dask_geopandas.GeoDataFrame

dask_geopandas.GeoDataFrame#

class dask_geopandas.GeoDataFrame(expr, spatial_partitions=None)#

Parallel GeoPandas GeoDataFrame

Do not use this class directly. Instead use functions like dask_geopandas.read_parquet(),or dask_geopandas.from_geopandas().

__init__(expr, spatial_partitions=None)#

Methods

__init__(expr[, spatial_partitions])

abs()

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

add(other[, axis, level, fill_value])

add_prefix(prefix)

Prefix labels with string prefix.

add_suffix(suffix)

Suffix labels with string suffix.

affine_transform(matrix)

Return a GeoSeries with translated geometries.

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])

Outputs statistics about every node in the expression.

any([axis, skipna, split_every])

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

apply(function, *args[, meta, axis])

Parallel version of pandas.DataFrame.apply

assign(**pairs)

Assign new columns to a DataFrame.

astype(dtypes)

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.

buffer(distance[, resolution])

Returns a GeoSeries of geometries representing all points within a given distance of each geometric object.

calculate_spatial_partitions()

Calculate spatial partitions

categorize([columns, index, split_every])

Convert columns of the DataFrame to category dtype.

clear_divisions()

Forget division information.

clip(mask[, keep_geom_type])

Clip points, lines, or polygon geometries to the mask extent.

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([fuse])

Compute this DataFrame.

compute_current_divisions([col, set_divisions])

Compute the current divisions of the DataFrame.

contains(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that contains other.

copy()

Make a copy of the dataframe

corr([method, min_periods, numeric_only, ...])

Compute pairwise correlation of columns, excluding NA/null values.

count([axis, numeric_only, split_every])

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.

covered_by(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that is entirely covered by other.

covers(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that is entirely covering other.

crosses(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that cross other.

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.

difference(other, *args, **kwargs)

Returns a GeoSeries of the points in each aligned geometry that are not in other.

disjoint(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry disjoint to other.

dissolve([by, aggfunc, split_out])

Dissolve geometries within groupby into a single geometry.

distance(other, *args, **kwargs)

Returns a Series containing the distance to aligned other.

div(other[, axis, level, fill_value])

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

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[, level, axis])

eval(expr, **kwargs)

Evaluate a string describing operations on DataFrame columns.

explain([stage, format])

Create a graph representation of the Expression.

explode([column, ignore_index, index_parts])

Explode multi-part geometries into multiple single geometries.

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[, axis, level, fill_value])

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

Construct a Dask DataFrame from a Python Dictionary

ge(other[, level, axis])

geohash([as_string, precision])

Calculate geohash based on the middle points of the geometry bounds for a given precision.

geom_almost_equals(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True if each aligned geometry is approximately equal to other.

geom_equals(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry equal to other.

geom_equals_exact(other, tolerance)

Return True for all geometries that equal aligned other to a given tolerance, else False.

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[, level, axis])

head([n, npartitions, compute])

First n rows of the dataset

hilbert_distance([total_bounds, level])

Calculate the distance along a Hilbert curve.

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

Return index of first occurrence of maximum over requested axis.

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

Return index of first occurrence of minimum over requested axis.

info([buf, verbose, memory_usage])

Concise summary of a Dask DataFrame

interpolate(distance[, normalized])

Return a point at the specified distance along each geometry

intersection(other, *args, **kwargs)

Returns a GeoSeries of the intersection of points in each aligned geometry with other.

intersects(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that intersects other.

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.

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, axis])

lower_once()

lt(other[, level, axis])

map(func[, na_action, meta])

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([axis, skipna, numeric_only, split_every])

Return the mean of the values over the requested axis.

median([axis, numeric_only])

Return the median of the values over the requested axis.

median_approximate([axis, method, numeric_only])

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

melt([id_vars, value_vars, var_name, ...])

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

memory_usage([deep, index])

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, numeric_only, split_every])

Return the minimum of the values over the requested axis.

mod(other[, axis, level, fill_value])

mode([dropna, split_every, numeric_only])

Get the mode(s) of each element along the selected axis.

morton_distance([total_bounds, level])

Calculate the distance of geometries along the Morton curve

mul(other[, axis, level, fill_value])

ne(other[, level, axis])

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([axis, dropna, split_every])

Count number of distinct elements in specified axis.

nunique_approx([split_every])

Approximate number of unique rows.

optimize([fuse])

Optimizes the DataFrame.

overlaps(other, *args, **kwargs)

Returns True for all aligned geometries that overlap other, else False.

persist([fuse])

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])

pprint()

Outputs a string representation of the DataFrame.

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

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.

project(other, *args, **kwargs)

Return the distance along each geometry nearest to other

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])

random_split(frac[, random_state, shuffle])

Pseudorandomly split dataframe into different pieces row-wise

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

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

Generic row-wise reductions.

relate(other, *args, **kwargs)

Returns the DE-9IM intersection matrices for the geometries

rename([index, columns])

Rename columns or index labels.

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

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

rename_geometry(col)

Renames the GeoDataFrame geometry column to the specified name.

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

Repartition a collection

replace([to_replace, value, regex])

Replace values given in to_replace with value.

representative_point()

Returns a GeoSeries of (cheaply computed) points that are guaranteed to be within each geometry.

resample(rule[, closed, label])

Resample time-series data.

reset_index([drop])

Reset the index to the default index.

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

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

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

rolling(window, **kwargs)

Provides rolling transformations.

rotate(angle[, origin, use_radians])

Returns a GeoSeries with rotated geometries.

round([decimals])

Round a DataFrame to a variable number of decimal places.

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

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

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

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

Random sample of items

scale([xfact, yfact, zfact, origin])

Returns a GeoSeries with scaled geometries.

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_crs(value[, allow_override])

Set the Coordinate Reference System (CRS) of a GeoSeries.

set_geometry(col)

Set the GeoDataFrame geometry using either an existing column or the specified input.

set_index(*args, **kwargs)

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, ignore_index, npartitions, ...])

Rearrange DataFrame into new partitions

simplify(*args, **kwargs)

Returns a GeoSeries containing a simplified representation of each geometry.

sjoin(df[, how, predicate])

Spatial join of two GeoDataFrames.

skew([xs, ys, origin, use_radians])

Returns a GeoSeries with skewed geometries.

sort_values(by[, npartitions, ascending, ...])

Sort the dataset by a single column.

spatial_shuffle([by, level, ...])

Shuffle the data into spatially consistent partitions.

squeeze([axis])

Squeeze 1 dimensional axis objects into scalars.

std([axis, skipna, ddof, numeric_only, ...])

Return sample standard deviation over requested axis.

sub(other[, axis, level, fill_value])

sum([axis, skipna, numeric_only, min_count, ...])

Return the sum of the values over the requested axis.

symmetric_difference(other, *args, **kwargs)

Returns a GeoSeries of the symmetric difference of points in each aligned geometry with other.

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_crs([crs, epsg])

Returns a GeoSeries with all geometries transformed to a new coordinate reference system.

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()

Create a dask.dataframe object from a dask_geopandas object

to_delayed([optimize_graph])

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

to_feather(path, *args, **kwargs)

See dask_geopadandas.to_feather docstring for more information

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

See dd.to_hdf docstring for more information

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_legacy_dataframe([optimize])

Convert to a legacy dask-dataframe collection

to_orc(path, *args, **kwargs)

See dd.to_orc docstring for more information

to_parquet(path, *args, **kwargs)

See dask_geopadandas.to_parquet docstring for more information

to_records([index, lengths])

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

to_string([max_rows])

Render a DataFrame to a console-friendly tabular output.

to_timestamp([freq, how])

Cast to DatetimeIndex of timestamps, at beginning of period.

to_wkb([hex])

Encode all geometry columns in the GeoDataFrame to WKB.

to_wkt(**kwargs)

Encode all geometry columns in the GeoDataFrame to WKT.

touches(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that touches other.

translate([xoff, yoff, zoff])

Returns a GeoSeries with translated geometries.

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

union(other, *args, **kwargs)

Returns a GeoSeries of the union of points in each aligned geometry with other.

union_all()

var([axis, skipna, ddof, numeric_only, ...])

Return unbiased variance over requested axis.

visualize([tasks])

Visualize the expression or task graph

where(cond[, other])

Replace values where the condition is False.

within(other, *args, **kwargs)

Returns a Series of dtype('bool') with value True for each aligned geometry that is within other.

Attributes

area

Returns a Series containing the area of each geometry in the GeoSeries expressed in the units of the CRS.

axes

boundary

Returns a GeoSeries of lower dimensional objects representing each geometry's set-theoretic boundary.

bounds

Returns a DataFrame with columns minx, miny, maxx, maxy values containing the bounds for each geometry.

centroid

Returns a GeoSeries of points representing the centroid of each geometry.

columns

convex_hull

Returns a GeoSeries of geometries representing the convex hull of each geometry.

crs

The Coordinate Reference System (CRS) represented as a pyproj.CRS object.

cx

Coordinate based indexer to select by intersection with bounding box.

dask

divisions

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

dtypes

Return data types

empty

envelope

Returns a GeoSeries of geometries representing the envelope of each geometry.

expr

exterior

Returns a GeoSeries of LinearRings representing the outer boundary of each polygon in the GeoSeries.

geom_type

Returns a Series of strings specifying the Geometry Type of each object.

geometry

has_z

Returns a Series of dtype('bool') with value True for features that have a z-component.

iloc

Purely integer-location based indexing for selection by position.

index

Return dask Index instance

interiors

Returns a Series of List representing the inner rings of each polygon in the GeoSeries.

is_empty

Returns a Series of dtype('bool') with value True for empty geometries.

is_ring

Returns a Series of dtype('bool') with value True for features that are closed.

is_simple

Returns a Series of dtype('bool') with value True for geometries that do not cross themselves.

is_valid

Returns a Series of dtype('bool') with value True for geometries that are valid.

known_divisions

Whether the divisions are known.

length

Returns a Series containing the length of each geometry expressed in the units of the CRS.

loc

Purely label-location based indexer for selection by label.

nbytes

ndim

Return dimensionality

npartitions

Return number of partitions

partitions

Slice dataframe by partitions

shape

sindex

Need to figure out how to concatenate spatial indexes

size

Size of the Series or DataFrame as a Delayed object.

spatial_partitions

The spatial extent of each of the partitions of the dask GeoDataFrame.

total_bounds

Returns a tuple containing minx, miny, maxx, maxy values for the bounds of the series as a whole.

type

Return the geometry type of each geometry in the GeoSeries

unary_union

Returns a geometry containing the union of all geometries in the GeoSeries.

values

Return a dask.array of the values of this dataframe