dask_expr._collection.Index

dask_expr._collection.Index

class dask_expr._collection.Index(expr)[source]

Index-like Expr Collection

__init__(expr)

Methods

__init__(expr)

abs()

Return a Series/DataFrame with absolute numeric value of each element.

add(other[, level, fill_value, axis])

add_prefix(prefix)

Prefix labels with string prefix.

add_suffix(suffix)

Suffix labels with string suffix.

align(other[, join, axis, fill_value])

Align two objects on their axes with the specified join method.

all([axis, skipna, split_every])

Return whether all elements are True, potentially over an axis.

analyze([filename, format])

any([axis, skipna, split_every])

Return whether any element is True, potentially over an axis.

apply(function, *args[, meta, axis])

Parallel version of pandas.Series.apply

astype(dtypes)

Cast a pandas object to a specified dtype dtype.

autocorr([lag, split_every])

Compute the lag-N autocorrelation.

between(left, right[, inclusive])

Return boolean Series equivalent to left <= series <= right.

bfill([axis, limit])

Fill NA/NaN values by using the next valid observation to fill the gap.

case_when(caselist)

Replace values where the conditions are True.

clear_divisions()

Forget division information.

clip([lower, upper, axis])

Trim values at input threshold(s).

combine(other, func[, fill_value])

Combine the Series with a Series or scalar according to func.

combine_first(other)

Update null elements with value in the same location in other.

compute([fuse])

Compute this dask collection

compute_current_divisions([col, set_divisions])

Compute the current divisions of the DataFrame.

copy([deep])

Make a copy of the dataframe

corr(other[, method, min_periods, split_every])

Compute correlation with other Series, excluding missing values.

count([split_every])

Count non-NA cells for each column or row.

cov(other[, min_periods, split_every])

Compute covariance with Series, excluding missing values.

cummax([axis, skipna])

Return cumulative maximum over a DataFrame or Series axis.

cummin([axis, skipna])

Return cumulative minimum over a DataFrame or Series axis.

cumprod([axis, skipna])

Return cumulative product over a DataFrame or Series axis.

cumsum([axis, skipna])

Return cumulative sum over a DataFrame or Series axis.

describe([split_every, percentiles, ...])

Generate descriptive statistics.

diff([periods, axis])

First discrete difference of element.

div(other[, level, fill_value, axis])

divide(other[, level, fill_value, axis])

dot(other[, meta])

Compute the dot product between the Series and the columns of other.

drop_duplicates([ignore_index, split_every, ...])

dropna()

Return a new Series with missing values removed.

enforce_runtime_divisions()

Enforce the current divisions at runtime.

eq(other[, level, fill_value, axis])

explain([stage, format])

explode()

Transform each element of a list-like to a row.

ffill([axis, limit])

Fill NA/NaN values by propagating the last valid observation to next valid.

fillna([value, axis])

Fill NA/NaN values using the specified method.

floordiv(other[, level, fill_value, axis])

from_dict(data, *[, npartitions, orient, ...])

Construct a Dask DataFrame from a Python Dictionary

ge(other[, level, fill_value, axis])

get_partition(n)

Get a dask DataFrame/Series representing the nth partition.

groupby(by, **kwargs)

Group Series using a mapper or by a Series of columns.

gt(other[, level, fill_value, axis])

head([n, npartitions, compute])

First n rows of the dataset

idxmax(*args, **kwargs)

Return index of first occurrence of maximum over requested axis.

idxmin(*args, **kwargs)

Return index of first occurrence of minimum over requested axis.

isin(values)

Whether each element in the DataFrame is contained in values.

isna()

Detect missing values.

isnull()

DataFrame.isnull is an alias for DataFrame.isna.

kurt([axis, fisher, bias, nan_policy, ...])

Return unbiased kurtosis over requested axis.

kurtosis([axis, fisher, bias, nan_policy, ...])

Return unbiased kurtosis over requested axis.

le(other[, level, fill_value, axis])

lower_once()

lt(other[, level, fill_value, axis])

map(arg[, na_action, meta, is_monotonic])

Map values using an input mapping or function.

map_overlap(func, before, after, *args[, ...])

Apply a function to each partition, sharing rows with adjacent partitions.

map_partitions(func, *args[, meta, ...])

Apply a Python function to each partition

mask(cond[, other])

Replace values where the condition is True.

max([axis, skipna, numeric_only, split_every])

Return the maximum of the values over the requested axis.

mean(*args, **kwargs)

Return the mean of the values over the requested axis.

median()

Return the median of the values over the requested axis.

median_approximate([method])

Return the approximate median of the values over the requested axis.

memory_usage([deep])

Memory usage of the values.

memory_usage_per_partition([index, deep])

Return the memory usage of each partition

min([axis, skipna, numeric_only, split_every])

Return the minimum of the values over the requested axis.

mod(other[, level, fill_value, axis])

mode([dropna, split_every])

Return the mode(s) of the Series.

mul(other[, level, fill_value, axis])

ne(other[, level, fill_value, axis])

nlargest([n, split_every])

Return the largest n elements.

notnull()

DataFrame.notnull is an alias for DataFrame.notna.

nsmallest([n, split_every])

Return the smallest n elements.

nunique([dropna, split_every, split_out])

Return number of unique elements in the object.

nunique_approx([split_every])

Approximate number of unique rows.

optimize([fuse])

persist([fuse])

Persist this dask collection into memory

pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

pow(other[, level, fill_value, axis])

prod(*args, **kwargs)

Return the product of the values over the requested axis.

product([axis, skipna, numeric_only, ...])

Return the product of the values over the requested axis.

quantile([q, method])

Approximate quantiles of Series

radd(other[, level, fill_value, axis])

random_split(frac[, random_state, shuffle])

Pseudorandomly split dataframe into different pieces row-wise

rdiv(other[, level, fill_value, axis])

reduction(chunk[, aggregate, combine, meta, ...])

Generic row-wise reductions.

rename(index[, sorted_index])

Alter Series index labels or name

rename_axis([mapper, index, columns, axis])

Set the name of the axis for the index or columns.

repartition([divisions, npartitions, ...])

Repartition a collection

replace([to_replace, value, regex])

Replace values given in to_replace with value.

resample(rule[, closed, label])

Resample time-series data.

reset_index([drop])

Reset the index to the default index.

rfloordiv(other[, level, fill_value, axis])

rmod(other[, level, fill_value, axis])

rmul(other[, level, fill_value, axis])

rolling(window, **kwargs)

Provides rolling transformations.

round([decimals])

Round a DataFrame to a variable number of decimal places.

rpow(other[, level, fill_value, axis])

rsub(other[, level, fill_value, axis])

rtruediv(other[, level, fill_value, axis])

sample([n, frac, replace, random_state])

Random sample of items

sem([axis, skipna, ddof, split_every, ...])

Return unbiased standard error of the mean over requested axis.

shift([periods, freq])

Shift index by desired number of periods with an optional time freq.

shuffle([on, ignore_index, npartitions, ...])

Rearrange DataFrame into new partitions

simplify()

skew([axis, bias, nan_policy, numeric_only])

Return unbiased skew over requested axis.

squeeze()

Squeeze 1 dimensional axis objects into scalars.

std(*args, **kwargs)

Return sample standard deviation over requested axis.

sub(other[, level, fill_value, axis])

sum(*args, **kwargs)

Return the sum of the values over the requested axis.

tail([n, compute])

Last n rows of the dataset

to_backend([backend])

Move to a new DataFrame backend

to_bag([index, format])

Create a Dask Bag from a Series

to_csv(filename, **kwargs)

See dd.to_csv docstring for more information

to_dask_array([lengths, meta, optimize])

Convert a dask DataFrame to a dask array.

to_dask_dataframe(*args, **kwargs)

Convert to a legacy dask-dataframe collection

to_delayed([optimize_graph])

Convert into a list of dask.delayed objects, one per partition.

to_frame([index, name])

Create a DataFrame with a column containing the Index.

to_hdf(path_or_buf, key[, mode, append])

See dd.to_hdf docstring for more information

to_json(filename, *args, **kwargs)

See dd.to_json docstring for more information

to_legacy_dataframe([optimize])

Convert to a legacy dask-dataframe collection

to_orc(path, *args, **kwargs)

See dd.to_orc docstring for more information

to_records([index, lengths])

to_series([index, name])

Create a Series with both index and values equal to the index keys.

to_sql(name, uri[, schema, if_exists, ...])

to_string([max_rows])

Render a string representation of the Series.

to_timestamp([freq, how])

Cast to DatetimeIndex of timestamps, at beginning of period.

truediv(other[, level, fill_value, axis])

unique([split_every, split_out, shuffle_method])

Return Series of unique values in the object.

value_counts([sort, ascending, dropna, ...])

Return a Series containing counts of unique values.

var(*args, **kwargs)

Return unbiased variance over requested axis.

visualize([tasks])

Visualize the expression or task graph

where(cond[, other])

Replace values where the condition is False.

Attributes

axes

columns

dask

divisions

Tuple of npartitions + 1 values, in ascending order, marking the lower/upper bounds of each partition's index.

dtype

dtypes

Return data types

expr

index

Return dask Index instance

is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

known_divisions

Whether the divisions are known.

loc

Purely label-location based indexer for selection by label.

name

nbytes

Number of bytes

ndim

Return dimensionality

npartitions

Return number of partitions

partitions

Slice dataframe by partitions

shape

Return a tuple representing the dimensionality of the DataFrame.

size

Size of the Series or DataFrame as a Delayed object.

values

Return a dask.array of the values of this dataframe