awkward

Submodules

behaviors
builder
contents
cppyy
errors
forms
forth
highlevel
index
jax
numba
operations
prettyprint
record
types
typetracer

Attributes

behavior
__all__

Classes

Array
ArrayBuilder
Record

Functions

mixin_class(registry[, name])
mixin_class_method(ufunc[, rhs, transpose])
all(array[, axis, keepdims, mask_identity, highlevel, ...])	Returns whether all elements are True over one or all levels of nesting.
almost_equal(left, right, *[, rtol, atol, ...])	Returns True if two arrays are equal within the given tolerances and options.
angle(val[, deg, highlevel, behavior, attrs])	Returns the counterclockwise angle of each complex element in radians or degrees.
any(array[, axis, keepdims, mask_identity, highlevel, ...])	Returns whether any elements are True over one or all levels of nesting.
argcartesian(arrays[, axis, nested, parameters, ...])	Computes the Cartesian product of arrays, returning integer indexes.
argcombinations(array, n, *[, replacement, axis, ...])	Computes combinations of n items from an array, returning integer indexes.
argmax(array[, axis, keepdims, mask_identity, ...])	Returns the index of the maximum value over one or all levels of nesting.
nanargmax(array[, axis, keepdims, mask_identity, ...])	Returns the index of the maximum value, treating NaN values as missing.
argmin(array[, axis, keepdims, mask_identity, ...])	Returns the index of the minimum value over one or all levels of nesting.
nanargmin(array[, axis, keepdims, mask_identity, ...])	Returns the index of the minimum value, treating NaN values as missing.
argsort(array[, axis, ascending, stable, highlevel, ...])	Sorts an array along an axis, returning integer indexes.
array_equal(a1, a2[, equal_nan, dtype_exact, ...])	Returns True if two arrays have the same shape and elements.
backend(*arrays)	Returns the name of the backend used by the given arrays.
broadcast_arrays(*arrays[, depth_limit, ...])	Broadcasts arrays together so they can be combined element-by-element.
broadcast_fields(*arrays[, highlevel, behavior, attrs])	Returns a list of arrays whose record types contain the same fields.
cartesian(arrays[, axis, nested, parameters, ...])	Computes the Cartesian product (cross product) of data from a set of arrays.
categories(array[, highlevel, behavior, attrs])	Returns the categories of a categorical array.
combinations(array, n, *[, replacement, axis, fields, ...])	Computes combinations of n items from an array, without replacement.
concatenate(arrays[, axis, mergebool, highlevel, ...])	Returns an array with the given arrays concatenated along an axis.
copy(array)	Returns a deep copy of the array (no memory shared with original).
corr(x, y[, weight, axis, keepdims, mask_identity, ...])	Computes the correlation of x and y over one or all levels of nesting.
count(array[, axis, keepdims, mask_identity, ...])	Counts an array's elements over one or all levels of nesting.
count_nonzero(array[, axis, keepdims, mask_identity, ...])	Counts an array's nonzero elements over one or all levels of nesting.
covar(x, y[, weight, axis, keepdims, mask_identity, ...])	Computes the covariance of x and y over one or all levels of nesting.
drop_none(array[, axis, highlevel, behavior, attrs])
enforce_type(array, type, *[, highlevel, behavior, attrs])	Returns an array whose structure is modified to match the given type.
fields(array)
fill_none(array, value[, axis, highlevel, behavior, attrs])
firsts(array[, axis, highlevel, behavior, attrs])
flatten(array[, axis, highlevel, behavior, attrs])	Returns an array with one or all levels of nesting removed.
from_arrow(array, *[, generate_bitmasks, highlevel, ...])	Converts an Apache Arrow array into an Awkward Array.
from_arrow_schema(schema)	Converts an Apache Arrow schema into an Awkward Form.
from_avro_file(file[, limit_entries, debug_forth, ...])	Reads an Avro file as an Awkward Array.
from_buffers(form, length, container[, buffer_key, ...])	Reconstitutes an Awkward Array from a Form, length, and memory buffers.
from_categorical(array, *[, highlevel, behavior, attrs])	Replaces categorical data with equivalent non-categorical data.
from_cupy(array, *[, regulararray, highlevel, ...])
from_dlpack(array, *[, prefer_cpu, regulararray, ...])
from_feather(path, *[, columns, use_threads, ...])	Reads a Feather file as an Awkward Array (through pyarrow).
from_iter(iterable, *[, allow_record, highlevel, ...])	Converts Python data into an Awkward Array.
from_jax(array, *[, regulararray, highlevel, ...])
from_json(source, *[, line_delimited, schema, ...])	Reads JSON from a string, bytes, file, or URL into an Awkward Array.
from_numpy(array, *[, regulararray, recordarray, ...])
from_parquet(path, *[, columns, row_groups, ...])	Reads data from a local or remote Parquet file or collection of files.
from_raggedtensor(array)
from_rdataframe(rdf, columns, *[, keep_order, ...])
from_regular(array[, axis, highlevel, behavior, attrs])	Converts one or all regular axes into irregular ones.
from_safetensors(source, *[, storage_options, ...])	Reads a safetensors file as an Awkward Array.
from_tensorflow(array)
from_torch(array)
full_like(array, fill_value, *[, dtype, ...])	Returns an array with the same structure as the input, filled with a given value.
imag(val[, highlevel, behavior, attrs])	Returns the imaginary components of the given array elements.
is_categorical(array)	Returns True if the array is categorical.
is_none(array[, axis, highlevel, behavior, attrs])
is_tuple(array)	Returns True if a record, or the outermost record of an array, is a tuple.
is_valid(array, *[, exception])	Returns True if the array has no structural errors and False otherwise.
isclose(a, b[, rtol, atol, equal_nan, highlevel, ...])	Returns a boolean array of element-wise approximate-equality between two arrays.
linear_fit(x, y[, weight, axis, keepdims, ...])	Computes the linear fit of y against x over one or all levels of nesting.
local_index(array[, axis, highlevel, behavior, attrs])	Returns the within-list index of each element at a given axis depth.
mask(array, mask, *[, valid_when, highlevel, ...])
materialize(array[, highlevel, behavior, attrs])	Materializes any virtual buffers in the array.
max(array[, axis, keepdims, initial, mask_identity, ...])	Returns the maximum value over one or all levels of nesting.
nanmax(array[, axis, keepdims, initial, ...])	Returns the maximum value, treating NaN values as missing.
mean(x[, weight, axis, keepdims, mask_identity, ...])	Computes the mean over one or all levels of nesting.
nanmean(x[, weight, axis, keepdims, mask_identity, ...])	Computes the mean, treating NaN values as missing.
merge_option_of_records(array[, axis, highlevel, ...])	Simplifies options of records into records of options.
merge_union_of_records(array[, axis, highlevel, ...])	Simplifies unions of records into records of options.
metadata_from_parquet(path, *[, storage_options, ...])
min(array[, axis, keepdims, initial, mask_identity, ...])	Returns the minimum value over one or all levels of nesting.
nanmin(array[, axis, keepdims, initial, ...])	Returns the minimum value, treating NaN values as missing.
moment(x, n[, weight, axis, keepdims, mask_identity, ...])	Computes the `n`th moment over one or all levels of nesting.
nan_to_none(array, *[, highlevel, behavior, attrs])
nan_to_num(array[, copy, nan, posinf, neginf, ...])
num(array[, axis, highlevel, behavior, attrs])	Returns the number of elements at a given axis depth.
ones_like(array, *[, dtype, including_unknown, ...])	Returns an array with the same structure as the input, filled with ones.
pad_none(array, target[, axis, clip, highlevel, ...])	Increases the lengths of lists to a target length by adding None values.
parameters(array)
nanprod(array[, axis, keepdims, mask_identity, ...])	Multiplies an array's elements, treating NaN values as missing.
prod(array[, axis, keepdims, mask_identity, ...])	Multiplies an array's elements over one or all levels of nesting.
ptp(array[, axis, keepdims, mask_identity, highlevel, ...])	Returns the range of values over one or all levels of nesting.
ravel(array, *[, highlevel, behavior, attrs])	Returns an array with all levels of nesting removed.
real(array[, highlevel, behavior, attrs])	Returns the real components of the given array elements.
round(array[, decimals, out, highlevel, behavior, attrs])	Rounds each array element to the given number of decimals.
run_lengths(array, *[, highlevel, behavior, attrs])	Returns the lengths of runs of identical values at the deepest level.
singletons(array[, axis, highlevel, behavior, attrs])
softmax(x[, axis, keepdims, mask_identity, highlevel, ...])	Computes the softmax over the innermost level of nesting.
sort(array[, axis, ascending, stable, highlevel, ...])	Returns an array with elements sorted along an axis.
nanstd(x[, weight, ddof, axis, keepdims, ...])	Computes the standard deviation, treating NaN values as missing.
std(x[, weight, ddof, axis, keepdims, mask_identity, ...])	Computes the standard deviation over one or all levels of nesting.
strings_astype(array, to, *[, highlevel, behavior, attrs])	Converts all strings in the array to a new type, leaving the structure untouched.
nansum(array[, axis, keepdims, mask_identity, ...])	Sums an array's elements, treating NaN values as missing.
sum(array[, axis, keepdims, mask_identity, highlevel, ...])	Sums an array's elements over one or all levels of nesting.
to_arrow(array, *[, list_to32, string_to32, ...])
to_arrow_table(array, *[, list_to32, string_to32, ...])
to_backend(array, backend, *[, highlevel, behavior, attrs])
to_buffers(array[, container, buffer_key, form_key, ...])
to_cudf(array)
to_cupy(array)
to_dataframe(array, *[, how, levelname, anonymous])
to_feather(array, destination, *[, list_to32, ...])
to_jax(array)
to_json(array[, file, line_delimited, ...])
to_layout(array, *[, allow_record, allow_unknown, ...])
to_list(array)
to_numpy(array, *[, allow_missing])
to_packed(array, *[, highlevel, behavior, attrs])
to_parquet(array, destination, *[, list_to32, ...])
to_parquet_dataset(directory[, filenames, storage_options])
to_parquet_row_groups(iterator, destination, *[, ...])
to_raggedtensor(array)
to_rdataframe(arrays, *[, flatlist_as_rvec])
to_regular(array[, axis, highlevel, behavior, attrs])
to_safetensors(array, destination, *[, ...])
to_tensorflow(array)
to_torch(array)
transform(transformation, array, *more_arrays[, ...])	Applies a transformation function to every node of one or more arrays.
type(array, *[, behavior])	Returns the high-level type of an array as a Type object.
unflatten(array, counts[, axis, highlevel, behavior, ...])	Returns an array with an additional level of nesting.
unzip(array, *[, how, highlevel, behavior, attrs])	Splits records or tuples into a tuple or dict of arrays, one per field.
validity_error(array, *[, exception])	Returns an error message if the array has a structural error, or empty if valid.
values_astype(array, to, *[, including_unknown, ...])	Converts all numbers in the array to a new type, leaving the structure untouched.
nanvar(x[, weight, ddof, axis, keepdims, ...])	Computes the variance, treating NaN values as missing.
var(x[, weight, ddof, axis, keepdims, mask_identity, ...])	Computes the variance over one or all levels of nesting.
where(condition, *args[, mergebool, highlevel, ...])	Selects elements from x or y by a condition, or finds where it is True.
with_field(array, what[, where, highlevel, behavior, ...])
with_name(array, name, *[, highlevel, behavior, attrs])
with_named_axis(array, named_axis, *[, highlevel, ...])
with_parameter(array, parameter, value, *[, ...])
without_field(array, where, *[, highlevel, behavior, ...])
without_named_axis(array, *[, highlevel, behavior, attrs])
without_parameters(array, *[, highlevel, behavior, attrs])
zeros_like(array, *[, dtype, including_unknown, ...])	Returns an array with the same structure as the input, filled with zeros.
zip(arrays[, depth_limit, parameters, with_name, ...])	Combines arrays into records or tuples, broadcasting them together.
zip_no_broadcast(arrays, *[, parameters, with_name, ...])	Combines arrays into a collection of records or tuples without broadcasting.

Package Contents

awkward.behavior: dict

awkward.__all__