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def copy(self, deep=True):
""" Make deep or shallow copy of BlockManager Parameters deep : boolean o rstring, default True If False, return shallow copy (do not ... |
# this preserves the notion of view copying of axes
if deep:
if deep == 'all':
copy = lambda ax: ax.copy(deep=True)
else:
copy = lambda ax: ax.view()
new_axes = [copy(ax) for ax in self.axes]
else:
new_axes = list(s... |
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def as_array(self, transpose=False, items=None):
"""Convert the blockmanager data into an numpy array. Parameters transpose : boolean, default False If True, tra... |
if len(self.blocks) == 0:
arr = np.empty(self.shape, dtype=float)
return arr.transpose() if transpose else arr
if items is not None:
mgr = self.reindex_axis(items, axis=0)
else:
mgr = self
if self._is_single_block and mgr.blocks[0].is_da... |
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def _interleave(self):
""" Return ndarray from blocks with specified item order Items must be contained in the blocks """ |
from pandas.core.dtypes.common import is_sparse
dtype = _interleaved_dtype(self.blocks)
# TODO: https://github.com/pandas-dev/pandas/issues/22791
# Give EAs some input on what happens here. Sparse needs this.
if is_sparse(dtype):
dtype = dtype.subtype
elif i... |
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def fast_xs(self, loc):
""" get a cross sectional for a given location in the items ; handle dups return the result, is *could* be a view in the case of a single... |
if len(self.blocks) == 1:
return self.blocks[0].iget((slice(None), loc))
items = self.items
# non-unique (GH4726)
if not items.is_unique:
result = self._interleave()
if self.ndim == 2:
result = result.T
return result[loc]... |
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def consolidate(self):
""" Join together blocks having same dtype Returns ------- y : BlockManager """ |
if self.is_consolidated():
return self
bm = self.__class__(self.blocks, self.axes)
bm._is_consolidated = False
bm._consolidate_inplace()
return bm |
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def iget(self, i, fastpath=True):
""" Return the data as a SingleBlockManager if fastpath=True and possible Otherwise return as a ndarray """ |
block = self.blocks[self._blknos[i]]
values = block.iget(self._blklocs[i])
if not fastpath or not block._box_to_block_values or values.ndim != 1:
return values
# fastpath shortcut for select a single-dim from a 2-dim BM
return SingleBlockManager(
[block.... |
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def insert(self, loc, item, value, allow_duplicates=False):
""" Insert item at selected position. Parameters loc : int item : hashable value : array_like allow_d... |
if not allow_duplicates and item in self.items:
# Should this be a different kind of error??
raise ValueError('cannot insert {}, already exists'.format(item))
if not isinstance(loc, int):
raise TypeError("loc must be int")
# insert to the axis; this could p... |
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def reindex_axis(self, new_index, axis, method=None, limit=None, fill_value=None, copy=True):
""" Conform block manager to new index. """ |
new_index = ensure_index(new_index)
new_index, indexer = self.axes[axis].reindex(new_index, method=method,
limit=limit)
return self.reindex_indexer(new_index, indexer, axis=axis,
fill_value=fill_value, cop... |
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def take(self, indexer, axis=1, verify=True, convert=True):
""" Take items along any axis. """ |
self._consolidate_inplace()
indexer = (np.arange(indexer.start, indexer.stop, indexer.step,
dtype='int64')
if isinstance(indexer, slice)
else np.asanyarray(indexer, dtype='int64'))
n = self.shape[axis]
if convert:
... |
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def unstack(self, unstacker_func, fill_value):
"""Return a blockmanager with all blocks unstacked. Parameters unstacker_func : callable A (partially-applied) ``p... |
n_rows = self.shape[-1]
dummy = unstacker_func(np.empty((0, 0)), value_columns=self.items)
new_columns = dummy.get_new_columns()
new_index = dummy.get_new_index()
new_blocks = []
columns_mask = []
for blk in self.blocks:
blocks, mask = blk._unstack(
... |
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def delete(self, item):
""" Delete single item from SingleBlockManager. Ensures that self.blocks doesn't become empty. """ |
loc = self.items.get_loc(item)
self._block.delete(loc)
self.axes[0] = self.axes[0].delete(loc) |
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def concat(self, to_concat, new_axis):
""" Concatenate a list of SingleBlockManagers into a single SingleBlockManager. Used for pd.concat of Series objects with ... |
non_empties = [x for x in to_concat if len(x) > 0]
# check if all series are of the same block type:
if len(non_empties) > 0:
blocks = [obj.blocks[0] for obj in non_empties]
if len({b.dtype for b in blocks}) == 1:
new_block = blocks[0].concat_same_type(b... |
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def from_array(cls, arr, index=None, name=None, copy=False, fill_value=None, fastpath=False):
"""Construct SparseSeries from array. .. deprecated:: 0.23.0 Use th... |
warnings.warn("'from_array' is deprecated and will be removed in a "
"future version. Please use the pd.SparseSeries(..) "
"constructor instead.", FutureWarning, stacklevel=2)
return cls(arr, index=index, name=name, copy=copy,
fill_value=fi... |
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def as_sparse_array(self, kind=None, fill_value=None, copy=False):
""" return my self as a sparse array, do not copy by default """ |
if fill_value is None:
fill_value = self.fill_value
if kind is None:
kind = self.kind
return SparseArray(self.values, sparse_index=self.sp_index,
fill_value=fill_value, kind=kind, copy=copy) |
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def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None, filter_type=None, **kwds):
""" perform a reduction operation """ |
return op(self.get_values(), skipna=skipna, **kwds) |
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def _ixs(self, i, axis=0):
""" Return the i-th value or values in the SparseSeries by location Parameters i : int, slice, or sequence of integers Returns -------... |
label = self.index[i]
if isinstance(label, Index):
return self.take(i, axis=axis)
else:
return self._get_val_at(i) |
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def abs(self):
""" Return an object with absolute value taken. Only applicable to objects that are all numeric Returns ------- abs: same type as caller """ |
return self._constructor(np.abs(self.values),
index=self.index).__finalize__(self) |
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def get(self, label, default=None):
""" Returns value occupying requested label, default to specified missing value if not present. Analogous to dict.get Paramet... |
if label in self.index:
loc = self.index.get_loc(label)
return self._get_val_at(loc)
else:
return default |
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def get_value(self, label, takeable=False):
""" Retrieve single value at passed index label .. deprecated:: 0.21.0 Please use .at[] or .iat[] accessors. Paramete... |
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(label, takeable=takeable) |
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def set_value(self, label, value, takeable=False):
""" Quickly set single value at passed label. If label is not contained, a new object is created with the labe... |
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(label, value, takeable=takeable) |
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def to_dense(self):
""" Convert SparseSeries to a Series. Returns ------- s : Series """ |
return Series(self.values.to_dense(), index=self.index,
name=self.name) |
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def copy(self, deep=True):
""" Make a copy of the SparseSeries. Only the actual sparse values need to be copied """ |
# TODO: https://github.com/pandas-dev/pandas/issues/22314
# We skip the block manager till that is resolved.
new_data = self.values.copy(deep=deep)
return self._constructor(new_data, sparse_index=self.sp_index,
fill_value=self.fill_value,
... |
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def sparse_reindex(self, new_index):
""" Conform sparse values to new SparseIndex Parameters new_index : {BlockIndex, IntIndex} Returns ------- reindexed : Spars... |
if not isinstance(new_index, splib.SparseIndex):
raise TypeError("new index must be a SparseIndex")
values = self.values
values = values.sp_index.to_int_index().reindex(
values.sp_values.astype('float64'), values.fill_value, new_index)
values = SparseArray(values... |
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def dropna(self, axis=0, inplace=False, **kwargs):
""" Analogous to Series.dropna. If fill_value=NaN, returns a dense Series """ |
# TODO: make more efficient
# Validate axis
self._get_axis_number(axis or 0)
dense_valid = self.to_dense().dropna()
if inplace:
raise NotImplementedError("Cannot perform inplace dropna"
" operations on a SparseSeries")
if... |
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def combine_first(self, other):
""" Combine Series values, choosing the calling Series's values first. Result index will be the union of the two indexes Paramete... |
if isinstance(other, SparseSeries):
other = other.to_dense()
dense_combined = self.to_dense().combine_first(other)
return dense_combined.to_sparse(fill_value=self.fill_value) |
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def _maybe_cache(arg, format, cache, convert_listlike):
""" Create a cache of unique dates from an array of dates Parameters arg : integer, float, string, dateti... |
from pandas import Series
cache_array = Series()
if cache:
# Perform a quicker unique check
from pandas import Index
unique_dates = Index(arg).unique()
if len(unique_dates) < len(arg):
cache_dates = convert_listlike(unique_dates.to_numpy(),
... |
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def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
""" Convert array of dates with a cache and box the result Parameters arg : integer, float,... |
from pandas import Series, DatetimeIndex, Index
result = Series(arg).map(cache_array)
if box:
if errors == 'ignore':
return Index(result, name=name)
else:
return DatetimeIndex(result, name=name)
return result.values |
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def _return_parsed_timezone_results(result, timezones, box, tz, name):
""" Return results from array_strptime if a %z or %Z directive was passed. Parameters resu... |
if tz is not None:
raise ValueError("Cannot pass a tz argument when "
"parsing strings with timezone "
"information.")
tz_results = np.array([Timestamp(res).tz_localize(zone) for res, zone
in zip(result, timezones)])
if bo... |
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def _adjust_to_origin(arg, origin, unit):
""" Helper function for to_datetime. Adjust input argument to the specified origin Parameters arg : list, tuple, ndarra... |
if origin == 'julian':
original = arg
j0 = Timestamp(0).to_julian_date()
if unit != 'D':
raise ValueError("unit must be 'D' for origin='julian'")
try:
arg = arg - j0
except TypeError:
raise ValueError("incompatible 'arg' type for given "
... |
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def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin=... |
if arg is None:
return None
if origin != 'unix':
arg = _adjust_to_origin(arg, origin, unit)
tz = 'utc' if utc else None
convert_listlike = partial(_convert_listlike_datetimes, tz=tz, unit=unit,
dayfirst=dayfirst, yearfirst=yearfirst,
... |
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def deprecate(name, alternative, version, alt_name=None, klass=None, stacklevel=2, msg=None):
""" Return a new function that emits a deprecation warning on use. ... |
alt_name = alt_name or alternative.__name__
klass = klass or FutureWarning
warning_msg = msg or '{} is deprecated, use {} instead'.format(name,
alt_name)
@wraps(alternative)
def wrapper(*args, **kwargs):
warnings.warn(warn... |
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def deprecate_kwarg(old_arg_name, new_arg_name, mapping=None, stacklevel=2):
""" Decorator to deprecate a keyword argument of a function. Parameters old_arg_name... |
if mapping is not None and not hasattr(mapping, 'get') and \
not callable(mapping):
raise TypeError("mapping from old to new argument values "
"must be dict or callable!")
def _deprecate_kwarg(func):
@wraps(func)
def wrapper(*args, **kwargs):
... |
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def make_signature(func):
""" Returns a tuple containing the paramenter list with defaults and parameter list. Examples -------- (['a', 'b', 'c=2'], ['a', 'b', '... |
spec = inspect.getfullargspec(func)
if spec.defaults is None:
n_wo_defaults = len(spec.args)
defaults = ('',) * n_wo_defaults
else:
n_wo_defaults = len(spec.args) - len(spec.defaults)
defaults = ('',) * n_wo_defaults + tuple(spec.defaults)
args = []
for var, default... |
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def from_range(cls, data, name=None, dtype=None, **kwargs):
""" Create RangeIndex from a range object. """ |
if not isinstance(data, range):
raise TypeError(
'{0}(...) must be called with object coercible to a '
'range, {1} was passed'.format(cls.__name__, repr(data)))
start, stop, step = data.start, data.stop, data.step
return RangeIndex(start, stop, step,... |
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def min(self, axis=None, skipna=True):
"""The minimum value of the RangeIndex""" |
nv.validate_minmax_axis(axis)
return self._minmax('min') |
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def max(self, axis=None, skipna=True):
"""The maximum value of the RangeIndex""" |
nv.validate_minmax_axis(axis)
return self._minmax('max') |
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def argsort(self, *args, **kwargs):
""" Returns the indices that would sort the index and its underlying data. Returns ------- argsorted : numpy array See Also -... |
nv.validate_argsort(args, kwargs)
if self._step > 0:
return np.arange(len(self))
else:
return np.arange(len(self) - 1, -1, -1) |
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def _min_fitting_element(self, lower_limit):
"""Returns the smallest element greater than or equal to the limit""" |
no_steps = -(-(lower_limit - self._start) // abs(self._step))
return self._start + abs(self._step) * no_steps |
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def _max_fitting_element(self, upper_limit):
"""Returns the largest element smaller than or equal to the limit""" |
no_steps = (upper_limit - self._start) // abs(self._step)
return self._start + abs(self._step) * no_steps |
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def union(self, other, sort=None):
""" Form the union of two Index objects and sorts if possible Parameters other : Index or array-like sort : False or None, def... |
self._assert_can_do_setop(other)
if len(other) == 0 or self.equals(other) or len(self) == 0:
return super().union(other, sort=sort)
if isinstance(other, RangeIndex) and sort is None:
start_s, step_s = self._start, self._step
end_s = self._start + self._step ... |
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def _add_numeric_methods_binary(cls):
""" add in numeric methods, specialized to RangeIndex """ |
def _make_evaluate_binop(op, step=False):
"""
Parameters
----------
op : callable that accepts 2 parms
perform the binary op
step : callable, optional, default to False
op to apply to the step parm if not None
... |
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def adjoin(space, *lists, **kwargs):
""" Glues together two sets of strings using the amount of space requested. The idea is to prettify. space : int number of s... |
strlen = kwargs.pop('strlen', len)
justfunc = kwargs.pop('justfunc', justify)
out_lines = []
newLists = []
lengths = [max(map(strlen, x)) + space for x in lists[:-1]]
# not the last one
lengths.append(max(map(len, lists[-1])))
maxLen = max(map(len, lists))
for i, lst in enumerate(l... |
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def read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, auth_local_webserver=False, dialect=None, location=None, configuration=None, cr... |
pandas_gbq = _try_import()
kwargs = {}
# START: new kwargs. Don't populate unless explicitly set.
if use_bqstorage_api is not None:
kwargs["use_bqstorage_api"] = use_bqstorage_api
# END: new kwargs
# START: deprecated kwargs. Don't populate unless explicitly set.
if verbose is ... |
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def andrews_curves(frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds):
""" Generate a matplotlib plot of Andrews curves, for visualisi... |
from math import sqrt, pi
import matplotlib.pyplot as plt
def function(amplitudes):
def f(t):
x1 = amplitudes[0]
result = x1 / sqrt(2.0)
# Take the rest of the coefficients and resize them
# appropriately. Take a copy of amplitudes as otherwise nump... |
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def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
""" Bootstrap plot on mean, median and mid-range statistics. The bootstrap plot is used to es... |
import random
import matplotlib.pyplot as plt
# random.sample(ndarray, int) fails on python 3.3, sigh
data = list(series.values)
samplings = [random.sample(data, size) for _ in range(samples)]
means = np.array([np.mean(sampling) for sampling in samplings])
medians = np.array([np.median(sa... |
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def autocorrelation_plot(series, ax=None, **kwds):
""" Autocorrelation plot for time series. Parameters: series: Time series ax: Matplotlib axis object, optional... |
import matplotlib.pyplot as plt
n = len(series)
data = np.asarray(series)
if ax is None:
ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0))
mean = np.mean(data)
c0 = np.sum((data - mean) ** 2) / float(n)
def r(h):
return ((data[:n - h] - mean) *
(data[h:] - mean)).... |
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def _any_pandas_objects(terms):
"""Check a sequence of terms for instances of PandasObject.""" |
return any(isinstance(term.value, pd.core.generic.PandasObject)
for term in terms) |
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def _align(terms):
"""Align a set of terms""" |
try:
# flatten the parse tree (a nested list, really)
terms = list(com.flatten(terms))
except TypeError:
# can't iterate so it must just be a constant or single variable
if isinstance(terms.value, pd.core.generic.NDFrame):
typ = type(terms.value)
return t... |
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def tsplot(series, plotf, ax=None, **kwargs):
import warnings """ Plots a Series on the given Matplotlib axes or the current axes Parameters axes : Axes series :... |
warnings.warn("'tsplot' is deprecated and will be removed in a "
"future version. Please use Series.plot() instead.",
FutureWarning, stacklevel=2)
# Used inferred freq is possible, need a test case for inferred
if ax is None:
import matplotlib.pyplot as plt
... |
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def _decorate_axes(ax, freq, kwargs):
"""Initialize axes for time-series plotting""" |
if not hasattr(ax, '_plot_data'):
ax._plot_data = []
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, 'legendlabels'):
ax.legendlabels = [kwargs.get('label', None)]
else:
ax.legendlabels.append(kwargs.get('label', None))
ax.view_interval = ... |
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def _is_homogeneous_type(self):
""" Whether all the columns in a DataFrame have the same type. Returns ------- bool Examples -------- True False Items with the s... |
if self._data.any_extension_types:
return len({block.dtype for block in self._data.blocks}) == 1
else:
return not self._data.is_mixed_type |
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def _repr_html_(self):
""" Return a html representation for a particular DataFrame. Mainly for IPython notebook. """ |
if self._info_repr():
buf = StringIO("")
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace('<', r'<', 1)
val = val.replace('>', r'>', 1)
return '<pre>' + val + '</pre>'
i... |
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def itertuples(self, index=True, name="Pandas"):
""" Iterate over DataFrame rows as namedtuples. Parameters index : bool, default True If True, return the index ... |
arrays = []
fields = list(self.columns)
if index:
arrays.append(self.index)
fields.insert(0, "Index")
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
# Python 3 s... |
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def dot(self, other):
""" Compute the matrix mutiplication between the DataFrame and other. This method computes the matrix product between the DataFrame and the... |
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if (len(common) > len(self.columns) or
len(common) > len(other.index)):
raise ValueError('matrices are not aligned')
left = self.reindex(columns=common, c... |
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def from_dict(cls, data, orient='columns', dtype=None, columns=None):
""" Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dic... |
index = None
orient = orient.lower()
if orient == 'index':
if len(data) > 0:
# TODO speed up Series case
if isinstance(list(data.values())[0], (Series, dict)):
data = _from_nested_dict(data)
else:
... |
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def to_numpy(self, dtype=None, copy=False):
""" Convert the DataFrame to a NumPy array. .. versionadded:: 0.24.0 By default, the dtype of the returned array will... |
result = np.array(self.values, dtype=dtype, copy=copy)
return result |
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def to_dict(self, orient='dict', into=dict):
""" Convert the DataFrame to a dictionary. The type of the key-value pairs can be customized with the parameters (se... |
if not self.columns.is_unique:
warnings.warn("DataFrame columns are not unique, some "
"columns will be omitted.", UserWarning,
stacklevel=2)
# GH16122
into_c = com.standardize_mapping(into)
if orient.lower().startswith('d'... |
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def to_records(self, index=True, convert_datetime64=None, column_dtypes=None, index_dtypes=None):
""" Convert DataFrame to a NumPy record array. Index will be in... |
if convert_datetime64 is not None:
warnings.warn("The 'convert_datetime64' parameter is "
"deprecated and will be removed in a future "
"version",
FutureWarning, stacklevel=2)
if index:
if is_datetim... |
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def from_items(cls, items, columns=None, orient='columns'):
""" Construct a DataFrame from a list of tuples. .. deprecated:: 0.23.0 `from_items` is deprecated an... |
warnings.warn("from_items is deprecated. Please use "
"DataFrame.from_dict(dict(items), ...) instead. "
"DataFrame.from_dict(OrderedDict(items)) may be used to "
"preserve the key order.",
FutureWarning, stacklevel=2)
... |
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def to_sparse(self, fill_value=None, kind='block'):
""" Convert to SparseDataFrame. Implement the sparse version of the DataFrame meaning that any data matching ... |
from pandas.core.sparse.api import SparseDataFrame
return SparseDataFrame(self._series, index=self.index,
columns=self.columns, default_kind=kind,
default_fill_value=fill_value) |
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def to_stata(self, fname, convert_dates=None, write_index=True, encoding="latin-1", byteorder=None, time_stamp=None, data_label=None, variable_labels=None, versio... |
kwargs = {}
if version not in (114, 117):
raise ValueError('Only formats 114 and 117 supported.')
if version == 114:
if convert_strl is not None:
raise ValueError('strl support is only available when using '
'format 117')
... |
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def to_feather(self, fname):
""" Write out the binary feather-format for DataFrames. .. versionadded:: 0.20.0 Parameters fname : str string file path """ |
from pandas.io.feather_format import to_feather
to_feather(self, fname) |
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def to_parquet(self, fname, engine='auto', compression='snappy', index=None, partition_cols=None, **kwargs):
""" Write a DataFrame to the binary parquet format. ... |
from pandas.io.parquet import to_parquet
to_parquet(self, fname, engine,
compression=compression, index=index,
partition_cols=partition_cols, **kwargs) |
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def memory_usage(self, index=True, deep=False):
""" Return the memory usage of each column in bytes. The memory usage can optionally include the contribution of ... |
result = Series([c.memory_usage(index=False, deep=deep)
for col, c in self.iteritems()], index=self.columns)
if index:
result = Series(self.index.memory_usage(deep=deep),
index=['Index']).append(result)
return result |
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def transpose(self, *args, **kwargs):
""" Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. Th... |
nv.validate_transpose(args, dict())
return super().transpose(1, 0, **kwargs) |
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def get_value(self, index, col, takeable=False):
""" Quickly retrieve single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accesso... |
warnings.warn("get_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._get_value(index, col, takeable=takeable) |
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def set_value(self, index, col, value, takeable=False):
""" Put single value at passed column and index. .. deprecated:: 0.21.0 Use .at[] or .iat[] accessors ins... |
warnings.warn("set_value is deprecated and will be removed "
"in a future release. Please use "
".at[] or .iat[] accessors instead", FutureWarning,
stacklevel=2)
return self._set_value(index, col, value, takeable=takeable) |
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def query(self, expr, inplace=False, **kwargs):
""" Query the columns of a DataFrame with a boolean expression. Parameters expr : str The query string to evaluat... |
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(expr, str):
msg = "expr must be a string to be evaluated, {0} given"
raise ValueError(msg.format(type(expr)))
kwargs['level'] = kwargs.pop('level', 0) + 1
kwargs['target'] = None
res = se... |
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def eval(self, expr, inplace=False, **kwargs):
""" Evaluate a string describing operations on DataFrame columns. Operates on columns only, not specific rows or e... |
from pandas.core.computation.eval import eval as _eval
inplace = validate_bool_kwarg(inplace, 'inplace')
resolvers = kwargs.pop('resolvers', None)
kwargs['level'] = kwargs.pop('level', 0) + 1
if resolvers is None:
index_resolvers = self._get_index_resolvers()
... |
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def select_dtypes(self, include=None, exclude=None):
""" Return a subset of the DataFrame's columns based on the column dtypes. Parameters include, exclude : sca... |
def _get_info_slice(obj, indexer):
"""Slice the info axis of `obj` with `indexer`."""
if not hasattr(obj, '_info_axis_number'):
msg = 'object of type {typ!r} has no info axis'
raise TypeError(msg.format(typ=type(obj).__name__))
slices = [slice... |
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def _box_col_values(self, values, items):
""" Provide boxed values for a column. """ |
klass = self._constructor_sliced
return klass(values, index=self.index, name=items, fastpath=True) |
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def _ensure_valid_index(self, value):
""" Ensure that if we don't have an index, that we can create one from the passed value. """ |
# GH5632, make sure that we are a Series convertible
if not len(self.index) and is_list_like(value):
try:
value = Series(value)
except (ValueError, NotImplementedError, TypeError):
raise ValueError('Cannot set a frame with no defined index '
... |
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def _set_item(self, key, value):
""" Add series to DataFrame in specified column. If series is a numpy-array (not a Series/TimeSeries), it must be the same lengt... |
self._ensure_valid_index(value)
value = self._sanitize_column(key, value)
NDFrame._set_item(self, key, value)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_seti... |
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def insert(self, loc, column, value, allow_duplicates=False):
""" Insert column into DataFrame at specified location. Raises a ValueError if `column` is already ... |
self._ensure_valid_index(value)
value = self._sanitize_column(column, value, broadcast=False)
self._data.insert(loc, column, value,
allow_duplicates=allow_duplicates) |
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def assign(self, **kwargs):
r""" Assign new columns to a DataFrame. Returns a new object with all original columns in addition to new ones. Existing columns that... |
data = self.copy()
# >= 3.6 preserve order of kwargs
if PY36:
for k, v in kwargs.items():
data[k] = com.apply_if_callable(v, data)
else:
# <= 3.5: do all calculations first...
results = OrderedDict()
for k, v in kwargs.ite... |
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def lookup(self, row_labels, col_labels):
""" Label-based "fancy indexing" function for DataFrame. Given equal-length arrays of row and column labels, return an ... |
n = len(row_labels)
if n != len(col_labels):
raise ValueError('Row labels must have same size as column labels')
thresh = 1000
if not self._is_mixed_type or n > thresh:
values = self.values
ridx = self.index.get_indexer(row_labels)
cidx =... |
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def _reindex_multi(self, axes, copy, fill_value):
""" We are guaranteed non-Nones in the axes. """ |
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
if row_indexer is not None and col_indexer is not None:
indexer = row_indexer, col_indexer
new_values = algorithms.take_2d_multi(self.values, ind... |
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def drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise'):
""" Drop specified labels from rows or columns. Remove ... |
return super().drop(labels=labels, axis=axis, index=index,
columns=columns, level=level, inplace=inplace,
errors=errors) |
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def rename(self, *args, **kwargs):
""" Alter axes labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as... |
axes = validate_axis_style_args(self, args, kwargs, 'mapper', 'rename')
kwargs.update(axes)
# Pop these, since the values are in `kwargs` under different names
kwargs.pop('axis', None)
kwargs.pop('mapper', None)
return super().rename(**kwargs) |
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def dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False):
""" Remove missing values. See the :ref:`User Guide <missing_data>` for more on whi... |
inplace = validate_bool_kwarg(inplace, 'inplace')
if isinstance(axis, (tuple, list)):
# GH20987
msg = ("supplying multiple axes to axis is deprecated and "
"will be removed in a future version.")
warnings.warn(msg, FutureWarning, stacklevel=2)
... |
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def drop_duplicates(self, subset=None, keep='first', inplace=False):
""" Return DataFrame with duplicate rows removed, optionally only considering certain column... |
if self.empty:
return self.copy()
inplace = validate_bool_kwarg(inplace, 'inplace')
duplicated = self.duplicated(subset, keep=keep)
if inplace:
inds, = (-duplicated)._ndarray_values.nonzero()
new_data = self._data.take(inds)
self._update... |
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def duplicated(self, subset=None, keep='first'):
""" Return boolean Series denoting duplicate rows, optionally only considering certain columns. Parameters subse... |
from pandas.core.sorting import get_group_index
from pandas._libs.hashtable import duplicated_int64, _SIZE_HINT_LIMIT
if self.empty:
return Series(dtype=bool)
def f(vals):
labels, shape = algorithms.factorize(
vals, size_hint=min(len(self), _SIZ... |
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def nlargest(self, n, columns, keep='first'):
""" Return the first `n` rows ordered by `columns` in descending order. Return the first `n` rows with the largest ... |
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nlargest() |
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def nsmallest(self, n, columns, keep='first'):
""" Return the first `n` rows ordered by `columns` in ascending order. Return the first `n` rows with the smallest... |
return algorithms.SelectNFrame(self,
n=n,
keep=keep,
columns=columns).nsmallest() |
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def swaplevel(self, i=-2, j=-1, axis=0):
""" Swap levels i and j in a MultiIndex on a particular axis. Parameters i, j : int, string (can be mixed) Level of inde... |
result = self.copy()
axis = self._get_axis_number(axis)
if axis == 0:
result.index = result.index.swaplevel(i, j)
else:
result.columns = result.columns.swaplevel(i, j)
return result |
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def reorder_levels(self, order, axis=0):
""" Rearrange index levels using input order. May not drop or duplicate levels. Parameters order : list of int or list o... |
axis = self._get_axis_number(axis)
if not isinstance(self._get_axis(axis),
MultiIndex): # pragma: no cover
raise TypeError('Can only reorder levels on a hierarchical axis.')
result = self.copy()
if axis == 0:
result.index = result.ind... |
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def combine(self, other, func, fill_value=None, overwrite=True):
""" Perform column-wise combine with another DataFrame. Combines a DataFrame with `other` DataFr... |
other_idxlen = len(other.index) # save for compare
this, other = self.align(other, copy=False)
new_index = this.index
if other.empty and len(new_index) == len(self.index):
return self.copy()
if self.empty and len(other) == other_idxlen:
return other.c... |
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def combine_first(self, other):
""" Update null elements with value in the same location in `other`. Combine two DataFrame objects by filling null values in one ... |
import pandas.core.computation.expressions as expressions
def extract_values(arr):
# Does two things:
# 1. maybe gets the values from the Series / Index
# 2. convert datelike to i8
if isinstance(arr, (ABCIndexClass, ABCSeries)):
arr = arr... |
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def update(self, other, join='left', overwrite=True, filter_func=None, errors='ignore'):
""" Modify in place using non-NA values from another DataFrame. Aligns o... |
import pandas.core.computation.expressions as expressions
# TODO: Support other joins
if join != 'left': # pragma: no cover
raise NotImplementedError("Only left join is supported")
if errors not in ['ignore', 'raise']:
raise ValueError("The parameter errors must... |
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def apply(self, func, axis=0, broadcast=None, raw=False, reduce=None, result_type=None, args=(), **kwds):
""" Apply a function along an axis of the DataFrame. Ob... |
from pandas.core.apply import frame_apply
op = frame_apply(self,
func=func,
axis=axis,
broadcast=broadcast,
raw=raw,
reduce=reduce,
result_type=result_ty... |
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def applymap(self, func):
""" Apply a function to a Dataframe elementwise. This method applies a function that accepts and returns a scalar to every element of a... |
# if we have a dtype == 'M8[ns]', provide boxed values
def infer(x):
if x.empty:
return lib.map_infer(x, func)
return lib.map_infer(x.astype(object).values, func)
return self.apply(infer) |
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def append(self, other, ignore_index=False, verify_integrity=False, sort=None):
""" Append rows of `other` to the end of caller, returning a new object. Columns ... |
if isinstance(other, (Series, dict)):
if isinstance(other, dict):
other = Series(other)
if other.name is None and not ignore_index:
raise TypeError('Can only append a Series if ignore_index=True'
' or if the Series has a na... |
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def join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False):
""" Join columns of another DataFrame. Join columns with `other` DataFrame either... |
# For SparseDataFrame's benefit
return self._join_compat(other, on=on, how=how, lsuffix=lsuffix,
rsuffix=rsuffix, sort=sort) |
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def round(self, decimals=0, *args, **kwargs):
""" Round a DataFrame to a variable number of decimal places. Parameters decimals : int, dict, Series Number of dec... |
from pandas.core.reshape.concat import concat
def _dict_round(df, decimals):
for col, vals in df.iteritems():
try:
yield _series_round(vals, decimals[col])
except KeyError:
yield vals
def _series_round(s, deci... |
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def corrwith(self, other, axis=0, drop=False, method='pearson'):
""" Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Se... |
axis = self._get_axis_number(axis)
this = self._get_numeric_data()
if isinstance(other, Series):
return this.apply(lambda x: other.corr(x, method=method),
axis=axis)
other = other._get_numeric_data()
left, right = this.align(other, joi... |
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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.in... |
axis = self._get_axis_number(axis)
if level is not None:
return self._count_level(level, axis=axis,
numeric_only=numeric_only)
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
# GH #42... |
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def nunique(self, axis=0, dropna=True):
""" Count distinct observations over requested axis. Return Series with number of distinct observations. Can ignore NaN v... |
return self.apply(Series.nunique, axis=axis, dropna=dropna) |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
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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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
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 o... |
self._check_percentile(q)
data = self._get_numeric_data() if numeric_only else self
axis = self._get_axis_number(axis)
is_transposed = axis == 1
if is_transposed:
data = data.T
result = data._data.quantile(qs=q,
axis=1,... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def isin(self, values):
""" Whether each element in the DataFrame is contained in values. Parameters values : iterable, Series, DataFrame or dict The result will... |
if isinstance(values, dict):
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
return concat((self.iloc[:, [i]].isin(values[col])
for i, col in enumerate(self.columns)), axis=1)
elif isinstance(val... |
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