High Level Expressions for Dask
Project description
Dask Expressions
Dask Dataframes with query optimization.
This is a proof-of-concept rewrite of Dask dataframe that includes query optimization and generally improved organization.
Example
import dask_expr as dx
df = dx.datasets.timeseries()
df.head()
df.groupby("name").x.mean().compute()
Query Representation
Dask-expr encodes user code in an expression tree:
>>> df.x.mean().pprint()
Mean:
Projection: columns='x'
Timeseries: seed=1896674884
This expression tree will be optimized and modified before execution:
>>> df.x.mean().optimize().pprint()
Div:
Sum:
Fused(375f9):
| Projection: columns='x'
| Timeseries: dtypes={'x': <class 'float'>} seed=1896674884
Count:
Fused(375f9):
| Projection: columns='x'
| Timeseries: dtypes={'x': <class 'float'>} seed=1896674884
Stability
This project is a work in progress and will be changed without notice or deprecation warning. Please provide feedback, but it's best to avoid use in production settings.
API Coverage
dask_expr.DataFrame
abs
add_prefix
add_sufix
align
all
any
apply
assign
astype
clip
combine_first
copy
count
dask
drop
drop_duplicates
dropna
eval
explode
fillna
groupby
head
idxmax
idxmin
index
isin
isna
join
map
map_partitions
max
memory_usage
merge
min
min
mode
nlargest
nsmallest
nunique_approx
partitions
pivot_table
prod
rename
rename_axis
repartition
replace
reset_index
round
sample
sort_values
select_dtypes
set_index
shuffle
std
sum
tail
to_parquet
to_timestamp
var
visualize
dask_expr.Series
abs
align
all
any
apply
astype
between
clip
combine_first
copy
count
dask
drop_duplicates
dropna
explode
fillna
groupby
head
idxmax
idxmin
index
isin
isna
map
map_partitions
max
memory_usage
min
min
mode
nlargest
nsmallest
nunique_approx
partitions
prod
rename_axis
repartition
replace
reset_index
round
shuffle
std
sum
tail
to_frame
to_timestamp
unique
value_counts
var
visualize
dask_expr.Index
abs
align
all
any
apply
astype
clip
combine_first
copy
count
dask
fillna
groupby
head
idxmax
idxmin
index
isin
isna
map_partitions
max
memory_usage
min
min
mode
nunique_approx
partitions
prod
rename_axis
repartition
replace
reset_index
round
shuffle
std
sum
tail
to_frame
to_timestamp
var
visualize
dask_expr._groupby.GroupBy
agg
aggregate
count
first
last
max
mean
min
prod
size
std
sum
value_counts
var
Binary operators (DataFrame
, Series
, and Index
):
__add__
__radd__
__sub__
__rsub__
__mul__
__rmul__
__truediv__
__rtruediv__
__lt__
__rlt__
__gt__
__rgt__
__le__
__rle__
__ge__
__rge__
__eq__
__ne__
__and__
__rand__
__or__
__ror__
__xor__
__rxor__
Unary operators (DataFrame
, Series
, and Index
):
__invert__
__neg__
__pos__
Accessors:
CategoricalAccessor
DatetimeAccessor
StringAccessor
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for dask_expr-0.1.10-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 18e27771056b49d9ad1df08c5d86c5f3260d8500f4418a8274d0faef3fb0722c |
|
MD5 | 18c006ceedde5dd471b90e259b3ed04c |
|
BLAKE2b-256 | 226009c8d94280d665b7134cafa5cc64e816c975fe13f7ba87d59f1c4c7eb949 |