INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Ensure that if we don't have an index, that we can create one from the
passed value. | 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 =... |
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
Insertion index. Must verify 0 <= loc <= len(columns)
column ... | 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... |
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 Series}
The column names are... | 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... |
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
broadcast : bool, default True
If ``key`` matches multiple duplicate col... | 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... |
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 : sequence
The row labels to use for lookup
col_lab... | 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... |
We are guaranteed non-Nones in the axes. | 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... |
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. When using a
multi-index, labels on different levels can be removed by specifying
the level.
Parameters
... | 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... |
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.
Parameters
----------
mapper : dict-like... | 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... |
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 replace the
existing index or expand on it.
Parameters
----------
keys : label or array-like or list ... | 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... |
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
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine if rows or columns which contain miss... | 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
----------
... |
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
levels.
Parameters
----------
level : int, str, tuple, or list, default None
Only remo... | 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
... |
Return DataFrame with duplicate rows removed, optionally only
considering certain columns. Indexes, including time indexes
are ignored.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicate... | 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... |
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 identifying duplicates, by
default use all of the columns
... | 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... |
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 ordering.
This method is equivalent to
``df.sort_va... | 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... |
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 ordering.
This method is equivalent to
``df.sort_val... | 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... |
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
-------
DataFrame
.. versionchanged:: 0.18.1
The index... | 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... |
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
(position) or by key (label).
axis : int
Where to... | 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... |
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 union of the two.
Parameters
----------
other : DataFrame
... | 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... |
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 union of the two.
Parameters
-----... | 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... |
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coercible into a DataFrame
Should have at least one matching index/column label
with the original DataFram... | 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... |
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 pivoting the
columns of the current dataframe:
... | 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... |
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : 1,2
requested ndim of result
subset : object, default None
subset to act on | 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
----------
... |
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 DataFrame's columns
(``axis=1``). By default (``result_type=None``), the final return type
is inferred from the return type ... | 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 ... |
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 will be a Series
(the analogue of stack when the columns are ... | 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... |
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 : DataFrame or Series/dict-like object, or list of these
The data to append.
ignore_index : boole... | 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 : ... |
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 from a single value.
Returns
... | 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... |
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
----------
periods : int, default 1
Periods to s... | 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
----------
... |
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 list.
Parameters
----------
other : DataFrame, Series, or list of DataFrame
I... | 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... |
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 same number of places.
Otherwise dict and Series round ... | 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... |
Compute pairwise correlation of columns, excluding NA/null values.
Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman... | 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 : ... |
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 columns
of the DataFrame.
Both NA and... | 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... |
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
----------
other : DataFrame, Series
Object with which to comput... | 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
... |
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 : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index' counts... | 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 :... |
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'}, default 0
The axis to use. 0 or 'index' for row... | 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'},... |
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 column-wise
skipna : boolean, default True
E... | 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... |
Let's be explicit about this. | 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) |
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 'columns'}, default 0
The axis to iterate over while searching for ... | 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... |
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 quantile(s) to compute.
axis : {0, 1, 'index', 'columns'} (default 0)
Equals 0 or 'index' for row-wis... | 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... |
Cast to DatetimeIndex of timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. en... | 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'}
... |
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's the index. If
`values` is a di... | 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'... |
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
------
TypeError if incompatible types | 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
------
... |
Safely cast the values to the dtype if they
are equivalent, meaning floats must be equivalent to the
ints. | 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)
... |
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
Returns
-------
tuple of (values, mask) | 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
... |
Construction from a string, raise a TypeError if not
possible | 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)) |
coerce to an ndarary of object dtype | 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 |
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 not necessary. If False,
a copy is made only i... | 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... |
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
-------
counts : Series
See Also... | 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
... |
Return values for sorting.
Returns
-------
ndarray
The transformed values should maintain the ordering between values
within the array.
See Also
--------
ExtensionArray.argsort | 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
"""
... |
Parameters
----------
result : array-like
mask : array-like bool
other : scalar or array-like
op_name : str | 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... |
return the length of a single non-tuple indexer which could be a slice | 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
... |
if we are index sliceable, then return my slicer, otherwise return None | 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
... |
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
----------
indexer : sequence
The key for the setitem
... | 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
----... |
reverse convert a missing indexer, which is a dict
return the scalar indexer and a boolean indicating if we converted | 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... |
create a filtered indexer that doesn't have any missing indexers | 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, _... |
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 indices that we are to convert.
n : int
The ... | 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... |
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
--------
>>> validate_indices([1, 2], 3)
# OK
>>> valid... | 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... |
We likely want to take the cross-product | 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 |
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. | 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']]
... |
want nice defaults for background_gradient that don't break
with non-numeric data. But if slice_ is passed go with that. | 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)
... |
check the key for valid keys across my indexer | 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:
... |
validate that an positional indexer cannot enlarge its target
will raise if needed, does not modify the indexer externally | 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"
... |
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 specified by `indexer`
multiindex_indexer : boolean, optional
Defau... | 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... |
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 axis
Returns
-------
boolean: Whet... | 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... |
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 : tuple
Tuple of indexers, one per axis... | 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... |
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
raise_missing: bool
Whether to raise a KeyError if some labels are not f... | 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
... |
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
Dimension on which the indexing is being made
R... | 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
... |
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 labels (only used to show correct error message)
indexer: array-like of booleans
... | 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... |
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 indexing is being made
Returns
-------
list-... | 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... |
this is pretty simple as we just have to deal with labels | 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... |
Translate any partial string timestamp matches in key, returning the
new key (GH 10331) | 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 ... |
Check that 'key' is a valid position in the desired axis.
Parameters
----------
key : int
Requested position
axis : int
Desired axis
Returns
-------
None
Raises
------
IndexError
If 'key' is not a vali... | 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
... |
Return Series values by list or array of integers
Parameters
----------
key : list-like positional indexer
axis : int (can only be zero)
Returns
-------
Series object | 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... |
much simpler as we only have to deal with our valid types | 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... |
require they keys to be the same type as the index (so we don't
fallback) | 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():
... |
require integer args (and convert to label arguments) | 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")
... |
create and return the block manager from a dataframe of series,
columns, index | 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... |
Only makes sense when fill_value is NaN | 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 = []
... |
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. Should put some more thought
into this
... | 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... |
Init self from ndarray or list of lists. | 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... |
Init self from scipy.sparse matrix. | 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... |
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 of dtype=object. See Notes.
No... | 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... |
Original pickle format | 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... |
Convert to dense DataFrame
Returns
-------
df : DataFrame | 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) |
Get new SparseDataFrame applying func to each 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,
... |
Make a copy of this SparseDataFrame | 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 |
Ratio of non-sparse points to total (dense) data points
represented in the frame | 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... |
Creates a new SparseArray from the input value.
Parameters
----------
key : object
value : scalar, Series, or array-like
kwargs : dict
Returns
-------
sanitized_column : SparseArray | 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
... |
Returns a row (cross-section) from the SparseDataFrame as a Series
object.
Parameters
----------
key : some index contained in the index
Returns
-------
xs : Series | 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:
... |
Returns a DataFrame with the rows/columns switched. | 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... |
Return SparseDataFrame of cumulative sums over requested axis.
Parameters
----------
axis : {0, 1}
0 for row-wise, 1 for column-wise
Returns
-------
y : SparseDataFrame | 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... |
Analogous to DataFrame.apply, for SparseDataFrame
Parameters
----------
func : function
Function to apply to each column
axis : {0, 1, 'index', 'columns'}
broadcast : bool, default False
For aggregation functions, return object of same size with values
... | 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'}
... |
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 is defined with a single
equal (e.g. ``pan... | 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... |
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 : str
Path to the pip file with dependencies (e.g. `... | 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... |
try to do platform conversion, allow ndarray or list here | 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'):
... |
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. | 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):
... |
try to cast to the specified dtype (e.g. convert back to bool/int
or could be an astype of float64->float32 | 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':
... |
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
----------
result : ndarray
The destinatio... | 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... |
interpret the dtype from a scalar or array. This is a convenience
routines to infer dtype from a scalar or an array
Parameters
----------
pandas_dtype : bool, default False
whether to infer dtype including pandas extension types.
If False, scalar/array belongs to pandas extension types ... | 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.
... |
interpret the dtype from a scalar
Parameters
----------
pandas_dtype : bool, default False
whether to infer dtype including pandas extension types.
If False, scalar belongs to pandas extension types is inferred as
object | def infer_dtype_from_scalar(val, pandas_dtype=False):
"""
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... |
infer the dtype from a scalar or array
Parameters
----------
arr : scalar or array
pandas_dtype : bool, default False
whether to infer dtype including pandas extension types.
If False, array belongs to pandas extension types
is inferred as object
Returns
-------
tup... | 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... |
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