INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
check whether ax has data | 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) |
Return result axes | def result(self):
"""
Return result axes
"""
if self.subplots:
if self.layout is not None and not is_list_like(self.ax):
return self.axes.reshape(*self.layout)
else:
return self.axes
else:
sec_true = isinstance(s... |
Common post process for each axes | def _post_plot_logic_common(self, ax, data):
"""Common post process for each axes"""
def get_label(i):
try:
return pprint_thing(data.index[i])
except Exception:
return ''
if self.orientation == 'vertical' or self.orientation is None:
... |
Common post process unrelated to data | def _adorn_subplots(self):
"""Common post process unrelated to data"""
if len(self.axes) > 0:
all_axes = self._get_subplots()
nrows, ncols = self._get_axes_layout()
_handle_shared_axes(axarr=all_axes, nplots=len(all_axes),
naxes=nrows *... |
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. | 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... |
get left ( primary ) or right ( secondary ) axes | 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) |
Manage style and color based on column number and its label. Returns tuple of appropriate style and kwds which color may be added. | 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... |
Look for error keyword arguments and return the actual errorbar data or return the error DataFrame/ dict | 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... |
merge BoxPlot/ KdePlot properties to passed kwds | 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 |
Plot DataFrame columns as lines. | 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.
... |
Vertical bar plot. | 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... |
Make a horizontal bar plot. | 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
... |
Draw one histogram of the DataFrame s columns. | 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... |
Draw a stacked area plot. | 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... |
Create a scatter plot with varying marker point size and color. | 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... |
Generate a hexagonal binning plot. | 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... |
Extract combined index: return intersection or union ( depending on the value of intersect ) of indexes on given axis or None if all objects lack indexes ( e. g. they are numpy arrays ). | def _get_objs_combined_axis(objs, intersect=False, axis=0, sort=True):
"""
Extract combined index: return intersection or union (depending on the
value of "intersect") of indexes on given axis, or None if all objects
lack indexes (e.g. they are numpy arrays).
Parameters
----------
objs : li... |
Return a list with distinct elements of objs ( different ids ). Preserves order. | def _get_distinct_objs(objs):
"""
Return a list with distinct elements of "objs" (different ids).
Preserves order.
"""
ids = set()
res = []
for obj in objs:
if not id(obj) in ids:
ids.add(id(obj))
res.append(obj)
return res |
Return the union or intersection of indexes. | 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
If True, calculate the ... |
Return the union of indexes. | def _union_indexes(indexes, sort=True):
"""
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
... |
Verify the type of indexes and convert lists to Index. | def _sanitize_and_check(indexes):
"""
Verify the type of indexes and convert lists to Index.
Cases:
- [list, list, ...]: Return ([list, list, ...], 'list')
- [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
Lists are sorted and converted to Index.
- [Index, Index, ..... |
Give a consensus names to indexes. | def _get_consensus_names(indexes):
"""
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... |
Determine if all indexes contain the same elements. | 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]
for index in ind... |
Convert SQL and params args to DBAPI2. 0 compliant format. | 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 |
Process parse_dates argument for read_sql functions | 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 = []
elif not hasattr(parse_dates, '__iter__'):
... |
Force non - datetime columns to be read as such. Supports both string formatted and integer timestamp columns. | 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 ... |
Wrap result set of query in a DataFrame. | 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... |
Execute the given SQL query using the provided connection object. | 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... |
Read SQL database table into a DataFrame. | 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... |
Read SQL query into a DataFrame. | 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.
Returns a DataFrame corresponding to the result set of the query
string. Optionally provide an `index_col` parameter to use one of the
c... |
Read SQL query or database table into a DataFrame. | def read_sql(sql, con, index_col=None, coerce_float=True, params=None,
parse_dates=None, columns=None, chunksize=None):
"""
Read SQL query or database table into a DataFrame.
This function is a convenience wrapper around ``read_sql_table`` and
``read_sql_query`` (for backward compatibility... |
Write records stored in a DataFrame to a SQL database. | def to_sql(frame, name, con, schema=None, if_exists='fail', index=True,
index_label=None, chunksize=None, dtype=None, method=None):
"""
Write records stored in a DataFrame to a SQL database.
Parameters
----------
frame : DataFrame, Series
name : string
Name of SQL table.
... |
Check if DataBase has named table. | def has_table(table_name, con, schema=None):
"""
Check if DataBase has named table.
Parameters
----------
table_name: string
Name of SQL table.
con: SQLAlchemy connectable(engine/connection) or sqlite3 DBAPI2 connection
Using SQLAlchemy makes it possible to use any DB supported ... |
Returns a SQLAlchemy engine from a URI ( if con is a string ) else it just return con without modifying it. | def _engine_builder(con):
"""
Returns a SQLAlchemy engine from a URI (if con is a string)
else it just return con without modifying it.
"""
global _SQLALCHEMY_INSTALLED
if isinstance(con, str):
try:
import sqlalchemy
except ImportError:
_SQLALCHEMY_INSTALL... |
Convenience function to return the correct PandasSQL subclass based on the provided parameters. | def pandasSQL_builder(con, schema=None, meta=None,
is_cursor=False):
"""
Convenience function to return the correct PandasSQL subclass based on the
provided parameters.
"""
# When support for DBAPI connections is removed,
# is_cursor should not be necessary.
con = _engi... |
Get the SQL db table schema for the given frame. | def get_schema(frame, name, keys=None, con=None, dtype=None):
"""
Get the SQL db table schema for the given frame.
Parameters
----------
frame : DataFrame
name : string
name of SQL table
keys : string or sequence, default: None
columns to use a primary key
con: an open S... |
Execute SQL statement inserting data | def _execute_insert(self, conn, keys, data_iter):
"""Execute SQL statement inserting data
Parameters
----------
conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
keys : list of str
Column names
data_iter : generator of list
Each item c... |
Return generator through chunked result set. | def _query_iterator(self, result, chunksize, columns, coerce_float=True,
parse_dates=None):
"""Return generator through chunked result set."""
while True:
data = result.fetchmany(chunksize)
if not data:
break
else:
... |
Make the DataFrame s column types align with the SQL table column types. Need to work around limited NA value support. Floats are always fine ints must always be floats if there are Null values. Booleans are hard because converting bool column with None replaces all Nones with false. Therefore only convert bool if ther... | def _harmonize_columns(self, parse_dates=None):
"""
Make the DataFrame's column types align with the SQL table
column types.
Need to work around limited NA value support. Floats are always
fine, ints must always be floats if there are Null values.
Booleans are hard becaus... |
Read SQL database table into a DataFrame. | def read_table(self, table_name, index_col=None, coerce_float=True,
parse_dates=None, columns=None, schema=None,
chunksize=None):
"""Read SQL database table into a DataFrame.
Parameters
----------
table_name : string
Name of SQL table in... |
Return generator through chunked result set | def _query_iterator(result, chunksize, columns, index_col=None,
coerce_float=True, parse_dates=None):
"""Return generator through chunked result set"""
while True:
data = result.fetchmany(chunksize)
if not data:
break
else:
... |
Read SQL query into a DataFrame. | def read_query(self, sql, index_col=None, coerce_float=True,
parse_dates=None, params=None, chunksize=None):
"""Read SQL query into a DataFrame.
Parameters
----------
sql : string
SQL query to be executed.
index_col : string, optional, default: Non... |
Write records stored in a DataFrame to a SQL database. | def to_sql(self, frame, name, if_exists='fail', index=True,
index_label=None, schema=None, chunksize=None, dtype=None,
method=None):
"""
Write records stored in a DataFrame to a SQL database.
Parameters
----------
frame : DataFrame
name : st... |
Return a list of SQL statements that creates a table reflecting the structure of a DataFrame. The first entry will be a CREATE TABLE statement while the rest will be CREATE INDEX statements. | def _create_table_setup(self):
"""
Return a list of SQL statements that creates a table reflecting the
structure of a DataFrame. The first entry will be a CREATE TABLE
statement while the rest will be CREATE INDEX statements.
"""
column_names_and_types = self._get_column... |
Return generator through chunked result set | 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... |
Write records stored in a DataFrame to a SQL database. | def to_sql(self, frame, name, if_exists='fail', index=True,
index_label=None, schema=None, chunksize=None, dtype=None,
method=None):
"""
Write records stored in a DataFrame to a SQL database.
Parameters
----------
frame: DataFrame
name: stri... |
Coerce to a categorical if a series is given. | 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)
return array |
Helper for membership check for key in cat. | def contains(cat, key, container):
"""
Helper for membership check for ``key`` in ``cat``.
This is a helper method for :method:`__contains__`
and :class:`CategoricalIndex.__contains__`.
Returns True if ``key`` is in ``cat.categories`` and the
location of ``key`` in ``categories`` is in ``conta... |
utility routine to turn values into codes given the specified categories | 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)
if dtype_equal:
# To prevent errone... |
Convert a set of codes for to a new set of categories | 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.... |
Factorize an input values into categories and codes. Preserves categorical dtype in categories. | def _factorize_from_iterable(values):
"""
Factorize an input `values` into `categories` and `codes`. Preserves
categorical dtype in `categories`.
*This is an internal function*
Parameters
----------
values : list-like
Returns
-------
codes : ndarray
categories : Index
... |
A higher - level wrapper over _factorize_from_iterable. | def _factorize_from_iterables(iterables):
"""
A higher-level wrapper over `_factorize_from_iterable`.
*This is an internal function*
Parameters
----------
iterables : list-like of list-likes
Returns
-------
codes_list : list of ndarrays
categories_list : list of Indexes
N... |
Copy constructor. | def copy(self):
"""
Copy constructor.
"""
return self._constructor(values=self._codes.copy(),
dtype=self.dtype,
fastpath=True) |
Coerce this type to another dtype | def astype(self, dtype, copy=True):
"""
Coerce this type to another dtype
Parameters
----------
dtype : numpy dtype or pandas type
copy : bool, default True
By default, astype always returns a newly allocated object.
If copy is set to False and dt... |
Construct a Categorical from inferred values. | def _from_inferred_categories(cls, inferred_categories, inferred_codes,
dtype, true_values=None):
"""
Construct a Categorical from inferred values.
For inferred categories (`dtype` is None) the categories are sorted.
For explicit `dtype`, the `inferred_... |
Make a Categorical type from codes and categories or dtype. | 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
categories/dtype and so do not need the (computation intensive)
factorization step, whi... |
Get the codes. | 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
return v |
Sets new categories inplace | 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... |
Internal method for directly updating the CategoricalDtype | def _set_dtype(self, dtype):
"""
Internal method for directly updating the CategoricalDtype
Parameters
----------
dtype : CategoricalDtype
Notes
-----
We don't do any validation here. It's assumed that the dtype is
a (valid) instance of `Categori... |
Set the ordered attribute to the boolean value. | 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
Whether or not to set th... |
Set the Categorical to be ordered. | def as_ordered(self, inplace=False):
"""
Set the Categorical to be ordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to True.
"""
... |
Set the Categorical to be unordered. | def as_unordered(self, inplace=False):
"""
Set the Categorical to be unordered.
Parameters
----------
inplace : bool, default False
Whether or not to set the ordered attribute in-place or return
a copy of this categorical with ordered set to False.
... |
Set the categories to the specified new_categories. | 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... |
Rename categories. | def rename_categories(self, new_categories, inplace=False):
"""
Rename categories.
Parameters
----------
new_categories : list-like, dict-like or callable
* list-like: all items must be unique and the number of items in
the new categories must match the ... |
Reorder categories as specified in new_categories. | def reorder_categories(self, new_categories, ordered=None, inplace=False):
"""
Reorder categories as specified in new_categories.
`new_categories` need to include all old categories and no new category
items.
Parameters
----------
new_categories : Index-like
... |
Add new categories. | def add_categories(self, new_categories, inplace=False):
"""
Add new categories.
`new_categories` will be included at the last/highest place in the
categories and will be unused directly after this call.
Parameters
----------
new_categories : category or list-li... |
Remove the specified categories. | def remove_categories(self, removals, inplace=False):
"""
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 cate... |
Remove categories which are not used. | 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... |
Map categories using input correspondence ( dict Series or function ). | def map(self, mapper):
"""
Map categories using input correspondence (dict, Series, or function).
Maps the categories to new categories. If the mapping correspondence is
one-to-one the result is a :class:`~pandas.Categorical` which has the
same order property as the original, ot... |
Shift Categorical by desired number of periods. | 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... |
Memory usage of my values | 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
No... |
Return a Series containing counts of each category. | 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
... |
Return the values. | 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
Index if datetime / periods.
"""
# if w... |
Sort the Categorical by category value returning a new Categorical by default. | def sort_values(self, inplace=False, ascending=True, na_position='last'):
"""
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 togeth... |
For correctly ranking ordered categorical data. See GH#15420 | 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
... |
Fill NA/ NaN values using the specified method. | def fillna(self, value=None, method=None, limit=None):
"""
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, dict, Series
If a scalar value is passed it is used to fill all missing values.
Alternatively, a Series or dict... |
Take elements from the Categorical. | def take_nd(self, indexer, allow_fill=None, fill_value=None):
"""
Take elements from the Categorical.
Parameters
----------
indexer : sequence of int
The indices in `self` to take. The meaning of negative values in
`indexer` depends on the value of `allow... |
Return a slice of myself. | def _slice(self, slicer):
"""
Return a slice of myself.
For internal compatibility with numpy arrays.
"""
# only allow 1 dimensional slicing, but can
# in a 2-d case be passd (slice(None),....)
if isinstance(slicer, tuple) and len(slicer) == 2:
if no... |
a short repr displaying only max_vals and an optional ( but default footer ) | def _tidy_repr(self, max_vals=10, footer=True):
""" a short repr displaying only max_vals and an optional (but default
footer)
"""
num = max_vals // 2
head = self[:num]._get_repr(length=False, footer=False)
tail = self[-(max_vals - num):]._get_repr(length=False, footer=Fa... |
return the base repr for the categories | def _repr_categories(self):
"""
return the base repr for the categories
"""
max_categories = (10 if get_option("display.max_categories") == 0 else
get_option("display.max_categories"))
from pandas.io.formats import format as fmt
if len(self.categ... |
Returns a string representation of the footer. | def _repr_categories_info(self):
"""
Returns a string representation of the footer.
"""
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str',
str(self.categories.dtype))
levheader = "Categories ({length}, {dtype}):... |
return an indexer coerced to the codes dtype | 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 |
Compute the inverse of a categorical returning a dict of categories - > indexers. | 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... |
The minimum value of the object. | 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... |
Returns the mode ( s ) of the Categorical. | 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... |
Return the Categorical which categories and codes are unique. Unused categories are NOT returned. | def unique(self):
"""
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... |
Returns True if categorical arrays are equal. | 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):
... |
Returns True if categoricals are the same dtype same categories and same ordered | 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) ... |
Describes this Categorical | 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... |
Check whether values are contained in Categorical. | 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... |
Convert argument to timedelta. | 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... |
Convert string r to a timedelta object. | def _coerce_scalar_to_timedelta_type(r, unit='ns', box=True, errors='raise'):
"""Convert string 'r' to a timedelta object."""
try:
result = Timedelta(r, unit)
if not box:
# explicitly view as timedelta64 for case when result is pd.NaT
result = result.asm8.view('timedelta... |
Convert a list of objects to a timedelta index object. | def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
"""Convert a list of objects to a timedelta index object."""
if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
# This is needed only to ensure that in the case where we end up
# returning arg (errors == "... |
Generates a sequence of dates corresponding to the specified time offset. Similar to dateutil. rrule except uses pandas DateOffset objects to represent time increments. | 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 ... |
Vectorized apply of DateOffset to DatetimeIndex raises NotImplentedError for offsets without a vectorized implementation. | def apply_index(self, i):
"""
Vectorized apply of DateOffset to DatetimeIndex,
raises NotImplentedError for offsets without a
vectorized implementation.
Parameters
----------
i : DatetimeIndex
Returns
-------
y : DatetimeIndex
"""... |
Roll provided date backward to next offset only if not on offset. | 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 |
Roll provided date forward to next offset only if not on offset. | 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 |
Used for moving to next business day. | 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,
... |
If n is positive return tomorrow s business day opening time. Otherwise yesterday s business day s opening time. | 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.
... |
Return business hours in a day by seconds. | 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... |
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