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19,700 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.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... | python | 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... | [
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19,701 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.corrwith | 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
... | python | 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.
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... | [
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19,702 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.count | 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 :... | python | 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 :... | [
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19,703 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.nunique | 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'},... | python | 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'},... | [
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19,704 | pandas-dev/pandas | pandas/core/frame.py | DataFrame._get_agg_axis | 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) | python | 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) | [
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19,705 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.quantile | 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... | python | 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)
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19,706 | pandas-dev/pandas | pandas/core/frame.py | DataFrame.isin | 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'... | python | 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
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19,707 | pandas-dev/pandas | pandas/core/arrays/integer.py | integer_array | 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
------
... | python | 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
------
... | [
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19,708 | pandas-dev/pandas | pandas/core/arrays/integer.py | safe_cast | def safe_cast(values, dtype, copy):
"""
Safely cast the values to the dtype if they
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"""
try:
return values.astype(dtype, casting='safe', copy=copy)
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... | python | def safe_cast(values, dtype, copy):
"""
Safely cast the values to the dtype if they
are equivalent, meaning floats must be equivalent to the
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"""
try:
return values.astype(dtype, casting='safe', copy=copy)
except TypeError:
casted = values.astype(dtype, copy=copy)
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19,709 | pandas-dev/pandas | pandas/core/arrays/integer.py | coerce_to_array | 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
... | python | 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
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19,710 | pandas-dev/pandas | pandas/core/arrays/integer.py | _IntegerDtype.construct_from_string | 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)) | python | 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 "
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19,711 | pandas-dev/pandas | pandas/core/arrays/integer.py | IntegerArray._coerce_to_ndarray | 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 | python | 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
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19,712 | pandas-dev/pandas | pandas/core/arrays/integer.py | IntegerArray.astype | 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... | python | 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
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19,713 | pandas-dev/pandas | pandas/core/arrays/integer.py | IntegerArray.value_counts | 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
... | python | 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
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19,714 | pandas-dev/pandas | pandas/core/arrays/integer.py | IntegerArray._values_for_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
"""
... | python | def _values_for_argsort(self) -> np.ndarray:
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Returns
-------
ndarray
The transformed values should maintain the ordering between values
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See Also
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ExtensionArray.argsort
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Returns
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See Also
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19,715 | pandas-dev/pandas | pandas/core/indexing.py | length_of_indexer | 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
... | python | 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
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19,716 | pandas-dev/pandas | pandas/core/indexing.py | convert_to_index_sliceable | 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
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"""
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')
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# we are an actual column
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19,717 | pandas-dev/pandas | pandas/core/indexing.py | check_setitem_lengths | 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
----... | python | 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.
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19,718 | pandas-dev/pandas | pandas/core/indexing.py | convert_missing_indexer | 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... | python | 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)
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19,719 | pandas-dev/pandas | pandas/core/indexing.py | convert_from_missing_indexer_tuple | 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)
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"""
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
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19,720 | pandas-dev/pandas | pandas/core/indexing.py | maybe_convert_indices | 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... | python | 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.
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19,721 | pandas-dev/pandas | pandas/core/indexing.py | validate_indices | 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... | python | 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
--------
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19,722 | pandas-dev/pandas | pandas/core/indexing.py | maybe_convert_ix | 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 | python | 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)
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return args | [
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19,723 | pandas-dev/pandas | pandas/core/indexing.py | _non_reducing_slice | 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']]
... | python | 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
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19,724 | pandas-dev/pandas | pandas/core/indexing.py | _maybe_numeric_slice | def _maybe_numeric_slice(df, slice_, include_bool=False):
"""
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"""
if slice_ is None:
dtypes = [np.number]
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... | python | def _maybe_numeric_slice(df, slice_, include_bool=False):
"""
want nice defaults for background_gradient that don't break
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19,725 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._has_valid_tuple | 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:
... | python | def _has_valid_tuple(self, key):
""" check the key for valid keys across my indexer """
for i, k in enumerate(key):
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raise IndexingError('Too many indexers')
try:
self._validate_key(k, i)
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19,726 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._has_valid_positional_setitem_indexer | def _has_valid_positional_setitem_indexer(self, indexer):
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will raise if needed, does not modify the indexer externally
"""
if isinstance(indexer, dict):
raise IndexError("{0} cannot enlarge its target object"
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if isinstance(indexer, dict):
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19,727 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._multi_take_opportunity | 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... | python | def _multi_take_opportunity(self, tup):
"""
Check whether there is the possibility to use ``_multi_take``.
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19,728 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._multi_take | 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... | python | def _multi_take(self, tup):
"""
Create the indexers for the passed tuple of keys, and execute the take
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19,729 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._get_listlike_indexer | 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
... | python | 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
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19,730 | pandas-dev/pandas | pandas/core/indexing.py | _NDFrameIndexer._convert_to_indexer | def _convert_to_indexer(self, obj, axis=None, is_setter=False,
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"""
Convert indexing key into something we can use to do actual fancy
indexing on an ndarray
Examples
ix[:5] -> slice(0, 5)
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ix[... | python | def _convert_to_indexer(self, obj, axis=None, is_setter=False,
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"""
Convert indexing key into something we can use to do actual fancy
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19,731 | pandas-dev/pandas | pandas/core/indexing.py | _LocationIndexer._get_slice_axis | 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... | python | def _get_slice_axis(self, slice_obj, axis=None):
""" this is pretty simple as we just have to deal with labels """
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axis = self.axis or 0
obj = self.obj
if not need_slice(slice_obj):
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19,732 | pandas-dev/pandas | pandas/core/indexing.py | _iLocIndexer._validate_integer | 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
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"""
Check that 'key' is a valid position in the desired axis.
Parameters
----------
key : int
Requested position
axis : int
Desired axis
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-------
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19,733 | pandas-dev/pandas | pandas/core/indexing.py | _iLocIndexer._get_list_axis | 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
"""
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"""
Return Series values by list or array of integers
Parameters
----------
key : list-like positional indexer
axis : int (can only be zero)
Returns
-------
Series object
"""
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19,734 | pandas-dev/pandas | pandas/core/indexing.py | _iLocIndexer._convert_to_indexer | def _convert_to_indexer(self, obj, axis=None, is_setter=False):
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if axis is None:
axis = self.axis or 0
# make need to convert a float key
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""" much simpler as we only have to deal with our valid types """
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axis = self.axis or 0
# make need to convert a float key
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19,735 | pandas-dev/pandas | pandas/core/sparse/frame.py | to_manager | 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... | python | 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(
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19,736 | pandas-dev/pandas | pandas/core/sparse/frame.py | stack_sparse_frame | 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 = []
... | python | 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 = []
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19,737 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame._init_matrix | 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... | python | def _init_matrix(self, data, index, columns, dtype=None):
"""
Init self from ndarray or list of lists.
"""
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index, columns = self._prep_index(data, index, columns)
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19,738 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame._init_spmatrix | def _init_spmatrix(self, data, index, columns, dtype=None,
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"""
Init self from scipy.sparse matrix.
"""
index, columns = self._prep_index(data, index, columns)
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19,739 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame.to_coo | 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... | python | def to_coo(self):
"""
Return the contents of the frame as a sparse SciPy COO matrix.
.. versionadded:: 0.20.0
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-------
coo_matrix : scipy.sparse.spmatrix
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19,740 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame._unpickle_sparse_frame_compat | 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... | python | def _unpickle_sparse_frame_compat(self, state):
"""
Original pickle format
"""
series, cols, idx, fv, kind = state
if not isinstance(cols, Index): # pragma: no cover
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columns = _unpickle_array(cols)
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19,741 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame.to_dense | def to_dense(self):
"""
Convert to dense DataFrame
Returns
-------
df : DataFrame
"""
data = {k: v.to_dense() for k, v in self.items()}
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-------
df : DataFrame
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19,742 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame._apply_columns | def _apply_columns(self, func):
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19,743 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame.copy | 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 | python | 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
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19,744 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame._sanitize_column | 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
... | python | def _sanitize_column(self, key, value, **kwargs):
"""
Creates a new SparseArray from the input value.
Parameters
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key : object
value : scalar, Series, or array-like
kwargs : dict
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sanitized_column : SparseArray
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19,745 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame.cumsum | def cumsum(self, axis=0, *args, **kwargs):
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axis : {0, 1}
0 for row-wise, 1 for column-wise
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y : SparseDataFrame
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"""
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19,746 | pandas-dev/pandas | pandas/core/sparse/frame.py | SparseDataFrame.apply | def apply(self, func, axis=0, broadcast=None, reduce=None,
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"""
Analogous to DataFrame.apply, for SparseDataFrame
Parameters
----------
func : function
Function to apply to each column
axis : {0, 1, 'index', 'columns'}
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result_type=None):
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Analogous to DataFrame.apply, for SparseDataFrame
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19,747 | pandas-dev/pandas | scripts/generate_pip_deps_from_conda.py | conda_package_to_pip | 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... | python | 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`)
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19,748 | pandas-dev/pandas | pandas/core/dtypes/cast.py | maybe_convert_platform | 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'):
... | python | 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'):
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19,749 | pandas-dev/pandas | pandas/core/dtypes/cast.py | maybe_upcast_putmask | 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... | python | 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.
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19,750 | pandas-dev/pandas | pandas/core/dtypes/cast.py | infer_dtype_from | 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.
... | python | 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.
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19,751 | pandas-dev/pandas | pandas/core/dtypes/cast.py | infer_dtype_from_scalar | 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
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"""
interpret the dtype from a scalar
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pandas_dtype : bool, default False
whether to infer dtype including pandas extension types.
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19,752 | pandas-dev/pandas | pandas/core/dtypes/cast.py | infer_dtype_from_array | 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.
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"""
infer the dtype from a scalar or array
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arr : scalar or array
pandas_dtype : bool, default False
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19,753 | pandas-dev/pandas | pandas/core/dtypes/cast.py | maybe_infer_dtype_type | def maybe_infer_dtype_type(element):
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Uses `element.dtype` if that's available.
Objects implementing the iterator protocol are cast to a NumPy array,
and from there the array's type is used.
Parameters
----------
element : object
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"""Try to infer an object's dtype, for use in arithmetic ops
Uses `element.dtype` if that's available.
Objects implementing the iterator protocol are cast to a NumPy array,
and from there the array's type is used.
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----------
element : object
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19,754 | pandas-dev/pandas | pandas/core/dtypes/cast.py | maybe_upcast | def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False):
""" provide explicit type promotion and coercion
Parameters
----------
values : the ndarray that we want to maybe upcast
fill_value : what we want to fill with
dtype : if None, then use the dtype of the values, else coerce to ... | python | def maybe_upcast(values, fill_value=np.nan, dtype=None, copy=False):
""" provide explicit type promotion and coercion
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values : the ndarray that we want to maybe upcast
fill_value : what we want to fill with
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19,755 | pandas-dev/pandas | pandas/core/dtypes/cast.py | coerce_indexer_dtype | def coerce_indexer_dtype(indexer, categories):
""" coerce the indexer input array to the smallest dtype possible """
length = len(categories)
if length < _int8_max:
return ensure_int8(indexer)
elif length < _int16_max:
return ensure_int16(indexer)
elif length < _int32_max:
re... | python | def coerce_indexer_dtype(indexer, categories):
""" coerce the indexer input array to the smallest dtype possible """
length = len(categories)
if length < _int8_max:
return ensure_int8(indexer)
elif length < _int16_max:
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19,756 | pandas-dev/pandas | pandas/core/dtypes/cast.py | coerce_to_dtypes | def coerce_to_dtypes(result, dtypes):
"""
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"""
if len(result) != len(dtypes):
raise AssertionError("_coerce_to_dtypes requires equal len arrays")
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try:
if isna(r):
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"""
given a dtypes and a result set, coerce the result elements to the
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19,757 | pandas-dev/pandas | pandas/core/dtypes/cast.py | find_common_type | def find_common_type(types):
"""
Find a common data type among the given dtypes.
Parameters
----------
types : list of dtypes
Returns
-------
pandas extension or numpy dtype
See Also
--------
numpy.find_common_type
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"""
Find a common data type among the given dtypes.
Parameters
----------
types : list of dtypes
Returns
-------
pandas extension or numpy dtype
See Also
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numpy.find_common_type
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pandas extension or numpy dtype
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19,758 | pandas-dev/pandas | pandas/core/dtypes/cast.py | cast_scalar_to_array | def cast_scalar_to_array(shape, value, dtype=None):
"""
create np.ndarray of specified shape and dtype, filled with values
Parameters
----------
shape : tuple
value : scalar value
dtype : np.dtype, optional
dtype to coerce
Returns
-------
ndarray of shape, filled with v... | python | def cast_scalar_to_array(shape, value, dtype=None):
"""
create np.ndarray of specified shape and dtype, filled with values
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----------
shape : tuple
value : scalar value
dtype : np.dtype, optional
dtype to coerce
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19,759 | pandas-dev/pandas | pandas/core/dtypes/cast.py | construct_1d_object_array_from_listlike | def construct_1d_object_array_from_listlike(values):
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Parameters
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values : any iterable which has a len()
Raises
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Returns
... | python | def construct_1d_object_array_from_listlike(values):
"""
Transform any list-like object in a 1-dimensional numpy array of object
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Parameters
----------
values : any iterable which has a len()
Raises
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19,760 | pandas-dev/pandas | pandas/core/dtypes/cast.py | construct_1d_ndarray_preserving_na | def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False):
"""
Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
Parameters
----------
values : Sequence
dtype : numpy.dtype, optional
copy : bool, default False
Note that copies may still be made with ... | python | def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False):
"""
Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
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values : Sequence
dtype : numpy.dtype, optional
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19,761 | pandas-dev/pandas | pandas/plotting/_core.py | scatter_plot | def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False,
**kwargs):
"""
Make a scatter plot from two DataFrame columns
Parameters
----------
data : DataFrame
x : Column name for the x-axis values
y : Column name for the y-axis values
ax : Matplotlib axis... | python | def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False,
**kwargs):
"""
Make a scatter plot from two DataFrame columns
Parameters
----------
data : DataFrame
x : Column name for the x-axis values
y : Column name for the y-axis values
ax : Matplotlib axis... | [
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19,762 | pandas-dev/pandas | pandas/plotting/_core.py | hist_frame | def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
sharey=False, figsize=None, layout=None, bins=10, **kwds):
"""
Make a histogram of the DataFrame's.
A `histogram`_ is a representation of the di... | python | def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
sharey=False, figsize=None, layout=None, bins=10, **kwds):
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Make a histogram of the DataFrame's.
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19,763 | pandas-dev/pandas | pandas/plotting/_core.py | hist_series | def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, figsize=None,
bins=10, **kwds):
"""
Draw histogram of the input series using matplotlib.
Parameters
----------
by : object, optional
If passed, then use... | python | def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, figsize=None,
bins=10, **kwds):
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Draw histogram of the input series using matplotlib.
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19,764 | pandas-dev/pandas | pandas/plotting/_core.py | boxplot_frame_groupby | def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
rot=0, grid=True, ax=None, figsize=None,
layout=None, sharex=False, sharey=True, **kwds):
"""
Make box plots from DataFrameGroupBy data.
Parameters
----------
grouped : ... | python | def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
rot=0, grid=True, ax=None, figsize=None,
layout=None, sharex=False, sharey=True, **kwds):
"""
Make box plots from DataFrameGroupBy data.
Parameters
----------
grouped : ... | [
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19,765 | pandas-dev/pandas | pandas/plotting/_core.py | MPLPlot._has_plotted_object | def _has_plotted_object(self, ax):
"""check whether ax has data"""
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len(ax.artists) != 0 or
len(ax.containers) != 0) | python | def _has_plotted_object(self, ax):
"""check whether ax has data"""
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19,766 | pandas-dev/pandas | pandas/plotting/_core.py | MPLPlot.result | def result(self):
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Return result axes
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19,767 | pandas-dev/pandas | pandas/plotting/_core.py | MPLPlot._post_plot_logic_common | def _post_plot_logic_common(self, ax, data):
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except Exception:
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19,768 | pandas-dev/pandas | pandas/plotting/_core.py | MPLPlot._adorn_subplots | def _adorn_subplots(self):
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19,769 | pandas-dev/pandas | pandas/plotting/_core.py | MPLPlot._apply_style_colors | def _apply_style_colors(self, colors, kwds, col_num, label):
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Manage style and color based on column number and its label.
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"""
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19,770 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.line | def line(self, x=None, y=None, **kwds):
"""
Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
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"""
Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
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19,771 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.bar | def bar(self, x=None, y=None, **kwds):
"""
Vertical bar plot.
A bar plot is a plot that presents categorical data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the pl... | python | def bar(self, x=None, y=None, **kwds):
"""
Vertical bar plot.
A bar plot is a plot that presents categorical data with
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19,772 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.barh | def barh(self, x=None, y=None, **kwds):
"""
Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
... | python | def barh(self, x=None, y=None, **kwds):
"""
Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
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19,773 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.hist | def hist(self, by=None, bins=10, **kwds):
"""
Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
into bins and draws all bins in one :class:`matplotlib.a... | python | def hist(self, by=None, bins=10, **kwds):
"""
Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
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19,774 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.area | def area(self, x=None, y=None, **kwds):
"""
Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
Coordinates for the X axis... | python | def area(self, x=None, y=None, **kwds):
"""
Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
Coordinates for the X axis... | [
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19,775 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.scatter | def scatter(self, x, y, s=None, c=None, **kwds):
"""
Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
useful to see complex... | python | def scatter(self, x, y, s=None, c=None, **kwds):
"""
Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
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19,776 | pandas-dev/pandas | pandas/plotting/_core.py | FramePlotMethods.hexbin | def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
**kwds):
"""
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
(the default), this is a histogram of the number of occurrences
of the obser... | python | def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
**kwds):
"""
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
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19,777 | pandas-dev/pandas | pandas/core/indexes/api.py | _get_combined_index | def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
intersect : bool, default False
If True, calculate the ... | python | def _get_combined_index(indexes, intersect=False, sort=False):
"""
Return the union or intersection of indexes.
Parameters
----------
indexes : list of Index or list objects
When intersect=True, do not accept list of lists.
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19,778 | pandas-dev/pandas | pandas/core/indexes/api.py | _union_indexes | def _union_indexes(indexes, sort=True):
"""
Return the union of indexes.
The behavior of sort and names is not consistent.
Parameters
----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
Returns
... | python | def _union_indexes(indexes, sort=True):
"""
Return the union of indexes.
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----------
indexes : list of Index or list objects
sort : bool, default True
Whether the result index should come out sorted or not.
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19,779 | pandas-dev/pandas | pandas/core/indexes/api.py | _sanitize_and_check | def _sanitize_and_check(indexes):
"""
Verify the type of indexes and convert lists to Index.
Cases:
- [list, list, ...]: Return ([list, list, ...], 'list')
- [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
Lists are sorted and converted to Index.
- [Index, Index, ..... | python | def _sanitize_and_check(indexes):
"""
Verify the type of indexes and convert lists to Index.
Cases:
- [list, list, ...]: Return ([list, list, ...], 'list')
- [list, Index, ...]: Return _sanitize_and_check([Index, Index, ...])
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19,780 | pandas-dev/pandas | pandas/core/indexes/api.py | _get_consensus_names | def _get_consensus_names(indexes):
"""
Give a consensus 'names' to indexes.
If there's exactly one non-empty 'names', return this,
otherwise, return empty.
Parameters
----------
indexes : list of Index objects
Returns
-------
list
A list representing the consensus 'nam... | python | def _get_consensus_names(indexes):
"""
Give a consensus 'names' to indexes.
If there's exactly one non-empty 'names', return this,
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Parameters
----------
indexes : list of Index objects
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19,781 | pandas-dev/pandas | pandas/core/indexes/api.py | _all_indexes_same | def _all_indexes_same(indexes):
"""
Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise.
"""
first = indexes[0]
for index in ind... | python | def _all_indexes_same(indexes):
"""
Determine if all indexes contain the same elements.
Parameters
----------
indexes : list of Index objects
Returns
-------
bool
True if all indexes contain the same elements, False otherwise.
"""
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19,782 | pandas-dev/pandas | pandas/io/sql.py | _convert_params | def _convert_params(sql, params):
"""Convert SQL and params args to DBAPI2.0 compliant format."""
args = [sql]
if params is not None:
if hasattr(params, 'keys'): # test if params is a mapping
args += [params]
else:
args += [list(params)]
return args | python | def _convert_params(sql, params):
"""Convert SQL and params args to DBAPI2.0 compliant format."""
args = [sql]
if params is not None:
if hasattr(params, 'keys'): # test if params is a mapping
args += [params]
else:
args += [list(params)]
return args | [
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19,783 | pandas-dev/pandas | pandas/io/sql.py | _process_parse_dates_argument | def _process_parse_dates_argument(parse_dates):
"""Process parse_dates argument for read_sql functions"""
# handle non-list entries for parse_dates gracefully
if parse_dates is True or parse_dates is None or parse_dates is False:
parse_dates = []
elif not hasattr(parse_dates, '__iter__'):
... | python | def _process_parse_dates_argument(parse_dates):
"""Process parse_dates argument for read_sql functions"""
# handle non-list entries for parse_dates gracefully
if parse_dates is True or parse_dates is None or parse_dates is False:
parse_dates = []
elif not hasattr(parse_dates, '__iter__'):
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19,784 | pandas-dev/pandas | pandas/io/sql.py | _parse_date_columns | def _parse_date_columns(data_frame, parse_dates):
"""
Force non-datetime columns to be read as such.
Supports both string formatted and integer timestamp columns.
"""
parse_dates = _process_parse_dates_argument(parse_dates)
# we want to coerce datetime64_tz dtypes for now to UTC
# we could ... | python | def _parse_date_columns(data_frame, parse_dates):
"""
Force non-datetime columns to be read as such.
Supports both string formatted and integer timestamp columns.
"""
parse_dates = _process_parse_dates_argument(parse_dates)
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19,785 | pandas-dev/pandas | pandas/io/sql.py | _wrap_result | def _wrap_result(data, columns, index_col=None, coerce_float=True,
parse_dates=None):
"""Wrap result set of query in a DataFrame."""
frame = DataFrame.from_records(data, columns=columns,
coerce_float=coerce_float)
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frame = DataFrame.from_records(data, columns=columns,
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19,786 | pandas-dev/pandas | pandas/io/sql.py | execute | def execute(sql, con, cur=None, params=None):
"""
Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
Using SQLAlchemy makes it possib... | python | def execute(sql, con, cur=None, params=None):
"""
Execute the given SQL query using the provided connection object.
Parameters
----------
sql : string
SQL query to be executed.
con : SQLAlchemy connectable(engine/connection) or sqlite3 connection
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Using SQLAlchemy makes it possible to use any DB supported by the
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19,787 | pandas-dev/pandas | pandas/io/sql.py | has_table | def has_table(table_name, con, schema=None):
"""
Check if DataBase has named table.
Parameters
----------
table_name: string
Name of SQL table.
con: SQLAlchemy connectable(engine/connection) or sqlite3 DBAPI2 connection
Using SQLAlchemy makes it possible to use any DB supported ... | python | def has_table(table_name, con, schema=None):
"""
Check if DataBase has named table.
Parameters
----------
table_name: string
Name of SQL table.
con: SQLAlchemy connectable(engine/connection) or sqlite3 DBAPI2 connection
Using SQLAlchemy makes it possible to use any DB supported ... | [
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19,788 | pandas-dev/pandas | pandas/io/sql.py | pandasSQL_builder | def pandasSQL_builder(con, schema=None, meta=None,
is_cursor=False):
"""
Convenience function to return the correct PandasSQL subclass based on the
provided parameters.
"""
# When support for DBAPI connections is removed,
# is_cursor should not be necessary.
con = _engi... | python | def pandasSQL_builder(con, schema=None, meta=None,
is_cursor=False):
"""
Convenience function to return the correct PandasSQL subclass based on the
provided parameters.
"""
# When support for DBAPI connections is removed,
# is_cursor should not be necessary.
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19,789 | pandas-dev/pandas | pandas/io/sql.py | get_schema | def get_schema(frame, name, keys=None, con=None, dtype=None):
"""
Get the SQL db table schema for the given frame.
Parameters
----------
frame : DataFrame
name : string
name of SQL table
keys : string or sequence, default: None
columns to use a primary key
con: an open S... | python | def get_schema(frame, name, keys=None, con=None, dtype=None):
"""
Get the SQL db table schema for the given frame.
Parameters
----------
frame : DataFrame
name : string
name of SQL table
keys : string or sequence, default: None
columns to use a primary key
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19,790 | pandas-dev/pandas | pandas/io/sql.py | SQLTable._execute_insert | def _execute_insert(self, conn, keys, data_iter):
"""Execute SQL statement inserting data
Parameters
----------
conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
keys : list of str
Column names
data_iter : generator of list
Each item c... | python | def _execute_insert(self, conn, keys, data_iter):
"""Execute SQL statement inserting data
Parameters
----------
conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection
keys : list of str
Column names
data_iter : generator of list
Each item c... | [
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19,791 | pandas-dev/pandas | pandas/io/sql.py | SQLTable._query_iterator | def _query_iterator(self, result, chunksize, columns, coerce_float=True,
parse_dates=None):
"""Return generator through chunked result set."""
while True:
data = result.fetchmany(chunksize)
if not data:
break
else:
... | python | def _query_iterator(self, result, chunksize, columns, coerce_float=True,
parse_dates=None):
"""Return generator through chunked result set."""
while True:
data = result.fetchmany(chunksize)
if not data:
break
else:
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19,792 | pandas-dev/pandas | pandas/io/sql.py | SQLTable._harmonize_columns | def _harmonize_columns(self, parse_dates=None):
"""
Make the DataFrame's column types align with the SQL table
column types.
Need to work around limited NA value support. Floats are always
fine, ints must always be floats if there are Null values.
Booleans are hard becaus... | python | def _harmonize_columns(self, parse_dates=None):
"""
Make the DataFrame's column types align with the SQL table
column types.
Need to work around limited NA value support. Floats are always
fine, ints must always be floats if there are Null values.
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19,793 | pandas-dev/pandas | pandas/io/sql.py | SQLiteTable._create_table_setup | def _create_table_setup(self):
"""
Return a list of SQL statements that creates a table reflecting the
structure of a DataFrame. The first entry will be a CREATE TABLE
statement while the rest will be CREATE INDEX statements.
"""
column_names_and_types = self._get_column... | python | def _create_table_setup(self):
"""
Return a list of SQL statements that creates a table reflecting the
structure of a DataFrame. The first entry will be a CREATE TABLE
statement while the rest will be CREATE INDEX statements.
"""
column_names_and_types = self._get_column... | [
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19,794 | pandas-dev/pandas | pandas/core/arrays/categorical.py | _maybe_to_categorical | def _maybe_to_categorical(array):
"""
Coerce to a categorical if a series is given.
Internal use ONLY.
"""
if isinstance(array, (ABCSeries, ABCCategoricalIndex)):
return array._values
elif isinstance(array, np.ndarray):
return Categorical(array)
return array | python | def _maybe_to_categorical(array):
"""
Coerce to a categorical if a series is given.
Internal use ONLY.
"""
if isinstance(array, (ABCSeries, ABCCategoricalIndex)):
return array._values
elif isinstance(array, np.ndarray):
return Categorical(array)
return array | [
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19,795 | pandas-dev/pandas | pandas/core/arrays/categorical.py | contains | def contains(cat, key, container):
"""
Helper for membership check for ``key`` in ``cat``.
This is a helper method for :method:`__contains__`
and :class:`CategoricalIndex.__contains__`.
Returns True if ``key`` is in ``cat.categories`` and the
location of ``key`` in ``categories`` is in ``conta... | python | def contains(cat, key, container):
"""
Helper for membership check for ``key`` in ``cat``.
This is a helper method for :method:`__contains__`
and :class:`CategoricalIndex.__contains__`.
Returns True if ``key`` is in ``cat.categories`` and the
location of ``key`` in ``categories`` is in ``conta... | [
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This is a helper method for :method:`__contains__`
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Returns True if ``key`` is in ``cat.categories`` and the
location of ``key`` in ``categories`` is in ``container``.
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----------
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19,796 | pandas-dev/pandas | pandas/core/arrays/categorical.py | _get_codes_for_values | def _get_codes_for_values(values, categories):
"""
utility routine to turn values into codes given the specified categories
"""
from pandas.core.algorithms import _get_data_algo, _hashtables
dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
if dtype_equal:
# To prevent errone... | python | def _get_codes_for_values(values, categories):
"""
utility routine to turn values into codes given the specified categories
"""
from pandas.core.algorithms import _get_data_algo, _hashtables
dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
if dtype_equal:
# To prevent errone... | [
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19,797 | pandas-dev/pandas | pandas/core/arrays/categorical.py | _recode_for_categories | def _recode_for_categories(codes, old_categories, new_categories):
"""
Convert a set of codes for to a new set of categories
Parameters
----------
codes : array
old_categories, new_categories : Index
Returns
-------
new_codes : array
Examples
--------
>>> old_cat = pd.... | python | def _recode_for_categories(codes, old_categories, new_categories):
"""
Convert a set of codes for to a new set of categories
Parameters
----------
codes : array
old_categories, new_categories : Index
Returns
-------
new_codes : array
Examples
--------
>>> old_cat = pd.... | [
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19,798 | pandas-dev/pandas | pandas/core/arrays/categorical.py | _factorize_from_iterable | def _factorize_from_iterable(values):
"""
Factorize an input `values` into `categories` and `codes`. Preserves
categorical dtype in `categories`.
*This is an internal function*
Parameters
----------
values : list-like
Returns
-------
codes : ndarray
categories : Index
... | python | def _factorize_from_iterable(values):
"""
Factorize an input `values` into `categories` and `codes`. Preserves
categorical dtype in `categories`.
*This is an internal function*
Parameters
----------
values : list-like
Returns
-------
codes : ndarray
categories : Index
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19,799 | pandas-dev/pandas | pandas/core/arrays/categorical.py | _factorize_from_iterables | def _factorize_from_iterables(iterables):
"""
A higher-level wrapper over `_factorize_from_iterable`.
*This is an internal function*
Parameters
----------
iterables : list-like of list-likes
Returns
-------
codes_list : list of ndarrays
categories_list : list of Indexes
N... | python | def _factorize_from_iterables(iterables):
"""
A higher-level wrapper over `_factorize_from_iterable`.
*This is an internal function*
Parameters
----------
iterables : list-like of list-likes
Returns
-------
codes_list : list of ndarrays
categories_list : list of Indexes
N... | [
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