 | pandas-dev/pandas | pandas/core/ops.py | _align_method_FRAME | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
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if axis is not None and left._get_axis_name(axis) == '... | python | def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """
def to_series(right):
msg = ('Unable to coerce to Series, length must be {req_len}: '
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19,967 | pandas-dev/pandas | pandas/core/ops.py | _cast_sparse_series_op | def _cast_sparse_series_op(left, right, opname):
"""
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to have NaN or inf values
Parameters
----------
left : SparseArray
right : SparseArray
opname : str
Returns
-------
left : SparseArray
right : Sp... | python | def _cast_sparse_series_op(left, right, opname):
"""
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----------
left : SparseArray
right : SparseArray
opname : str
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19,968 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | validate_inferred_freq | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
------... | python | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
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----------
freq : DateOffset or None
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19,969 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | maybe_infer_freq | def maybe_infer_freq(freq):
"""
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"""
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19,970 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | _ensure_datetimelike_to_i8 | def _ensure_datetimelike_to_i8(other, to_utc=False):
"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
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"""
Helper for coercing an input scalar or array to i8.
Parameters
----------
other : 1d array
to_utc : bool, default False
If True, convert the values to UTC before extracting the i8 values
If False, extract the i8 values dir... | [
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19,971 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._scalar_from_string | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
Period, Timestamp, or Timedelta, or NaT... | python | def _scalar_from_string(
self,
value: str,
) -> Union[Period, Timestamp, Timedelta, NaTType]:
"""
Construct a scalar type from a string.
Parameters
----------
value : str
Returns
-------
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Returns
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Period, Timestamp, or Timedelta, or NaT
Whatever the type of ``self._scalar_type`` is.
Notes
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This should call ``self._check_compatible_wit... | [
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19,972 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._unbox_scalar | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
... | python | def _unbox_scalar(
self,
value: Union[Period, Timestamp, Timedelta, NaTType],
) -> int:
"""
Unbox the integer value of a scalar `value`.
Parameters
----------
value : Union[Period, Timestamp, Timedelta]
Returns
-------
int
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Examples
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>>> self._unbox_scalar(Timedelta('10s')) # DOCTEST: +SKIP
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19,973 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | AttributesMixin._check_compatible_with | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
... | python | def _check_compatible_with(
self,
other: Union[Period, Timestamp, Timedelta, NaTType],
) -> None:
"""
Verify that `self` and `other` are compatible.
* DatetimeArray verifies that the timezones (if any) match
* PeriodArray verifies that the freq matches
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* Timedelta has no verification
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19,974 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatelikeOps.strftime | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string for... | python | def strftime(self, date_format):
"""
Convert to Index using specified date_format.
Return an Index of formatted strings specified by date_format, which
supports the same string format as the python standard library. Details
of the string format can be found in `python string for... | [
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19,975 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.repeat | def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
values = self._data.repeat(repeats)
return type(self)(values.view('i8'), dtype=self.dtype) | python | def repeat(self, repeats, *args, **kwargs):
"""
Repeat elements of an array.
See Also
--------
numpy.ndarray.repeat
"""
nv.validate_repeat(args, kwargs)
values = self._data.repeat(repeats)
return type(self)(values.view('i8'), dtype=self.dtype) | [
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19,976 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_delta | def _add_delta(self, other):
"""
Add a timedelta-like, Tick or TimedeltaIndex-like object
to self, yielding an int64 numpy array
Parameters
----------
delta : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
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"""
Add a timedelta-like, Tick or TimedeltaIndex-like object
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Parameters
----------
delta : {timedelta, np.timedelta64, Tick,
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Returns
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19,977 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_timedeltalike_scalar | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
if isna(other):
# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
new_values = np.empty(len(self), dtype='i8')
new_va... | python | def _add_timedeltalike_scalar(self, other):
"""
Add a delta of a timedeltalike
return the i8 result view
"""
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# i.e np.timedelta64("NaT"), not recognized by delta_to_nanoseconds
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19,978 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_delta_tdi | def _add_delta_tdi(self, other):
"""
Add a delta of a TimedeltaIndex
return the i8 result view
"""
if len(self) != len(other):
raise ValueError("cannot add indices of unequal length")
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19,979 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._add_nat | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
# GH#19124 pd.NaT is treate... | python | def _add_nat(self):
"""
Add pd.NaT to self
"""
if is_period_dtype(self):
raise TypeError('Cannot add {cls} and {typ}'
.format(cls=type(self).__name__,
typ=type(NaT).__name__))
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19,980 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._sub_nat | def _sub_nat(self):
"""
Subtract pd.NaT from self
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# GH#19124 Timedelta - datetime is not in general well-defined.
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"""
Subtract pd.NaT from self
"""
# GH#19124 Timedelta - datetime is not in general well-defined.
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19,981 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._addsub_int_array | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
`_time_shift` pointwise.
Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
... | python | def _addsub_int_array(self, other, op):
"""
Add or subtract array-like of integers equivalent to applying
`_time_shift` pointwise.
Parameters
----------
other : Index, ExtensionArray, np.ndarray
integer-dtype
op : {operator.add, operator.sub}
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19,982 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._addsub_offset_array | def _addsub_offset_array(self, other, op):
"""
Add or subtract array-like of DateOffset objects
Parameters
----------
other : Index, np.ndarray
object-dtype containing pd.DateOffset objects
op : {operator.add, operator.sub}
Returns
-------
... | python | def _addsub_offset_array(self, other, op):
"""
Add or subtract array-like of DateOffset objects
Parameters
----------
other : Index, np.ndarray
object-dtype containing pd.DateOffset objects
op : {operator.add, operator.sub}
Returns
-------
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19,983 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin._ensure_localized | def _ensure_localized(self, arg, ambiguous='raise', nonexistent='raise',
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"""
Ensure that we are re-localized.
This is for compat as we can then call this on all datetimelike
arrays generally (ignored for Period/Timedelta)
Parameters
... | python | def _ensure_localized(self, arg, ambiguous='raise', nonexistent='raise',
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"""
Ensure that we are re-localized.
This is for compat as we can then call this on all datetimelike
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Parameters
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19,984 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.min | def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Array or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Index.min : Return the minimum value in an Index.
Series.min : Return the minimum value in a Se... | python | def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Array or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Index.min : Return the minimum value in an Index.
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19,985 | pandas-dev/pandas | pandas/core/arrays/datetimelike.py | DatetimeLikeArrayMixin.max | def max(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the maximum value of the Array or maximum along
an axis.
See Also
--------
numpy.ndarray.max
Index.max : Return the maximum value in an Index.
Series.max : Return the maximum value in a Se... | python | def max(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the maximum value of the Array or maximum along
an axis.
See Also
--------
numpy.ndarray.max
Index.max : Return the maximum value in an Index.
Series.max : Return the maximum value in a Se... | [
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19,986 | pandas-dev/pandas | pandas/core/arrays/period.py | _period_array_cmp | def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
def wrapper(self, other):
op = getattr(self.asi8, opname)
if isinstance(other, (ABCDataFrame, ... | python | def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = opname == '__ne__'
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19,987 | pandas-dev/pandas | pandas/core/arrays/period.py | _raise_on_incompatible | def _raise_on_incompatible(left, right):
"""
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
Raises
------
IncompatibleFrequency
"""
... | python | def _raise_on_incompatible(left, right):
"""
Helper function to render a consistent error message when raising
IncompatibleFrequency.
Parameters
----------
left : PeriodArray
right : DateOffset, Period, ndarray, or timedelta-like
Raises
------
IncompatibleFrequency
"""
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19,988 | pandas-dev/pandas | pandas/core/arrays/period.py | period_array | def period_array(
data: Sequence[Optional[Period]],
freq: Optional[Tick] = None,
copy: bool = False,
) -> PeriodArray:
"""
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
data : Sequence of Period objects
A sequence of Period obje... | python | def period_array(
data: Sequence[Optional[Period]],
freq: Optional[Tick] = None,
copy: bool = False,
) -> PeriodArray:
"""
Construct a new PeriodArray from a sequence of Period scalars.
Parameters
----------
data : Sequence of Period objects
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19,989 | pandas-dev/pandas | pandas/core/arrays/period.py | validate_dtype_freq | def validate_dtype_freq(dtype, freq):
"""
If both a dtype and a freq are available, ensure they match. If only
dtype is available, extract the implied freq.
Parameters
----------
dtype : dtype
freq : DateOffset or None
Returns
-------
freq : DateOffset
Raises
------
... | python | def validate_dtype_freq(dtype, freq):
"""
If both a dtype and a freq are available, ensure they match. If only
dtype is available, extract the implied freq.
Parameters
----------
dtype : dtype
freq : DateOffset or None
Returns
-------
freq : DateOffset
Raises
------
... | [
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19,990 | pandas-dev/pandas | pandas/core/arrays/period.py | dt64arr_to_periodarr | def dt64arr_to_periodarr(data, freq, tz=None):
"""
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
freq : Optional[Union[str, Tick]]
Must match the `freq` on the `data` if `data` i... | python | def dt64arr_to_periodarr(data, freq, tz=None):
"""
Convert an datetime-like array to values Period ordinals.
Parameters
----------
data : Union[Series[datetime64[ns]], DatetimeIndex, ndarray[datetime64ns]]
freq : Optional[Union[str, Tick]]
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19,991 | pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._from_datetime64 | def _from_datetime64(cls, data, freq, tz=None):
"""
Construct a PeriodArray from a datetime64 array
Parameters
----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
PeriodAr... | python | def _from_datetime64(cls, data, freq, tz=None):
"""
Construct a PeriodArray from a datetime64 array
Parameters
----------
data : ndarray[datetime64[ns], datetime64[ns, tz]]
freq : str or Tick
tz : tzinfo, optional
Returns
-------
PeriodAr... | [
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19,992 | pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._format_native_types | def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs):
"""
actually format my specific types
"""
values = self.astype(object)
if date_format:
formatter = lambda dt: dt.strftime(date_format)
else:
formatter = lambda dt: '%s' % dt... | python | def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs):
"""
actually format my specific types
"""
values = self.astype(object)
if date_format:
formatter = lambda dt: dt.strftime(date_format)
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19,993 | pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._add_delta | def _add_delta(self, other):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new PeriodArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
... | python | def _add_delta(self, other):
"""
Add a timedelta-like, Tick, or TimedeltaIndex-like object
to self, yielding a new PeriodArray
Parameters
----------
other : {timedelta, np.timedelta64, Tick,
TimedeltaIndex, ndarray[timedelta64]}
Returns
... | [
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19,994 | pandas-dev/pandas | pandas/core/arrays/period.py | PeriodArray._check_timedeltalike_freq_compat | def _check_timedeltalike_freq_compat(self, other):
"""
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are only valid if `other` is an integer multiple of `self.freq`.
If the operation is valid, find that integer multiple. Otherwise,
raise because the operatio... | python | def _check_timedeltalike_freq_compat(self, other):
"""
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19,995 | pandas-dev/pandas | pandas/core/dtypes/missing.py | _isna_old | def _isna_old(obj):
"""Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
return libmissing.checknull_old(obj)
# hack (for now) because MI regist... | python | def _isna_old(obj):
"""Detect missing values. Treat None, NaN, INF, -INF as null.
Parameters
----------
arr: ndarray or object value
Returns
-------
boolean ndarray or boolean
"""
if is_scalar(obj):
return libmissing.checknull_old(obj)
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19,996 | pandas-dev/pandas | pandas/core/dtypes/missing.py | _maybe_fill | def _maybe_fill(arr, fill_value=np.nan):
"""
if we have a compatible fill_value and arr dtype, then fill
"""
if _isna_compat(arr, fill_value):
arr.fill(fill_value)
return arr | python | def _maybe_fill(arr, fill_value=np.nan):
"""
if we have a compatible fill_value and arr dtype, then fill
"""
if _isna_compat(arr, fill_value):
arr.fill(fill_value)
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19,997 | pandas-dev/pandas | pandas/core/dtypes/missing.py | na_value_for_dtype | def na_value_for_dtype(dtype, compat=True):
"""
Return a dtype compat na value
Parameters
----------
dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
--------
>>> na_value_for_dtype(np.dtype('int64'))
0
>... | python | def na_value_for_dtype(dtype, compat=True):
"""
Return a dtype compat na value
Parameters
----------
dtype : string / dtype
compat : boolean, default True
Returns
-------
np.dtype or a pandas dtype
Examples
--------
>>> na_value_for_dtype(np.dtype('int64'))
0
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19,998 | pandas-dev/pandas | pandas/plotting/_tools.py | table | def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
Helper function to convert DataFrame and Series to matplotlib.table
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
keyword arg... | python | def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
Helper function to convert DataFrame and Series to matplotlib.table
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
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] | 9feb3ad92cc0397a04b665803a49299ee7aa1037 | https://github.com/pandas-dev/pandas/blob/9feb3ad92cc0397a04b665803a49299ee7aa1037/pandas/plotting/_tools.py#L23-L60 |
19,999 | pandas-dev/pandas | pandas/plotting/_tools.py | _subplots | def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, ax=None, layout=None, layout_type='box',
**fig_kw):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, includi... | python | def _subplots(naxes=None, sharex=False, sharey=False, squeeze=True,
subplot_kw=None, ax=None, layout=None, layout_type='box',
**fig_kw):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, includi... | [
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This utility wrapper makes it convenient to create common layouts of
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Keyword arguments:
naxes : int
Number of required axes. Exceeded axes are set invisible. Default is
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