JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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interpret the dtype from a scalar or array. This is a convenience routines to infer dtype from a scalar or an array Parameters ---------- pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, scalar/array belongs to pandas extension types ...
interpret the dtype from a scalar Parameters ---------- pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, scalar belongs to pandas extension types is inferred as object def infer_dtype_from_scalar(val, pandas_dtype=False): """...
infer the dtype from a scalar or array Parameters ---------- arr : scalar or array pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, array belongs to pandas extension types is inferred as object Returns ------- tup...
Try to infer an object's dtype, for use in arithmetic ops Uses `element.dtype` if that's available. Objects implementing the iterator protocol are cast to a NumPy array, and from there the array's type is used. Parameters ---------- element : object Possibly has a `.dtype` attribute, a...
provide explicit type promotion and coercion Parameters ---------- values : the ndarray that we want to maybe upcast fill_value : what we want to fill with dtype : if None, then use the dtype of the values, else coerce to this type copy : if True always make a copy even if no upcast is required...
Change string like dtypes to object for ``DataFrame.select_dtypes()``. def invalidate_string_dtypes(dtype_set): """Change string like dtypes to object for ``DataFrame.select_dtypes()``. """ non_string_dtypes = dtype_set - {np.dtype('S').type, np.dtype('<U').type} if non_string_dtypes != dtype_s...
coerce the indexer input array to the smallest dtype possible def coerce_indexer_dtype(indexer, categories): """ coerce the indexer input array to the smallest dtype possible """ length = len(categories) if length < _int8_max: return ensure_int8(indexer) elif length < _int16_max: return...
given a dtypes and a result set, coerce the result elements to the dtypes def coerce_to_dtypes(result, dtypes): """ given a dtypes and a result set, coerce the result elements to the dtypes """ if len(result) != len(dtypes): raise AssertionError("_coerce_to_dtypes requires equal len arr...
Cast the elements of an array to a given dtype a nan-safe manner. Parameters ---------- arr : ndarray dtype : np.dtype copy : bool, default True If False, a view will be attempted but may fail, if e.g. the item sizes don't align. skipna: bool, default False Whether or no...
if we have an object dtype, try to coerce dates and/or numbers def maybe_convert_objects(values, convert_dates=True, convert_numeric=True, convert_timedeltas=True, copy=True): """ if we have an object dtype, try to coerce dates and/or numbers """ # if we have passed in a list or scal...
if we have an object dtype, try to coerce dates and/or numbers def soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, coerce=False, copy=True): """ if we have an object dtype, try to coerce dates and/or numbers """ conversion_count = sum((datetime, numeric, time...
we might have a array (or single object) that is datetime like, and no dtype is passed don't change the value unless we find a datetime/timedelta set this is pretty strict in that a datetime/timedelta is REQUIRED in addition to possible nulls/string likes Parameters ---------- value : np.a...
try to cast the array/value to a datetimelike dtype, converting float nan to iNaT def maybe_cast_to_datetime(value, dtype, errors='raise'): """ try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """ from pandas.core.tools.timedeltas import to_timedelta from pandas...
Find a common data type among the given dtypes. Parameters ---------- types : list of dtypes Returns ------- pandas extension or numpy dtype See Also -------- numpy.find_common_type def find_common_type(types): """ Find a common data type among the given dtypes. Para...
create np.ndarray of specified shape and dtype, filled with values Parameters ---------- shape : tuple value : scalar value dtype : np.dtype, optional dtype to coerce Returns ------- ndarray of shape, filled with value, of specified / inferred dtype def cast_scalar_to_array(sh...
create a np.ndarray / pandas type of specified shape and dtype filled with values Parameters ---------- value : scalar value length : int dtype : pandas_dtype / np.dtype Returns ------- np.ndarray / pandas type of length, filled with value def construct_1d_arraylike_from_scalar(va...
Transform any list-like object in a 1-dimensional numpy array of object dtype. Parameters ---------- values : any iterable which has a len() Raises ------ TypeError * If `values` does not have a len() Returns ------- 1-dimensional numpy array of dtype object def const...
Construct a new ndarray, coercing `values` to `dtype`, preserving NA. Parameters ---------- values : Sequence dtype : numpy.dtype, optional copy : bool, default False Note that copies may still be made with ``copy=False`` if casting is required. Returns ------- arr : nd...
Takes any dtype and returns the casted version, raising for when data is incompatible with integer/unsigned integer dtypes. .. versionadded:: 0.24.0 Parameters ---------- arr : array-like The array to cast. dtype : str, np.dtype The integer dtype to cast the array to. copy:...
Make a scatter plot from two DataFrame columns Parameters ---------- data : DataFrame x : Column name for the x-axis values y : Column name for the y-axis values ax : Matplotlib axis object figsize : A tuple (width, height) in inches grid : Setting this to True will show the grid kw...
Make a histogram of the DataFrame's. A `histogram`_ is a representation of the distribution of data. This function calls :meth:`matplotlib.pyplot.hist`, on each series in the DataFrame, resulting in one histogram per column. .. _histogram: https://en.wikipedia.org/wiki/Histogram Parameters --...
Draw histogram of the input series using matplotlib. Parameters ---------- by : object, optional If passed, then used to form histograms for separate groups ax : matplotlib axis object If not passed, uses gca() grid : bool, default True Whether to show axis grid lines xl...
Grouped histogram Parameters ---------- data : Series/DataFrame column : object, optional by : object, optional ax : axes, optional bins : int, default 50 figsize : tuple, optional layout : optional sharex : bool, default False sharey : bool, default False rot : int, def...
Make box plots from DataFrameGroupBy data. Parameters ---------- grouped : Grouped DataFrame subplots : bool * ``False`` - no subplots will be used * ``True`` - create a subplot for each group column : column name or list of names, or vector Can be any valid input to groupby...
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...
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...
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), ...
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=No...
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...
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...
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. 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...
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 ...
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...
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 ...
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, ...
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...
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], ...
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 : list of objects Each object will only be considered if it has a _get_axis ...
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.ad...
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 intersection between indexes. Otherwise, calculate the union. s...
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 ------- Index def _union_indexes(indexes, sor...
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, ...]: Return ([Index, Index, ...], TYPE) ...
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 'names' found. def _get_consensus_names(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. def _all_indexes_same(indexes): """ Determine if all indexes contain the same elements...
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: ...
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 = [] ...
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 = _proc...
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) ...
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. ...
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...
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...
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). It will delegate to the specific function depending on the provided input. A SQL query will be routed to ``read_sql_query``, while a d...
Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame, Series name : string Name of SQL table. con : SQLAlchemy connectable(engine/connection) or database string URI or sqlite3 DBAPI2 connection Using SQLAlchemy makes it possible to u...
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 by that library. If a DBAPI2 object, only...
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(co...
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. """ # Wh...
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 SQL database connection object or a SQLAlchemy connectable Using SQ...
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 contains a list of values to be inserted def _execute_insert(...
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: ...
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 f...
Read SQL database table into a DataFrame. Parameters ---------- table_name : string Name of SQL table in database. index_col : string, optional, default: None Column to set as index. coerce_float : boolean, default True Attempts to convert val...
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...
Read SQL query into a DataFrame. Parameters ---------- 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 Attem...
Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame name : string Name of SQL table. if_exists : {'fail', 'replace', 'append'}, default 'fail' - fail: If table exists, do nothing. - replace: If table ...
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 reflect...
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(dat...
Write records stored in a DataFrame to a SQL database. Parameters ---------- frame: DataFrame name: string Name of SQL table. if_exists: {'fail', 'replace', 'append'}, default 'fail' fail: If table exists, do nothing. replace: If table exists,...
Coerce to a categorical if a series is given. Internal use ONLY. 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.ndarra...
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 ``container``. Parameters ---------- cat ...
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...
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...
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 If `values` has a categorical dtype, then `cat...
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 Notes ----- See `_factorize_from_iterable` for ...
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 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 dtype is categorical, the original object is r...
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 inferred_codes ...
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, which is usually done on the constructor. If your data does not follow this conventio...
Get the codes. Returns ------- codes : integer array view A non writable view of the `codes` array. def _get_codes(self): """ Get the codes. Returns ------- codes : integer array view A non writable view of the `codes` array. ...
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...
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 `CategoricalDtype`. def _set_dtype(self, dtype): ...
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 ...
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. def as_ordered(self, inplace=False): """ Set the Cate...
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. def as_unordered(self, inplace=False): """ Set the...
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...
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 existing number of categories. * dict-like: specifies a mapping from...
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 The categories in new order. ordered : bool, optional Whether or not...
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-like of category The new categories to be included. inplace ...
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. inplace ...
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...
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, otherwise a :class:`~pandas.Index` is...
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....
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 ...
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 ...
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. def get_values(self): """ Return the 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 ...
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 def _values_for_rank(self): """ For correctly ranking ordered categorical data. S...
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 can be used to fill in different values for each index. The va...

interpret the dtype from a scalar or array. This is a convenience routines to infer dtype from a scalar or an array Parameters ---------- pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, scalar/array belongs to pandas extension types ...

interpret the dtype from a scalar Parameters ---------- pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, scalar belongs to pandas extension types is inferred as object def infer_dtype_from_scalar(val, pandas_dtype=False): """...

infer the dtype from a scalar or array Parameters ---------- arr : scalar or array pandas_dtype : bool, default False whether to infer dtype including pandas extension types. If False, array belongs to pandas extension types is inferred as object Returns ------- tup...

Try to infer an object's dtype, for use in arithmetic ops Uses `element.dtype` if that's available. Objects implementing the iterator protocol are cast to a NumPy array, and from there the array's type is used. Parameters ---------- element : object Possibly has a `.dtype` attribute, a...

provide explicit type promotion and coercion Parameters ---------- values : the ndarray that we want to maybe upcast fill_value : what we want to fill with dtype : if None, then use the dtype of the values, else coerce to this type copy : if True always make a copy even if no upcast is required...

Change string like dtypes to object for ``DataFrame.select_dtypes()``. def invalidate_string_dtypes(dtype_set): """Change string like dtypes to object for ``DataFrame.select_dtypes()``. """ non_string_dtypes = dtype_set - {np.dtype('S').type, np.dtype('<U').type} if non_string_dtypes != dtype_s...

coerce the indexer input array to the smallest dtype possible def coerce_indexer_dtype(indexer, categories): """ coerce the indexer input array to the smallest dtype possible """ length = len(categories) if length < _int8_max: return ensure_int8(indexer) elif length < _int16_max: return...

given a dtypes and a result set, coerce the result elements to the dtypes def coerce_to_dtypes(result, dtypes): """ given a dtypes and a result set, coerce the result elements to the dtypes """ if len(result) != len(dtypes): raise AssertionError("_coerce_to_dtypes requires equal len arr...

Cast the elements of an array to a given dtype a nan-safe manner. Parameters ---------- arr : ndarray dtype : np.dtype copy : bool, default True If False, a view will be attempted but may fail, if e.g. the item sizes don't align. skipna: bool, default False Whether or no...

if we have an object dtype, try to coerce dates and/or numbers def maybe_convert_objects(values, convert_dates=True, convert_numeric=True, convert_timedeltas=True, copy=True): """ if we have an object dtype, try to coerce dates and/or numbers """ # if we have passed in a list or scal...

if we have an object dtype, try to coerce dates and/or numbers def soft_convert_objects(values, datetime=True, numeric=True, timedelta=True, coerce=False, copy=True): """ if we have an object dtype, try to coerce dates and/or numbers """ conversion_count = sum((datetime, numeric, time...

we might have a array (or single object) that is datetime like, and no dtype is passed don't change the value unless we find a datetime/timedelta set this is pretty strict in that a datetime/timedelta is REQUIRED in addition to possible nulls/string likes Parameters ---------- value : np.a...

try to cast the array/value to a datetimelike dtype, converting float nan to iNaT def maybe_cast_to_datetime(value, dtype, errors='raise'): """ try to cast the array/value to a datetimelike dtype, converting float nan to iNaT """ from pandas.core.tools.timedeltas import to_timedelta from pandas...

Find a common data type among the given dtypes. Parameters ---------- types : list of dtypes Returns ------- pandas extension or numpy dtype See Also -------- numpy.find_common_type def find_common_type(types): """ Find a common data type among the given dtypes. Para...

create np.ndarray of specified shape and dtype, filled with values Parameters ---------- shape : tuple value : scalar value dtype : np.dtype, optional dtype to coerce Returns ------- ndarray of shape, filled with value, of specified / inferred dtype def cast_scalar_to_array(sh...

create a np.ndarray / pandas type of specified shape and dtype filled with values Parameters ---------- value : scalar value length : int dtype : pandas_dtype / np.dtype Returns ------- np.ndarray / pandas type of length, filled with value def construct_1d_arraylike_from_scalar(va...

Transform any list-like object in a 1-dimensional numpy array of object dtype. Parameters ---------- values : any iterable which has a len() Raises ------ TypeError * If `values` does not have a len() Returns ------- 1-dimensional numpy array of dtype object def const...

Construct a new ndarray, coercing `values` to `dtype`, preserving NA. Parameters ---------- values : Sequence dtype : numpy.dtype, optional copy : bool, default False Note that copies may still be made with ``copy=False`` if casting is required. Returns ------- arr : nd...

Takes any dtype and returns the casted version, raising for when data is incompatible with integer/unsigned integer dtypes. .. versionadded:: 0.24.0 Parameters ---------- arr : array-like The array to cast. dtype : str, np.dtype The integer dtype to cast the array to. copy:...

Make a scatter plot from two DataFrame columns Parameters ---------- data : DataFrame x : Column name for the x-axis values y : Column name for the y-axis values ax : Matplotlib axis object figsize : A tuple (width, height) in inches grid : Setting this to True will show the grid kw...

Make a histogram of the DataFrame's. A `histogram`_ is a representation of the distribution of data. This function calls :meth:`matplotlib.pyplot.hist`, on each series in the DataFrame, resulting in one histogram per column. .. _histogram: https://en.wikipedia.org/wiki/Histogram Parameters --...

Draw histogram of the input series using matplotlib. Parameters ---------- by : object, optional If passed, then used to form histograms for separate groups ax : matplotlib axis object If not passed, uses gca() grid : bool, default True Whether to show axis grid lines xl...

Grouped histogram Parameters ---------- data : Series/DataFrame column : object, optional by : object, optional ax : axes, optional bins : int, default 50 figsize : tuple, optional layout : optional sharex : bool, default False sharey : bool, default False rot : int, def...

Make box plots from DataFrameGroupBy data. Parameters ---------- grouped : Grouped DataFrame subplots : bool * ``False`` - no subplots will be used * ``True`` - create a subplot for each group column : column name or list of names, or vector Can be any valid input to groupby...

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...

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...

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), ...

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=No...

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...

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...

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. 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...

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 ...

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...

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 ...

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, ...

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...

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], ...

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 : list of objects Each object will only be considered if it has a _get_axis ...

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.ad...

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 intersection between indexes. Otherwise, calculate the union. s...

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 ------- Index def _union_indexes(indexes, sor...

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, ...]: Return ([Index, Index, ...], TYPE) ...

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 'names' found. def _get_consensus_names(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. def _all_indexes_same(indexes): """ Determine if all indexes contain the same elements...

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: ...

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 = [] ...

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 = _proc...

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) ...

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. ...

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...

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...

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). It will delegate to the specific function depending on the provided input. A SQL query will be routed to ``read_sql_query``, while a d...

Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame, Series name : string Name of SQL table. con : SQLAlchemy connectable(engine/connection) or database string URI or sqlite3 DBAPI2 connection Using SQLAlchemy makes it possible to u...

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 by that library. If a DBAPI2 object, only...

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(co...

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. """ # Wh...

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 SQL database connection object or a SQLAlchemy connectable Using SQ...

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 contains a list of values to be inserted def _execute_insert(...

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: ...

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 f...

Read SQL database table into a DataFrame. Parameters ---------- table_name : string Name of SQL table in database. index_col : string, optional, default: None Column to set as index. coerce_float : boolean, default True Attempts to convert val...

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...

Read SQL query into a DataFrame. Parameters ---------- 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 Attem...

Write records stored in a DataFrame to a SQL database. Parameters ---------- frame : DataFrame name : string Name of SQL table. if_exists : {'fail', 'replace', 'append'}, default 'fail' - fail: If table exists, do nothing. - replace: If table ...

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 reflect...

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(dat...

Write records stored in a DataFrame to a SQL database. Parameters ---------- frame: DataFrame name: string Name of SQL table. if_exists: {'fail', 'replace', 'append'}, default 'fail' fail: If table exists, do nothing. replace: If table exists,...

Coerce to a categorical if a series is given. Internal use ONLY. 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.ndarra...

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 ``container``. Parameters ---------- cat ...

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...

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...

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 If `values` has a categorical dtype, then `cat...

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 Notes ----- See `_factorize_from_iterable` for ...

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 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 dtype is categorical, the original object is r...

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 inferred_codes ...

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, which is usually done on the constructor. If your data does not follow this conventio...

Get the codes. Returns ------- codes : integer array view A non writable view of the `codes` array. def _get_codes(self): """ Get the codes. Returns ------- codes : integer array view A non writable view of the `codes` array. ...

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...

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 `CategoricalDtype`. def _set_dtype(self, dtype): ...

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 ...

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. def as_ordered(self, inplace=False): """ Set the Cate...

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. def as_unordered(self, inplace=False): """ Set the...

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...

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 existing number of categories. * dict-like: specifies a mapping from...

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 The categories in new order. ordered : bool, optional Whether or not...

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-like of category The new categories to be included. inplace ...

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. inplace ...

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...

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, otherwise a :class:`~pandas.Index` is...

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....

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 ...

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 ...

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. def get_values(self): """ Return the 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 ...

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 def _values_for_rank(self): """ For correctly ranking ordered categorical data. S...

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 can be used to fill in different values for each index. The va...