py2lispIDyOM.extract.MelodyInfo

class py2lispIDyOM.extract.MelodyInfo(exp_pitch_element_list, parent_experiment, *args, **kw)

A melody object (pd.DataFrame) that contains all data in a single melody inherit from the parent Experiment.

Public Data Attributes:

Inherited from DataFrame

axes	Return a list representing the axes of the DataFrame.
shape	Return a tuple representing the dimensionality of the DataFrame.
style	Returns a Styler object.
T	rtype: DataFrame
index	The index (row labels) of the DataFrame.
columns	The column labels of the DataFrame.
values	Return a Numpy representation of the DataFrame.

Inherited from NDFrame

attrs	Dictionary of global attributes of this dataset.
flags	Get the properties associated with this pandas object.
shape	Return a tuple of axis dimensions
axes	Return index label(s) of the internal NDFrame
ndim	Return an int representing the number of axes / array dimensions.
size	Return an int representing the number of elements in this object.
empty	Indicator whether Series/DataFrame is empty.
values
dtypes	Return the dtypes in the DataFrame.

Inherited from IndexingMixin

iloc	Purely integer-location based indexing for selection by position.
loc	Access a group of rows and columns by label(s) or a boolean array.
at	Access a single value for a row/column label pair.
iat	Access a single value for a row/column pair by integer position.

Public Methods:

__init__(exp_pitch_element_list, ...)
access_idyom_output_keywords(output_keywords)	Access certain idyom output(s) via its (their) keyword(s).
get_idyom_output_nparray(idyom_output_key)	Get the IDyOM output via its key as a np.array
get_idyom_output_keyword_list()	Get a list of available IDyOM output keyword for this melody.
compute_properties_means(idyom_outputs)	Compute the mean values of the idyom outputs.

Inherited from DataFrame

__init__([data, index, columns, dtype, copy])
__dataframe__([nan_as_null, allow_copy])	Return the dataframe interchange object implementing the interchange protocol.
__repr__()	Return a string representation for a particular DataFrame.
to_string()	Render a DataFrame to a console-friendly tabular output.
items()	Iterate over (column name, Series) pairs.
iteritems()	Iterate over (column name, Series) pairs.
iterrows()	Iterate over DataFrame rows as (index, Series) pairs.
itertuples([index, name])	Iterate over DataFrame rows as namedtuples.
__len__()	Returns length of info axis, but here we use the index.
dot()	Compute the matrix multiplication between the DataFrame and other.
__matmul__()	Matrix multiplication using binary @ operator in Python>=3.5.
__rmatmul__(other)	Matrix multiplication using binary @ operator in Python>=3.5.
from_dict(data[, orient, dtype, columns])	Construct DataFrame from dict of array-like or dicts.
to_numpy([dtype, copy, na_value])	Convert the DataFrame to a NumPy array.
to_dict()	Convert the DataFrame to a dictionary.
to_gbq(destination_table[, project_id, ...])	Write a DataFrame to a Google BigQuery table.
from_records(data[, index, exclude, ...])	Convert structured or record ndarray to DataFrame.
to_records([index, column_dtypes, index_dtypes])	Convert DataFrame to a NumPy record array.
to_stata(path[, convert_dates, write_index, ...])	Export DataFrame object to Stata dta format.
to_feather(path, **kwargs)	Write a DataFrame to the binary Feather format.
to_markdown([buf, mode, index, storage_options])	Print DataFrame in Markdown-friendly format.
to_parquet()	Write a DataFrame to the binary parquet format.
to_orc([path, engine, index, engine_kwargs])	Write a DataFrame to the ORC format.
to_html()	Render a DataFrame as an HTML table.
to_xml([path_or_buffer, index, root_name, ...])	Render a DataFrame to an XML document.
info([verbose, buf, max_cols, memory_usage, ...])	Print a concise summary of a DataFrame.
memory_usage([index, deep])	Return the memory usage of each column in bytes.
transpose(*args[, copy])	Transpose index and columns.
__getitem__(key)
isetitem(loc, value)	Set the given value in the column with position 'loc'.
__setitem__(key, value)
query()	Query the columns of a DataFrame with a boolean expression.
eval()	Evaluate a string describing operations on DataFrame columns.
select_dtypes([include, exclude])	Return a subset of the DataFrame's columns based on the column dtypes.
insert(loc, column, value[, allow_duplicates])	Insert column into DataFrame at specified location.
assign(**kwargs)	Assign new columns to a DataFrame.
lookup(row_labels, col_labels)	Label-based "fancy indexing" function for DataFrame.
align(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
set_axis()	Assign desired index to given axis.
reindex([labels, index, columns, axis, ...])	Conform Series/DataFrame to new index with optional filling logic.
drop()	Drop specified labels from rows or columns.
rename()	Alter axes labels.
fillna()	Fill NA/NaN values using the specified method.
pop(item)	Return item and drop from frame.
replace()	Replace values given in to_replace with value.
shift([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
set_index()	Set the DataFrame index using existing columns.
reset_index()	Reset the index, or a level of it.
isna()	Detect missing values.
isnull()	DataFrame.isnull is an alias for DataFrame.isna.
notna()	Detect existing (non-missing) values.
notnull()	DataFrame.notnull is an alias for DataFrame.notna.
dropna()	Remove missing values.
drop_duplicates([subset, keep, inplace, ...])	Return DataFrame with duplicate rows removed.
duplicated([subset, keep])	Return boolean Series denoting duplicate rows.
sort_values()	Sort by the values along either axis.
sort_index()	Sort object by labels (along an axis).
value_counts([subset, normalize, sort, ...])	Return a Series containing counts of unique rows in the DataFrame.
nlargest(n, columns[, keep])	Return the first n rows ordered by columns in descending order.
nsmallest(n, columns[, keep])	Return the first n rows ordered by columns in ascending order.
swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex.
reorder_levels(order[, axis])	Rearrange index levels using input order.
__divmod__(other)	rtype: tuple[DataFrame, DataFrame]
__rdivmod__(other)	rtype: tuple[DataFrame, DataFrame]
compare(other[, align_axis, keep_shape, ...])	Compare to another DataFrame and show the differences.
combine(other, func[, fill_value, overwrite])	Perform column-wise combine with another DataFrame.
combine_first(other)	Update null elements with value in the same location in other.
update(other[, join, overwrite, ...])	Modify in place using non-NA values from another DataFrame.
groupby([by, axis, level, as_index, sort, ...])	Group DataFrame using a mapper or by a Series of columns.
pivot([index, columns, values])	Return reshaped DataFrame organized by given index / column values.
pivot_table([values, index, columns, ...])	Create a spreadsheet-style pivot table as a DataFrame.
stack([level, dropna])	Stack the prescribed level(s) from columns to index.
explode(column[, ignore_index])	Transform each element of a list-like to a row, replicating index values.
unstack([level, fill_value])	Pivot a level of the (necessarily hierarchical) index labels.
melt([id_vars, value_vars, var_name, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
diff([periods, axis])	First discrete difference of element.
aggregate([func, axis])	Aggregate using one or more operations over the specified axis.
agg([func, axis])	Aggregate using one or more operations over the specified axis.
any(*[, axis, bool_only, skipna, level])	Return whether any element is True, potentially over an axis.
transform(func[, axis])	Call func on self producing a DataFrame with the same axis shape as self.
apply(func[, axis, raw, result_type, args])	Apply a function along an axis of the DataFrame.
applymap(func[, na_action])	Apply a function to a Dataframe elementwise.
append(other[, ignore_index, ...])	Append rows of other to the end of caller, returning a new object.
join(other[, on, how, lsuffix, rsuffix, ...])	Join columns of another DataFrame.
merge(right[, how, on, left_on, right_on, ...])	Merge DataFrame or named Series objects with a database-style join.
round([decimals])	Round a DataFrame to a variable number of decimal places.
corr([method, min_periods, numeric_only])	Compute pairwise correlation of columns, excluding NA/null values.
cov([min_periods, ddof, numeric_only])	Compute pairwise covariance of columns, excluding NA/null values.
corrwith(other[, axis, drop, method, ...])	Compute pairwise correlation.
count([axis, level, numeric_only])	Count non-NA cells for each column or row.
nunique([axis, dropna])	Count number of distinct elements in specified axis.
idxmin([axis, skipna, numeric_only])	Return index of first occurrence of minimum over requested axis.
idxmax([axis, skipna, numeric_only])	Return index of first occurrence of maximum over requested axis.
mode([axis, numeric_only, dropna])	Get the mode(s) of each element along the selected axis.
quantile()	Return values at the given quantile over requested axis.
asfreq(freq[, method, how, normalize, ...])	Convert time series to specified frequency.
resample(rule[, axis, closed, label, ...])	Resample time-series data.
to_timestamp([freq, how, axis, copy])	Cast to DatetimeIndex of timestamps, at beginning of period.
to_period([freq, axis, copy])	Convert DataFrame from DatetimeIndex to PeriodIndex.
isin(values)	Whether each element in the DataFrame is contained in values.
plot	alias of PlotAccessor
hist([column, by, grid, xlabelsize, xrot, ...])	Make a histogram of the DataFrame's columns.
boxplot([column, by, ax, fontsize, rot, ...])	Make a box plot from DataFrame columns.
sparse	alias of SparseFrameAccessor
ffill()	Synonym for DataFrame.fillna() with method='ffill'.
bfill()	Synonym for DataFrame.fillna() with method='bfill'.
clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
interpolate([method, axis, limit, inplace, ...])	Fill NaN values using an interpolation method.
where()	Replace values where the condition is False.
mask()	Replace values where the condition is True.
all([axis, bool_only, skipna, level])	Return whether all elements are True, potentially over an axis.
mad([axis, skipna, level])	Return the mean absolute deviation of the values over the requested axis.
sem([axis, skipna, level, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
var([axis, skipna, level, ddof, numeric_only])	Return unbiased variance over requested axis.
std([axis, skipna, level, ddof, numeric_only])	Return sample standard deviation over requested axis.
cummin([axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
cummax([axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
cumsum([axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
cumprod([axis, skipna])	Return cumulative product over a DataFrame or Series axis.
sum([axis, skipna, level, numeric_only, ...])	Return the sum of the values over the requested axis.
prod([axis, skipna, level, numeric_only, ...])	Return the product of the values over the requested axis.
product([axis, skipna, level, numeric_only, ...])	Return the product of the values over the requested axis.
mean([axis, skipna, level, numeric_only])	Return the mean of the values over the requested axis.
skew([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis.
kurt([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis.
kurtosis([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis.
median([axis, skipna, level, numeric_only])	Return the median of the values over the requested axis.
max([axis, skipna, level, numeric_only])	Return the maximum of the values over the requested axis.
min([axis, skipna, level, numeric_only])	Return the minimum of the values over the requested axis.
add(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator add).
radd(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator radd).
sub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator sub).
mul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator mul).
truediv(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
floordiv(other[, axis, level, fill_value])	Get Integer division of dataframe and other, element-wise (binary operator floordiv).
mod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator mod).
pow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator pow).
rmul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator rmul).
rsub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator rsub).
rtruediv(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
rfloordiv(other[, axis, level, fill_value])	Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
rpow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator rpow).
rmod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator rmod).
div(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
rdiv(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
eq(other[, axis, level])	Get Equal to of dataframe and other, element-wise (binary operator eq).
ne(other[, axis, level])	Get Not equal to of dataframe and other, element-wise (binary operator ne).
lt(other[, axis, level])	Get Less than of dataframe and other, element-wise (binary operator lt).
gt(other[, axis, level])	Get Greater than of dataframe and other, element-wise (binary operator gt).
le(other[, axis, level])	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
ge(other[, axis, level])	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
multiply(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator mul).
subtract(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator sub).
divide(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).

Inherited from NDFrame

__init__(data[, copy, attrs])
set_flags(*[, copy, allows_duplicate_labels])	Return a new object with updated flags.
set_axis()	Assign desired index to given axis.
swapaxes(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately.
droplevel(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
pop(item)	rtype: Union[Series, Any]
squeeze([axis])	Squeeze 1 dimensional axis objects into scalars.
rename_axis()	Set the name of the axis for the index or columns.
equals(other)	Test whether two objects contain the same elements.
__neg__()	rtype: TypeVar(NDFrameT, bound= NDFrame)
__pos__()	rtype: TypeVar(NDFrameT, bound= NDFrame)
__invert__()	rtype: TypeVar(NDFrameT, bound= NDFrame)
__nonzero__()	rtype: NoReturn
__bool__()	rtype: NoReturn
bool()	Return the bool of a single element Series or DataFrame.
abs()	Return a Series/DataFrame with absolute numeric value of each element.
__abs__()	rtype: TypeVar(NDFrameT, bound= NDFrame)
__round__([decimals])	rtype: TypeVar(NDFrameT, bound= NDFrame)
__iter__()	Iterate over info axis.
keys()	Get the 'info axis' (see Indexing for more).
items()	Iterate over (label, values) on info axis
__len__()	Returns length of info axis
__contains__(key)	True if the key is in the info axis
__array__([dtype])
__array_wrap__(result[, context])	Gets called after a ufunc and other functions.
__array_ufunc__(ufunc, method, *inputs, **kwargs)
__getstate__()	rtype: dict[str, Any]
__setstate__(state)	rtype: None
__repr__()	Return a string representation for a particular object.
to_excel(excel_writer[, sheet_name, na_rep, ...])	Write object to an Excel sheet.
to_json([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
to_hdf(path_or_buf, key[, mode, complevel, ...])	Write the contained data to an HDF5 file using HDFStore.
to_sql(name, con[, schema, if_exists, ...])	Write records stored in a DataFrame to a SQL database.
to_pickle(path[, compression, protocol, ...])	Pickle (serialize) object to file.
to_clipboard([excel, sep])	Copy object to the system clipboard.
to_xarray()	Return an xarray object from the pandas object.
to_latex()	Render object to a LaTeX tabular, longtable, or nested table.
to_csv()	Write object to a comma-separated values (csv) file.
take(indices[, axis, is_copy])	Return the elements in the given positional indices along an axis.
xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.
__getitem__(item)
__delitem__(key)	Delete item
get(key[, default])	Get item from object for given key (ex: DataFrame column).
reindex_like(other[, method, copy, limit, ...])	Return an object with matching indices as other object.
drop()	rtype: Optional[TypeVar(NDFrameT, bound= NDFrame)]
add_prefix(prefix)	Prefix labels with string prefix.
add_suffix(suffix)	Suffix labels with string suffix.
sort_values()	Sort by the values along either axis.
sort_index()	rtype: Optional[TypeVar(NDFrameT, bound= NDFrame)]
reindex(*args, **kwargs)	Conform Series/DataFrame to new index with optional filling logic.
filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
head([n])	Return the first n rows.
tail([n])	Return the last n rows.
sample([n, frac, replace, weights, ...])	Return a random sample of items from an axis of object.
pipe(func, *args, **kwargs)	Apply chainable functions that expect Series or DataFrames.
__finalize__(other[, method])	Propagate metadata from other to self.
__getattr__(name)	After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.
__setattr__(name, value)	After regular attribute access, try setting the name This allows simpler access to columns for interactive use.
astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
copy([deep])	Make a copy of this object's indices and data.
__copy__([deep])	rtype: TypeVar(NDFrameT, bound= NDFrame)
__deepcopy__([memo])	Parameters memo, default None Standard signature. Unused
infer_objects()	Attempt to infer better dtypes for object columns.
convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
fillna()	Fill NA/NaN values using the specified method.
ffill()	Synonym for DataFrame.fillna() with method='ffill'.
pad([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
bfill()	Synonym for DataFrame.fillna() with method='bfill'.
backfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
replace()	Replace values given in to_replace with value.
interpolate([method, axis, limit, inplace, ...])	Fill NaN values using an interpolation method.
asof(where[, subset])	Return the last row(s) without any NaNs before where.
isna()	Detect missing values.
isnull()	Detect missing values.
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
asfreq(freq[, method, how, normalize, ...])	Convert time series to specified frequency.
at_time(time[, asof, axis])	Select values at particular time of day (e.g., 9:30AM).
between_time(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
resample(rule[, axis, closed, label, ...])	Resample time-series data.
first(offset)	Select initial periods of time series data based on a date offset.
last(offset)	Select final periods of time series data based on a date offset.
rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
compare(other[, align_axis, keep_shape, ...])	Compare to another Series/DataFrame and show the differences.
align(other[, join, axis, level, copy, ...])	Align two objects on their axes with the specified join method.
where()	Replace values where the condition is False.
mask()	Replace values where the condition is True.
shift([periods, freq, axis, fill_value])	Shift index by desired number of periods with an optional time freq.
slice_shift([periods, axis])	Equivalent to shift without copying data.
tshift([periods, freq, axis])	Shift the time index, using the index's frequency if available.
truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.
tz_convert(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
tz_localize(tz[, axis, level, copy, ...])	Localize tz-naive index of a Series or DataFrame to target time zone.
describe([percentiles, include, exclude, ...])	Generate descriptive statistics.
pct_change([periods, fill_method, limit, freq])	Percentage change between the current and a prior element.
any([axis, bool_only, skipna, level])	rtype: DataFrame | Series | bool
all([axis, bool_only, skipna, level])	rtype: Series | bool
cummax([axis, skipna])
cummin([axis, skipna])
cumsum([axis, skipna])
cumprod([axis, skipna])
sem([axis, skipna, level, ddof, numeric_only])	rtype: Series | float
var([axis, skipna, level, ddof, numeric_only])	rtype: Series | float
std([axis, skipna, level, ddof, numeric_only])	rtype: Series | float
min([axis, skipna, level, numeric_only])
max([axis, skipna, level, numeric_only])
mean([axis, skipna, level, numeric_only])	rtype: Series | float
median([axis, skipna, level, numeric_only])	rtype: Series | float
skew([axis, skipna, level, numeric_only])	rtype: Series | float
kurt([axis, skipna, level, numeric_only])	rtype: Series | float
kurtosis([axis, skipna, level, numeric_only])	rtype: Series | float
sum([axis, skipna, level, numeric_only, ...])
prod([axis, skipna, level, numeric_only, ...])
product([axis, skipna, level, numeric_only, ...])
mad([axis, skipna, level])	{desc}
rolling(window[, min_periods, center, ...])	Provide rolling window calculations.
expanding([min_periods, center, axis, method])	Provide expanding window calculations.
ewm([com, span, halflife, alpha, ...])	Provide exponentially weighted (EW) calculations.
__iadd__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__isub__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__imul__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__itruediv__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__ifloordiv__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__imod__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__ipow__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__iand__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__ior__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
__ixor__(other)	rtype: TypeVar(NDFrameT, bound= NDFrame)
first_valid_index()	Return index for first non-NA value or None, if no non-NA value is found.
last_valid_index()	Return index for last non-NA value or None, if no non-NA value is found.

Inherited from PandasObject

__repr__()	Return a string representation for a particular object.
__sizeof__()	Generates the total memory usage for an object that returns either a value or Series of values

Inherited from DirNamesMixin

__dir__()

Provide method name lookup and completion.

Inherited from OpsMixin

__eq__(other)	Return self==value.
__ne__(other)	Return self!=value.
__lt__(other)	Return self<value.
__le__(other)	Return self<=value.
__gt__(other)	Return self>value.
__ge__(other)	Return self>=value.
__and__(other)
__rand__(other)
__or__(other)	Return self|value.
__ror__(other)	Return value|self.
__xor__(other)
__rxor__(other)
__add__(other)
__radd__(other)
__sub__(other)
__rsub__(other)
__mul__(other)
__rmul__(other)
__truediv__(other)
__rtruediv__(other)
__floordiv__(other)
__rfloordiv__(other)
__mod__(other)
__rmod__(other)
__divmod__(other)
__rdivmod__(other)
__pow__(other)
__rpow__(other)

Private Data Attributes:

_metadata

Inherited from DataFrame

_internal_names_set
_typ
_HANDLED_TYPES
_accessors
_hidden_attrs
_constructor	Used when a manipulation result has the same dimensions as the original.
_is_homogeneous_type	Whether all the columns in a DataFrame have the same type.
_can_fast_transpose	Can we transpose this DataFrame without creating any new array objects.
_values	Analogue to ._values that may return a 2D ExtensionArray.
_series
_agg_summary_and_see_also_doc
_agg_examples_doc
_AXIS_ORDERS
_AXIS_TO_AXIS_NUMBER
_AXIS_LEN
_info_axis_number
_info_axis_name
_AXIS_NUMBERS	Deprecated since version 1.1.0.
_AXIS_NAMES	Deprecated since version 1.1.0.
_mgr
_internal_names
_metadata
_is_copy

Inherited from NDFrame

_internal_names
_internal_names_set
_accessors
_hidden_attrs
_metadata
_is_copy
_constructor	Used when a manipulation result has the same dimensions as the original.
_data
_stat_axis_number
_stat_axis_name
_AXIS_TO_AXIS_NUMBER
_AXIS_NUMBERS	Deprecated since version 1.1.0.
_AXIS_NAMES	Deprecated since version 1.1.0.
_info_axis	rtype: Index
_stat_axis	rtype: Index
_is_view	Return boolean indicating if self is view of another array
_is_mixed_type	rtype: bool
_values	internal implementation
_mgr
_attrs
_typ
_AXIS_ORDERS
_info_axis_number
_info_axis_name
_AXIS_LEN

Inherited from PandasObject

_constructor	Class constructor (for this class it's just __class__.
_cache

Inherited from DirNamesMixin

_accessors
_hidden_attrs

Private Methods:

_get_onset_time_in_seconds()
_get_onset_beat_nparray()
_get_melody_name_pprint()	rtype: str
_get_pianoroll_pitch_distribution()
_get_pianoroll_original()
_get_onset_time_vector()
_get_surprisal_array()

Inherited from DataFrame

_repr_fits_vertical_()	Check length against max_rows.
_repr_fits_horizontal_([ignore_width])	Check if full repr fits in horizontal boundaries imposed by the display options width and max_columns.
_info_repr()	True if the repr should show the info view.
_repr_html_()	Return a html representation for a particular DataFrame.
_from_arrays(arrays, columns, index[, ...])	Create DataFrame from a list of arrays corresponding to the columns.
_ixs(i[, axis])	Parameters i : int axis : int
_get_column_array(i)	Get the values of the i'th column (ndarray or ExtensionArray, as stored in the Block)
_iter_column_arrays()	Iterate over the arrays of all columns in order.
_getitem_bool_array(key)
_getitem_multilevel(key)
_get_value(index, col[, takeable])	Quickly retrieve single value at passed column and index.
_setitem_slice(key, value)
_setitem_array(key, value)
_iset_not_inplace(key, value)
_setitem_frame(key, value)
_set_item_frame_value(key, value)	rtype: None
_iset_item_mgr(loc, value[, inplace])	rtype: None
_set_item_mgr(key, value)	rtype: None
_iset_item(loc, value)	rtype: None
_set_item(key, value)	Add series to DataFrame in specified column.
_set_value(index, col, value[, takeable])	Put single value at passed column and index.
_ensure_valid_index(value)	Ensure that if we don't have an index, that we can create one from the passed value.
_box_col_values(values, loc)	Provide boxed values for a column.
_clear_item_cache()	rtype: None
_get_item_cache(item)	Return the cached item, item represents a label indexer.
_reset_cacher()	Reset the cacher.
_maybe_cache_changed(item, value, inplace)	The object has called back to us saying maybe it has changed.
_sanitize_column(value)	Ensures new columns (which go into the BlockManager as new blocks) are always copied and converted into an array.
_reindex_axes(axes, level, limit, tolerance, ...)	Perform the reindex for all the axes.
_reindex_index(new_index, method, copy, level)
_reindex_columns(new_columns, method, copy, ...)
_reindex_multi(axes, copy, fill_value)	We are guaranteed non-Nones in the axes.
_replace_columnwise(mapping, inplace, regex)	Dispatch to Series.replace column-wise.
_cmp_method(other, op)
_arith_method(other, op)
_logical_method(other, op)
_dispatch_frame_op(right, func[, axis])	Evaluate the frame operation func(left, right) by evaluating column-by-column, dispatching to the Series implementation.
_combine_frame(other, func[, fill_value])
_construct_result(result)	Wrap the result of an arithmetic, comparison, or logical operation.
_gotitem(key, ndim[, subset])	Sub-classes to define.
_append(other[, ignore_index, ...])	rtype: DataFrame
_join_compat(other[, on, how, lsuffix, ...])
_count_level(level[, axis, numeric_only])
_reduce(op, name, *[, axis, skipna, ...])
_reduce_axis1(name, func, skipna)	Special case for _reduce to try to avoid a potentially-expensive transpose.
_get_agg_axis(axis_num)	Let's be explicit about this.
_to_dict_of_blocks([copy])	Return a dict of dtype -> Constructor Types that each is a homogeneous dtype.

Inherited from NDFrame

_init_mgr(mgr, axes[, dtype, copy])	passed a manager and a axes dict
_as_manager(typ[, copy])	Private helper function to create a DataFrame with specific manager.
_validate_dtype(dtype)	validate the passed dtype
_construct_axes_dict([axes])	Return an axes dictionary for myself.
_construct_axes_from_arguments(args, kwargs)	Construct and returns axes if supplied in args/kwargs.
_get_axis_number(axis)	rtype: int
_get_axis_name(axis)	rtype: str
_get_axis(axis)	rtype: Index
_get_block_manager_axis(axis)	Map the axis to the block_manager axis.
_get_axis_resolvers(axis)	rtype: dict[str, Series | MultiIndex]
_get_index_resolvers()	rtype: dict[Hashable, Series | MultiIndex]
_get_cleaned_column_resolvers()	Return the special character free column resolvers of a dataframe.
_set_axis_nocheck(labels, axis, inplace, copy)
_set_axis(axis, labels)	rtype: None
_rename([mapper, index, columns, axis, ...])	rtype: Optional[TypeVar(NDFrameT, bound= NDFrame)]
_set_axis_name(name[, axis, inplace])	Set the name(s) of the axis.
_indexed_same(other)	rtype: bool
_is_level_reference(key[, axis])	Test whether a key is a level reference for a given axis.
_is_label_reference(key[, axis])	Test whether a key is a label reference for a given axis.
_is_label_or_level_reference(key[, axis])	Test whether a key is a label or level reference for a given axis.
_check_label_or_level_ambiguity(key[, axis])	Check whether key is ambiguous.
_get_label_or_level_values(key[, axis])	Return a 1-D array of values associated with key, a label or level from the given axis.
_drop_labels_or_levels(keys[, axis])	Drop labels and/or levels for the given axis.
_repr_latex_()	Returns a LaTeX representation for a particular object.
_repr_data_resource_()	Not a real Jupyter special repr method, but we use the same naming convention.
_reset_cacher()	Reset the cacher.
_maybe_update_cacher([clear, ...])	See if we need to update our parent cacher if clear, then clear our cache.
_clear_item_cache()	rtype: None
_take(indices[, axis, convert_indices])	Internal version of the take allowing specification of additional args.
_take_with_is_copy(indices[, axis])	Internal version of the take method that sets the _is_copy attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning).
_slice(slobj[, axis])	Construct a slice of this container.
_set_is_copy(ref[, copy])	rtype: None
_check_is_chained_assignment_possible()	Check if we are a view, have a cacher, and are of mixed type.
_check_setitem_copy([t, force])	Parameters t : str, the type of setting error force : bool, default False :5: (ERROR/3) Unexpected indentation. If True, then force showing an error.
_check_inplace_and_allows_duplicate_labels(inplace)
_drop_axis(labels, axis[, level, errors, ...])	Drop labels from specified axis.
_update_inplace(result[, verify_is_copy])	Replace self internals with result.
_reindex_axes(axes, level, limit, tolerance, ...)	Perform the reindex for all the axes.
_needs_reindex_multi(axes, method, level)	Check if we do need a multi reindex.
_reindex_multi(axes, copy, fill_value)
_reindex_with_indexers(reindexers[, ...])	allow_dups indicates an internal call here
_dir_additions()	add the string-like attributes from the info_axis.
_protect_consolidate(f)	Consolidate _mgr -- if the blocks have changed, then clear the cache
_consolidate_inplace()	Consolidate data in place and return None
_consolidate()	Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray).
_check_inplace_setting(value)	check whether we allow in-place setting with this type of value
_get_numeric_data()	rtype: TypeVar(NDFrameT, bound= NDFrame)
_get_bool_data()
_convert([datetime, numeric, timedelta])	Attempt to infer better dtype for object columns.
_clip_with_scalar(lower, upper[, inplace])
_clip_with_one_bound(threshold, method, ...)
_align_frame(other[, join, axis, level, ...])
_align_series(other[, join, axis, level, ...])
_where(cond[, other, inplace, axis, level])	Equivalent to public method where, except that other is not applied as a function even if callable.
_agg_by_level(name[, axis, level, skipna])
_logical_func(name, func[, axis, bool_only, ...])	rtype: Series | bool
_accum_func(name, func[, axis, skipna])
_stat_function_ddof(name, func[, axis, ...])	rtype: Series | float
_stat_function(name, func[, axis, skipna, ...])
_min_count_stat_function(name, func[, axis, ...])
_add_numeric_operations()	Add the operations to the cls; evaluate the doc strings again
_inplace_method(other, op)	Wrap arithmetic method to operate inplace.
_find_valid_index(*, how)	Retrieves the index of the first valid value.

Inherited from PandasObject

_reset_cache([key])

Reset cached properties.

Inherited from DirNamesMixin

_dir_deletions()	Delete unwanted __dir__ for this object.
_dir_additions()	Add additional __dir__ for this object.

Inherited from OpsMixin

_cmp_method(other, op)
_logical_method(other, op)
_arith_method(other, op)

---

access_idyom_output_keywords(output_keywords)

Access certain idyom output(s) via its (their) keyword(s).

Parameters:

output_keywords (List[str]) – A list of IDyOM output keywords (e.g., [‘cpitch.information.content’, ‘onset’, ‘entropy’])

Returns:

a dataframe containing all data of the selected IDyOM outputs according to the specified keywords.

Return type:

pd.DataFrame

compute_properties_means(idyom_outputs)

Compute the mean values of the idyom outputs.

Parameters:

idyom_outputs (List[str]) – list of idyom output keyword to compute the means

Type:

List[str]

Returns:

the mean values of selected idyom outputs

Return type:

DataFrame

get_idyom_output_keyword_list()

Get a list of available IDyOM output keyword for this melody.

Returns:

a list of available IDyOM output keyword

Return type:

list(str)

get_idyom_output_nparray(idyom_output_key)

Get the IDyOM output via its key as a np.array

Parameters:

idyom_output_key (str) – list of str

Returns:

an array of the specified output values

Return type:

np.array