BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def infer_dtype_from(val, pandas_dtype=False): """ 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. ...
def infer_dtype_from_scalar(val, pandas_dtype=False): """ 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 obj...
def infer_dtype_from_array(arr, 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 typ...
def maybe_infer_dtype_type(element): """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 ...
def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): """ 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 ...
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_set: raise TypeError("string dtypes are not allowed, use 'object' ins...
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 ensure_int16(indexer) elif length < _int32_max: re...
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 arrays") def conv(r, dtype): try: if isna(r): ...
def astype_nansafe(arr, dtype, copy=True, skipna=False): """ 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 siz...
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 scalar if isinstance(values, (list, tuple)): values = np...
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, timedelta)) if conversion_count == 0: raise ValueError('...
def maybe_infer_to_datetimelike(value, convert_dates=False): """ 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 po...
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.core.tools.datetimes import to_datetime if dtype is not None: if isinstan...
def find_common_type(types): """ 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 """ if len(types) == 0: raise ValueError...
def cast_scalar_to_array(shape, value, dtype=None): """ 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 v...
def construct_1d_arraylike_from_scalar(value, length, dtype): """ 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 ty...
def construct_1d_object_array_from_listlike(values): """ 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 ...
def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False): """ 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 ...
def maybe_cast_to_integer_array(arr, dtype, copy=False): """ 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 : st...
def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False, **kwargs): """ 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...
def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds): """ Make a histogram of the DataFrame's. A `histogram`_ is a representation of the di...
def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds): """ Draw histogram of the input series using matplotlib. Parameters ---------- by : object, optional If passed, then use...
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- ...
def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, sharex=False, sharey=True, **kwds): """ Make box plots from DataFrameGroupBy data. Parameters ---------- grouped : ...
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 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...
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: ...
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 *...
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 _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 _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 _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 _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 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 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 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 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 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 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 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 _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...
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
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 ...
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 ...
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, .....
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...
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...
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 _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__'): ...
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 _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 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 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_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...
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...
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. ...
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 ...
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...
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...
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...
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...
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: ...
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...
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...
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: ...
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...
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...
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...
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 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...
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
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...
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...
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 _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 ...
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...
def copy(self): """ Copy constructor. """ return self._constructor(values=self._codes.copy(), dtype=self.dtype, fastpath=True)
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...
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_...
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...
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
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_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...
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...
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. """ ...
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. ...
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 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 ...
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 ...
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...
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...
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 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...
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 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...
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 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...
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...
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 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...

def infer_dtype_from(val, pandas_dtype=False): """ 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. ...

def infer_dtype_from_scalar(val, pandas_dtype=False): """ 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 obj...

def infer_dtype_from_array(arr, 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 typ...

def maybe_infer_dtype_type(element): """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 ...

def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False): """ 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 ...

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_set: raise TypeError("string dtypes are not allowed, use 'object' ins...

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 ensure_int16(indexer) elif length < _int32_max: re...

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 arrays") def conv(r, dtype): try: if isna(r): ...

def astype_nansafe(arr, dtype, copy=True, skipna=False): """ 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 siz...

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 scalar if isinstance(values, (list, tuple)): values = np...

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, timedelta)) if conversion_count == 0: raise ValueError('...

def maybe_infer_to_datetimelike(value, convert_dates=False): """ 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 po...

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.core.tools.datetimes import to_datetime if dtype is not None: if isinstan...

def find_common_type(types): """ 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 """ if len(types) == 0: raise ValueError...

def cast_scalar_to_array(shape, value, dtype=None): """ 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 v...

def construct_1d_arraylike_from_scalar(value, length, dtype): """ 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 ty...

def construct_1d_object_array_from_listlike(values): """ 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 ...

def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False): """ 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 ...

def maybe_cast_to_integer_array(arr, dtype, copy=False): """ 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 : st...

def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False, **kwargs): """ 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...

def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds): """ Make a histogram of the DataFrame's. A `histogram`_ is a representation of the di...

def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds): """ Draw histogram of the input series using matplotlib. Parameters ---------- by : object, optional If passed, then use...

def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- ...

def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, sharex=False, sharey=True, **kwds): """ Make box plots from DataFrameGroupBy data. Parameters ---------- grouped : ...

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 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...

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: ...

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 *...

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 _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 _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 _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 _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 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 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 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 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 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 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 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 _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...

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

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 ...

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 ...

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, .....

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...

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...

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 _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__'): ...

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 _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 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 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_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...

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...

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. ...

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 ...

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...

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...

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...

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...

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: ...

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...

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...

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: ...

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...

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...

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...

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 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...

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

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...

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...

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 _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 ...

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...

def copy(self): """ Copy constructor. """ return self._constructor(values=self._codes.copy(), dtype=self.dtype, fastpath=True)

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...

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_...

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...

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

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_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...

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...

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. """ ...

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. ...

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 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 ...

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 ...

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...

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...

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 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...

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 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...

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 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...

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...

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 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...