concrete-python | Python modules and scripts for working with Concrete | Data Manipulation library
kandi X-RAY | concrete-python Summary
kandi X-RAY | concrete-python Summary
Python modules and scripts for working with Concrete, a data serialization format for NLP
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Top functions reviewed by kandi - BETA
- Helper function to format CoLL - style
- Returns a list of character offsets for a given tokenization
- Return a list of dependencies for the given tokenization
- Return a list of all dep tags for the given tokenization
- Create a Communication object
- Create an instance of the model
- Split a string into sections
- Print the metadata of the givenCommunication
- Return lst
- Load a tar file
- Create a communication term
- Prints a list of tokens with entity mentions
- Read the content of an IProt
- Add POS tagging tags
- Inspect the dependency parse tree
- Prints a list of entities
- Write obj to redis
- Prints a list of Situations
- Print section of comm
- Filter annotations based on annotations
- Prints a list of situation mentions
- Prints the tokens associated with a comm
- Process getCommunicationIDs request
- Process a register search result
- Read this object from an IProt
- Compress the given comm
- Creates a Communication object from a Tweet
concrete-python Key Features
concrete-python Examples and Code Snippets
voxels.cell_centers().plot(render_points_as_spheres=True)
import pyvista as pv
tetra = pv.voxelize(pv.Tetrahedron(), density=0.1)
tetra.plot(scalars='vtkOriginalCellIds')
from pyvista.demo
# Register datasets
from detectron2.data.datasets import register_coco_instances
for d in ["train", "validation"]:
register_coco_instances(f"segmentation_{d}", {}, f"/content/drive/MyDrive/Segmentation Annotations/{d}.json", f"/content
length = float(input('Length of park (m):'))
width = float(input('Width of park (m):'))
squared = float(input('Litres per square metre:'))
total = length * width * squared # calculate total metres to get answer
print("Litres Required =
# Uses pseudo-namespacing to avoid collisions.
_EXT_SUFFIX = "___"
_NEXT_EXT_INDEX = 0
def is_ext_var(element) -> bool:
return element.endswith(_EXT_SUFFIX)
def ext_var() -> str:
global _NEXT_EXT_INDEX
ext_index = _NE
from abc import ABC, abstractmethod
class Language():
English = 'english'
class AbstractSpeaker(ABC):
@abstractmethod
def say_hello(self)->None:
pass
"""... other abstract and maybe even concrete methods ..."
[-2.39 -2.54 -2.23 -2.24 -2.29 -2.37 -2.39 -2.10 -2.34 -2.20]
[-2.28 -2.43 -2.25 -2.33 -2.28 -2.42 -2.26 -2.19 -2.37 -2.25]
[-2.43 -2.44 -2.25 -2.33 -2.33 -2.37 -2.30 -2.10 -2.37 -2.17]
[-2.33 -2.43 -2.28 -2.27 -2.34 -2.34 -2.28 -2.16 -2.3
class TestAnimal(Animal):
def description(self):
return "foo"
assert TestAnimal().zoo_str() == "fooGet more info at zoo.com!"
A.func(A(), *args, **kwargs) == A().func(*args, **kwargs)
import functools
import inspect
class A:
def __init__(self, y):
self.y = y
def sum(self, a: int, b: int):
return (a + b) * self.y
from itertools import groupby
def modified_encode(alist):
def ctr_ele(el):
if len(el)>1:
return [len(el), el[0]]
else:
return [ el[0] ] # <--- return list with single element
re
from collections import Counter
c = Counter()
for name, value in inputs:
c[name] += float(value)
Community Discussions
Trending Discussions on Data Manipulation
QUESTION
I am working with the R programming language.
I have the following dataset:
...ANSWER
Answered 2022-Apr-10 at 05:36Up front, "1,3,4" != 1
. It seems you should look to split the strings using strsplit(., ",")
.
QUESTION
I've the following table
Owner Pet Housing_Type A Cats;Dog;Rabbit 3 B Dog;Rabbit 2 C Cats 2 D Cats;Rabbit 3 E Cats;Fish 1The code is as follows:
...ANSWER
Answered 2022-Mar-15 at 08:48One approach is to define a helper function that matches for a specific animal, then bind the columns to the original frame.
Note that some wrangling is done to get rid of whitespace to identify the unique animals to query.
QUESTION
I have this data frame:
...ANSWER
Answered 2022-Mar-10 at 04:12We can use stri_replace_all_regex
to replace your color_1
into integers together with the arithmetic operator.
Here I've stored your values into a vector color_1_convert
. We can use this as the input in stri_replace_all_regex
for better management of the values.
QUESTION
I have a database with columns M1
, M2
and M3
. These M values correspond to the values obtained by each method. My idea is now to make a rank column for each of them. For M1
and M2
, the rank will be from the highest value to the lowest value and M3
in reverse. I made the output table for you to see.
ANSWER
Answered 2022-Mar-07 at 14:15Using rank
and relocate
:
QUESTION
I working on a Python project that has a DataFrame like this:
...ANSWER
Answered 2022-Feb-24 at 20:48You could use the idxmax
method on axis:
QUESTION
I would like to know of a fast/efficient way in any program (awk/perl/python) to split a csv file (say 10k columns) into multiple small files each containing 2 columns. I would be doing this on a unix machine.
...ANSWER
Answered 2021-Dec-12 at 05:22With your show samples, attempts; please try following awk
code. Since you are opening files all together it may fail with infamous "too many files opened error" So to avoid that have all values into an array and in END
block of this awk
code print them one by one and I am closing them ASAP all contents are getting printed to output file.
QUESTION
Good afternoon, friends!
I'm currently performing some calculations in R (df is displayed below). My goal is to display in a new column the first non-null value from selected cells for each row.
My df is:
...ANSWER
Answered 2022-Feb-03 at 11:16One option with dplyr
could be:
QUESTION
I am again struggling with transforming a wide df into a long one using pivot_longer
The data frame is a result of power analysis for different effect sizes and sample sizes, this is how the original df looks like:
ANSWER
Answered 2022-Feb-03 at 10:59library(tidyverse)
example %>%
pivot_longer(cols = starts_with("es"), names_to = "type", names_prefix = "es_", values_to = "es") %>%
pivot_longer(cols = starts_with("pwr"), names_to = "pwr", names_prefix = "pwr_") %>%
filter(substr(type, 1, 3) == substr(pwr, 1, 3)) %>%
mutate(pwr = parse_number(pwr)) %>%
arrange(pwr, es, type)
QUESTION
Suppose I have the following 10 variables (num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5):
...ANSWER
Answered 2021-Dec-26 at 10:11You may define a function FUN(n)
that creates a data set as shown in OP.
QUESTION
I am trying to tidy up some data that is all contained in 1 column called "game_info" as a string. This data contains college basketball upcoming game data, with the Date, Time, Team IDs, Team Names, etc. Ideally each one of those would be their own column. I have tried separating with a space delimiter, but that has not worked well since there are teams such as "Duke" with 1 part to their name, and teams with 2 to 3 parts to their name (Michigan State, South Dakota State, etc). There also teams with "-" dashes in their name.
Here is my data:
...ANSWER
Answered 2021-Dec-16 at 15:25Here's one with regex. See regex101 link for the regex explanations
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install concrete-python
You can use concrete-python like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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