dask_geopandas.GeoSeries

class dask_geopandas.GeoSeries(expr, spatial_partitions=None)

Parallel GeoPandas GeoSeries

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[, level, fill_value, axis])
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.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.
buffer(distance[, resolution])	Return a GeoSeries of geometries representing all points within a given distance of each geometric object.
calculate_spatial_partitions()	Calculate spatial partitions
case_when(caselist)	Replace values where the conditions are True.
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])	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, set_divisions])	Compute the current divisions of the DataFrame.
contains(other, *args, **kwargs)	Return a Series of dtype('bool') with value True for each aligned geometry that contains other.
copy()	Make a copy of the dataframe
corr(other[, method, min_periods, split_every])	Compute correlation with other Series, excluding missing values.
count([axis, numeric_only, split_every])	Count non-NA cells for each column or row.
cov(other[, min_periods, split_every])	Compute covariance with Series, excluding missing values.
covered_by(other, *args, **kwargs)	Return a Series of dtype('bool') with value True for each aligned geometry that is entirely covered by other.
covers(other, *args, **kwargs)	Return a Series of dtype('bool') with value True for each aligned geometry that is entirely covering other.
crosses(other, *args, **kwargs)	Return 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)	Return a GeoSeries of the points in each aligned geometry that are not in other.
disjoint(other, *args, **kwargs)	Return a Series of dtype('bool') with value True for each aligned geometry disjoint to other.
distance(other, *args, **kwargs)	Return a Series containing the distance to aligned other.
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])	Create a graph representation of the Expression.
explode([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 with value.
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])
geohash([as_string, precision])	Calculate geohash based on the middle points of the geometry bounds for a given precision.
geom_equals(other, *args, **kwargs)	Return 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, **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
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.
interpolate(distance[, normalized])	Return a point at the specified distance along each geometry.
intersection(other, *args, **kwargs)	Return a GeoSeries of the intersection of points in each aligned geometry with other.
intersects(other, *args, **kwargs)	Return 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.
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])	Map values of Series according to 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([axis, skipna, numeric_only, split_every])	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, index])	Return the memory usage of the Series.
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.
morton_distance([total_bounds, level])	Calculate the distance of geometries along the Morton curve
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])	Optimizes the DataFrame.
overlaps(other, *args, **kwargs)	Return 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.
pow(other[, level, fill_value, axis])
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, 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.
relate(other, *args, **kwargs)	Return the DE-9IM intersection matrices for the geometries.
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.
representative_point()	Return 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[, level, fill_value, axis])
rmod(other[, level, fill_value, axis])
rmul(other[, level, fill_value, axis])
rolling(window, **kwargs)	Provides rolling transformations.
rotate(angle[, origin, use_radians])	Return a GeoSeries with rotated geometries.
round([decimals])	Round numeric columns in 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
scale([xfact, yfact, zfact, origin])	Return a GeoSeries with scaled geometries.
sem([axis, skipna, ddof, split_every, ...])	Return unbiased standard error of the mean over requested axis.
set_crs(value[, allow_override])	Set the value of the crs on a new object
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)	Return a GeoSeries containing a simplified representation of each geometry.
skew([xs, ys, origin, use_radians])	Return a GeoSeries with skewed geometries.
squeeze()	Squeeze 1 dimensional axis objects into scalars.
std([axis, skipna, ddof, numeric_only, ...])	Return sample standard deviation over requested axis.
sub(other[, level, fill_value, axis])
sum([axis, skipna, numeric_only, min_count, ...])	Return the sum of the values over the requested axis.
symmetric_difference(other, *args, **kwargs)	Return 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])	Return 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_frame([name])	Convert Series to DataFrame.
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_orc(path, *args, **kwargs)	See dd.to_orc docstring for more information
to_records([index, lengths])
to_sql(name, uri[, schema, if_exists, ...])
to_string([max_rows])	Render a string representation of the Series.
to_timestamp([freq, how])	Cast PeriodIndex 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)	Return a Series of dtype('bool') with value True for each aligned geometry that touches other.
translate([xoff, yoff, zoff])	Return a GeoSeries with translated geometries.
truediv(other[, level, fill_value, axis])
union(other, *args, **kwargs)	Return a GeoSeries of the union of points in each aligned geometry with other.
union_all()
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([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)	Return a Series of dtype('bool') with value True for each aligned geometry that is within other.

Attributes

area	Return a Series containing the area of each geometry in the GeoSeries expressed in the units of the CRS.
axes
boundary	Return a GeoSeries of lower dimensional objects representing each geometry's set-theoretic boundary.
bounds	Return a DataFrame with columns minx, miny, maxx, maxy values containing the bounds for each geometry.
centroid	Return a GeoSeries of points representing the centroid of each geometry.
columns
convex_hull	Return a GeoSeries of geometries representing the convex hull of each geometry.
crs	The Coordinate Reference System (CRS) 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.
dtype
dtypes	Return data types
envelope	Return a GeoSeries of geometries representing the envelope of each geometry.
expr
exterior	Return 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	Return a Series of dtype('bool') with value True for features that have a z-component.
index	Return dask Index instance
interiors	Return 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_monotonic_decreasing	Return True if values in the object are monotonically decreasing.
is_monotonic_increasing	Return True if values in the object are monotonically increasing.
is_ring	Return a Series of dtype('bool') with value True for features that are closed.
is_simple	Return a Series of dtype('bool') with value True for geometries that do not cross themselves.
is_valid	Return a Series of dtype('bool') with value True for geometries that are valid.
known_divisions	Whether the divisions are known.
length	Return 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.
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.
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	Return 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	Return a geometry containing the union of all geometries in the GeoSeries.
values	Return a dask.array of the values of this dataframe
x	Return the x location of point geometries in a GeoSeries.
y	Return the y location of point geometries in a GeoSeries.
z	Return the z location of point geometries in a GeoSeries.