BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def _box_col_values(self, values, items): """ Provide boxed values for a column. """ klass = self._constructor_sliced return klass(values, index=self.index, name=items, fastpath=True)
def _ensure_valid_index(self, value): """ Ensure that if we don't have an index, that we can create one from the passed value. """ # GH5632, make sure that we are a Series convertible if not len(self.index) and is_list_like(value): try: value =...
def _set_item(self, key, value): """ Add series to DataFrame in specified column. If series is a numpy-array (not a Series/TimeSeries), it must be the same length as the DataFrames index or an error will be thrown. Series/TimeSeries will be conformed to the DataFrames index to ...
def insert(self, loc, column, value, allow_duplicates=False): """ Insert column into DataFrame at specified location. Raises a ValueError if `column` is already contained in the DataFrame, unless `allow_duplicates` is set to True. Parameters ---------- loc : int...
def assign(self, kwargs): r""" Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters ---------- kwargs : dict of {str: callable or Seri...
def _sanitize_column(self, key, value, broadcast=True): """ Ensures new columns (which go into the BlockManager as new blocks) are always copied and converted into an array. Parameters ---------- key : object value : scalar, Series, or array-like broadcas...
def lookup(self, row_labels, col_labels): """ Label-based "fancy indexing" function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Parameters ---------- row_labels : sequenc...
def _reindex_multi(self, axes, copy, fill_value): """ We are guaranteed non-Nones in the axes. """ new_index, row_indexer = self.index.reindex(axes['index']) new_columns, col_indexer = self.columns.reindex(axes['columns']) if row_indexer is not None and col_indexer is n...
def drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise'): """ Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names...
def rename(self, args, *kwargs): """ Alter axes labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. See the :ref:`user guide <basics.rename>` for more. P...
def set_index(self, keys, drop=True, append=False, inplace=False, verify_integrity=False): """ Set the DataFrame index using existing columns. Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can repla...
def reset_index(self, level=None, drop=False, inplace=False, col_level=0, col_fill=''): """ Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more ...
def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False): """ Remove missing values. See the :ref:`User Guide <missing_data>` for more on which values are considered missing, and how to work with missing data. Parameters ---------- ...
def drop_duplicates(self, subset=None, keep='first', inplace=False): """ Return DataFrame with duplicate rows removed, optionally only considering certain columns. Indexes, including time indexes are ignored. Parameters ---------- subset : column label or sequenc...
def duplicated(self, subset=None, keep='first'): """ Return boolean Series denoting duplicate rows, optionally only considering certain columns. Parameters ---------- subset : column label or sequence of labels, optional Only consider certain columns for iden...
def nlargest(self, n, columns, keep='first'): """ Return the first `n` rows ordered by `columns` in descending order. Return the first `n` rows with the largest values in `columns`, in descending order. The columns that are not specified are returned as well, but not used for or...
def nsmallest(self, n, columns, keep='first'): """ Return the first `n` rows ordered by `columns` in ascending order. Return the first `n` rows with the smallest values in `columns`, in ascending order. The columns that are not specified are returned as well, but not used for or...
def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis. Parameters ---------- i, j : int, string (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- DataFr...
def reorder_levels(self, order, axis=0): """ Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int or list of str List representing new level order. Reference level by number (positio...
def combine(self, other, func, fill_value=None, overwrite=True): """ Perform column-wise combine with another DataFrame. Combines a DataFrame with `other` DataFrame using `func` to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the un...
def combine_first(self, other): """ Update null elements with value in the same location in `other`. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the...
def update(self, other, join='left', overwrite=True, filter_func=None, errors='ignore'): """ Modify in place using non-NA values from another DataFrame. Aligns on indices. There is no return value. Parameters ---------- other : DataFrame, or object coerci...
def stack(self, level=-1, dropna=True): """ Stack the prescribed level(s) from columns to index. Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pi...
def unstack(self, level=-1, fill_value=None): """ Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output wil...
def diff(self, periods=1, axis=0): """ First discrete difference of element. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is the element in the same column of the previous row). Parameters ---------- ...
def _gotitem(self, key: Union[str, List[str]], ndim: int, subset: Optional[Union[Series, ABCDataFrame]] = None, ) -> Union[Series, ABCDataFrame]: """ Sub-classes to define. Return a sliced object. Parameters ---------- ...
def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds): """ Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame's index (``axis=0``) or the ...
def applymap(self, func): """ Apply a function to a Dataframe elementwise. This method applies a function that accepts and returns a scalar to every element of a DataFrame. Parameters ---------- func : callable Python function, returns a single value...
def append(self, other, ignore_index=False, verify_integrity=False, sort=None): """ Append rows of `other` to the end of caller, returning a new object. Columns in `other` that are not in the caller are added as new columns. Parameters ---------- other : ...
def join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False): """ Join columns of another DataFrame. Join columns with `other` DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a l...
def round(self, decimals=0, args, *kwargs): """ Round a DataFrame to a variable number of decimal places. Parameters ---------- decimals : int, dict, Series Number of decimal places to round each column to. If an int is given, round each column to the s...
def corr(self, method='pearson', min_periods=1): """ Compute pairwise correlation of columns, excluding NA/null values. Parameters ---------- method : {'pearson', 'kendall', 'spearman'} or callable * pearson : standard correlation coefficient * kendall : ...
def cov(self, min_periods=None): """ Compute pairwise covariance of columns, excluding NA/null values. Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the `covariance matrix <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the c...
def corrwith(self, other, axis=0, drop=False, method='pearson'): """ Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations. Parameters ...
def count(self, axis=0, level=None, numeric_only=False): """ Count non-NA cells for each column or row. The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending on `pandas.options.mode.use_inf_as_na`) are considered NA. Parameters ---------- axis :...
def nunique(self, axis=0, dropna=True): """ Count distinct observations over requested axis. Return Series with number of distinct observations. Can ignore NaN values. .. versionadded:: 0.20.0 Parameters ---------- axis : {0 or 'index', 1 or 'columns'},...
def idxmin(self, axis=0, skipna=True): """ Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 0 or 'index' for row-wise, 1 or 'columns' for colum...
def _get_agg_axis(self, axis_num): """ Let's be explicit about this. """ if axis_num == 0: return self.columns elif axis_num == 1: return self.index else: raise ValueError('Axis must be 0 or 1 (got %r)' % axis_num)
def mode(self, axis=0, numeric_only=False, dropna=True): """ Get the mode(s) of each element along the selected axis. The mode of a set of values is the value that appears most often. It can be multiple values. Parameters ---------- axis : {0 or 'index', 1 or 'c...
def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation='linear'): """ Return values at the given quantile over requested axis. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value between 0 <= q <= 1, the quanti...
def to_timestamp(self, freq=None, how='start', axis=0, copy=True): """ Cast to DatetimeIndex of timestamps, at beginning of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} ...
def isin(self, values): """ Whether each element in the DataFrame is contained in values. Parameters ---------- values : iterable, Series, DataFrame or dict The result will only be true at a location if all the labels match. If `values` is a Series, that'...
def integer_array(values, dtype=None, copy=False): """ Infer and return an integer array of the values. Parameters ---------- values : 1D list-like dtype : dtype, optional dtype to coerce copy : boolean, default False Returns ------- IntegerArray Raises ------ ...
def safe_cast(values, dtype, copy): """ Safely cast the values to the dtype if they are equivalent, meaning floats must be equivalent to the ints. """ try: return values.astype(dtype, casting='safe', copy=copy) except TypeError: casted = values.astype(dtype, copy=copy) ...
def coerce_to_array(values, dtype, mask=None, copy=False): """ Coerce the input values array to numpy arrays with a mask Parameters ---------- values : 1D list-like dtype : integer dtype mask : boolean 1D array, optional copy : boolean, default False if True, copy the input ...
def construct_from_string(cls, string): """ Construction from a string, raise a TypeError if not possible """ if string == cls.name: return cls() raise TypeError("Cannot construct a '{}' from " "'{}'".format(cls, string))
def _coerce_to_ndarray(self): """ coerce to an ndarary of object dtype """ # TODO(jreback) make this better data = self._data.astype(object) data[self._mask] = self._na_value return data
def astype(self, dtype, copy=True): """ Cast to a NumPy array or IntegerArray with 'dtype'. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. copy : bool, default True Whether to copy the data, even if no...
def value_counts(self, dropna=True): """ Returns a Series containing counts of each category. Every category will have an entry, even those with a count of 0. Parameters ---------- dropna : boolean, default True Don't include counts of NaN. Returns ...
def _values_for_argsort(self) -> np.ndarray: """Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort """ ...
def _maybe_mask_result(self, result, mask, other, op_name): """ Parameters ---------- result : array-like mask : array-like bool other : scalar or array-like op_name : str """ # may need to fill infs # and mask wraparound if is_flo...
def length_of_indexer(indexer, target=None): """ return the length of a single non-tuple indexer which could be a slice """ if target is not None and isinstance(indexer, slice): target_len = len(target) start = indexer.start stop = indexer.stop step = indexer.step ...
def convert_to_index_sliceable(obj, key): """ if we are index sliceable, then return my slicer, otherwise return None """ idx = obj.index if isinstance(key, slice): return idx._convert_slice_indexer(key, kind='getitem') elif isinstance(key, str): # we are an actual column ...
def check_setitem_lengths(indexer, value, values): """ Validate that value and indexer are the same length. An special-case is allowed for when the indexer is a boolean array and the number of true values equals the length of ``value``. In this case, no exception is raised. Parameters ----...
def convert_missing_indexer(indexer): """ reverse convert a missing indexer, which is a dict return the scalar indexer and a boolean indicating if we converted """ if isinstance(indexer, dict): # a missing key (but not a tuple indexer) indexer = indexer['key'] if isinstanc...
def convert_from_missing_indexer_tuple(indexer, axes): """ create a filtered indexer that doesn't have any missing indexers """ def get_indexer(_i, _idx): return (axes[_i].get_loc(_idx['key']) if isinstance(_idx, dict) else _idx) return tuple(get_indexer(_i, _idx) for _i, _...
def maybe_convert_indices(indices, n): """ Attempt to convert indices into valid, positive indices. If we have negative indices, translate to positive here. If we have indices that are out-of-bounds, raise an IndexError. Parameters ---------- indices : array-like The array of indic...
def validate_indices(indices, n): """ Perform bounds-checking for an indexer. -1 is allowed for indicating missing values. Parameters ---------- indices : ndarray n : int length of the array being indexed Raises ------ ValueError Examples -------- >>> vali...
def maybe_convert_ix(args): """ We likely want to take the cross-product """ ixify = True for arg in args: if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)): ixify = False if ixify: return np.ix_(args) else: return args
def _non_reducing_slice(slice_): """ Ensurse that a slice doesn't reduce to a Series or Scalar. Any user-paseed `subset` should have this called on it to make sure we're always working with DataFrames. """ # default to column slice, like DataFrame # ['A', 'B'] -> IndexSlices[:, ['A', 'B']] ...
def _maybe_numeric_slice(df, slice_, include_bool=False): """ want nice defaults for background_gradient that don't break with non-numeric data. But if slice_ is passed go with that. """ if slice_ is None: dtypes = [np.number] if include_bool: dtypes.append(bool) ...
def _has_valid_tuple(self, key): """ check the key for valid keys across my indexer """ for i, k in enumerate(key): if i >= self.obj.ndim: raise IndexingError('Too many indexers') try: self._validate_key(k, i) except ValueError: ...
def _has_valid_positional_setitem_indexer(self, indexer): """ validate that an positional indexer cannot enlarge its target will raise if needed, does not modify the indexer externally """ if isinstance(indexer, dict): raise IndexError("{0} cannot enlarge its target object" ...
def _align_series(self, indexer, ser, multiindex_indexer=False): """ Parameters ---------- indexer : tuple, slice, scalar The indexer used to get the locations that will be set to `ser` ser : pd.Series The values to assign to the locations spe...
def _multi_take_opportunity(self, tup): """ Check whether there is the possibility to use ``_multi_take``. Currently the limit is that all axes being indexed must be indexed with list-likes. Parameters ---------- tup : tuple Tuple of indexers, one per...
def _multi_take(self, tup): """ Create the indexers for the passed tuple of keys, and execute the take operation. This allows the take operation to be executed all at once - rather than once for each dimension - improving efficiency. Parameters ---------- tup : t...
def _get_listlike_indexer(self, key, axis, raise_missing=False): """ Transform a list-like of keys into a new index and an indexer. Parameters ---------- key : list-like Target labels axis: int Dimension on which the indexing is being made ...
def _getitem_iterable(self, key, axis=None): """ Index current object with an an iterable key (which can be a boolean indexer, or a collection of keys). Parameters ---------- key : iterable Target labels, or boolean indexer axis: int, default None ...
def _validate_read_indexer(self, key, indexer, axis, raise_missing=False): """ Check that indexer can be used to return a result (e.g. at least one element was found, unless the list of keys was actually empty). Parameters ---------- key : list-like Target la...
def _convert_to_indexer(self, obj, axis=None, is_setter=False, raise_missing=False): """ Convert indexing key into something we can use to do actual fancy indexing on an ndarray Examples ix[:5] -> slice(0, 5) ix[[1,2,3]] -> [1,2,3] ix[...
def _convert_for_reindex(self, key, axis=None): """ Transform a list of keys into a new array ready to be used as axis of the object we return (e.g. including NaNs). Parameters ---------- key : list-like Target labels axis: int Where the i...
def _get_slice_axis(self, slice_obj, axis=None): """ this is pretty simple as we just have to deal with labels """ if axis is None: axis = self.axis or 0 obj = self.obj if not need_slice(slice_obj): return obj.copy(deep=False) labels = obj._get_axis(axis...
def _get_partial_string_timestamp_match_key(self, key, labels): """Translate any partial string timestamp matches in key, returning the new key (GH 10331)""" if isinstance(labels, MultiIndex): if (isinstance(key, str) and labels.levels[0].is_all_dates): # Convert key ...
def _validate_integer(self, key, axis): """ Check that 'key' is a valid position in the desired axis. Parameters ---------- key : int Requested position axis : int Desired axis Returns ------- None Raises ...
def _get_list_axis(self, key, axis=None): """ Return Series values by list or array of integers Parameters ---------- key : list-like positional indexer axis : int (can only be zero) Returns ------- Series object """ if axis is No...
def _convert_to_indexer(self, obj, axis=None, is_setter=False): """ much simpler as we only have to deal with our valid types """ if axis is None: axis = self.axis or 0 # make need to convert a float key if isinstance(obj, slice): return self._convert_slice_index...
def _convert_key(self, key, is_setter=False): """ require they keys to be the same type as the index (so we don't fallback) """ # allow arbitrary setting if is_setter: return list(key) for ax, i in zip(self.obj.axes, key): if ax.is_integer(): ...
def _convert_key(self, key, is_setter=False): """ require integer args (and convert to label arguments) """ for a, i in zip(self.obj.axes, key): if not is_integer(i): raise ValueError("iAt based indexing can only have integer " "indexers") ...
def to_manager(sdf, columns, index): """ create and return the block manager from a dataframe of series, columns, index """ # from BlockManager perspective axes = [ensure_index(columns), ensure_index(index)] return create_block_manager_from_arrays( [sdf[c] for c in columns], columns, a...
def stack_sparse_frame(frame): """ Only makes sense when fill_value is NaN """ lengths = [s.sp_index.npoints for _, s in frame.items()] nobs = sum(lengths) # this is pretty fast minor_codes = np.repeat(np.arange(len(frame.columns)), lengths) inds_to_concat = [] vals_to_concat = [] ...
def homogenize(series_dict): """ Conform a set of SparseSeries (with NaN fill_value) to a common SparseIndex corresponding to the locations where they all have data Parameters ---------- series_dict : dict or DataFrame Notes ----- Using the dumbest algorithm I could think of. Shoul...
def _init_matrix(self, data, index, columns, dtype=None): """ Init self from ndarray or list of lists. """ data = prep_ndarray(data, copy=False) index, columns = self._prep_index(data, index, columns) data = {idx: data[:, i] for i, idx in enumerate(columns)} retur...
def _init_spmatrix(self, data, index, columns, dtype=None, fill_value=None): """ Init self from scipy.sparse matrix. """ index, columns = self._prep_index(data, index, columns) data = data.tocoo() N = len(index) # Construct a dict of Sparse...
def to_coo(self): """ Return the contents of the frame as a sparse SciPy COO matrix. .. versionadded:: 0.20.0 Returns ------- coo_matrix : scipy.sparse.spmatrix If the caller is heterogeneous and contains booleans or objects, the result will be o...
def _unpickle_sparse_frame_compat(self, state): """ Original pickle format """ series, cols, idx, fv, kind = state if not isinstance(cols, Index): # pragma: no cover from pandas.io.pickle import _unpickle_array columns = _unpickle_array(cols) els...
def to_dense(self): """ Convert to dense DataFrame Returns ------- df : DataFrame """ data = {k: v.to_dense() for k, v in self.items()} return DataFrame(data, index=self.index, columns=self.columns)
def _apply_columns(self, func): """ Get new SparseDataFrame applying func to each columns """ new_data = {col: func(series) for col, series in self.items()} return self._constructor( data=new_data, index=self.index, columns=self.columns, ...
def copy(self, deep=True): """ Make a copy of this SparseDataFrame """ result = super().copy(deep=deep) result._default_fill_value = self._default_fill_value result._default_kind = self._default_kind return result
def density(self): """ Ratio of non-sparse points to total (dense) data points represented in the frame """ tot_nonsparse = sum(ser.sp_index.npoints for _, ser in self.items()) tot = len(self.index) * len(self.columns) return tot_nonspa...
def _sanitize_column(self, key, value, **kwargs): """ Creates a new SparseArray from the input value. Parameters ---------- key : object value : scalar, Series, or array-like kwargs : dict Returns ------- sanitized_column : SparseArray ...
def xs(self, key, axis=0, copy=False): """ Returns a row (cross-section) from the SparseDataFrame as a Series object. Parameters ---------- key : some index contained in the index Returns ------- xs : Series """ if axis == 1: ...
def transpose(self, args, *kwargs): """ Returns a DataFrame with the rows/columns switched. """ nv.validate_transpose(args, kwargs) return self._constructor( self.values.T, index=self.columns, columns=self.index, default_fill_value=self._default_fill_val...
def cumsum(self, axis=0, args, *kwargs): """ Return SparseDataFrame of cumulative sums over requested axis. Parameters ---------- axis : {0, 1} 0 for row-wise, 1 for column-wise Returns ------- y : SparseDataFrame """ nv.val...
def apply(self, func, axis=0, broadcast=None, reduce=None, result_type=None): """ Analogous to DataFrame.apply, for SparseDataFrame Parameters ---------- func : function Function to apply to each column axis : {0, 1, 'index', 'columns'} ...
def conda_package_to_pip(package): """ Convert a conda package to its pip equivalent. In most cases they are the same, those are the exceptions: - Packages that should be excluded (in `EXCLUDE`) - Packages that should be renamed (in `RENAME`) - A package requiring a specific version, in conda i...
def main(conda_fname, pip_fname, compare=False): """ Generate the pip dependencies file from the conda file, or compare that they are synchronized (``compare=True``). Parameters ---------- conda_fname : str Path to the conda file with dependencies (e.g. `environment.yml`). pip_fname...
def maybe_convert_platform(values): """ try to do platform conversion, allow ndarray or list here """ if isinstance(values, (list, tuple)): values = construct_1d_object_array_from_listlike(list(values)) if getattr(values, 'dtype', None) == np.object_: if hasattr(values, '_values'): ...
def is_nested_object(obj): """ return a boolean if we have a nested object, e.g. a Series with 1 or more Series elements This may not be necessarily be performant. """ if isinstance(obj, ABCSeries) and is_object_dtype(obj): if any(isinstance(v, ABCSeries) for v in obj.values): ...
def maybe_downcast_to_dtype(result, dtype): """ try to cast to the specified dtype (e.g. convert back to bool/int or could be an astype of float64->float32 """ if is_scalar(result): return result def trans(x): return x if isinstance(dtype, str): if dtype == 'infer': ...
def maybe_upcast_putmask(result, mask, other): """ A safe version of putmask that potentially upcasts the result. The result is replaced with the first N elements of other, where N is the number of True values in mask. If the length of other is shorter than N, other will be repeated. Parameters...

def _box_col_values(self, values, items): """ Provide boxed values for a column. """ klass = self._constructor_sliced return klass(values, index=self.index, name=items, fastpath=True)

def _ensure_valid_index(self, value): """ Ensure that if we don't have an index, that we can create one from the passed value. """ # GH5632, make sure that we are a Series convertible if not len(self.index) and is_list_like(value): try: value =...

def _set_item(self, key, value): """ Add series to DataFrame in specified column. If series is a numpy-array (not a Series/TimeSeries), it must be the same length as the DataFrames index or an error will be thrown. Series/TimeSeries will be conformed to the DataFrames index to ...

def insert(self, loc, column, value, allow_duplicates=False): """ Insert column into DataFrame at specified location. Raises a ValueError if `column` is already contained in the DataFrame, unless `allow_duplicates` is set to True. Parameters ---------- loc : int...

def assign(self, **kwargs): r""" Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. Parameters ---------- **kwargs : dict of {str: callable or Seri...

def _sanitize_column(self, key, value, broadcast=True): """ Ensures new columns (which go into the BlockManager as new blocks) are always copied and converted into an array. Parameters ---------- key : object value : scalar, Series, or array-like broadcas...

def lookup(self, row_labels, col_labels): """ Label-based "fancy indexing" function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Parameters ---------- row_labels : sequenc...

def _reindex_multi(self, axes, copy, fill_value): """ We are guaranteed non-Nones in the axes. """ new_index, row_indexer = self.index.reindex(axes['index']) new_columns, col_indexer = self.columns.reindex(axes['columns']) if row_indexer is not None and col_indexer is n...

def drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise'): """ Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names...

def rename(self, *args, **kwargs): """ Alter axes labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. See the :ref:`user guide <basics.rename>` for more. P...

def set_index(self, keys, drop=True, append=False, inplace=False, verify_integrity=False): """ Set the DataFrame index using existing columns. Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can repla...

def reset_index(self, level=None, drop=False, inplace=False, col_level=0, col_fill=''): """ Reset the index, or a level of it. Reset the index of the DataFrame, and use the default one instead. If the DataFrame has a MultiIndex, this method can remove one or more ...

def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False): """ Remove missing values. See the :ref:`User Guide <missing_data>` for more on which values are considered missing, and how to work with missing data. Parameters ---------- ...

def drop_duplicates(self, subset=None, keep='first', inplace=False): """ Return DataFrame with duplicate rows removed, optionally only considering certain columns. Indexes, including time indexes are ignored. Parameters ---------- subset : column label or sequenc...

def duplicated(self, subset=None, keep='first'): """ Return boolean Series denoting duplicate rows, optionally only considering certain columns. Parameters ---------- subset : column label or sequence of labels, optional Only consider certain columns for iden...

def nlargest(self, n, columns, keep='first'): """ Return the first `n` rows ordered by `columns` in descending order. Return the first `n` rows with the largest values in `columns`, in descending order. The columns that are not specified are returned as well, but not used for or...

def nsmallest(self, n, columns, keep='first'): """ Return the first `n` rows ordered by `columns` in ascending order. Return the first `n` rows with the smallest values in `columns`, in ascending order. The columns that are not specified are returned as well, but not used for or...

def swaplevel(self, i=-2, j=-1, axis=0): """ Swap levels i and j in a MultiIndex on a particular axis. Parameters ---------- i, j : int, string (can be mixed) Level of index to be swapped. Can pass level name as string. Returns ------- DataFr...

def reorder_levels(self, order, axis=0): """ Rearrange index levels using input order. May not drop or duplicate levels. Parameters ---------- order : list of int or list of str List representing new level order. Reference level by number (positio...

def combine(self, other, func, fill_value=None, overwrite=True): """ Perform column-wise combine with another DataFrame. Combines a DataFrame with `other` DataFrame using `func` to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the un...

def combine_first(self, other): """ Update null elements with value in the same location in `other`. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the...

def update(self, other, join='left', overwrite=True, filter_func=None, errors='ignore'): """ Modify in place using non-NA values from another DataFrame. Aligns on indices. There is no return value. Parameters ---------- other : DataFrame, or object coerci...

def stack(self, level=-1, dropna=True): """ Stack the prescribed level(s) from columns to index. Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pi...

def unstack(self, level=-1, fill_value=None): """ Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output wil...

def diff(self, periods=1, axis=0): """ First discrete difference of element. Calculates the difference of a DataFrame element compared with another element in the DataFrame (default is the element in the same column of the previous row). Parameters ---------- ...

def _gotitem(self, key: Union[str, List[str]], ndim: int, subset: Optional[Union[Series, ABCDataFrame]] = None, ) -> Union[Series, ABCDataFrame]: """ Sub-classes to define. Return a sliced object. Parameters ---------- ...

def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds): """ Apply a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame's index (``axis=0``) or the ...

def applymap(self, func): """ Apply a function to a Dataframe elementwise. This method applies a function that accepts and returns a scalar to every element of a DataFrame. Parameters ---------- func : callable Python function, returns a single value...

def append(self, other, ignore_index=False, verify_integrity=False, sort=None): """ Append rows of `other` to the end of caller, returning a new object. Columns in `other` that are not in the caller are added as new columns. Parameters ---------- other : ...

def join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False): """ Join columns of another DataFrame. Join columns with `other` DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a l...

def round(self, decimals=0, *args, **kwargs): """ Round a DataFrame to a variable number of decimal places. Parameters ---------- decimals : int, dict, Series Number of decimal places to round each column to. If an int is given, round each column to the s...

def corr(self, method='pearson', min_periods=1): """ Compute pairwise correlation of columns, excluding NA/null values. Parameters ---------- method : {'pearson', 'kendall', 'spearman'} or callable * pearson : standard correlation coefficient * kendall : ...

def cov(self, min_periods=None): """ Compute pairwise covariance of columns, excluding NA/null values. Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the `covariance matrix <https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the c...

def corrwith(self, other, axis=0, drop=False, method='pearson'): """ Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations. Parameters ...

def count(self, axis=0, level=None, numeric_only=False): """ Count non-NA cells for each column or row. The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending on `pandas.options.mode.use_inf_as_na`) are considered NA. Parameters ---------- axis :...

def nunique(self, axis=0, dropna=True): """ Count distinct observations over requested axis. Return Series with number of distinct observations. Can ignore NaN values. .. versionadded:: 0.20.0 Parameters ---------- axis : {0 or 'index', 1 or 'columns'},...

def idxmin(self, axis=0, skipna=True): """ Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 0 or 'index' for row-wise, 1 or 'columns' for colum...

def _get_agg_axis(self, axis_num): """ Let's be explicit about this. """ if axis_num == 0: return self.columns elif axis_num == 1: return self.index else: raise ValueError('Axis must be 0 or 1 (got %r)' % axis_num)

def mode(self, axis=0, numeric_only=False, dropna=True): """ Get the mode(s) of each element along the selected axis. The mode of a set of values is the value that appears most often. It can be multiple values. Parameters ---------- axis : {0 or 'index', 1 or 'c...

def quantile(self, q=0.5, axis=0, numeric_only=True, interpolation='linear'): """ Return values at the given quantile over requested axis. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value between 0 <= q <= 1, the quanti...

def to_timestamp(self, freq=None, how='start', axis=0, copy=True): """ Cast to DatetimeIndex of timestamps, at *beginning* of period. Parameters ---------- freq : str, default frequency of PeriodIndex Desired frequency. how : {'s', 'e', 'start', 'end'} ...

def isin(self, values): """ Whether each element in the DataFrame is contained in values. Parameters ---------- values : iterable, Series, DataFrame or dict The result will only be true at a location if all the labels match. If `values` is a Series, that'...

def integer_array(values, dtype=None, copy=False): """ Infer and return an integer array of the values. Parameters ---------- values : 1D list-like dtype : dtype, optional dtype to coerce copy : boolean, default False Returns ------- IntegerArray Raises ------ ...

def safe_cast(values, dtype, copy): """ Safely cast the values to the dtype if they are equivalent, meaning floats must be equivalent to the ints. """ try: return values.astype(dtype, casting='safe', copy=copy) except TypeError: casted = values.astype(dtype, copy=copy) ...

def coerce_to_array(values, dtype, mask=None, copy=False): """ Coerce the input values array to numpy arrays with a mask Parameters ---------- values : 1D list-like dtype : integer dtype mask : boolean 1D array, optional copy : boolean, default False if True, copy the input ...

def construct_from_string(cls, string): """ Construction from a string, raise a TypeError if not possible """ if string == cls.name: return cls() raise TypeError("Cannot construct a '{}' from " "'{}'".format(cls, string))

def _coerce_to_ndarray(self): """ coerce to an ndarary of object dtype """ # TODO(jreback) make this better data = self._data.astype(object) data[self._mask] = self._na_value return data

def astype(self, dtype, copy=True): """ Cast to a NumPy array or IntegerArray with 'dtype'. Parameters ---------- dtype : str or dtype Typecode or data-type to which the array is cast. copy : bool, default True Whether to copy the data, even if no...

def value_counts(self, dropna=True): """ Returns a Series containing counts of each category. Every category will have an entry, even those with a count of 0. Parameters ---------- dropna : boolean, default True Don't include counts of NaN. Returns ...

def _values_for_argsort(self) -> np.ndarray: """Return values for sorting. Returns ------- ndarray The transformed values should maintain the ordering between values within the array. See Also -------- ExtensionArray.argsort """ ...

def _maybe_mask_result(self, result, mask, other, op_name): """ Parameters ---------- result : array-like mask : array-like bool other : scalar or array-like op_name : str """ # may need to fill infs # and mask wraparound if is_flo...

def length_of_indexer(indexer, target=None): """ return the length of a single non-tuple indexer which could be a slice """ if target is not None and isinstance(indexer, slice): target_len = len(target) start = indexer.start stop = indexer.stop step = indexer.step ...

def convert_to_index_sliceable(obj, key): """ if we are index sliceable, then return my slicer, otherwise return None """ idx = obj.index if isinstance(key, slice): return idx._convert_slice_indexer(key, kind='getitem') elif isinstance(key, str): # we are an actual column ...

def check_setitem_lengths(indexer, value, values): """ Validate that value and indexer are the same length. An special-case is allowed for when the indexer is a boolean array and the number of true values equals the length of ``value``. In this case, no exception is raised. Parameters ----...

def convert_missing_indexer(indexer): """ reverse convert a missing indexer, which is a dict return the scalar indexer and a boolean indicating if we converted """ if isinstance(indexer, dict): # a missing key (but not a tuple indexer) indexer = indexer['key'] if isinstanc...

def convert_from_missing_indexer_tuple(indexer, axes): """ create a filtered indexer that doesn't have any missing indexers """ def get_indexer(_i, _idx): return (axes[_i].get_loc(_idx['key']) if isinstance(_idx, dict) else _idx) return tuple(get_indexer(_i, _idx) for _i, _...

def maybe_convert_indices(indices, n): """ Attempt to convert indices into valid, positive indices. If we have negative indices, translate to positive here. If we have indices that are out-of-bounds, raise an IndexError. Parameters ---------- indices : array-like The array of indic...

def validate_indices(indices, n): """ Perform bounds-checking for an indexer. -1 is allowed for indicating missing values. Parameters ---------- indices : ndarray n : int length of the array being indexed Raises ------ ValueError Examples -------- >>> vali...

def maybe_convert_ix(*args): """ We likely want to take the cross-product """ ixify = True for arg in args: if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)): ixify = False if ixify: return np.ix_(*args) else: return args

def _non_reducing_slice(slice_): """ Ensurse that a slice doesn't reduce to a Series or Scalar. Any user-paseed `subset` should have this called on it to make sure we're always working with DataFrames. """ # default to column slice, like DataFrame # ['A', 'B'] -> IndexSlices[:, ['A', 'B']] ...

def _maybe_numeric_slice(df, slice_, include_bool=False): """ want nice defaults for background_gradient that don't break with non-numeric data. But if slice_ is passed go with that. """ if slice_ is None: dtypes = [np.number] if include_bool: dtypes.append(bool) ...

def _has_valid_tuple(self, key): """ check the key for valid keys across my indexer """ for i, k in enumerate(key): if i >= self.obj.ndim: raise IndexingError('Too many indexers') try: self._validate_key(k, i) except ValueError: ...

def _has_valid_positional_setitem_indexer(self, indexer): """ validate that an positional indexer cannot enlarge its target will raise if needed, does not modify the indexer externally """ if isinstance(indexer, dict): raise IndexError("{0} cannot enlarge its target object" ...

def _align_series(self, indexer, ser, multiindex_indexer=False): """ Parameters ---------- indexer : tuple, slice, scalar The indexer used to get the locations that will be set to `ser` ser : pd.Series The values to assign to the locations spe...

def _multi_take_opportunity(self, tup): """ Check whether there is the possibility to use ``_multi_take``. Currently the limit is that all axes being indexed must be indexed with list-likes. Parameters ---------- tup : tuple Tuple of indexers, one per...

def _multi_take(self, tup): """ Create the indexers for the passed tuple of keys, and execute the take operation. This allows the take operation to be executed all at once - rather than once for each dimension - improving efficiency. Parameters ---------- tup : t...

def _get_listlike_indexer(self, key, axis, raise_missing=False): """ Transform a list-like of keys into a new index and an indexer. Parameters ---------- key : list-like Target labels axis: int Dimension on which the indexing is being made ...

def _getitem_iterable(self, key, axis=None): """ Index current object with an an iterable key (which can be a boolean indexer, or a collection of keys). Parameters ---------- key : iterable Target labels, or boolean indexer axis: int, default None ...

def _validate_read_indexer(self, key, indexer, axis, raise_missing=False): """ Check that indexer can be used to return a result (e.g. at least one element was found, unless the list of keys was actually empty). Parameters ---------- key : list-like Target la...

def _convert_to_indexer(self, obj, axis=None, is_setter=False, raise_missing=False): """ Convert indexing key into something we can use to do actual fancy indexing on an ndarray Examples ix[:5] -> slice(0, 5) ix[[1,2,3]] -> [1,2,3] ix[...

def _convert_for_reindex(self, key, axis=None): """ Transform a list of keys into a new array ready to be used as axis of the object we return (e.g. including NaNs). Parameters ---------- key : list-like Target labels axis: int Where the i...

def _get_slice_axis(self, slice_obj, axis=None): """ this is pretty simple as we just have to deal with labels """ if axis is None: axis = self.axis or 0 obj = self.obj if not need_slice(slice_obj): return obj.copy(deep=False) labels = obj._get_axis(axis...

def _get_partial_string_timestamp_match_key(self, key, labels): """Translate any partial string timestamp matches in key, returning the new key (GH 10331)""" if isinstance(labels, MultiIndex): if (isinstance(key, str) and labels.levels[0].is_all_dates): # Convert key ...

def _validate_integer(self, key, axis): """ Check that 'key' is a valid position in the desired axis. Parameters ---------- key : int Requested position axis : int Desired axis Returns ------- None Raises ...

def _get_list_axis(self, key, axis=None): """ Return Series values by list or array of integers Parameters ---------- key : list-like positional indexer axis : int (can only be zero) Returns ------- Series object """ if axis is No...

def _convert_to_indexer(self, obj, axis=None, is_setter=False): """ much simpler as we only have to deal with our valid types """ if axis is None: axis = self.axis or 0 # make need to convert a float key if isinstance(obj, slice): return self._convert_slice_index...

def _convert_key(self, key, is_setter=False): """ require they keys to be the same type as the index (so we don't fallback) """ # allow arbitrary setting if is_setter: return list(key) for ax, i in zip(self.obj.axes, key): if ax.is_integer(): ...

def _convert_key(self, key, is_setter=False): """ require integer args (and convert to label arguments) """ for a, i in zip(self.obj.axes, key): if not is_integer(i): raise ValueError("iAt based indexing can only have integer " "indexers") ...

def to_manager(sdf, columns, index): """ create and return the block manager from a dataframe of series, columns, index """ # from BlockManager perspective axes = [ensure_index(columns), ensure_index(index)] return create_block_manager_from_arrays( [sdf[c] for c in columns], columns, a...

def stack_sparse_frame(frame): """ Only makes sense when fill_value is NaN """ lengths = [s.sp_index.npoints for _, s in frame.items()] nobs = sum(lengths) # this is pretty fast minor_codes = np.repeat(np.arange(len(frame.columns)), lengths) inds_to_concat = [] vals_to_concat = [] ...

def homogenize(series_dict): """ Conform a set of SparseSeries (with NaN fill_value) to a common SparseIndex corresponding to the locations where they all have data Parameters ---------- series_dict : dict or DataFrame Notes ----- Using the dumbest algorithm I could think of. Shoul...

def _init_matrix(self, data, index, columns, dtype=None): """ Init self from ndarray or list of lists. """ data = prep_ndarray(data, copy=False) index, columns = self._prep_index(data, index, columns) data = {idx: data[:, i] for i, idx in enumerate(columns)} retur...

def _init_spmatrix(self, data, index, columns, dtype=None, fill_value=None): """ Init self from scipy.sparse matrix. """ index, columns = self._prep_index(data, index, columns) data = data.tocoo() N = len(index) # Construct a dict of Sparse...

def to_coo(self): """ Return the contents of the frame as a sparse SciPy COO matrix. .. versionadded:: 0.20.0 Returns ------- coo_matrix : scipy.sparse.spmatrix If the caller is heterogeneous and contains booleans or objects, the result will be o...

def _unpickle_sparse_frame_compat(self, state): """ Original pickle format """ series, cols, idx, fv, kind = state if not isinstance(cols, Index): # pragma: no cover from pandas.io.pickle import _unpickle_array columns = _unpickle_array(cols) els...

def to_dense(self): """ Convert to dense DataFrame Returns ------- df : DataFrame """ data = {k: v.to_dense() for k, v in self.items()} return DataFrame(data, index=self.index, columns=self.columns)

def _apply_columns(self, func): """ Get new SparseDataFrame applying func to each columns """ new_data = {col: func(series) for col, series in self.items()} return self._constructor( data=new_data, index=self.index, columns=self.columns, ...

def copy(self, deep=True): """ Make a copy of this SparseDataFrame """ result = super().copy(deep=deep) result._default_fill_value = self._default_fill_value result._default_kind = self._default_kind return result

def density(self): """ Ratio of non-sparse points to total (dense) data points represented in the frame """ tot_nonsparse = sum(ser.sp_index.npoints for _, ser in self.items()) tot = len(self.index) * len(self.columns) return tot_nonspa...

def _sanitize_column(self, key, value, **kwargs): """ Creates a new SparseArray from the input value. Parameters ---------- key : object value : scalar, Series, or array-like kwargs : dict Returns ------- sanitized_column : SparseArray ...

def xs(self, key, axis=0, copy=False): """ Returns a row (cross-section) from the SparseDataFrame as a Series object. Parameters ---------- key : some index contained in the index Returns ------- xs : Series """ if axis == 1: ...

def transpose(self, *args, **kwargs): """ Returns a DataFrame with the rows/columns switched. """ nv.validate_transpose(args, kwargs) return self._constructor( self.values.T, index=self.columns, columns=self.index, default_fill_value=self._default_fill_val...

def cumsum(self, axis=0, *args, **kwargs): """ Return SparseDataFrame of cumulative sums over requested axis. Parameters ---------- axis : {0, 1} 0 for row-wise, 1 for column-wise Returns ------- y : SparseDataFrame """ nv.val...

def apply(self, func, axis=0, broadcast=None, reduce=None, result_type=None): """ Analogous to DataFrame.apply, for SparseDataFrame Parameters ---------- func : function Function to apply to each column axis : {0, 1, 'index', 'columns'} ...

def conda_package_to_pip(package): """ Convert a conda package to its pip equivalent. In most cases they are the same, those are the exceptions: - Packages that should be excluded (in `EXCLUDE`) - Packages that should be renamed (in `RENAME`) - A package requiring a specific version, in conda i...

def main(conda_fname, pip_fname, compare=False): """ Generate the pip dependencies file from the conda file, or compare that they are synchronized (``compare=True``). Parameters ---------- conda_fname : str Path to the conda file with dependencies (e.g. `environment.yml`). pip_fname...

def maybe_convert_platform(values): """ try to do platform conversion, allow ndarray or list here """ if isinstance(values, (list, tuple)): values = construct_1d_object_array_from_listlike(list(values)) if getattr(values, 'dtype', None) == np.object_: if hasattr(values, '_values'): ...

def is_nested_object(obj): """ return a boolean if we have a nested object, e.g. a Series with 1 or more Series elements This may not be necessarily be performant. """ if isinstance(obj, ABCSeries) and is_object_dtype(obj): if any(isinstance(v, ABCSeries) for v in obj.values): ...

def maybe_downcast_to_dtype(result, dtype): """ try to cast to the specified dtype (e.g. convert back to bool/int or could be an astype of float64->float32 """ if is_scalar(result): return result def trans(x): return x if isinstance(dtype, str): if dtype == 'infer': ...

def maybe_upcast_putmask(result, mask, other): """ A safe version of putmask that potentially upcasts the result. The result is replaced with the first N elements of other, where N is the number of True values in mask. If the length of other is shorter than N, other will be repeated. Parameters...