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train | _maybe_convert_usecols | Convert `usecols` into a compatible format for parsing in `parsers.py`.
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usecols : object
The use-columns object to potentially convert.
Returns
-------
converted : object
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Convert `usecols` into a compatible format for parsing in `parsers.py`.
Parameters
----------
usecols : object
The use-columns object to potentially convert.
Returns
-------
converted : object
The compatible format of `usecols`.
... | def _maybe_convert_usecols(usecols):
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Convert `usecols` into a compatible format for parsing in `parsers.py`.
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usecols : object
The use-columns object to potentially convert.
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The compatible format of `usecols`.
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train | _fill_mi_header | Forward fill blank entries in row but only inside the same parent index.
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List of items in a single row.
control_row : list of bool
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train | _pop_header_name | Pop the header name for MultiIndex parsing.
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train | _ensure_scope | Ensure that we are grabbing the correct scope. | pandas/core/computation/scope.py | def _ensure_scope(level, global_dict=None, local_dict=None, resolvers=(),
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train | _replacer | Replace a number with its hexadecimal representation. Used to tag
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train | _raw_hex_id | Return the padded hexadecimal id of ``obj``. | pandas/core/computation/scope.py | def _raw_hex_id(obj):
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train | _get_pretty_string | Return a prettier version of obj
Parameters
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obj : object
Object to pretty print
Returns
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s : str
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Parameters
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obj : object
Object to pretty print
Returns
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s : str
Pretty print object repr
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obj : object
Object to pretty print
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s : str
Pretty print object repr
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train | Scope.resolve | Resolve a variable name in a possibly local context
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key : str
A variable name
is_local : bool
Flag indicating whether the variable is local or not (prefixed with
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Returns
-------
value : ob... | pandas/core/computation/scope.py | def resolve(self, key, is_local):
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Parameters
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key : str
A variable name
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Parameters
----------
key : str
A variable name
is_local : bool
Flag indicating whether the variable is local or not (prefixed with
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train | Scope.swapkey | Replace a variable name, with a potentially new value.
Parameters
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old_key : str
Current variable name to replace
new_key : str
New variable name to replace `old_key` with
new_value : object
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Current variable name to replace
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train | Scope._get_vars | Get specifically scoped variables from a list of stack frames.
Parameters
----------
stack : list
A list of stack frames as returned by ``inspect.stack()``
scopes : sequence of strings
A sequence containing valid stack frame attribute names that
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stack : list
A list of stack frames as returned by ``inspect.stack()``
scopes : sequence of strings
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train | Scope.update | Update the current scope by going back `level` levels.
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train | Scope.add_tmp | Add a temporary variable to the scope.
Parameters
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value : object
An arbitrary object to be assigned to a temporary variable.
Returns
-------
name : basestring
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Parameters
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value : object
An arbitrary object to be assigned to a temporary variable.
Returns
-------
name : basestring
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An arbitrary object to be assigned to a temporary variable.
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train | Scope.full_scope | Return the full scope for use with passing to engines transparently
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vars : DeepChainMap
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train | read_sas | Read SAS files stored as either XPORT or SAS7BDAT format files.
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filepath_or_buffer : string or file-like object
Path to the SAS file.
format : string {'xport', 'sas7bdat'} or None
If None, file format is inferred from file extension. If 'xport' or
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Read SAS files stored as either XPORT or SAS7BDAT format files.
Parameters
----------
filepath_or_buffer : string or file-like object
Path to the SAS file.
format :... | def read_sas(filepath_or_buffer, format=None, index=None, encoding=None,
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Read SAS files stored as either XPORT or SAS7BDAT format files.
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train | _coerce_method | Install the scalar coercion methods. | pandas/core/series.py | def _coerce_method(converter):
"""
Install the scalar coercion methods.
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Install the scalar coercion methods.
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Data used to populate the new Series
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Derive the "_data" and "index" attributes of a new Series from a
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Data used to populate the new Series
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train | Series.from_array | Construct Series from array.
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.. deprecated :: 0.23.0
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train | Series.compress | Return selected slices of an array along given axis as a Series.
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"""
Return selected slices of an array along given axis as a Series.
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Return selected slices of an array along given axis as a Series.
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train | Series.nonzero | Return the *integer* indices of the elements that are non-zero.
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Return the *integer* indices of the elements that are non-zero.
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Return the *integer* indices of the elements that are non-zero.
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train | Series.view | Create a new view of the Series.
This function will return a new Series with a view of the same
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type. The new data type must preserve the same size in bytes as to not
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... | pandas/core/series.py | def view(self, dtype=None):
"""
Create a new view of the Series.
This function will return a new Series with a view of the same
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Create a new view of the Series.
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train | Series._ixs | Return the i-th value or values in the Series by location.
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i : int, slice, or sequence of integers
Returns
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train | Series.repeat | Repeat elements of a Series.
Returns a new Series where each element of the current Series
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Parameters
----------
repeats : int or array of ints
The number of repetitions for each element. This should be a
... | pandas/core/series.py | def repeat(self, repeats, axis=None):
"""
Repeat elements of a Series.
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Parameters
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repeats : int or array of ints
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"""
Repeat elements of a Series.
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train | Series.reset_index | Generate a new DataFrame or Series with the index reset.
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when the index is meaningless and needs to be reset to the default
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Parameters
----------
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Generate a new DataFrame or Series with the index reset.
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train | Series.to_string | Render a string representation of the Series.
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buf : StringIO-like, optional
Buffer to write to.
na_rep : str, optional
String representation of NaN to use, default 'NaN'.
float_format : one-parameter function, optional
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"""
Render a string representation of the Series.
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Render a string representation of the Series.
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into : class, default dict
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Convert Series to {label -> value} dict or dict-like object.
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Parameters
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name : object, default None
The passed name should substitute for the series name (if it has
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Returns
-------
DataFrame
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... | pandas/core/series.py | def to_frame(self, name=None):
"""
Convert Series to DataFrame.
Parameters
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name : object, default None
The passed name should substitute for the series name (if it has
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Returns
-------
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name : object, default None
The passed name should substitute for the series name (if it has
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-------
DataFrame
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train | Series.to_sparse | Convert Series to SparseSeries.
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kind : {'block', 'integer'}, default 'block'
fill_value : float, defaults to NaN (missing)
Value to use for filling NaN values.
Returns
-------
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"""
Convert Series to SparseSeries.
Parameters
----------
kind : {'block', 'integer'}, default 'block'
fill_value : float, defaults to NaN (missing)
Value to use for filling NaN values.
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Convert Series to SparseSeries.
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kind : {'block', 'integer'}, default 'block'
fill_value : float, defaults to NaN (missing)
Value to use for filling NaN values.
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train | Series._set_name | Set the Series name.
Parameters
----------
name : str
inplace : bool
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"""
Set the Series name.
Parameters
----------
name : str
inplace : bool
whether to modify `self` directly or return a copy
"""
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train | Series.count | Return number of non-NA/null observations in the Series.
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level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a smaller Series.
Returns
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Return number of non-NA/null observations in the Series.
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Number of decimal places to round to (default: 0).
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train | Series.quantile | Return value at the given quantile.
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interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
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train | Series.corr | Compute correlation with `other` Series, excluding missing values.
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Series with which to compute the covariance.
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Minimum number of observations needed to have a valid result.
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Compute covariance with Series, excluding missing values.
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train | Series.append | Concatenate two or more Series.
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to_append : Series or list/tuple of Series
Series to append with self.
ignore_index : bool, default False
If True, do not use the index labels.
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Concatenate two or more Series.
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train | Series._binop | Perform generic binary operation with optional fill value.
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other : Series
func : binary operator
fill_value : float or object
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Perform generic binary operation with optional fill value.
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train | Series.combine | Combine the Series with a Series or scalar according to `func`.
Combine the Series and `other` using `func` to perform elementwise
selection for combined Series.
`fill_value` is assumed when value is missing at some index
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other : Series
The value(s) to be combined with the `Series`.
Returns
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Series
The result of combining the Series with the other object.
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Combine Series values, choosing the calling Series's values first.
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train | Series.update | Modify Series in place using non-NA values from passed
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other : Series
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>>> s = pd.Series([1, 2, 3])
>>> s.update(pd.Series([4, 5, 6]))
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other : Series
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>>> s = pd.Series([1, 2, 3])
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train | Series.sort_values | Sort by the values.
Sort a Series in ascending or descending order by some
criterion.
Parameters
----------
axis : {0 or 'index'}, default 0
Axis to direct sorting. The value 'index' is accepted for
compatibility with DataFrame.sort_values.
ascen... | pandas/core/series.py | def sort_values(self, axis=0, ascending=True, inplace=False,
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"""
Sort by the values.
Sort a Series in ascending or descending order by some
criterion.
Parameters
----------
axis : {0 or 'index'}, default... | def sort_values(self, axis=0, ascending=True, inplace=False,
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Sort by the values.
Sort a Series in ascending or descending order by some
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train | Series.sort_index | Sort Series by index labels.
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Axis to direct sorting. This can only be 0 for Series.
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train | Series.argsort | Override ndarray.argsort. Argsorts the value, omitting NA/null values,
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axis : int
Has no effect but is accepted for compatibility with numpy.
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train | Series.nlargest | Return the largest `n` elements.
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Return this many descending sorted values.
keep : {'first', 'last', 'all'}, default 'first'
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Return the largest `n` elements.
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train | Series.nsmallest | Return the smallest `n` elements.
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Return this many ascending sorted values.
keep : {'first', 'last', 'all'}, default 'first'
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Return this many ascending sorted values.
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train | Series.swaplevel | Swap levels i and j in a MultiIndex.
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----------
i, j : int, str (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
Series
Series with levels swapped in MultiIndex.
.. versionchanged:: 0... | pandas/core/series.py | def swaplevel(self, i=-2, j=-1, copy=True):
"""
Swap levels i and j in a MultiIndex.
Parameters
----------
i, j : int, str (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
Series
Series w... | def swaplevel(self, i=-2, j=-1, copy=True):
"""
Swap levels i and j in a MultiIndex.
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----------
i, j : int, str (can be mixed)
Level of index to be swapped. Can pass level name as string.
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train | Series.reorder_levels | Rearrange index levels using input order.
May not drop or duplicate levels.
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----------
order : list of int representing new level order
(reference level by number or key)
Returns
-------
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Rearrange index levels using input order.
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----------
order : list of int representing new level order
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-------
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Rearrange index levels using input order.
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-------
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train | Series.map | Map values of Series according to input correspondence.
Used for substituting each value in a Series with another value,
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arg : function, dict, or Series
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Map values of Series according to input correspondence.
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Map values of Series according to input correspondence.
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Can be ufunc (a NumPy function that applies to the entire Series)
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Parameters
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func : function
Python function or NumPy ufunc to apply.
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Invoke function on values of Series.
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Invoke function on values of Series.
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train | Series._reduce | Perform a reduction operation.
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train | Series.rename | Alter Series index labels or name.
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a dict / Series will be left as-is. Extra labels listed don't throw an
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Alternatively, change ``Series.name`` with a scalar value.
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train | Series.reindex_axis | Conform Series to new index with optional filling logic.
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"""
Conform Series to new index with optional filling logic.
.. deprecated:: 0.21.0
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"""
# for compatibility with higher dims
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Conform Series to new index with optional filling logic.
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train | Series.memory_usage | Return the memory usage of the Series.
The memory usage can optionally include the contribution of
the index and of elements of `object` dtype.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the Series index.
... | pandas/core/series.py | def memory_usage(self, index=True, deep=False):
"""
Return the memory usage of the Series.
The memory usage can optionally include the contribution of
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Parameters
----------
index : bool, default True
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Return the memory usage of the Series.
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train | Series.isin | Check whether `values` are contained in Series.
Return a boolean Series showing whether each element in the Series
matches an element in the passed sequence of `values` exactly.
Parameters
----------
values : set or list-like
The sequence of values to test. Passing ... | pandas/core/series.py | def isin(self, values):
"""
Check whether `values` are contained in Series.
Return a boolean Series showing whether each element in the Series
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Parameters
----------
values : set or list-like
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Check whether `values` are contained in Series.
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----------
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train | Series.between | Return boolean Series equivalent to left <= series <= right.
This function returns a boolean vector containing `True` wherever the
corresponding Series element is between the boundary values `left` and
`right`. NA values are treated as `False`.
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----------
lef... | pandas/core/series.py | def between(self, left, right, inclusive=True):
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Return boolean Series equivalent to left <= series <= right.
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train | Series.from_csv | Read CSV file.
.. deprecated:: 0.21.0
Use :func:`pandas.read_csv` instead.
It is preferable to use the more powerful :func:`pandas.read_csv`
for most general purposes, but ``from_csv`` makes for an easy
roundtrip to and from a file (the exact counterpart of
``to_csv... | pandas/core/series.py | def from_csv(cls, path, sep=',', parse_dates=True, header=None,
index_col=0, encoding=None, infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`pandas.read_csv` instead.
It is preferable to use the more powerful :func:`pandas.... | def from_csv(cls, path, sep=',', parse_dates=True, header=None,
index_col=0, encoding=None, infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`pandas.read_csv` instead.
It is preferable to use the more powerful :func:`pandas.... | [
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train | Series.dropna | Return a new Series with missing values removed.
See the :ref:`User Guide <missing_data>` for more on which values are
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Parameters
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axis : {0 or 'index'}, default 0
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Return a new Series with missing values removed.
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Return a new Series with missing values removed.
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train | Series.valid | Return Series without null values.
.. deprecated:: 0.23.0
Use :meth:`Series.dropna` instead. | pandas/core/series.py | def valid(self, inplace=False, **kwargs):
"""
Return Series without null values.
.. deprecated:: 0.23.0
Use :meth:`Series.dropna` instead.
"""
warnings.warn("Method .valid will be removed in a future version. "
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"""
Return Series without null values.
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train | Series.to_timestamp | Cast to DatetimeIndex of Timestamps, at *beginning* of period.
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freq : str, default frequency of PeriodIndex
Desired frequency.
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Cast to DatetimeIndex of Timestamps, at *beginning* of period.
Parameters
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freq : str, default frequency of PeriodIndex
Desired frequency.
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Cast to DatetimeIndex of Timestamps, at *beginning* of period.
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freq : str, default frequency of PeriodIndex
Desired frequency.
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Frequency associated with the PeriodIndex.
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freq : str, default None
Frequency associated with the PeriodIndex.
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Convert Series from DatetimeIndex to PeriodIndex with desired
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freq : str, default None
Frequency associated with the PeriodIndex.
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train | to_numeric | Convert argument to a numeric type.
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train | _get_fill | Create a 0-dim ndarray containing the fill value
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arr : SparseArray
Returns
-------
fill_value : ndarray
0-dim ndarray with just the fill value.
Notes
-----
coerce fill_value to arr dtype if possible
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Create a 0-dim ndarray containing the fill value
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----------
arr : SparseArray
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-------
fill_value : ndarray
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Notes
-----
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Create a 0-dim ndarray containing the fill value
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arr : SparseArray
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train | _sparse_array_op | Perform a binary operation between two arrays.
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left : Union[SparseArray, ndarray]
right : Union[SparseArray, ndarray]
op : Callable
The binary operation to perform
name str
Name of the callable.
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right: ABCSparseArray,
op: Callable,
name: str
) -> Any:
"""
Perform a binary operation between two arrays.
Parameters
----------
left : Union[SparseArray, ndarray]
right : Union[SparseArray, ndarray]
op : Callable
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name: str
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Perform a binary operation between two arrays.
Parameters
----------
left : Union[SparseArray, ndarray]
right : Union[SparseArray, ndarray]
op : Callable
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train | _wrap_result | wrap op result to have correct dtype | pandas/core/arrays/sparse.py | def _wrap_result(name, data, sparse_index, fill_value, dtype=None):
"""
wrap op result to have correct dtype
"""
if name.startswith('__'):
# e.g. __eq__ --> eq
name = name[2:-2]
if name in ('eq', 'ne', 'lt', 'gt', 'le', 'ge'):
dtype = np.bool
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"""
wrap op result to have correct dtype
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# e.g. __eq__ --> eq
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train | _maybe_to_sparse | array must be SparseSeries or SparseArray | pandas/core/arrays/sparse.py | def _maybe_to_sparse(array):
"""
array must be SparseSeries or SparseArray
"""
if isinstance(array, ABCSparseSeries):
array = array.values.copy()
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"""
array must be SparseSeries or SparseArray
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train | _sanitize_values | return an ndarray for our input,
in a platform independent manner | pandas/core/arrays/sparse.py | def _sanitize_values(arr):
"""
return an ndarray for our input,
in a platform independent manner
"""
if hasattr(arr, 'values'):
arr = arr.values
else:
# scalar
if is_scalar(arr):
arr = [arr]
# ndarray
if isinstance(arr, np.ndarray):
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"""
return an ndarray for our input,
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# scalar
if is_scalar(arr):
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train | make_sparse | Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
dtype : np.dtype, optional
copy : bool, default False
Returns
-------
(sparse_values, index, fill_value) : (ndarray, SparseIndex, Scalar) | pandas/core/arrays/sparse.py | def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False):
"""
Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
dtype : np.dtype, optional
copy : bool, default False
Returns
... | def make_sparse(arr, kind='block', fill_value=None, dtype=None, copy=False):
"""
Convert ndarray to sparse format
Parameters
----------
arr : ndarray
kind : {'block', 'integer'}
fill_value : NaN or another value
dtype : np.dtype, optional
copy : bool, default False
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train | SparseArray.density | The percent of non- ``fill_value`` points, as decimal.
Examples
--------
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
>>> s.density
0.6 | pandas/core/arrays/sparse.py | def density(self):
"""
The percent of non- ``fill_value`` points, as decimal.
Examples
--------
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
>>> s.density
0.6
"""
r = float(self.sp_index.npoints) / float(self.sp_index.length)
return ... | def density(self):
"""
The percent of non- ``fill_value`` points, as decimal.
Examples
--------
>>> s = SparseArray([0, 0, 1, 1, 1], fill_value=0)
>>> s.density
0.6
"""
r = float(self.sp_index.npoints) / float(self.sp_index.length)
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train | SparseArray.fillna | Fill missing values with `value`.
Parameters
----------
value : scalar, optional
method : str, optional
.. warning::
Using 'method' will result in high memory use,
as all `fill_value` methods will be converted to
an in-memory nd... | pandas/core/arrays/sparse.py | def fillna(self, value=None, method=None, limit=None):
"""
Fill missing values with `value`.
Parameters
----------
value : scalar, optional
method : str, optional
.. warning::
Using 'method' will result in high memory use,
as a... | def fillna(self, value=None, method=None, limit=None):
"""
Fill missing values with `value`.
Parameters
----------
value : scalar, optional
method : str, optional
.. warning::
Using 'method' will result in high memory use,
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train | SparseArray._first_fill_value_loc | Get the location of the first missing value.
Returns
-------
int | pandas/core/arrays/sparse.py | def _first_fill_value_loc(self):
"""
Get the location of the first missing value.
Returns
-------
int
"""
if len(self) == 0 or self.sp_index.npoints == len(self):
return -1
indices = self.sp_index.to_int_index().indices
if not len(ind... | def _first_fill_value_loc(self):
"""
Get the location of the first missing value.
Returns
-------
int
"""
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train | SparseArray.all | Tests whether all elements evaluate True
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all : bool
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"""
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all : bool
See Also
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train | SparseArray.any | Tests whether at least one of elements evaluate True
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train | SparseArray.sum | Sum of non-NA/null values
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train | SparseArray.mean | Mean of non-NA/null values
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Mean of non-NA/null values
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mean : float
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train | tokenize_string | Tokenize a Python source code string.
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----------
source : str
A Python source code string
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tok : tuple of int, str
ints correspond to the all caps constants in the tokenize module
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tok : tuple of int, str
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train | _clean_spaces_backtick_quoted_names | Clean up a column name if surrounded by backticks.
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train | _preparse | Compose a collection of tokenization functions
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A Python source code string
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train | _filter_nodes | Filter out AST nodes that are subclasses of ``superclass``. | pandas/core/computation/expr.py | def _filter_nodes(superclass, all_nodes=_all_nodes):
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node_names = (node.__name__ for node in all_nodes
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train | _node_not_implemented | Return a function that raises a NotImplementedError with a passed node
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"""Return a function that raises a NotImplementedError with a passed node
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train | disallow | Decorator to disallow certain nodes from parsing. Raises a
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disallowed : callable
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train | _op_maker | Return a function to create an op class with its symbol already passed.
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train | add_ops | Decorator to add default implementation of ops. | pandas/core/computation/expr.py | def add_ops(op_classes):
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train | Expr.names | Get the names in an expression | pandas/core/computation/expr.py | def names(self):
"""Get the names in an expression"""
if is_term(self.terms):
return frozenset([self.terms.name])
return frozenset(term.name for term in com.flatten(self.terms)) | def names(self):
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train | _is_convertible_to_index | return a boolean whether I can attempt conversion to a TimedeltaIndex | pandas/core/indexes/timedeltas.py | def _is_convertible_to_index(other):
"""
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train | timedelta_range | Return a fixed frequency TimedeltaIndex, with day as the default
frequency
Parameters
----------
start : string or timedelta-like, default None
Left bound for generating timedeltas
end : string or timedelta-like, default None
Right bound for generating timedeltas
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"""
Return a fixed frequency TimedeltaIndex, with day as the default
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Parameters
----------
start : string or timedelta-like, default None
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Return a fixed frequency TimedeltaIndex, with day as the default
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start : string or timedelta-like, default None
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train | FrozenList.union | Returns a FrozenList with other concatenated to the end of self.
Parameters
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other : array-like
The array-like whose elements we are concatenating.
Returns
-------
diff : FrozenList
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Returns a FrozenList with other concatenated to the end of self.
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other : array-like
The array-like whose elements we are concatenating.
Returns
-------
diff : FrozenList
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Returns a FrozenList with other concatenated to the end of self.
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other : array-like
The array-like whose elements we are concatenating.
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diff : FrozenList
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train | FrozenList.difference | Returns a FrozenList with elements from other removed from self.
Parameters
----------
other : array-like
The array-like whose elements we are removing self.
Returns
-------
diff : FrozenList
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Returns a FrozenList with elements from other removed from self.
Parameters
----------
other : array-like
The array-like whose elements we are removing self.
Returns
-------
diff : FrozenList
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Returns a FrozenList with elements from other removed from self.
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other : array-like
The array-like whose elements we are removing self.
Returns
-------
diff : FrozenList
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train | FrozenNDArray.searchsorted | Find indices to insert `value` so as to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : Equivalent function. | pandas/core/indexes/frozen.py | def searchsorted(self, value, side="left", sorter=None):
"""
Find indices to insert `value` so as to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : Equivalent function.
"""
# We are much more perf... | def searchsorted(self, value, side="left", sorter=None):
"""
Find indices to insert `value` so as to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : Equivalent function.
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train | arrays_to_mgr | Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases. | pandas/core/internals/construction.py | def arrays_to_mgr(arrays, arr_names, index, columns, dtype=None):
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
"""
# figure out the index, if necessary
if index is None:
index = extract_index(arrays)
else:
index = e... | def arrays_to_mgr(arrays, arr_names, index, columns, dtype=None):
"""
Segregate Series based on type and coerce into matrices.
Needs to handle a lot of exceptional cases.
"""
# figure out the index, if necessary
if index is None:
index = extract_index(arrays)
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