INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Return results from array_strptime if a %z or %Z directive was passed. | def _return_parsed_timezone_results(result, timezones, box, tz, name):
"""
Return results from array_strptime if a %z or %Z directive was passed.
Parameters
----------
result : ndarray
int64 date representations of the dates
timezones : ndarray
pytz timezone objects
box : bo... |
Helper function for to_datetime. Performs the conversions of 1D listlike of dates | def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
unit=None, errors=None,
infer_datetime_format=None, dayfirst=None,
yearfirst=None, exact=None):
"""
Helper function for to_datetime. Performs the ... |
Helper function for to_datetime. Adjust input argument to the specified origin | def _adjust_to_origin(arg, origin, unit):
"""
Helper function for to_datetime.
Adjust input argument to the specified origin
Parameters
----------
arg : list, tuple, ndarray, Series, Index
date to be adjusted
origin : 'julian' or Timestamp
origin offset for the arg
unit ... |
Convert argument to datetime. | 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='unix',
cache=False):
"""
Convert argument to datetime.
Parameters
----------
arg : integ... |
assemble the unit specified fields from the arg ( DataFrame ) Return a Series for actual parsing | def _assemble_from_unit_mappings(arg, errors, box, tz):
"""
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
Parameters
----------
arg : DataFrame
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsin... |
try to parse the YYYYMMDD/ %Y%m%d format try to deal with NaT - like arg is a passed in as an object dtype but could really be ints/ strings with nan - like/ or floats ( e. g. with nan ) | def _attempt_YYYYMMDD(arg, errors):
"""
try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
arg is a passed in as an object dtype, but could really be ints/strings
with nan-like/or floats (e.g. with nan)
Parameters
----------
arg : passed value
errors : 'raise','ignore',... |
Parse time strings to time objects using fixed strptime formats ( %H: %M %H%M %I: %M%p %I%M%p %H: %M: %S %H%M%S %I: %M: %S%p %I%M%S%p ) | def to_time(arg, format=None, infer_time_format=False, errors='raise'):
"""
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p")
Use infer_time_format if all the strings are in the same format to speed... |
Return a new function that emits a deprecation warning on use. | 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.
To use this method for a deprecated function, another function
`alternative` with the same signature must exist. The deprecated
... |
Decorator to deprecate a keyword argument of a function. | 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 : str
Name of argument in function to deprecate
new_arg_name : str or None
Name of preferred argument in f... |
Returns a tuple containing the paramenter list with defaults and parameter list. | def make_signature(func):
"""
Returns a tuple containing the paramenter list with defaults
and parameter list.
Examples
--------
>>> def f(a, b, c=2):
>>> return a * b * c
>>> print(make_signature(f))
(['a', 'b', 'c=2'], ['a', 'b', 'c'])
"""
spec = inspect.getfullargspe... |
Return a fixed frequency PeriodIndex with day ( calendar ) as the default frequency | def period_range(start=None, end=None, periods=None, freq=None, name=None):
"""
Return a fixed frequency PeriodIndex, with day (calendar) as the default
frequency
Parameters
----------
start : string or period-like, default None
Left bound for generating periods
end : string or peri... |
Create RangeIndex from a range object. | 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(... |
Return a list of tuples of the ( attr formatted_value ) | def _format_attrs(self):
"""
Return a list of tuples of the (attr, formatted_value)
"""
attrs = self._get_data_as_items()
if self.name is not None:
attrs.append(('name', ibase.default_pprint(self.name)))
return attrs |
The minimum value of the RangeIndex | def min(self, axis=None, skipna=True):
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('min') |
The maximum value of the RangeIndex | def max(self, axis=None, skipna=True):
"""The maximum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
return self._minmax('max') |
Returns the indices that would sort the index and its underlying data. | def argsort(self, *args, **kwargs):
"""
Returns the indices that would sort the index and its
underlying data.
Returns
-------
argsorted : numpy array
See Also
--------
numpy.ndarray.argsort
"""
nv.validate_argsort(args, kwargs)
... |
Determines if two Index objects contain the same elements. | def equals(self, other):
"""
Determines if two Index objects contain the same elements.
"""
if isinstance(other, RangeIndex):
ls = len(self)
lo = len(other)
return (ls == lo == 0 or
ls == lo == 1 and
self._start ... |
Form the intersection of two Index objects. | def intersection(self, other, sort=False):
"""
Form the intersection of two Index objects.
Parameters
----------
other : Index or array-like
sort : False or None, default False
Sort the resulting index if possible
.. versionadded:: 0.24.0
... |
Returns the smallest element greater than or equal to the limit | 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 |
Returns the largest element smaller than or equal to the limit | 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 |
Extended Euclidean algorithms to solve Bezout s identity: a * x + b * y = gcd ( x y ) Finds one particular solution for x y: s t Returns: gcd s t | def _extended_gcd(self, a, b):
"""
Extended Euclidean algorithms to solve Bezout's identity:
a*x + b*y = gcd(x, y)
Finds one particular solution for x, y: s, t
Returns: gcd, s, t
"""
s, old_s = 0, 1
t, old_t = 1, 0
r, old_r = b, a
while ... |
Form the union of two Index objects and sorts if possible | 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, default None
Whether to sort resulting index. ``sort=None`` returns a
mononot... |
add in numeric methods specialized to RangeIndex | 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 :... |
Convert the PandasArray to a: class: numpy. ndarray. | def to_numpy(self, dtype=None, copy=False):
"""
Convert the PandasArray to a :class:`numpy.ndarray`.
By default, this requires no coercion or copying of data.
Parameters
----------
dtype : numpy.dtype
The NumPy dtype to pass to :func:`numpy.asarray`.
... |
Glues together two sets of strings using the amount of space requested. The idea is to prettify. | 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 spaces for padding
lists : str
list of str which being joined
strlen : callable
function used to... |
Perform ljust center rjust against string or list - like | def justify(texts, max_len, mode='right'):
"""
Perform ljust, center, rjust against string or list-like
"""
if mode == 'left':
return [x.ljust(max_len) for x in texts]
elif mode == 'center':
return [x.center(max_len) for x in texts]
else:
return [x.rjust(max_len) for x in... |
internal. pprinter for iterables. you should probably use pprint_thing () rather then calling this directly. | def _pprint_seq(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
bounds length of printed sequence, depending on options
"""
if isinstance(seq, set):
fmt = "{{{body}}}"
else... |
internal. pprinter for iterables. you should probably use pprint_thing () rather then calling this directly. | def _pprint_dict(seq, _nest_lvl=0, max_seq_items=None, **kwds):
"""
internal. pprinter for iterables. you should probably use pprint_thing()
rather then calling this directly.
"""
fmt = "{{{things}}}"
pairs = []
pfmt = "{key}: {val}"
if max_seq_items is False:
nitems = len(seq)... |
This function is the sanctioned way of converting objects to a unicode representation. | def pprint_thing(thing, _nest_lvl=0, escape_chars=None, default_escapes=False,
quote_strings=False, max_seq_items=None):
"""
This function is the sanctioned way of converting objects
to a unicode representation.
properly handles nested sequences containing unicode strings
(unicode(... |
Return the formatted obj as a unicode string | def format_object_summary(obj, formatter, is_justify=True, name=None,
indent_for_name=True):
"""
Return the formatted obj as a unicode string
Parameters
----------
obj : object
must be iterable and support __getitem__
formatter : callable
string formatt... |
Return a list of tuples of the ( attr formatted_value ) for common attrs including dtype name length | def format_object_attrs(obj):
"""
Return a list of tuples of the (attr, formatted_value)
for common attrs, including dtype, name, length
Parameters
----------
obj : object
must be iterable
Returns
-------
list
"""
attrs = []
if hasattr(obj, 'dtype'):
at... |
Load data from Google BigQuery. | 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, credentials=None,
use_bqstorage_api=None, private_key=None, verbose=None):
"""
Load data from Google BigQuery.
... |
Draw a matrix of scatter plots. | def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
diagonal='hist', marker='.', density_kwds=None,
hist_kwds=None, range_padding=0.05, **kwds):
"""
Draw a matrix of scatter plots.
Parameters
----------
frame : DataFrame
alpha : float, o... |
Plot a multidimensional dataset in 2D. | def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
"""
Plot a multidimensional dataset in 2D.
Each Series in the DataFrame is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each Series. Highly corr... |
Generate a matplotlib plot of Andrews curves for visualising clusters of multivariate data. | def andrews_curves(frame, class_column, ax=None, samples=200, color=None,
colormap=None, **kwds):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t... |
Bootstrap plot on mean median and mid - range statistics. | 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 estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plot... |
Parallel coordinates plotting. | def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
use_columns=False, xticks=None, colormap=None,
axvlines=True, axvlines_kwds=None, sort_labels=False,
**kwds):
"""Parallel coordinates plotting.
Parameters
... |
Lag plot for time series. | def lag_plot(series, lag=1, ax=None, **kwds):
"""Lag plot for time series.
Parameters
----------
series : Time series
lag : lag of the scatter plot, default 1
ax : Matplotlib axis object, optional
kwds : Matplotlib scatter method keyword arguments, optional
Returns
-------
clas... |
Autocorrelation plot for time series. | def autocorrelation_plot(series, ax=None, **kwds):
"""
Autocorrelation plot for time series.
Parameters:
-----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns:
-----------
class:`matplo... |
Check a sequence of terms for instances of PandasObject. | 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) |
Align a set of terms | 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):
... |
Reconstruct an object given its type raw value and possibly empty ( None ) axes. | def _reconstruct_object(typ, obj, axes, dtype):
"""Reconstruct an object given its type, raw value, and possibly empty
(None) axes.
Parameters
----------
typ : object
A type
obj : object
The value to use in the type constructor
axes : dict
The axes to use to construc... |
Plots a Series on the given Matplotlib axes or the current axes | 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 : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot(... |
Initialize axes for time - series plotting | 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)]
... |
Get the freq attribute of the ax object if set. Also checks shared axes ( eg when using secondary yaxis sharex = True or twinx ) | def _get_ax_freq(ax):
"""
Get the freq attribute of the ax object if set.
Also checks shared axes (eg when using secondary yaxis, sharex=True
or twinx)
"""
ax_freq = getattr(ax, 'freq', None)
if ax_freq is None:
# check for left/right ax in case of secondary yaxis
if hasattr(... |
Convert seconds to D days HH: MM: SS. F | def format_timedelta_ticks(x, pos, n_decimals):
"""
Convert seconds to 'D days HH:MM:SS.F'
"""
s, ns = divmod(x, 1e9)
m, s = divmod(s, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
decimals = int(ns * 10**(n_decimals - 9))
s = r'{:02d}:{:02d}:{:02d}'.format(int(h), int(m), int(s))
... |
Pretty - formats the date axis ( x - axis ). | def format_dateaxis(subplot, freq, index):
"""
Pretty-formats the date axis (x-axis).
Major and minor ticks are automatically set for the frequency of the
current underlying series. As the dynamic mode is activated by
default, changing the limits of the x axis will intelligently change
the pos... |
Whether all the columns in a DataFrame have the same type. | def _is_homogeneous_type(self):
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.... |
Return a html representation for a particular DataFrame. | 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.
... |
Render a DataFrame to a console - friendly tabular output. % ( shared_params ) s line_width: int optional Width to wrap a line in characters. % ( returns ) s See Also -------- to_html: Convert DataFrame to HTML. | def to_string(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None,
max_rows=None, max_cols=None, show_dimensions=False,
decimal='.', ... |
r Iterator over ( column name Series ) pairs. | def iteritems(self):
r"""
Iterator over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterat... |
Iterate over DataFrame rows as ( index Series ) pairs. | def iterrows(self):
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
it : generator
... |
Iterate over DataFrame rows as namedtuples. | def itertuples(self, index=True, name="Pandas"):
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The... |
Compute the matrix mutiplication between the DataFrame and other. | def dot(self, other):
"""
Compute the matrix mutiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5... |
Construct DataFrame from dict of array - like or dicts. | def from_dict(cls, data, orient='columns', dtype=None, columns=None):
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
... |
Convert the DataFrame to a NumPy array. | 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 be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``fl... |
Convert the DataFrame to a dictionary. | 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
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'records', 'index'}
... |
Write a DataFrame to a Google BigQuery table. | def to_gbq(self, destination_table, project_id=None, chunksize=None,
reauth=False, if_exists='fail', auth_local_webserver=False,
table_schema=None, location=None, progress_bar=True,
credentials=None, verbose=None, private_key=None):
"""
Write a DataFrame to a... |
Convert structured or record ndarray to DataFrame. | def from_records(cls, data, index=None, exclude=None, columns=None,
coerce_float=False, nrows=None):
"""
Convert structured or record ndarray to DataFrame.
Parameters
----------
data : ndarray (structured dtype), list of tuples, dict, or DataFrame
in... |
Convert DataFrame to a NumPy record array. | 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 included as the first field of the record array if
requested.
Parameters
----------
... |
Construct a DataFrame from a list of tuples. | 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 and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
... |
Read CSV file. | def from_csv(cls, path, header=0, sep=',', index_col=0, parse_dates=True,
encoding=None, tupleize_cols=None,
infer_datetime_format=False):
"""
Read CSV file.
.. deprecated:: 0.21.0
Use :func:`read_csv` instead.
It is preferable to use the m... |
Convert to SparseDataFrame. | def to_sparse(self, fill_value=None, kind='block'):
"""
Convert to SparseDataFrame.
Implement the sparse version of the DataFrame meaning that any data
matching a specific value it's omitted in the representation.
The sparse DataFrame allows for a more efficient storage.
... |
Export DataFrame object to Stata dta format. | 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, version=114,
convert_strl=None):
"""
Export DataFrame object to Stata dta format.
Writes... |
Write out the binary feather - format for DataFrames. | 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, fnam... |
Write a DataFrame to the binary parquet format. | def to_parquet(self, fname, engine='auto', compression='snappy',
index=None, partition_cols=None, **kwargs):
"""
Write a DataFrame to the binary parquet format.
.. versionadded:: 0.21.0
This function writes the dataframe as a `parquet file
<https://parquet.ap... |
Render a DataFrame as an HTML table. % ( shared_params ) s bold_rows: bool default True Make the row labels bold in the output. classes: str or list or tuple default None CSS class ( es ) to apply to the resulting html table. escape: bool default True Convert the characters < > and & to HTML - safe sequences. notebook:... | def to_html(self, buf=None, columns=None, col_space=None, header=True,
index=True, na_rep='NaN', formatters=None, float_format=None,
sparsify=None, index_names=True, justify=None, max_rows=None,
max_cols=None, show_dimensions=False, decimal='.',
bold_rows=... |
Print a concise summary of a DataFrame. | def info(self, verbose=None, buf=None, max_cols=None, memory_usage=None,
null_counts=None):
"""
Print a concise summary of a DataFrame.
This method prints information about a DataFrame including
the index dtype and column dtypes, non-null values and memory usage.
P... |
Return the memory usage of each column in bytes. | 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
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
... |
Transpose index and columns. | def transpose(self, *args, **kwargs):
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
c... |
Quickly retrieve single value at passed column and index. | 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[] accessors instead.
Parameters
----------
index : row label
col : column label
takeable :... |
Put single value at passed column and index. | 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 instead.
Parameters
----------
index : row label
col : column label
value : scalar
... |
Parameters ---------- i: int slice or sequence of integers axis: int | def _ixs(self, i, axis=0):
"""
Parameters
----------
i : int, slice, or sequence of integers
axis : int
Notes
-----
If slice passed, the resulting data will be a view.
"""
# irow
if axis == 0:
if isinstance(i, slice):
... |
Query the columns of a DataFrame with a boolean expression. | def query(self, expr, inplace=False, **kwargs):
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' cha... |
Evaluate a string describing operations on DataFrame columns. | def eval(self, expr, inplace=False, **kwargs):
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user i... |
Return a subset of the DataFrame s columns based on the column dtypes. | def select_dtypes(self, include=None, exclude=None):
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
... |
Provide boxed values for a column. | 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) |
Ensure that if we don t have an index that we can create one from the passed value. | def _ensure_valid_index(self, value):
"""
Ensure that if we don't have an index, that we can create one from the
passed value.
"""
# GH5632, make sure that we are a Series convertible
if not len(self.index) and is_list_like(value):
try:
value =... |
Add series to DataFrame in specified column. | def _set_item(self, key, value):
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
... |
Insert column into DataFrame at specified location. | def insert(self, loc, column, value, allow_duplicates=False):
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int... |
r Assign new columns to a DataFrame. | def assign(self, **kwargs):
r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Seri... |
Ensures new columns ( which go into the BlockManager as new blocks ) are always copied and converted into an array. | def _sanitize_column(self, key, value, broadcast=True):
"""
Ensures new columns (which go into the BlockManager as new blocks) are
always copied and converted into an array.
Parameters
----------
key : object
value : scalar, Series, or array-like
broadcas... |
Label - based fancy indexing function for DataFrame. | def lookup(self, row_labels, col_labels):
"""
Label-based "fancy indexing" function for DataFrame.
Given equal-length arrays of row and column labels, return an
array of the values corresponding to each (row, col) pair.
Parameters
----------
row_labels : sequenc... |
We are guaranteed non - Nones in the axes. | def _reindex_multi(self, axes, copy, fill_value):
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes['index'])
new_columns, col_indexer = self.columns.reindex(axes['columns'])
if row_indexer is not None and col_indexer is n... |
Drop specified labels from rows or columns. | def drop(self, labels=None, axis=0, index=None, columns=None,
level=None, inplace=False, errors='raise'):
"""
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names... |
Alter axes labels. | def rename(self, *args, **kwargs):
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
See the :ref:`user guide <basics.rename>` for more.
P... |
Set the DataFrame index using existing columns. | def set_index(self, keys, drop=True, append=False, inplace=False,
verify_integrity=False):
"""
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing
columns or arrays (of the correct length). The index can repla... |
Reset the index or a level of it. | def reset_index(self, level=None, drop=False, inplace=False, col_level=0,
col_fill=''):
"""
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead.
If the DataFrame has a MultiIndex, this method can remove one or more
... |
Remove missing values. | def dropna(self, axis=0, how='any', thresh=None, subset=None,
inplace=False):
"""
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
... |
Return DataFrame with duplicate rows removed optionally only considering certain columns. Indexes including time indexes are ignored. | def drop_duplicates(self, subset=None, keep='first', inplace=False):
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns. Indexes, including time indexes
are ignored.
Parameters
----------
subset : column label or sequenc... |
Return boolean Series denoting duplicate rows optionally only considering certain columns. | def duplicated(self, subset=None, keep='first'):
"""
Return boolean Series denoting duplicate rows, optionally only
considering certain columns.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for iden... |
Return the first n rows ordered by columns in descending order. | def nlargest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for or... |
Return the first n rows ordered by columns in ascending order. | def nsmallest(self, n, columns, keep='first'):
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for or... |
Swap levels i and j in a MultiIndex on a particular axis. | def swaplevel(self, i=-2, j=-1, axis=0):
"""
Swap levels i and j in a MultiIndex on a particular axis.
Parameters
----------
i, j : int, string (can be mixed)
Level of index to be swapped. Can pass level name as string.
Returns
-------
DataFr... |
Rearrange index levels using input order. May not drop or duplicate levels. | def reorder_levels(self, order, axis=0):
"""
Rearrange index levels using input order. May not drop or
duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
(positio... |
Perform column - wise combine with another DataFrame. | def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the un... |
Update null elements with value in the same location in other. | def combine_first(self, other):
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the... |
Modify in place using non - NA values from another DataFrame. | def update(self, other, join='left', overwrite=True, filter_func=None,
errors='ignore'):
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coerci... |
Stack the prescribed level ( s ) from columns to index. | def stack(self, level=-1, dropna=True):
"""
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pi... |
Pivot a level of the ( necessarily hierarchical ) index labels returning a DataFrame having a new level of column labels whose inner - most level consists of the pivoted index labels. | def unstack(self, level=-1, fill_value=None):
"""
Pivot a level of the (necessarily hierarchical) index labels, returning
a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output wil... |
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