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train | MPLPlot._has_plotted_object | check whether ax has data | pandas/plotting/_core.py | def _has_plotted_object(self, ax):
"""check whether ax has data"""
return (len(ax.lines) != 0 or
len(ax.artists) != 0 or
len(ax.containers) != 0) | def _has_plotted_object(self, ax):
"""check whether ax has data"""
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len(ax.artists) != 0 or
len(ax.containers) != 0) | [
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train | MPLPlot.result | Return result axes | pandas/plotting/_core.py | def result(self):
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Return result axes
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if self.subplots:
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Return result axes
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train | MPLPlot._post_plot_logic_common | Common post process for each axes | pandas/plotting/_core.py | def _post_plot_logic_common(self, ax, data):
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def get_label(i):
try:
return pprint_thing(data.index[i])
except Exception:
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train | MPLPlot._adorn_subplots | Common post process unrelated to data | pandas/plotting/_core.py | def _adorn_subplots(self):
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if len(self.axes) > 0:
all_axes = self._get_subplots()
nrows, ncols = self._get_axes_layout()
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train | MPLPlot._apply_axis_properties | Tick creation within matplotlib is reasonably expensive and is
internally deferred until accessed as Ticks are created/destroyed
multiple times per draw. It's therefore beneficial for us to avoid
accessing unless we will act on the Tick. | pandas/plotting/_core.py | def _apply_axis_properties(self, axis, rot=None, fontsize=None):
""" Tick creation within matplotlib is reasonably expensive and is
internally deferred until accessed as Ticks are created/destroyed
multiple times per draw. It's therefore beneficial for us to avoid
accessing u... | def _apply_axis_properties(self, axis, rot=None, fontsize=None):
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train | MPLPlot._get_ax_layer | get left (primary) or right (secondary) axes | pandas/plotting/_core.py | def _get_ax_layer(cls, ax, primary=True):
"""get left (primary) or right (secondary) axes"""
if primary:
return getattr(ax, 'left_ax', ax)
else:
return getattr(ax, 'right_ax', ax) | def _get_ax_layer(cls, ax, primary=True):
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train | MPLPlot._apply_style_colors | Manage style and color based on column number and its label.
Returns tuple of appropriate style and kwds which "color" may be added. | pandas/plotting/_core.py | def _apply_style_colors(self, colors, kwds, col_num, label):
"""
Manage style and color based on column number and its label.
Returns tuple of appropriate style and kwds which "color" may be added.
"""
style = None
if self.style is not None:
if isinstance(self... | def _apply_style_colors(self, colors, kwds, col_num, label):
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Manage style and color based on column number and its label.
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train | MPLPlot._parse_errorbars | Look for error keyword arguments and return the actual errorbar data
or return the error DataFrame/dict
Error bars can be specified in several ways:
Series: the user provides a pandas.Series object of the same
length as the data
ndarray: provides a np.ndarray... | pandas/plotting/_core.py | def _parse_errorbars(self, label, err):
"""
Look for error keyword arguments and return the actual errorbar data
or return the error DataFrame/dict
Error bars can be specified in several ways:
Series: the user provides a pandas.Series object of the same
l... | def _parse_errorbars(self, label, err):
"""
Look for error keyword arguments and return the actual errorbar data
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Error bars can be specified in several ways:
Series: the user provides a pandas.Series object of the same
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train | HistPlot._make_plot_keywords | merge BoxPlot/KdePlot properties to passed kwds | pandas/plotting/_core.py | def _make_plot_keywords(self, kwds, y):
"""merge BoxPlot/KdePlot properties to passed kwds"""
# y is required for KdePlot
kwds['bottom'] = self.bottom
kwds['bins'] = self.bins
return kwds | def _make_plot_keywords(self, kwds, y):
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# y is required for KdePlot
kwds['bottom'] = self.bottom
kwds['bins'] = self.bins
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train | FramePlotMethods.line | Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
Either the location or the label of the columns to be u... | pandas/plotting/_core.py | def line(self, x=None, y=None, **kwds):
"""
Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
... | def line(self, x=None, y=None, **kwds):
"""
Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
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train | FramePlotMethods.bar | Vertical bar plot.
A bar plot is a plot that presents categorical data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the plot shows the specific categories being compared, and the
... | pandas/plotting/_core.py | def bar(self, x=None, y=None, **kwds):
"""
Vertical bar plot.
A bar plot is a plot that presents categorical data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the pl... | def bar(self, x=None, y=None, **kwds):
"""
Vertical bar plot.
A bar plot is a plot that presents categorical data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
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train | FramePlotMethods.barh | Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the plot shows the specific categories being... | pandas/plotting/_core.py | def barh(self, x=None, y=None, **kwds):
"""
Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
... | def barh(self, x=None, y=None, **kwds):
"""
Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
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train | FramePlotMethods.hist | Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
into bins and draws all bins in one :class:`matplotlib.axes.Axes`.
This is useful when the DataFrame's Series ... | pandas/plotting/_core.py | def hist(self, by=None, bins=10, **kwds):
"""
Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
into bins and draws all bins in one :class:`matplotlib.a... | def hist(self, by=None, bins=10, **kwds):
"""
Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
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train | FramePlotMethods.area | Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
Coordinates for the X axis. By default uses the index.
y : label or position, ... | pandas/plotting/_core.py | def area(self, x=None, y=None, **kwds):
"""
Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
Coordinates for the X axis... | def area(self, x=None, y=None, **kwds):
"""
Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
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train | FramePlotMethods.scatter | Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
useful to see complex correlations between two variables. Points could
be for inst... | pandas/plotting/_core.py | def scatter(self, x, y, s=None, c=None, **kwds):
"""
Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
useful to see complex... | def scatter(self, x, y, s=None, c=None, **kwds):
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Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
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train | FramePlotMethods.hexbin | Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
(the default), this is a histogram of the number of occurrences
of the observations at ``(x[i], y[i])``.
If `C` is specified, specifies values at given coordinates
``(x[i], ... | pandas/plotting/_core.py | def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
**kwds):
"""
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
(the default), this is a histogram of the number of occurrences
of the obser... | def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
**kwds):
"""
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
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of the obser... | [
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train | _get_objs_combined_axis | Extract combined index: return intersection or union (depending on the
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lack indexes (e.g. they are numpy arrays).
Parameters
----------
objs : list of objects
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"""
Extract combined index: return intersection or union (depending on the
value of "intersect") of indexes on given axis, or None if all objects
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----------
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train | _get_distinct_objs | Return a list with distinct elements of "objs" (different ids).
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"""
Return a list with distinct elements of "objs" (different ids).
Preserves order.
"""
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res = []
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train | _get_combined_index | Return the union or intersection of indexes.
Parameters
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indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
If True, calculate the intersection between indexes. Otherwise,
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s... | pandas/core/indexes/api.py | def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
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Return the union or intersection of indexes.
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indexes : list of Index or list objects
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train | _union_indexes | Return the union of indexes.
The behavior of sort and names is not consistent.
Parameters
----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
Returns
-------
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"""
Return the union of indexes.
The behavior of sort and names is not consistent.
Parameters
----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
Returns
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"""
Return the union of indexes.
The behavior of sort and names is not consistent.
Parameters
----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
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train | _sanitize_and_check | Verify the type of indexes and convert lists to Index.
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- [list, list, ...]: Return ([list, list, ...], 'list')
- [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
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"""
Verify the type of indexes and convert lists to Index.
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"""
Verify the type of indexes and convert lists to Index.
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- [list, list, ...]: Return ([list, list, ...], 'list')
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train | _get_consensus_names | Give a consensus 'names' to indexes.
If there's exactly one non-empty 'names', return this,
otherwise, return empty.
Parameters
----------
indexes : list of Index objects
Returns
-------
list
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"""
Give a consensus 'names' to indexes.
If there's exactly one non-empty 'names', return this,
otherwise, return empty.
Parameters
----------
indexes : list of Index objects
Returns
-------
list
A list representing the consensus 'nam... | def _get_consensus_names(indexes):
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Give a consensus 'names' to indexes.
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----------
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train | _all_indexes_same | Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise. | pandas/core/indexes/api.py | def _all_indexes_same(indexes):
"""
Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise.
"""
first = indexes[0]
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"""
Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise.
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train | _convert_params | Convert SQL and params args to DBAPI2.0 compliant format. | pandas/io/sql.py | def _convert_params(sql, params):
"""Convert SQL and params args to DBAPI2.0 compliant format."""
args = [sql]
if params is not None:
if hasattr(params, 'keys'): # test if params is a mapping
args += [params]
else:
args += [list(params)]
return args | def _convert_params(sql, params):
"""Convert SQL and params args to DBAPI2.0 compliant format."""
args = [sql]
if params is not None:
if hasattr(params, 'keys'): # test if params is a mapping
args += [params]
else:
args += [list(params)]
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train | _process_parse_dates_argument | Process parse_dates argument for read_sql functions | pandas/io/sql.py | def _process_parse_dates_argument(parse_dates):
"""Process parse_dates argument for read_sql functions"""
# handle non-list entries for parse_dates gracefully
if parse_dates is True or parse_dates is None or parse_dates is False:
parse_dates = []
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... | def _process_parse_dates_argument(parse_dates):
"""Process parse_dates argument for read_sql functions"""
# handle non-list entries for parse_dates gracefully
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train | _parse_date_columns | Force non-datetime columns to be read as such.
Supports both string formatted and integer timestamp columns. | pandas/io/sql.py | def _parse_date_columns(data_frame, parse_dates):
"""
Force non-datetime columns to be read as such.
Supports both string formatted and integer timestamp columns.
"""
parse_dates = _process_parse_dates_argument(parse_dates)
# we want to coerce datetime64_tz dtypes for now to UTC
# we could ... | def _parse_date_columns(data_frame, parse_dates):
"""
Force non-datetime columns to be read as such.
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parse_dates = _process_parse_dates_argument(parse_dates)
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train | _wrap_result | Wrap result set of query in a DataFrame. | pandas/io/sql.py | def _wrap_result(data, columns, index_col=None, coerce_float=True,
parse_dates=None):
"""Wrap result set of query in a DataFrame."""
frame = DataFrame.from_records(data, columns=columns,
coerce_float=coerce_float)
frame = _parse_date_columns(frame, parse... | def _wrap_result(data, columns, index_col=None, coerce_float=True,
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"""Wrap result set of query in a DataFrame."""
frame = DataFrame.from_records(data, columns=columns,
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train | execute | Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
Using SQLAlchemy makes it possible to use any DB supported by the
library.
... | pandas/io/sql.py | def execute(sql, con, cur=None, params=None):
"""
Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
Using SQLAlchemy makes it possib... | def execute(sql, con, cur=None, params=None):
"""
Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
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train | read_sql_table | Read SQL database table into a DataFrame.
Given a table name and a SQLAlchemy connectable, returns a DataFrame.
This function does not support DBAPI connections.
Parameters
----------
table_name : str
Name of SQL table in database.
con : SQLAlchemy connectable or str
A database... | pandas/io/sql.py | def read_sql_table(table_name, con, schema=None, index_col=None,
coerce_float=True, parse_dates=None, columns=None,
chunksize=None):
"""
Read SQL database table into a DataFrame.
Given a table name and a SQLAlchemy connectable, returns a DataFrame.
This function do... | def read_sql_table(table_name, con, schema=None, index_col=None,
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chunksize=None):
"""
Read SQL database table into a DataFrame.
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train | read_sql_query | Read SQL query into a DataFrame.
Returns a DataFrame corresponding to the result set of the query
string. Optionally provide an `index_col` parameter to use one of the
columns as the index, otherwise default integer index will be used.
Parameters
----------
sql : string SQL query or SQLAlchemy... | pandas/io/sql.py | def read_sql_query(sql, con, index_col=None, coerce_float=True, params=None,
parse_dates=None, chunksize=None):
"""Read SQL query into a DataFrame.
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Write records stored in a DataFrame to a SQL database.
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Write records stored in a DataFrame to a SQL database.
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name of SQL table
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table_name : string
Name of SQL table in database.
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Column to set as index.
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sql : string
SQL query to be executed.
index_col : string, optional, default: None
Column name to use as index for the returned DataFrame object.
coerce_float : boolean, default True
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----------
sql : string
SQL query to be executed.
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Write records stored in a DataFrame to a SQL database.
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train | SQLiteDatabase._query_iterator | Return generator through chunked result set | pandas/io/sql.py | def _query_iterator(cursor, chunksize, columns, index_col=None,
coerce_float=True, parse_dates=None):
"""Return generator through chunked result set"""
while True:
data = cursor.fetchmany(chunksize)
if type(data) == tuple:
data = list(data... | def _query_iterator(cursor, chunksize, columns, index_col=None,
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"""Return generator through chunked result set"""
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Write records stored in a DataFrame to a SQL database.
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Write records stored in a DataFrame to a SQL database.
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train | _maybe_to_categorical | Coerce to a categorical if a series is given.
Internal use ONLY. | pandas/core/arrays/categorical.py | def _maybe_to_categorical(array):
"""
Coerce to a categorical if a series is given.
Internal use ONLY.
"""
if isinstance(array, (ABCSeries, ABCCategoricalIndex)):
return array._values
elif isinstance(array, np.ndarray):
return Categorical(array)
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"""
Coerce to a categorical if a series is given.
Internal use ONLY.
"""
if isinstance(array, (ABCSeries, ABCCategoricalIndex)):
return array._values
elif isinstance(array, np.ndarray):
return Categorical(array)
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Returns True if ``key`` is in ``cat.categories`` and the
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"""
Helper for membership check for ``key`` in ``cat``.
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train | _get_codes_for_values | utility routine to turn values into codes given the specified categories | pandas/core/arrays/categorical.py | def _get_codes_for_values(values, categories):
"""
utility routine to turn values into codes given the specified categories
"""
from pandas.core.algorithms import _get_data_algo, _hashtables
dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
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# To prevent errone... | def _get_codes_for_values(values, categories):
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train | _recode_for_categories | Convert a set of codes for to a new set of categories
Parameters
----------
codes : array
old_categories, new_categories : Index
Returns
-------
new_codes : array
Examples
--------
>>> old_cat = pd.Index(['b', 'a', 'c'])
>>> new_cat = pd.Index(['a', 'b'])
>>> codes = n... | pandas/core/arrays/categorical.py | def _recode_for_categories(codes, old_categories, new_categories):
"""
Convert a set of codes for to a new set of categories
Parameters
----------
codes : array
old_categories, new_categories : Index
Returns
-------
new_codes : array
Examples
--------
>>> old_cat = pd.... | def _recode_for_categories(codes, old_categories, new_categories):
"""
Convert a set of codes for to a new set of categories
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----------
codes : array
old_categories, new_categories : Index
Returns
-------
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--------
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----------
values : list-like
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*This is an internal function*
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train | _factorize_from_iterables | A higher-level wrapper over `_factorize_from_iterable`.
*This is an internal function*
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train | Categorical.copy | Copy constructor. | pandas/core/arrays/categorical.py | def copy(self):
"""
Copy constructor.
"""
return self._constructor(values=self._codes.copy(),
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"""
Copy constructor.
"""
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train | Categorical.astype | Coerce this type to another dtype
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dtype : numpy dtype or pandas type
copy : bool, default True
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object is r... | pandas/core/arrays/categorical.py | def astype(self, dtype, copy=True):
"""
Coerce this type to another dtype
Parameters
----------
dtype : numpy dtype or pandas type
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By default, astype always returns a newly allocated object.
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"""
Coerce this type to another dtype
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----------
dtype : numpy dtype or pandas type
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train | Categorical._from_inferred_categories | Construct a Categorical from inferred values.
For inferred categories (`dtype` is None) the categories are sorted.
For explicit `dtype`, the `inferred_categories` are cast to the
appropriate type.
Parameters
----------
inferred_categories : Index
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"""
Construct a Categorical from inferred values.
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For explicit `dtype`, the `inferred_... | def _from_inferred_categories(cls, inferred_categories, inferred_codes,
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Construct a Categorical from inferred values.
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train | Categorical.from_codes | Make a Categorical type from codes and categories or dtype.
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If your data does not follow this conventio... | pandas/core/arrays/categorical.py | def from_codes(cls, codes, categories=None, ordered=None, dtype=None):
"""
Make a Categorical type from codes and categories or dtype.
This constructor is useful if you already have codes and
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train | Categorical._get_codes | Get the codes.
Returns
-------
codes : integer array view
A non writable view of the `codes` array. | pandas/core/arrays/categorical.py | def _get_codes(self):
"""
Get the codes.
Returns
-------
codes : integer array view
A non writable view of the `codes` array.
"""
v = self._codes.view()
v.flags.writeable = False
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"""
Get the codes.
Returns
-------
codes : integer array view
A non writable view of the `codes` array.
"""
v = self._codes.view()
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train | Categorical._set_categories | Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
>>> c = pd.Categorical(['a', 'b'])
>>> c
[a, b]
Categories (2, object... | pandas/core/arrays/categorical.py | def _set_categories(self, categories, fastpath=False):
"""
Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
>>> c = pd.Categor... | def _set_categories(self, categories, fastpath=False):
"""
Sets new categories inplace
Parameters
----------
fastpath : bool, default False
Don't perform validation of the categories for uniqueness or nulls
Examples
--------
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train | Categorical._set_dtype | Internal method for directly updating the CategoricalDtype
Parameters
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dtype : CategoricalDtype
Notes
-----
We don't do any validation here. It's assumed that the dtype is
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"""
Internal method for directly updating the CategoricalDtype
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dtype : CategoricalDtype
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We don't do any validation here. It's assumed that the dtype is
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"""
Internal method for directly updating the CategoricalDtype
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dtype : CategoricalDtype
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train | Categorical.set_ordered | Set the ordered attribute to the boolean value.
Parameters
----------
value : bool
Set whether this categorical is ordered (True) or not (False).
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this ... | pandas/core/arrays/categorical.py | def set_ordered(self, value, inplace=False):
"""
Set the ordered attribute to the boolean value.
Parameters
----------
value : bool
Set whether this categorical is ordered (True) or not (False).
inplace : bool, default False
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Set the ordered attribute to the boolean value.
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value : bool
Set whether this categorical is ordered (True) or not (False).
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train | Categorical.as_ordered | Set the Categorical to be ordered.
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inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
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Set the Categorical to be ordered.
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Whether or not to set the ordered attribute in-place or return
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Set the Categorical to be ordered.
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Whether or not to set the ordered attribute in-place or return
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train | Categorical.as_unordered | Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
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"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
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... | def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
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inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
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train | Categorical.set_categories | Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
unused categories) or remove old categories (which results in values
set to NaN). If `rename==True`, the categories will simple be renamed
(less or more items than in ol... | pandas/core/arrays/categorical.py | def set_categories(self, new_categories, ordered=None, rename=False,
inplace=False):
"""
Set the categories to the specified new_categories.
`new_categories` can include new categories (which will result in
unused categories) or remove old categories (which result... | def set_categories(self, new_categories, ordered=None, rename=False,
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Set the categories to the specified new_categories.
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train | Categorical.rename_categories | Rename categories.
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Rename categories.
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Rename categories.
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train | Categorical.reorder_categories | Reorder categories as specified in new_categories.
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Parameters
----------
new_categories : Index-like
The categories in new order.
ordered : bool, optional
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"""
Reorder categories as specified in new_categories.
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items.
Parameters
----------
new_categories : Index-like
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"""
Reorder categories as specified in new_categories.
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----------
new_categories : Index-like
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train | Categorical.add_categories | Add new categories.
`new_categories` will be included at the last/highest place in the
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Parameters
----------
new_categories : category or list-like of category
The new categories to be included.
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Add new categories.
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Parameters
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Add new categories.
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train | Categorical.remove_categories | Remove the specified categories.
`removals` must be included in the old categories. Values which were in
the removed categories will be set to NaN
Parameters
----------
removals : category or list of categories
The categories which should be removed.
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"""
Remove the specified categories.
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Parameters
----------
removals : category or list of cate... | def remove_categories(self, removals, inplace=False):
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Remove the specified categories.
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train | Categorical.remove_unused_categories | Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dropped.
Returns
-------
cat : Categorical with u... | pandas/core/arrays/categorical.py | def remove_unused_categories(self, inplace=False):
"""
Remove categories which are not used.
Parameters
----------
inplace : bool, default False
Whether or not to drop unused categories inplace or return a copy of
this categorical with unused categories dro... | def remove_unused_categories(self, inplace=False):
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Remove categories which are not used.
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Whether or not to drop unused categories inplace or return a copy of
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train | Categorical.map | Map categories using input correspondence (dict, Series, or function).
Maps the categories to new categories. If the mapping correspondence is
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same order property as the original, otherwise a :class:`~pandas.Index`
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Map categories using input correspondence (dict, Series, or function).
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train | Categorical.shift | Shift Categorical by desired number of periods.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
fill_value : object, optional
The scalar value to use for newly introduced missing values.
.. versionadded:: 0.... | pandas/core/arrays/categorical.py | def shift(self, periods, fill_value=None):
"""
Shift Categorical by desired number of periods.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative
fill_value : object, optional
The scalar value to use for new... | def shift(self, periods, fill_value=None):
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Shift Categorical by desired number of periods.
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Number of periods to move, can be positive or negative
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train | Categorical.memory_usage | Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include ... | pandas/core/arrays/categorical.py | def memory_usage(self, deep=False):
"""
Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
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"""
Memory usage of my values
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----------
deep : bool
Introspect the data deeply, interrogate
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-------
bytes used
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train | Categorical.value_counts | Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
... | pandas/core/arrays/categorical.py | def value_counts(self, dropna=True):
"""
Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
... | def value_counts(self, dropna=True):
"""
Return a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
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train | Categorical.get_values | Return the values.
For internal compatibility with pandas formatting.
Returns
-------
numpy.array
A numpy array of the same dtype as categorical.categories.dtype or
Index if datetime / periods. | pandas/core/arrays/categorical.py | def get_values(self):
"""
Return the values.
For internal compatibility with pandas formatting.
Returns
-------
numpy.array
A numpy array of the same dtype as categorical.categories.dtype or
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"""
# if w... | def get_values(self):
"""
Return the values.
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Returns
-------
numpy.array
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train | Categorical.sort_values | Sort the Categorical by category value returning a new
Categorical by default.
While an ordering is applied to the category values, sorting in this
context refers more to organizing and grouping together based on
matching category values. Thus, this function can be called on an
... | pandas/core/arrays/categorical.py | def sort_values(self, inplace=False, ascending=True, na_position='last'):
"""
Sort the Categorical by category value returning a new
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While an ordering is applied to the category values, sorting in this
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train | Categorical._values_for_rank | For correctly ranking ordered categorical data. See GH#15420
Ordered categorical data should be ranked on the basis of
codes with -1 translated to NaN.
Returns
-------
numpy.array | pandas/core/arrays/categorical.py | def _values_for_rank(self):
"""
For correctly ranking ordered categorical data. See GH#15420
Ordered categorical data should be ranked on the basis of
codes with -1 translated to NaN.
Returns
-------
numpy.array
"""
from pandas import Series
... | def _values_for_rank(self):
"""
For correctly ranking ordered categorical data. See GH#15420
Ordered categorical data should be ranked on the basis of
codes with -1 translated to NaN.
Returns
-------
numpy.array
"""
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train | Categorical.fillna | Fill NA/NaN values using the specified method.
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Fill NA/NaN values using the specified method.
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train | Categorical.take_nd | Take elements from the Categorical.
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indexer : sequence of int
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allow_fill : bool, default None
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Take elements from the Categorical.
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Take elements from the Categorical.
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"""
Return a slice of myself.
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"""
# only allow 1 dimensional slicing, but can
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Return a slice of myself.
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train | Categorical._tidy_repr | a short repr displaying only max_vals and an optional (but default
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""" a short repr displaying only max_vals and an optional (but default
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"""
num = max_vals // 2
head = self[:num]._get_repr(length=False, footer=False)
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train | Categorical._repr_categories | return the base repr for the categories | pandas/core/arrays/categorical.py | def _repr_categories(self):
"""
return the base repr for the categories
"""
max_categories = (10 if get_option("display.max_categories") == 0 else
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from pandas.io.formats import format as fmt
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"""
return the base repr for the categories
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train | Categorical._repr_categories_info | Returns a string representation of the footer. | pandas/core/arrays/categorical.py | def _repr_categories_info(self):
"""
Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str',
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Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
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train | Categorical._maybe_coerce_indexer | return an indexer coerced to the codes dtype | pandas/core/arrays/categorical.py | def _maybe_coerce_indexer(self, indexer):
"""
return an indexer coerced to the codes dtype
"""
if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i':
indexer = indexer.astype(self._codes.dtype)
return indexer | def _maybe_coerce_indexer(self, indexer):
"""
return an indexer coerced to the codes dtype
"""
if isinstance(indexer, np.ndarray) and indexer.dtype.kind == 'i':
indexer = indexer.astype(self._codes.dtype)
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train | Categorical._reverse_indexer | Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
dict of categories -> indexers
Example
-------
In [1]: c = pd.Categorical(list('aabca'))
In [2]: c
Out[2]:
... | pandas/core/arrays/categorical.py | def _reverse_indexer(self):
"""
Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
dict of categories -> indexers
Example
-------
In [1]: c = pd.Categorical(li... | def _reverse_indexer(self):
"""
Compute the inverse of a categorical, returning
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*This is an internal function*
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-------
dict of categories -> indexers
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train | Categorical.min | The minimum value of the object.
Only ordered `Categoricals` have a minimum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
min : the minimum of this `Categorical` | pandas/core/arrays/categorical.py | def min(self, numeric_only=None, **kwargs):
"""
The minimum value of the object.
Only ordered `Categoricals` have a minimum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
min : the minimum of this `Ca... | def min(self, numeric_only=None, **kwargs):
"""
The minimum value of the object.
Only ordered `Categoricals` have a minimum!
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------
TypeError
If the `Categorical` is not `ordered`.
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-------
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train | Categorical.mode | Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
Returns
-------
modes : `Categoric... | pandas/core/arrays/categorical.py | def mode(self, dropna=True):
"""
Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
R... | def mode(self, dropna=True):
"""
Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : bool, default True
Don't consider counts of NaN/NaT.
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train | Categorical.unique | Return the ``Categorical`` which ``categories`` and ``codes`` are
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- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, categories
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"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
unique. Unused categories are NOT returned.
- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, ca... | def unique(self):
"""
Return the ``Categorical`` which ``categories`` and ``codes`` are
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train | Categorical.equals | Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool | pandas/core/arrays/categorical.py | def equals(self, other):
"""
Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool
"""
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
... | def equals(self, other):
"""
Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
bool
"""
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
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train | Categorical.is_dtype_equal | Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
bool | pandas/core/arrays/categorical.py | def is_dtype_equal(self, other):
"""
Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
bool
"""
try:
return hash(self.dtype) ... | def is_dtype_equal(self, other):
"""
Returns True if categoricals are the same dtype
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Parameters
----------
other : Categorical
Returns
-------
bool
"""
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train | Categorical.describe | Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category. | pandas/core/arrays/categorical.py | def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
from pand... | def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
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train | Categorical.isin | Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
`values` exactly.
Parameters
----------
values : set or list-like
The sequence of v... | pandas/core/arrays/categorical.py | def isin(self, values):
"""
Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
`values` exactly.
Parameters
----------
values : se... | def isin(self, values):
"""
Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
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train | to_timedelta | Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
units (e.g. days, hours, minutes, seconds). This method converts
an argument from a recognized timedelta format / value into
a Timedelta type.
Parameters
----------
arg : str, timedelta, li... | pandas/core/tools/timedeltas.py | def to_timedelta(arg, unit='ns', box=True, errors='raise'):
"""
Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
units (e.g. days, hours, minutes, seconds). This method converts
an argument from a recognized timedelta format / value into
a Tim... | def to_timedelta(arg, unit='ns', box=True, errors='raise'):
"""
Convert argument to timedelta.
Timedeltas are absolute differences in times, expressed in difference
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train | _coerce_scalar_to_timedelta_type | Convert string 'r' to a timedelta object. | pandas/core/tools/timedeltas.py | def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'):
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try:
result = Timedelta(r, unit)
if not box:
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result = Timedelta(r, unit)
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train | _convert_listlike | Convert a list of objects to a timedelta index object. | pandas/core/tools/timedeltas.py | def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
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train | generate_range | Generates a sequence of dates corresponding to the specified time
offset. Similar to dateutil.rrule except uses pandas DateOffset
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Parameters
----------
start : datetime (default None)
end : datetime (default None)
periods : int, (default None)
offse... | pandas/tseries/offsets.py | def generate_range(start=None, end=None, periods=None, offset=BDay()):
"""
Generates a sequence of dates corresponding to the specified time
offset. Similar to dateutil.rrule except uses pandas DateOffset
objects to represent time increments.
Parameters
----------
start : datetime (default ... | def generate_range(start=None, end=None, periods=None, offset=BDay()):
"""
Generates a sequence of dates corresponding to the specified time
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start : datetime (default ... | [
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train | DateOffset.apply_index | Vectorized apply of DateOffset to DatetimeIndex,
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vectorized implementation.
Parameters
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i : DatetimeIndex
Returns
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"""
Vectorized apply of DateOffset to DatetimeIndex,
raises NotImplentedError for offsets without a
vectorized implementation.
Parameters
----------
i : DatetimeIndex
Returns
-------
y : DatetimeIndex
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"""
Vectorized apply of DateOffset to DatetimeIndex,
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----------
i : DatetimeIndex
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y : DatetimeIndex
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train | DateOffset.rollback | Roll provided date backward to next offset only if not on offset. | pandas/tseries/offsets.py | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds)
return dt | def rollback(self, dt):
"""
Roll provided date backward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt - self.__class__(1, normalize=self.normalize, **self.kwds)
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train | DateOffset.rollforward | Roll provided date forward to next offset only if not on offset. | pandas/tseries/offsets.py | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds)
return dt | def rollforward(self, dt):
"""
Roll provided date forward to next offset only if not on offset.
"""
dt = as_timestamp(dt)
if not self.onOffset(dt):
dt = dt + self.__class__(1, normalize=self.normalize, **self.kwds)
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train | BusinessHourMixin.next_bday | Used for moving to next business day. | pandas/tseries/offsets.py | def next_bday(self):
"""
Used for moving to next business day.
"""
if self.n >= 0:
nb_offset = 1
else:
nb_offset = -1
if self._prefix.startswith('C'):
# CustomBusinessHour
return CustomBusinessDay(n=nb_offset,
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"""
Used for moving to next business day.
"""
if self.n >= 0:
nb_offset = 1
else:
nb_offset = -1
if self._prefix.startswith('C'):
# CustomBusinessHour
return CustomBusinessDay(n=nb_offset,
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train | BusinessHourMixin._next_opening_time | If n is positive, return tomorrow's business day opening time.
Otherwise yesterday's business day's opening time.
Opening time always locates on BusinessDay.
Otherwise, closing time may not if business hour extends over midnight. | pandas/tseries/offsets.py | def _next_opening_time(self, other):
"""
If n is positive, return tomorrow's business day opening time.
Otherwise yesterday's business day's opening time.
Opening time always locates on BusinessDay.
Otherwise, closing time may not if business hour extends over midnight.
... | def _next_opening_time(self, other):
"""
If n is positive, return tomorrow's business day opening time.
Otherwise yesterday's business day's opening time.
Opening time always locates on BusinessDay.
Otherwise, closing time may not if business hour extends over midnight.
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train | BusinessHourMixin._get_business_hours_by_sec | Return business hours in a day by seconds. | pandas/tseries/offsets.py | def _get_business_hours_by_sec(self):
"""
Return business hours in a day by seconds.
"""
if self._get_daytime_flag:
# create dummy datetime to calculate businesshours in a day
dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute)
until = d... | def _get_business_hours_by_sec(self):
"""
Return business hours in a day by seconds.
"""
if self._get_daytime_flag:
# create dummy datetime to calculate businesshours in a day
dtstart = datetime(2014, 4, 1, self.start.hour, self.start.minute)
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