# dask.dataframe.Index

class dask.dataframe.Index(dsk, name, meta, divisions)
  __init__(dsk, name, meta, divisions)

  Methods

  `__init__`(dsk, name, meta, divisions) |
  `abs`() | Return a Series/DataFrame with absolute numeric value of each element.
  `add`(other[, level, fill_value, axis]) | Return Addition of series and other, element-wise (binary operator add).
  `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, out]) | Return whether all elements are True, potentially over an axis.
  `any`([axis, skipna, split_every, out]) | Return whether any element is True, potentially over an axis.
  `apply`(func[, convert_dtype, meta, args]) | Parallel version of pandas.Series.apply
  `astype`(dtype) | 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.
  `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`(**kwargs) | Compute this dask collection
  `compute_current_divisions`([col]) | 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]) | Return number of non-NA/null observations in the Series.
  `cov`(other[, min_periods, split_every]) | Compute covariance with Series, excluding missing values.
  `cummax`([axis, skipna, out]) | Return cumulative maximum over a DataFrame or Series axis.
  `cummin`([axis, skipna, out]) | Return cumulative minimum over a DataFrame or Series axis.
  `cumprod`([axis, skipna, dtype, out]) | Return cumulative product over a DataFrame or Series axis.
  `cumsum`([axis, skipna, dtype, out]) | 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]) | Return Floating division of series and other, element-wise (binary operator truediv).
  `divide`(other[, level, fill_value, axis]) | Return Floating division of series and other, element-wise (binary operator truediv).
  `dot`(other[, meta]) | Compute the dot product between the Series and the columns of other.
  `drop_duplicates`([split_every, split_out, ...]) | Return Index with duplicate values removed.
  `dropna`() | Return a new Series with missing values removed.
  `enforce_runtime_divisions`() | Enforce the current divisions at runtime
  `eq`(other[, level, fill_value, axis]) | Return Equal to of series and other, element-wise (binary operator eq).
  `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, method, limit, axis]) | Fill NA/NaN values using the specified method.
  `first`(offset) | Select initial periods of time series data based on a date offset.
  `floordiv`(other[, level, fill_value, axis]) | Return Integer division of series and other, element-wise (binary operator floordiv).
  `ge`(other[, level, fill_value, axis]) | Return Greater than or equal to of series and other, element-wise (binary operator ge).
  `get_partition`(n) | Get a dask DataFrame/Series representing the nth partition.
  `groupby`([by, group_keys, sort, observed, dropna]) | Group Series using a mapper or by a Series of columns.
  `gt`(other[, level, fill_value, axis]) | Return Greater than of series and other, element-wise (binary operator gt).
  `head`([n, compute]) | First n items of the Index.
  `idxmax`([axis, skipna, split_every, numeric_only]) | Return index of first occurrence of maximum over requested axis.
  `idxmin`([axis, skipna, split_every, numeric_only]) | Return index of first occurrence of minimum over requested axis.
  `isin`(values) | Whether elements in Series are contained in values.
  `isna`() | Detect missing values.
  `isnull`() | DataFrame.isnull is an alias for DataFrame.isna.
  `kurtosis`([axis, fisher, bias, nan_policy, ...]) | Return unbiased kurtosis over requested axis.
  `last`(offset) | Select final periods of time series data based on a date offset.
  `le`(other[, level, fill_value, axis]) | Return Less than or equal to of series and other, element-wise (binary operator le).
  `lt`(other[, level, fill_value, axis]) | Return Less than of series and other, element-wise (binary operator lt).
  `map`(arg[, na_action, meta, is_monotonic]) | Map values using an input mapping or function.
  `map_overlap`(func, before, after, *args, **kwargs) | Apply a function to each partition, sharing rows with adjacent partitions.
  `map_partitions`(func, *args, **kwargs) | Apply Python function on each DataFrame partition.
  `mask`(cond[, other]) | Replace values where the condition is True.
  `max`([split_every]) | Return the maximum value of the Index.
  `mean`([axis, skipna, split_every, dtype, ...]) | Return the mean of the values over the requested axis.
  `median`([method]) | 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`([split_every]) | Return the minimum value of the Index.
  `mod`(other[, level, fill_value, axis]) | Return Modulo of series and other, element-wise (binary operator mod).
  `mode`([dropna, split_every]) | Return the mode(s) of the Series.
  `mul`(other[, level, fill_value, axis]) | Return Multiplication of series and other, element-wise (binary operator mul).
  `ne`(other[, level, fill_value, axis]) | Return Not equal to of series and other, element-wise (binary operator ne).
  `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`([split_every, dropna]) | Return number of unique elements in the object.
  `nunique_approx`([split_every]) | Approximate number of unique rows.
  `persist`(**kwargs) | Persist this dask collection into memory
  `pipe`(func, *args, **kwargs) | Apply chainable functions that expect Series or DataFrames.
  `pow`(other[, level, fill_value, axis]) | Return Exponential power of series and other, element-wise (binary operator pow).
  `prod`([axis, skipna, split_every, dtype, ...]) | Return the product of the values over the requested axis.
  `product`([axis, skipna, split_every, dtype, ...]) | Return the product of the values over the requested axis.
  `quantile`([q, method]) | Approximate quantiles of Series
  `radd`(other[, level, fill_value, axis]) | Return Addition of series and other, element-wise (binary operator radd).
  `random_split`(frac[, random_state, shuffle]) | Pseudorandomly split dataframe into different pieces row-wise
  `rdiv`(other[, level, fill_value, axis]) | Return Floating division of series and other, element-wise (binary operator rtruediv).
  `reduction`(chunk[, aggregate, combine, meta, ...]) | Generic row-wise reductions.
  `rename`([index, inplace, sorted_index]) | Alter Series index labels or name
  `repartition`([divisions, npartitions, ...]) | Repartition dataframe along new divisions
  `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]) | Return Integer division of series and other, element-wise (binary operator rfloordiv).
  `rmod`(other[, level, fill_value, axis]) | Return Modulo of series and other, element-wise (binary operator rmod).
  `rmul`(other[, level, fill_value, axis]) | Return Multiplication of series and other, element-wise (binary operator rmul).
  `rolling`(window[, min_periods, center, ...]) | Provides rolling transformations.
  `round`([decimals]) | Round each value in a Series to the given number of decimals.
  `rpow`(other[, level, fill_value, axis]) | Return Exponential power of series and other, element-wise (binary operator rpow).
  `rsub`(other[, level, fill_value, axis]) | Return Subtraction of series and other, element-wise (binary operator rsub).
  `rtruediv`(other[, level, fill_value, axis]) | Return Floating division of series and other, element-wise (binary operator rtruediv).
  `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 time frequency increments.
  `shuffle`(on[, npartitions, max_branch, ...]) | Rearrange DataFrame into new partitions
  `skew`([axis, bias, nan_policy, out, numeric_only]) | Return unbiased skew over requested axis.
  `squeeze`() | Squeeze 1 dimensional axis objects into scalars.
  `std`([axis, skipna, ddof, split_every, ...]) | Return sample standard deviation over requested axis.
  `sub`(other[, level, fill_value, axis]) | Return Subtraction of series and other, element-wise (binary operator sub).
  `sum`([axis, skipna, split_every, dtype, out, ...]) | 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) | Store Dask DataFrame to CSV files
  `to_dask_array`([lengths, meta]) | Convert a dask DataFrame to a dask array.
  `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]) | Store Dask Dataframe to Hierarchical Data Format (HDF) files
  `to_json`(filename, *args, **kwargs) | See dd.to_json docstring for more information
  `to_series`() | Create a Series with both index and values equal to the index keys.
  `to_sql`(name, uri[, schema, if_exists, ...]) | See dd.to_sql docstring for more information
  `to_string`([max_rows]) | Render a string representation of the Series.
  `to_timestamp`([freq, how, axis]) | Cast to DatetimeIndex of Timestamps, at beginning of period.
  `truediv`(other[, level, fill_value, axis]) | Return Floating division of series and other, element-wise (binary operator truediv).
  `unique`([split_every, split_out]) | Return Series of unique values in the object.
  `value_counts`([sort, ascending, dropna, ...]) | Return a Series containing counts of unique values.
  `var`([axis, skipna, ddof, split_every, ...]) | Return unbiased variance over requested axis.
  `view`(dtype) | Create a new view of the Series.
  `visualize`([filename, format, optimize_graph]) | Render the computation of this object's task graph using graphviz.
  `where`(cond[, other]) | Replace values where the condition is False.

  Attributes

  `attrs` | Dictionary of global attributes of this dataset.
  `axes` |
  `divisions` | Tuple of `npartitions + 1` values, in ascending order, marking the lower/upper bounds of each partition's index.
  `dtype` | Return data type
  `index` | Return dask Index instance
  `is_monotonic_decreasing` | Return a boolean if the values are equal or decreasing.
  `is_monotonic_increasing` | Return a boolean if the values are equal or increasing.
  `known_divisions` | Whether divisions are already 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 a Series.
  `size` | Size of the Series or DataFrame as a Delayed object.
  `values` | Return a dask.array of the values of this dataframe