# dask.dataframe.DataFrame

class dask.dataframe.DataFrame(dsk, name, meta, divisions)

  Parallel Pandas DataFrame

  Do not use this class directly. Instead use functions like `dd.read_csv`, `dd.read_parquet`, or `dd.from_pandas`.

  Parameters:
    dsk: dict

      The dask graph to compute this DataFrame

    name: str

      The key prefix that specifies which keys in the dask comprise this particular DataFrame

    meta: pandas.DataFrame

      An empty `pandas.DataFrame` with names, dtypes, and index matching the expected output.

    divisions: tuple of index values

      Values along which we partition our blocks on the index

  __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[, axis, level, fill_value]) | Get Addition of dataframe 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[, axis, broadcast, raw, reduce, ...]) | Parallel version of pandas.DataFrame.apply
  `applymap`(func[, meta]) | Apply a function to a Dataframe elementwise.
  `assign`(**kwargs) | Assign new columns to a DataFrame.
  `astype`(dtype) | Cast a pandas object to a specified dtype `dtype`.
  `bfill`([axis, limit]) | Fill NA/NaN values by using the next valid observation to fill the gap.
  `categorize`([columns, index, split_every]) | Convert columns of the DataFrame to category dtype.
  `clear_divisions`() | Forget division information
  `clip`([lower, upper, axis]) | Trim values at input threshold(s).
  `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.
  `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`([method, min_periods, numeric_only, ...]) | Compute pairwise correlation of columns, excluding NA/null values.
  `count`([axis, split_every, numeric_only]) | Count non-NA cells for each column or row.
  `cov`([min_periods, numeric_only, split_every]) | Compute pairwise covariance of columns, excluding NA/null 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[, axis, level, fill_value]) | Get Floating division of dataframe and other, element-wise (binary operator truediv).
  `divide`(other[, axis, level, fill_value]) | Get Floating division of dataframe and other, element-wise (binary operator truediv).
  `dot`(other[, meta]) | Compute the dot product between the Series and the columns of other.
  `drop`([labels, axis, columns, errors]) | Drop specified labels from rows or columns.
  `drop_duplicates`([subset, split_every, ...]) | Return DataFrame with duplicate rows removed.
  `dropna`([how, subset, thresh]) | Remove missing values.
  `enforce_runtime_divisions`() | Enforce the current divisions at runtime
  `eq`(other[, axis, level]) | Get Equal to of dataframe and other, element-wise (binary operator eq).
  `eval`(expr[, inplace]) | Evaluate a string describing operations on DataFrame columns.
  `explode`(column) | Transform each element of a list-like to a row, replicating index values.
  `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[, axis, level, fill_value]) | Get Integer division of dataframe and other, element-wise (binary operator floordiv).
  `from_dict`(data, *, npartitions[, orient, ...]) | Construct a Dask DataFrame from a Python Dictionary
  `ge`(other[, axis, level]) | Get Greater than or equal to of dataframe 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 DataFrame using a mapper or by a Series of columns.
  `gt`(other[, axis, level]) | Get Greater than of dataframe and other, element-wise (binary operator gt).
  `head`([n, npartitions, compute]) | First n rows of the dataset
  `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.
  `info`([buf, verbose, memory_usage]) | Concise summary of a Dask DataFrame.
  `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.
  `items`() | Iterate over (column name, Series) pairs.
  `iterrows`() | Iterate over DataFrame rows as (index, Series) pairs.
  `itertuples`([index, name]) | Iterate over DataFrame rows as namedtuples.
  `join`(other[, on, how, lsuffix, rsuffix, ...]) | Join columns of another DataFrame.
  `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[, axis, level]) | Get Less than or equal to of dataframe and other, element-wise (binary operator le).
  `lt`(other[, axis, level]) | Get Less than of dataframe and other, element-wise (binary operator lt).
  `map`(func[, meta, na_action]) |
  `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`([axis, skipna, split_every, out, ...]) | Return the maximum of the values over the requested axis.
  `mean`([axis, skipna, split_every, dtype, ...]) | Return the mean of the values over the requested axis.
  `median`([axis, method]) | Return the median of the values over the requested axis.
  `median_approximate`([axis, method]) | Return the approximate median of the values over the requested axis.
  `melt`([id_vars, value_vars, var_name, ...]) | Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
  `memory_usage`([index, deep]) | Return the memory usage of each column in bytes.
  `memory_usage_per_partition`([index, deep]) | Return the memory usage of each partition
  `merge`(right[, how, on, left_on, right_on, ...]) | Merge the DataFrame with another DataFrame
  `min`([axis, skipna, split_every, out, ...]) | Return the minimum of the values over the requested axis.
  `mod`(other[, axis, level, fill_value]) | Get Modulo of dataframe and other, element-wise (binary operator mod).
  `mode`([dropna, split_every, numeric_only]) | Get the mode(s) of each element along the selected axis.
  `mul`(other[, axis, level, fill_value]) | Get Multiplication of dataframe and other, element-wise (binary operator mul).
  `ne`(other[, axis, level]) | Get Not equal to of dataframe and other, element-wise (binary operator ne).
  `nlargest`([n, columns, split_every]) | Return the first n rows ordered by columns in descending order.
  `notnull`() | DataFrame.notnull is an alias for DataFrame.notna.
  `nsmallest`([n, columns, split_every]) | Return the first n rows ordered by columns in ascending order.
  `nunique`([split_every, dropna, axis]) | Count number of distinct elements in specified axis.
  `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.
  `pivot_table`([index, columns, values, aggfunc]) | Create a spreadsheet-style pivot table as a DataFrame.
  `pop`(item) | Return item and drop from frame.
  `pow`(other[, axis, level, fill_value]) | Get Exponential power of dataframe 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, axis, numeric_only, method]) | Approximate row-wise and precise column-wise quantiles of DataFrame
  `query`(expr, **kwargs) | Filter dataframe with complex expression
  `radd`(other[, axis, level, fill_value]) | Get Addition of dataframe and other, element-wise (binary operator radd).
  `random_split`(frac[, random_state, shuffle]) | Pseudorandomly split dataframe into different pieces row-wise
  `rdiv`(other[, axis, level, fill_value]) | Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
  `reduction`(chunk[, aggregate, combine, meta, ...]) | Generic row-wise reductions.
  `rename`([index, columns]) | Rename columns or index labels.
  `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[, axis, level, fill_value]) | Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
  `rmod`(other[, axis, level, fill_value]) | Get Modulo of dataframe and other, element-wise (binary operator rmod).
  `rmul`(other[, axis, level, fill_value]) | Get Multiplication of dataframe and other, element-wise (binary operator rmul).
  `rolling`(window[, min_periods, center, ...]) | Provides rolling transformations.
  `round`([decimals]) | Round a DataFrame to a variable number of decimal places.
  `rpow`(other[, axis, level, fill_value]) | Get Exponential power of dataframe and other, element-wise (binary operator rpow).
  `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).
  `sample`([n, frac, replace, random_state]) | Random sample of items
  `select_dtypes`([include, exclude]) | Return a subset of the DataFrame's columns based on the column dtypes.
  `sem`([axis, skipna, ddof, split_every, ...]) | Return unbiased standard error of the mean over requested axis.
  `set_index`(other[, drop, sorted, ...]) | Set the DataFrame index (row labels) using an existing column.
  `shift`([periods, freq, axis]) | Shift index by desired number of periods with an optional time freq.
  `shuffle`(on[, npartitions, max_branch, ...]) | Rearrange DataFrame into new partitions
  `skew`([axis, bias, nan_policy, out, numeric_only]) | Return unbiased skew over requested axis.
  `sort_values`(by[, npartitions, ascending, ...]) | Sort the dataset by a single column.
  `squeeze`([axis]) | Squeeze 1 dimensional axis objects into scalars.
  `std`([axis, skipna, ddof, split_every, ...]) | Return sample standard deviation over requested axis.
  `sub`(other[, axis, level, fill_value]) | Get Subtraction of dataframe 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 Dask Bag from a Dask DataFrame
  `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_hdf`(path_or_buf, key[, mode, append]) | Store Dask Dataframe to Hierarchical Data Format (HDF) files
  `to_html`([max_rows]) | Render a DataFrame as an HTML table.
  `to_json`(filename, *args, **kwargs) | See dd.to_json docstring for more information
  `to_orc`(path, *args, **kwargs) | See dd.to_orc docstring for more information
  `to_parquet`(path, *args, **kwargs) | Store Dask.dataframe to Parquet files
  `to_records`([index, lengths]) | Create Dask Array from a Dask Dataframe
  `to_sql`(name, uri[, schema, if_exists, ...]) | See dd.to_sql docstring for more information
  `to_string`([max_rows]) | Render a DataFrame to a console-friendly tabular output.
  `to_timestamp`([freq, how, axis]) | Cast to DatetimeIndex of timestamps, at beginning of period.
  `truediv`(other[, axis, level, fill_value]) | Get Floating division of dataframe and other, element-wise (binary operator truediv).
  `var`([axis, skipna, ddof, split_every, ...]) | Return unbiased variance over requested axis.
  `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` |
  `columns` |
  `divisions` | Tuple of `npartitions + 1` values, in ascending order, marking the lower/upper bounds of each partition's index.
  `dtypes` | Return data types
  `empty` |
  `iloc` | Purely integer-location based indexing for selection by position.
  `index` | Return dask Index instance
  `known_divisions` | Whether divisions are already known
  `loc` | Purely label-location based indexer for selection by label.
  `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