QuestionId
int64 74.8M
79.8M
| UserId
int64 56
29.4M
| QuestionTitle
stringlengths 15
150
| QuestionBody
stringlengths 40
40.3k
| Tags
stringlengths 8
101
| CreationDate
stringdate 2022-12-10 09:42:47
2025-11-01 19:08:18
| AnswerCount
int64 0
44
| UserExpertiseLevel
int64 301
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| UserDisplayName
stringlengths 3
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⌀ |
|---|---|---|---|---|---|---|---|---|
76,375,036
| 34,935
|
Tool to automatically fix pylint "unnecessary-comprehension"?
|
<p><a href="https://stackoverflow.com/questions/65902301/how-to-fix-pylint-error-unnecessary-use-of-a-comprehension">This question</a> shows pylint reporting <code>unnecessary-comprehension</code> on this code:</p>
<pre><code>dict1 = {"A": "This is A", "B": "This is B"}
bools = [True, False]
dict2 = {key: value for key, value in zip(dict1.keys(), bools)}
</code></pre>
<p><a href="https://stackoverflow.com/questions/54586757/how-do-i-automatically-fix-lint-issues-reported-by-pylint">This question</a> asks in general if there is an automated tool to fix pylint issues. It mentions black, autopep8, autoflake8. None of those fix this.</p>
<p>Is there a tool to apply that would fix this pylint warning?</p>
|
<python><pylint>
|
2023-05-31 15:18:17
| 1
| 21,683
|
dfrankow
|
76,374,942
| 12,851,199
|
Is defining classmethods in Enum classes bad practice or not?
|
<p>I have Enum class:</p>
<pre><code>class Group(StrEnum):
admin = 'admin'
manager = 'manager'
user = 'user'
@classmethod
def get_staff_groups(cls):
return {cls.admin, cls.manager}
@classmethod
def get_user_groups(cls):
return {cls.user}
</code></pre>
<p>Is it good practice or not?</p>
<p>I tried like written above. Can Enum contains methods?</p>
|
<python>
|
2023-05-31 15:08:22
| 1
| 438
|
Vadim Beglov
|
76,374,927
| 1,473,517
|
How to specify -march=native with %%cython
|
<p>In ipython I can do the following:</p>
<pre><code>%%cython --compile-args=-Ofast
def f(x):
return 2.0*x
</code></pre>
<p>But how can I add <code>-march=native</code> as well?</p>
|
<python><ipython><cython>
|
2023-05-31 15:06:25
| 1
| 21,513
|
Simd
|
76,374,804
| 10,266,106
|
Correctly Computing Cumsum Along Third Dimension of ndarray
|
<p>I have a multi-dimensional array that I am performing summations along at each individual point, specifically for statistical analysis. The ndarray is 3-dimensional with shape <code>(1200,2600,200)</code>. The calculation I'm looking to execute specifically is the following:</p>
<pre><code>cumsum = np.divide(np.cumsum(array, axis=2), np.sum(array))
</code></pre>
<p>Where array is the ndarray defined above and the axis=2 call specifically instructs np.cumsum to sum the elements cumulatively down the third dimension at point i, j.</p>
<p>My issue is the cumsum at point i,j which is not identical when run as shown above (incorrect result) compared to when I execute it at a specific point i, j individually (correct result). Specifically, the result is incorrect to about eight decimal places at each point in the ndarray. The data at point i,j and results follow below:</p>
<p><strong>Code that produces the correct result</strong>:</p>
<pre><code>cumsum = np.divide(np.cumsum(array[i,j]), np.sum(array[i,j]))
</code></pre>
<p><strong>Data at point i, j in the array</strong>:</p>
<pre><code>[2.5896627e-01 7.8833267e-02 4.6881203e-02 3.3370260e-02 2.5926309e-02
2.1185182e-02 1.7896093e-02 1.5469486e-02 1.3617482e-02 1.2151432e-02
1.0961573e-02 9.9725584e-03 9.1435499e-03 8.4358063e-03 7.8244330e-03
7.2888890e-03 6.8194675e-03 6.4030001e-03 6.0309847e-03 5.6953467e-03
5.3933552e-03 5.1191193e-03 4.8689796e-03 4.6398928e-03 4.4284821e-03
4.2343135e-03 4.0546600e-03 3.8879570e-03 3.7322419e-03 3.5876189e-03
3.4524139e-03 3.3257415e-03 3.2063506e-03 3.0945288e-03 2.9891655e-03
2.8897219e-03 2.7953433e-03 2.7063680e-03 2.6220153e-03 2.5419386e-03
2.4658246e-03 2.3931006e-03 2.3241036e-03 2.2582957e-03 2.1954644e-03
2.1351755e-03 2.0777427e-03 2.0227525e-03 1.9700555e-03 1.9193111e-03
1.8708067e-03 1.8242144e-03 1.7794254e-03 1.7361674e-03 1.6947000e-03
1.6547571e-03 1.6162583e-03 1.5789805e-03 1.5431579e-03 1.5085702e-03
1.4751572e-03 1.4428621e-03 1.4115060e-03 1.3812946e-03 1.3520514e-03
1.3237320e-03 1.2961850e-03 1.2695953e-03 1.2438130e-03 1.2188030e-03
1.1944349e-03 1.1708769e-03 1.1479985e-03 1.1257721e-03 1.1040850e-03
1.0830887e-03 1.0626703e-03 1.0428075e-03 1.0234793e-03 1.0045893e-03
9.8627270e-04 9.6843322e-04 9.5105352e-04 9.3404873e-04 9.1754185e-04
9.0144749e-04 8.8575145e-04 8.7037822e-04 8.5544016e-04 8.4086129e-04
8.2662981e-04 8.1267761e-04 7.9910830e-04 7.8585331e-04 7.7290280e-04
7.6019607e-04 7.4782735e-04 7.3573570e-04 7.2391250e-04 7.1234960e-04
7.0099317e-04 6.8992859e-04 6.7910185e-04 6.6850579e-04 6.5809203e-04
6.4793869e-04 6.3799700e-04 6.2826090e-04 6.1868585e-04 6.0934469e-04
6.0019240e-04 5.9122394e-04 5.8239867e-04 5.7378388e-04 5.6533841e-04
5.5705803e-04 5.4890534e-04 5.4094271e-04 5.3313252e-04 5.2547100e-04
5.1795418e-04 5.1054877e-04 5.0331140e-04 4.9620820e-04 4.8923609e-04
4.8236389e-04 4.7564457e-04 4.6904699e-04 4.6256813e-04 4.5617929e-04
4.4993000e-04 4.4379136e-04 4.3776058e-04 4.3181126e-04 4.2598948e-04
4.2026848e-04 4.1464576e-04 4.0911944e-04 4.0366512e-04 3.9832521e-04
3.9307532e-04 3.8791337e-04 3.8281683e-04 3.7782549e-04 3.7291663e-04
3.6808843e-04 3.6331985e-04 3.5864810e-04 3.5405197e-04 3.4952996e-04
3.4506238e-04 3.4068426e-04 3.3637564e-04 3.3213521e-04 3.2794452e-04
3.2383655e-04 3.1979271e-04 3.1581166e-04 3.1189225e-04 3.0801745e-04
3.0421774e-04 3.0047612e-04 2.9679155e-04 2.9314787e-04 2.8957392e-04
2.8605366e-04 2.8258603e-04 2.7915623e-04 2.7579122e-04 2.7247594e-04
2.6920953e-04 2.6597784e-04 2.6280651e-04 2.5968134e-04 2.5660152e-04
2.5355379e-04 2.5056230e-04 2.4761367e-04 2.4470725e-04 2.4184212e-04
2.3900617e-04 2.3622185e-04 2.3347675e-04 2.3077012e-04 2.2809043e-04
2.2545905e-04 2.2286420e-04 2.2030527e-04 2.1777138e-04 2.1528259e-04
2.1282790e-04 2.1040675e-04 2.0800892e-04 2.0565331e-04 2.0332953e-04
2.0103710e-04 1.9877554e-04 1.9653517e-04 1.9433383e-04 1.9216179e-04]
</code></pre>
<p><strong>Performing calculation as shown above (incorrect result)</strong>:</p>
<pre><code>[3.61291841e-09 4.71274575e-09 5.36679989e-09 5.83235860e-09
6.19406482e-09 6.48962573e-09 6.73929978e-09 6.95511915e-09
7.14510096e-09 7.31462935e-09 7.46755813e-09 7.60668861e-09
7.73425235e-09 7.85194310e-09 7.96110378e-09 8.06279310e-09
8.15793388e-09 8.24726420e-09 8.33140401e-09 8.41086223e-09
8.48610604e-09 8.55752447e-09 8.62545324e-09 8.69018546e-09
8.75196893e-09 8.81104345e-09 8.86761065e-09 8.92185259e-09
8.97392294e-09 9.02397446e-09 9.07214037e-09 9.11853881e-09
9.16327192e-09 9.20644538e-09 9.24814803e-09 9.28846333e-09
9.32746147e-09 9.36521882e-09 9.40179934e-09 9.43726342e-09
9.47166523e-09 9.50505186e-09 9.53747659e-09 9.56898294e-09
9.59961266e-09 9.62940039e-09 9.65838787e-09 9.68660796e-09
9.71409264e-09 9.74087033e-09 9.76697034e-09 9.79242021e-09
9.81724568e-09 9.84146720e-09 9.86511051e-09 9.88819604e-09
9.91074511e-09 9.93277371e-09 9.95430316e-09 9.97535032e-09
9.99593031e-09 1.00160600e-08 1.00357518e-08 1.00550226e-08
1.00738857e-08 1.00923545e-08 1.01104369e-08 1.01281499e-08
1.01455022e-08 1.01625064e-08 1.01791704e-08 1.01955058e-08
1.02115214e-08 1.02272271e-08 1.02426299e-08 1.02577404e-08
1.02725659e-08 1.02871143e-08 1.03013935e-08 1.03154081e-08
1.03291686e-08 1.03426796e-08 1.03559481e-08 1.03689795e-08
1.03817808e-08 1.03943574e-08 1.04067137e-08 1.04188569e-08
1.04307922e-08 1.04425224e-08 1.04540554e-08 1.04653930e-08
1.04765423e-08 1.04875051e-08 1.04982885e-08 1.05088942e-08
1.05193267e-08 1.05295914e-08 1.05396909e-08 1.05496287e-08
1.05594085e-08 1.05690345e-08 1.05785078e-08 1.05878346e-08
1.05970166e-08 1.06060565e-08 1.06149569e-08 1.06237223e-08
1.06323537e-08 1.06408544e-08 1.06492282e-08 1.06574767e-08
1.06656017e-08 1.06736069e-08 1.06814939e-08 1.06892655e-08
1.06969233e-08 1.07044711e-08 1.07119087e-08 1.07192397e-08
1.07264659e-08 1.07335891e-08 1.07406102e-08 1.07475335e-08
1.07543592e-08 1.07610889e-08 1.07677245e-08 1.07742677e-08
1.07807221e-08 1.07870859e-08 1.07933635e-08 1.07995550e-08
1.08056621e-08 1.08116867e-08 1.08176295e-08 1.08234932e-08
1.08292788e-08 1.08349862e-08 1.08406173e-08 1.08461746e-08
1.08516591e-08 1.08570708e-08 1.08624123e-08 1.08676836e-08
1.08728866e-08 1.08780212e-08 1.08830900e-08 1.08880931e-08
1.08930323e-08 1.08979092e-08 1.09027232e-08 1.09074758e-08
1.09121689e-08 1.09168017e-08 1.09213776e-08 1.09258949e-08
1.09303562e-08 1.09347624e-08 1.09391136e-08 1.09434115e-08
1.09476552e-08 1.09518474e-08 1.09559881e-08 1.09600773e-08
1.09641176e-08 1.09681082e-08 1.09720499e-08 1.09759446e-08
1.09797922e-08 1.09835936e-08 1.09873497e-08 1.09910596e-08
1.09947260e-08 1.09983489e-08 1.10019291e-08 1.10054668e-08
1.10089626e-08 1.10124168e-08 1.10158309e-08 1.10192051e-08
1.10225393e-08 1.10258354e-08 1.10290923e-08 1.10323120e-08
1.10354943e-08 1.10386402e-08 1.10417497e-08 1.10448228e-08
1.10478613e-08 1.10508651e-08 1.10538343e-08 1.10567697e-08
1.10596723e-08 1.10625411e-08 1.10653771e-08 1.10681819e-08
1.10709557e-08 1.10736975e-08 1.10764082e-08 1.10790888e-08]
</code></pre>
<p><strong>Performing calculation above, but at individual point (correct result)</strong>:</p>
<pre><code>[0.3261025 0.42537308 0.48440808 0.5264295 0.55907714 0.5857545
0.6082901 0.62777 0.6449178 0.66021943 0.6740228 0.6865807
0.69809467 0.7087174 0.7185703 0.72774875 0.7363362 0.7443992
0.75199366 0.7591655 0.76595706 0.7724033 0.7785346 0.78437734
0.7899539 0.79528594 0.8003918 0.80528766 0.8099875 0.81450516
0.8188526 0.8230406 0.8270782 0.830975 0.8347391 0.83837795
0.84189796 0.8453059 0.8486077 0.85180867 0.8549138 0.85792726
0.8608539 0.86369765 0.8664623 0.869151 0.8717674 0.87431455
0.87679535 0.87921226 0.8815681 0.8838652 0.88610595 0.8882922
0.8904262 0.89250994 0.8945452 0.8965335 0.8984767 0.90037644
0.902234 0.90405095 0.90582836 0.90756774 0.9092703 0.91093725
0.91256946 0.9141682 0.91573447 0.91726923 0.9187733 0.9202477
0.92169327 0.9231109 0.9245012 0.92586505 0.92720324 0.9285163
0.92980516 0.93107015 0.93231213 0.93353164 0.9347293 0.93590546
0.9370609 0.93819606 0.9393114 0.94040745 0.9414847 0.9425435
0.9435845 0.9446078 0.9456141 0.94660366 0.9475769 0.9485342
0.9494758 0.9504024 0.9513139 0.9522109 0.95309365 0.95396245
0.9548176 0.95565945 0.95648813 0.95730406 0.9581075 0.9588986
0.9596777 0.960445 0.9612008 0.9619453 0.9626787 0.9634012
0.96411306 0.96481454 0.9655058 0.966187 0.96685827 0.96752
0.96817225 0.9688152 0.969449 0.9700738 0.9706899 0.9712973
0.9718963 0.9724869 0.9730694 0.97364384 0.97421044 0.9747693
0.9753205 0.9758643 0.97640073 0.97692996 0.97745216 0.9779673
0.97847563 0.9789772 0.9794722 0.9799607 0.98044276 0.9809186
0.9813882 0.9818517 0.98230916 0.9827608 0.9832066 0.98364675
0.98408127 0.98451024 0.9849338 0.98535204 0.985765 0.9861728
0.9865755 0.9869731 0.9873659 0.9877538 0.9881369 0.98851526
0.9888889 0.98925805 0.9896227 0.9899829 0.99033874 0.99069023
0.99103755 0.99138063 0.99171966 0.9920545 0.99238545 0.9927125
0.9930356 0.9933549 0.99367046 0.99398226 0.9942904 0.99459493
0.9948959 0.99519336 0.99548733 0.99577796 0.9960652 0.99634916
0.9966298 0.9969072 0.9971815 0.99745256 0.9977206 0.99798554
0.9982475 0.9985064 0.9987625 0.9990156 0.99926597 0.9995134
0.99975806 1.0000001 ]
</code></pre>
|
<python><numpy><numpy-ndarray><cumsum>
|
2023-05-31 14:53:40
| 1
| 431
|
TornadoEric
|
76,374,785
| 11,760,357
|
Keep original string values after pandas.series.str.extract() if the regex doesn't match
|
<p>I am trying to extract emails from strings, and want to make sure that if the original value is formatted how I expect already, that it is not changed to Nan and instead is kept as is.</p>
<p>Example input</p>
<pre><code><class 'pandas.core.series.Series'>
1 <doe.b.john@gmail.com>
2 <doe.c.jane@gmail.com>
3 person.anonymous@hotmail.com
4 dent.arthur@space.com
</code></pre>
<p>I am using</p>
<pre class="lang-py prettyprint-override"><code># curr_emails is <class 'pandas.core.series.Series'>
curr_emails = curr_emails.str.extract(r"<([^<>]+)>").squeeze()` # regex extracts text between < >
</code></pre>
<p>I receive back</p>
<pre><code>1 doe.b.john@gmail.com
2 doe.c.jane@gmail.com
3 NaN
4 Nan
</code></pre>
<p>But I instead would like</p>
<pre><code>1 doe.b.john@gmail.com
2 doe.c.jane@gmail.com
3 person.anonymous@hotmail.com
4 dent.arthur@space.com
</code></pre>
<p>A similar question is posted <a href="https://stackoverflow.com/questions/59191897/keep-original-string-values-after-pandas-str-extract-if-the-regex-doesnt-matc">here</a>, but I could not seem to make it work with my current approach.</p>
|
<python><pandas>
|
2023-05-31 14:51:52
| 2
| 375
|
Caleb Renfroe
|
76,374,682
| 4,019,495
|
Is there a way to require a mutually exclusive group, where one of the groups has multiple options?
|
<p>I would like a behavior where exactly one of</p>
<ul>
<li><code>--a</code></li>
<li><code>--b</code> AND <code>--c</code></li>
</ul>
<p>are required. I know how to do it if the second requirement were just <code>--b</code>:</p>
<pre><code>group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('--a', type=str)
group.add_argument('--b', type=str)
</code></pre>
|
<python><argparse>
|
2023-05-31 14:39:37
| 1
| 835
|
extremeaxe5
|
76,374,605
| 8,564,860
|
Kafka running in container - cannot connect from another container
|
<p>I've tried to follow the advice of some other excellent posts such as <a href="https://stackoverflow.com/questions/51630260/connect-to-kafka-running-in-docker/51634499#51634499">this one</a> and others, but still running into problems.</p>
<p>I have two docker compose networks, one running kafka/zookeeper and another running a python application which includes a kafka producer, so needs to write to the container running kafka. I've created a network <code>kafka_logging_network</code> which both containers are a part of. I've double checked the <code>KAFKA_ADVERTISED_LISTENERS</code> variable many times but cant see what I'm missing.</p>
<p>The below shows the two <code>docker-compose.yml</code> files, followed by the <code>Dockerfile</code> and <code>main.py</code> for the application. <code>main.py</code> should run without error but in reality it cannot find any kafka brokers at the given <code>KAFKA_HOST</code> and <code>KAFKA_PORT</code>, raising <code>kafka.errors.NoBrokersAvailable</code>. Where have I gone wrong?</p>
<h2>kafka/docker-compose.yml (for kafka/zookeeper)</h2>
<pre><code>version: '3.7'
services:
zoo1:
image: confluentinc/cp-zookeeper:7.3.2
hostname: zoo1
container_name: zoo1
ports:
- "2181:2181"
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_SERVER_ID: 1
ZOOKEEPER_SERVERS: zoo1:2888:3888
networks:
- logging_network
kafka1:
image: confluentinc/cp-enterprise-kafka:5.3.1
hostname: kafka1
container_name: kafka1
ports:
- "9092:9092"
environment:
KAFKA_BROKER_ID: 1
KAFKA_ZOOKEEPER_CONNECT: 'zookeeper:2181'
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:29092,PLAINTEXT_HOST://0.0.0.0:9092
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka1:29092,PLAINTEXT_HOST://localhost:9092
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
KAFKA_GROUP_INITIAL_REBALANCE_DELAY_MS: 0
depends_on:
- zoo1
restart: always
networks:
- logging_network
networks:
logging_network:
driver: bridge
</code></pre>
<h2>app_folder/docker-compose.yml (for python application)</h2>
<pre><code>version: '3.7'
services:
pyapp:
build:
context: .
dockerfile: Dockerfile
container_name: pyapp
networks:
- kafka_logging_network
environment:
KAFKA_HOST: kafka1
KAFKA_PORT: 29092
KAFKA_TOPIC: logs-topic
networks:
kafka_logging_network:
external: true
</code></pre>
<h2>Dockerfile</h2>
<pre><code>FROM python:3.9.7
COPY . .
RUN pip install kafka_python==2.0.2
ENTRYPOINT python main.py
</code></pre>
<h2>main.py</h2>
<pre><code>from kafka import KafkaProducer
import os
host = os.environ['KAFKA_HOST']
port = os.environ['KAFKA_PORT']
kp = KafkaProducer(bootstrap_servers=f'{host}:{port}')
print('success')
</code></pre>
|
<python><docker><apache-kafka><docker-compose>
|
2023-05-31 14:32:37
| 1
| 1,102
|
John F
|
76,374,535
| 520,601
|
Numpy np.float32 maximum number is 16M instead of 3.4e38 - unclear behavior of floats
|
<p>Max value here:</p>
<pre><code>import numpy
numpy.finfo(numpy.float32).max
3.4028235e+38
</code></pre>
<p>Then I run code</p>
<pre><code>a = np.array([0, 0, 0], dtype=np.float32)
for _ in range(100_000_000): a += 1.0
</code></pre>
<p>Why does it end up being</p>
<pre><code>array([16777216., 16777216., 16777216.], dtype=float32)
</code></pre>
<p>?</p>
|
<python><numpy>
|
2023-05-31 14:24:16
| 0
| 1,600
|
Tadas Šubonis
|
76,374,435
| 12,778,634
|
How to initialize a dataframe from dictionary translated into sparse matrix
|
<p>pI do:</p>
<pre><code>>>> import pandas as pd
>>>
>>> aa = {"a": ["group_a", "group_b", "group_c"]}
>>> ab = {"b": ["x", "y"]}
>>> ba = {"c": ["group_b"]}
>>>
>>> c = {**aa, **ab, **ba}
>>>
>>> df = pd.DataFrame.from_dict(c, orient="index")
>>>
>>> print(df)
0 1 2
a group_a group_b group_c
b x y None
c groub_b None None
</code></pre>
<p>But I am actually looking for a pythonic way to get:</p>
<pre><code> group_a group_b group_c x y
a 1 1 1 0 0
b 0 0 0 1 1
c 0 1 0 0 0
</code></pre>
|
<python><pandas>
|
2023-05-31 14:14:00
| 4
| 1,237
|
AndreasInfo
|
76,374,265
| 9,937,874
|
ONNX as a form of model managment
|
<p>I have am working on a project that requires the use of previously created model objects. They have all been built with scikit-learn but with different library versions (0.23.1, 0.23.2, 1.0.2, and 1.2.2). I have always been under the assumption that if you are using models to make predictions you should be using the version of scikit-learn that the model was built with. Since I only need to make predictions with these model objects I was considering converting them all to onnx objects in order to get around rebuilding all the model objects using the most recent version of scikit-learn. Is this the correct approach or is there a better solution?</p>
|
<python><python-3.x><scikit-learn><onnx>
|
2023-05-31 13:52:28
| 0
| 644
|
magladde
|
76,374,230
| 11,974,225
|
Using existing Python packages in VSCode and SSH
|
<p>I installed VSCode and its remote SSH and Python extensions.</p>
<p>I connected to a remote server, let's say by <code>ssh user_name@ip_address</code>.</p>
<p>Now, in the remote server, the path the folder with the Python packages I installed long time ago is: <code>/home/user_name/.local/lib/python3.7/site-packages</code>.</p>
<p>How do I use them when I create a Python script in VSCode and, for example, try to <code>import numpy as np</code>?</p>
|
<python><visual-studio-code><ssh>
|
2023-05-31 13:47:20
| 2
| 907
|
qwerty
|
76,374,184
| 11,251,373
|
Fastest way to find out whether row or one of set of rows exists in database accesed via SqlAlchemy
|
<p>Django has <code>queryset.exists()</code> method that fetch bool indicating whether or not at least 1 row with specific conditions exists.</p>
<p>for example</p>
<pre><code>User.objects.filter(id__in=[1, 2, 3, 4, 5], usernmae='test').exists()
</code></pre>
<p>It would just return bool <code>True</code> in case at least one row with these conditions exists and False other way around.</p>
<p><strong>Question</strong> - is it something like that could be done in SQLAlchemy > 2.0 with <strong>asyncpg</strong> driver?
Best I have found is <code>await session.get</code> but it potentially could fetch multiple objectds from DB, dezerialize them, etc, whereas I only need to find out if at least one of these rows does exists?</p>
|
<python><django><sqlalchemy>
|
2023-05-31 13:43:08
| 1
| 2,235
|
Aleksei Khatkevich
|
76,374,151
| 6,248,190
|
Error 1064 when inserting SQL data from a dump file into MariaDB using Python script
|
<p>I have written a Python script to import data from a SQL dump file into a MariaDB instance. However, when I run the script, I encounter the following error:</p>
<pre><code>Error: 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MariaDB server version for the right syntax to use near ''Small Room - 2.00 Hours \r\n(08:00 PM - 10:00 PM)\r\nPeak Hours' at line 2
</code></pre>
<p>Interestingly, when I use DBeaver and connect to the database, I can successfully insert the same data without any errors using the console in DBeaver.</p>
<p>Here is the Python script I am using:</p>
<pre><code>import mysql.connector
# Database connection details
host = 'localhost'
port = 3306
user = 'admin'
password = 'password'
database = 'example'
# SQL file path
sql_file = 'file_location'
try:
connection = mysql.connector.connect(
host=host,
port=port,
user=user,
password=password,
database=database
)
cursor = connection.cursor()
with open(sql_file, 'r') as file:
sql_statements = file.read()
statements = sql_statements.split(';')
for statement in statements:
if statement.strip():
cursor.execute(statement)
connection.commit()
print('Data imported successfully.')
except mysql.connector.Error as error:
print('Error:', error)
finally:
if 'connection' in locals() and connection.is_connected():
cursor.close()
connection.close()
</code></pre>
<p>Here is the SQL data I am trying to insert:</p>
<pre><code>INSERT INTO `transactions` (`id`, `profit`, `sub_total`, `grand_total`, `gst`, `status`, `created_on`, `content`, `receive_amount`, `change_amount`, `start_time`, `end_time`, `checkout`, `outlet_id`, `staff_id`, `server_transaction_id`, `outlet_transaction_id`) VALUES
(318151, 18.69, 20.00, 20.00, 1.31, 'paid', '1480319419', 'Premium Medium Room - 3 Hours \r\n(04:00 PM - 07:00 PM)\r\nHappy Hours 3 Hours Package;19;2016-11-28 16:00:00;2016-11-28 19:00:00;132', 20.00, 0.00, '1480320000', '1480330800', b'1', 7, 1006, 318151, 36079)
</code></pre>
<p>I'm not sure why I'm encountering this error when running the script, but I can successfully execute the same SQL statement using DBeaver. Any help or suggestions would be greatly appreciated. Thank you</p>
|
<python><mariadb>
|
2023-05-31 13:40:09
| 2
| 1,271
|
user6248190
|
76,374,021
| 7,441,757
|
Pip does install although reporting conflicts
|
<p>I am trying to prevent installing two packages that conflict. But when I do:</p>
<pre><code>pip install some-package
pip install another-package
</code></pre>
<blockquote>
<p>ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
some-package requires numpy==1.24.2, but you have numpy 1.24.3 which is incompatible.</p>
</blockquote>
<blockquote>
<p>Successfully installed numpy-1.24.3 botocore-1.29.140 ... another-package</p>
</blockquote>
<p>Now I am surprised I can import both <code>some-package</code> as <code>another-package</code> - shouldn't the second installation fail?</p>
<p><code>pip check</code> does show the conflict.</p>
<blockquote>
<p>some-package has requirement numpy==1.24.2, but you have numpy 1.24.3.</p>
</blockquote>
<p>I found <a href="https://github.com/pypa/pip/issues/988" rel="nofollow noreferrer">this issue</a> which suggests that it should be solved, but I am using pip 23.1.2.</p>
|
<python><pip>
|
2023-05-31 13:26:56
| 0
| 5,199
|
Roelant
|
76,373,921
| 12,040,751
|
What does coroutine mean in Python?
|
<p>Is there a consensus on what a coroutine is today in Python?</p>
<p>I found coroutines first mentioned in PEP <a href="https://peps.python.org/pep-0219/" rel="nofollow noreferrer">219</a> and <a href="https://peps.python.org/pep-0220/" rel="nofollow noreferrer">220</a> (2000, Python 2.1), then in <a href="https://peps.python.org/pep-0255/" rel="nofollow noreferrer">PEP 255 – Simple Generators</a> (2001, Python 2.2). The latter is the PEP that introduced generators and the <code>yield</code> statement, which are the basis of coroutines as of <a href="https://peps.python.org/pep-0342/" rel="nofollow noreferrer">PEP 342 - Coroutines via Enhanced Generators</a> (2005, Python 2.5).</p>
<p>If we stop then, a generator that you can send values to is - pretty much - a coroutine:</p>
<pre><code>def coroutine():
name = yield
print(f"Hello {name}")
hello = coroutine()
type(hello) # generator type
hello.send(None) # Initialize the coroutine
hello.send("Dave") # prints 'Hello Dave' (and raises StopIteration)
</code></pre>
<p>Fast forward to <a href="https://peps.python.org/pep-0492/" rel="nofollow noreferrer">PEP 492 – Coroutines with async and await syntax</a> (2015, Python 3.5), and this is called a coroutine:</p>
<pre><code>async def coroutine():
pass
type(coroutine()) # coroutine
</code></pre>
<p>Are both of these constructs commonly referred as coroutines, or is there some jargon to distinguish between the two?</p>
|
<python><coroutine>
|
2023-05-31 13:16:34
| 2
| 1,569
|
edd313
|
76,373,894
| 2,548,285
|
CMake: Could not find Python3 (found version 3.9.6)
|
<p>I am trying to configure CMake (v3.26.0) to find a local Anaconda installation of Python3 (on Ventura 13.3.1) I am using but I am running into an issue with CMake detecting Python. In my CMake configuration script, I have:</p>
<pre><code>find_package(Python3 COMPONENTS Interpreter REQUIRED
Python3_ROOT "${CMAKE_CURRENT_LIST_DIR}/../conda/bin")
</code></pre>
<p>This correctly points CMake to the location of the local Python3 interpreter. Moreover, when I run the configuration script, it <em>appears</em> to find Python but seems to think it hasn't:</p>
<pre><code>CMake Error at /usr/local/Cellar/cmake/3.26.0/share/cmake/Modules/FindPackageHandleStandardArgs.cmake:230 (message):
Could NOT find Python3 (missing: Python3_ROOT
/Users/xxx/xxxxx/cmake/../conda/bin) (found version "3.9.6")
</code></pre>
<p>I am confused; it seems to be picking up the right version of Python (i.e., I expect it to be 3.9.6) but CMake is somehow indicating that it wasn't found. What am I doing wrong here?</p>
|
<python><python-3.x><cmake>
|
2023-05-31 13:12:38
| 2
| 393
|
Professor Nuke
|
76,373,836
| 13,955,154
|
Delete a line from a PDF and creating a new PDF with these modifications
|
<p>I currently have this code:</p>
<pre><code>def delete_first_line_from_pages(pages_indices, input_pdf_path, output_pdf_path):
with open(input_pdf_path, 'rb') as input_file, open(output_pdf_path, 'wb') as output_file:
reader = PyPDF2.PdfReader(input_file)
for page_index in range(len(reader.pages)):
page = reader.pages[page_index]
if page_index in pages_indices:
content = page.extract_text()
lines = content.split("\n")
if lines:
first_line = next(line for line in lines if line.strip())
if first_line:
modified_content = content.replace(first_line, '', 1)
else:
modified_content = content
</code></pre>
<p>where I end up with modified_content which is basically the text of the page with the first line of text removed. From now on I want to create a page element that has the same structure of the source but this new modified_content as text.
My final goal is to save to an output_path the pdf I had as input but removing the first line of each page.
How can I do it?</p>
|
<python><pdf>
|
2023-05-31 13:07:21
| 1
| 720
|
Lorenzo Cutrupi
|
76,373,668
| 14,282,714
|
Change y-axis title hover in histogram plotly python
|
<p>I would like to change the y-axis title of a histogram in plotly python. We could use the <code>fig.update_layout(yaxis_title="New y-axis title")</code> to change the title of the y-axis. But this doesn't automatically change the hover title of the y-axis. We could use the <code>labels</code> argument to change the title of the x-axis and hover title but this doesn't work for the y-axis title with count. Here is some reproducible code:</p>
<pre><code>import plotly.express as px
df = px.data.tips()
fig = px.histogram(df, x="total_bill",
labels={"total_bill": "Total bill"})
fig.update_layout(yaxis_title="New y-axis title")
fig.show()
</code></pre>
<p>Output:</p>
<p><a href="https://i.sstatic.net/MxUSR.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MxUSR.png" alt="enter image description here" /></a></p>
<p>As you can see the y-axis title is nicely changed, but the hover title isn't changed. So I was wondering if anyone knows how to fix this issue?</p>
|
<python><histogram><plotly>
|
2023-05-31 12:50:21
| 1
| 42,724
|
Quinten
|
76,373,649
| 8,605,348
|
In Databricks, where does `spark.*` come from?
|
<p>When working in Azure Databricks, the <code>spark.sql()</code> function is available at startup; no imports necessary. But where does it come from?</p>
<p><code>spark.sql</code>'s docs say it's from <code>pyspark.sql.session</code>:</p>
<pre class="lang-py prettyprint-override"><code>help(spark.sql)
</code></pre>
<pre><code>Help on method sql in module pyspark.sql.session:
</code></pre>
<p>But when I try calling <code>sql()</code> from <code>pyspark.sql.session</code>, it fails:</p>
<pre class="lang-py prettyprint-override"><code>import pyspark.sql.session as spark
spark.sql
</code></pre>
<pre><code>AttributeError: module 'pyspark.sql.session' has no attribute 'sql'
</code></pre>
<p>I'm developing a Python package to be used in Databricks, but I can't test it on my local machine without <code>spark</code>, but the <code>spark</code> scope doesn't seem to come from any package I can install locally with Poetry.</p>
|
<python><databricks><azure-databricks>
|
2023-05-31 12:47:50
| 1
| 1,294
|
ardaar
|
76,373,626
| 14,649,310
|
Alembic ignores new model and does not add new table to revision
|
<p>How can I make alembic take a new file with a new model into account for a new revision? All the models are in the same directory <code>/models</code> but alembic seems to ignore it. How can it be included and tracked by alembic?</p>
|
<python><flask><alembic>
|
2023-05-31 12:45:25
| 1
| 4,999
|
KZiovas
|
76,373,541
| 8,929,189
|
Django m2m relationship only saving sometimes through django admin
|
<p>Have been banging my head against a wall on this one for a while. I have a model called Car, in this is has a models.ManyToManyField of colours.</p>
<pre><code>class Car(models.Model):
brand = models.CharField(max_length=70, default='', unique=True)
colours = models.ManyToManyField('Colours')
...
def fetch_data(self):
#some code which gets data from an api and adds it to some fields
def save(self, *args, **kwargs):
self.fetch_data()
super(Car, self).save(*args, **kwargs)
</code></pre>
<p>For a while this has been working seamlessly. A Car can be saved with numerous colours.</p>
<p>Recently, if I select a colour in django admin when adding a Car, I click save, the Car will be created. However, clicking back into the car in django admin and you can see there are no Colours attached to it. And looking at the database, the m2m values have not been added.</p>
<p>However, if I stay on that Car's edit screen for a while (5 mins) in django admin, re click on the colours, and press save, it will save the colours half of the time.</p>
<p>Does anyone have any idea what could be the issue with this? I have tried adding save_related() and save_model() in CarAdmin(admin.ModelAdmin) but to no avail.</p>
<p>Thanks!</p>
<p>EDIT:</p>
<p>Code within admin.py for the Car model looks like this currently:</p>
<pre><code>class CarAdmin(admin.ModelAdmin):
list_display = ('name','published')
list_filter = ['published','colours']
# This is needed to save the many to many on cars -> colours
# def save_related(self, request, form, formsets, change):
# super(CarAdmin, self).save_related(request, form, formsets, change)
# def save_model(self, request, obj, form, change):
# Car.fetch_data()
# super(Car, self).save_model(request, obj, form, change)
</code></pre>
|
<python><django><postgresql><django-models>
|
2023-05-31 12:34:10
| 0
| 546
|
rlou
|
76,373,508
| 15,148,200
|
Pycaret error : TypeError: format() got an unexpected keyword argument 'precision'
|
<p>I'm using the compare_models() function for the classification problem in pycaret. It was running well during the process but in the end, it does not print anything but an empty list.</p>
<pre><code>clf1 = setup(data = train_titanic_df, target = 'Survived')
compare_models(include = ['lightgbm', 'xgboost'], errors="raise")
</code></pre>
<p>After I adding <em>errors="raise"</em> to <em>compare_models()</em>, it gives me this error:</p>
<pre><code>---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[26], line 1
----> 1 compare_models(include = ['lightgbm', 'xgboost'], errors="raise") #'rf', 'ada','catboost'
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/utils/generic.py:965, in check_if_global_is_not_none.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
963 if globals_d[name] is None:
964 raise ValueError(message)
--> 965 return func(*args, **kwargs)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/classification/functional.py:822, in compare_models(include, exclude, fold, round, cross_validation, sort, n_select, budget_time, turbo, errors, fit_kwargs, groups, experiment_custom_tags, probability_threshold, engine, verbose, parallel)
680 @check_if_global_is_not_none(globals(), _CURRENT_EXPERIMENT_DECORATOR_DICT)
681 def compare_models(
682 include: Optional[List[Union[str, Any]]] = None,
(...)
698 parallel: Optional[ParallelBackend] = None,
699 ) -> Union[Any, List[Any]]:
700 """
701 This function trains and evaluates performance of all estimators available in the
702 model library using cross validation. The output of this function is a score grid
(...)
819 - No models are logged in ``MLFlow`` when ``cross_validation`` parameter is False.
820 """
--> 822 return _CURRENT_EXPERIMENT.compare_models(
823 include=include,
824 exclude=exclude,
825 fold=fold,
826 round=round,
827 cross_validation=cross_validation,
828 sort=sort,
829 n_select=n_select,
830 budget_time=budget_time,
831 turbo=turbo,
832 errors=errors,
833 fit_kwargs=fit_kwargs,
834 groups=groups,
835 experiment_custom_tags=experiment_custom_tags,
836 probability_threshold=probability_threshold,
837 engine=engine,
838 verbose=verbose,
839 parallel=parallel,
840 )
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/classification/oop.py:1190, in ClassificationExperiment.compare_models(self, include, exclude, fold, round, cross_validation, sort, n_select, budget_time, turbo, errors, fit_kwargs, groups, experiment_custom_tags, probability_threshold, engine, verbose, parallel)
1187 self._set_engine(estimator=estimator, engine=eng, severity="error")
1189 try:
-> 1190 return_values = super().compare_models(
1191 include=include,
1192 exclude=exclude,
1193 fold=fold,
1194 round=round,
1195 cross_validation=cross_validation,
1196 sort=sort,
1197 n_select=n_select,
1198 budget_time=budget_time,
1199 turbo=turbo,
1200 errors=errors,
1201 fit_kwargs=fit_kwargs,
1202 groups=groups,
1203 experiment_custom_tags=experiment_custom_tags,
1204 verbose=verbose,
1205 probability_threshold=probability_threshold,
1206 parallel=parallel,
1207 caller_params=caller_params,
1208 )
1209 finally:
1210 if engine is not None:
1211 # Reset the models back to the default engines
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:788, in _SupervisedExperiment.compare_models(self, include, exclude, fold, round, cross_validation, sort, n_select, budget_time, turbo, errors, fit_kwargs, groups, experiment_custom_tags, probability_threshold, verbose, parallel, caller_params)
786 results_columns_to_ignore = ["Object", "runtime", "cutoff"]
787 if errors == "raise":
--> 788 model, model_fit_time = self._create_model(**create_model_args)
789 model_results = self.pull(pop=True)
790 else:
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:1575, in _SupervisedExperiment._create_model(self, estimator, fold, round, cross_validation, predict, fit_kwargs, groups, refit, probability_threshold, experiment_custom_tags, verbose, system, add_to_model_list, X_train_data, y_train_data, metrics, display, model_only, return_train_score, **kwargs)
1570 self._master_model_container.append(
1571 {"model": model, "scores": model_results, "cv": cv}
1572 )
1574 # yellow the mean
-> 1575 model_results = self._highlight_and_round_model_results(
1576 model_results, return_train_score, round
1577 )
1578 if system:
1579 display.display(model_results)
File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py:1266, in _SupervisedExperiment._highlight_and_round_model_results(self, model_results, return_train_score, round)
1264 indices = ["Mean"]
1265 model_results = color_df(model_results, "yellow", indices, axis=1)
-> 1266 model_results = model_results.format(precision=round)
1267 return model_results
TypeError: format() got an unexpected keyword argument 'precision'
</code></pre>
<p>what I tried so far:</p>
<p>1- Upgrade pycaret to version <code>3.0.0.rc9</code>.</p>
<p>2- Trying other model other than lightgbm and xgboost.</p>
<p>3- Check my python version which is 3.8.5.</p>
<p>I searched online a lot but couldn't find anything that address this error! I appreciate any help with this error.</p>
|
<python><machine-learning><jupyter-notebook><anaconda><pycaret>
|
2023-05-31 12:30:09
| 1
| 547
|
Raha Moosavi
|
76,373,153
| 2,059,689
|
Converting pd.Series of lists to multi-column DataFrame
|
<p>Given Pandas <code>Series</code> of lists where all lists have the same length <code>N</code>. How can I reshape it into a <code>DataFrame</code> with <code>N</code> columns (keeping the index)?</p>
<p>For example, given</p>
<pre><code>s = pd.Series([[1, 10, 0.1], [2, 20, 0.2], [3, 30, 0.3]], index=['a', 'b', 'c'])
>>> print(s)
a [1, 10, 0.1]
b [2, 20, 0.2]
c [3, 30, 0.3]
dtype: object
</code></pre>
<p>I want the code to produce <code>DataFrame</code> looking like this</p>
<pre><code>df = pd.DataFrame([[1, 10, 0.1], [2, 20, 0.2], [3, 30, 0.3]],
index=['a', 'b', 'c'], columns=['X', 'Y'])
>>> print(df)
X Y Z
a 1 10 0.1
b 2 20 0.2
c 3 30 0.3
</code></pre>
|
<python><pandas><dataframe>
|
2023-05-31 11:47:35
| 2
| 3,200
|
vvv444
|
76,373,054
| 6,455,731
|
Pydantic: Why are field values referencing another model converted to dictionaries when using .dict?
|
<p>My problem is that when I iterate over a model instance using <code>.dict</code>, field values referencing other models are converted to dictionaries, e.g.:</p>
<pre class="lang-py prettyprint-override"><code>from pydantic import BaseModel
class Model(BaseModel):
attr: str
class AnotherModel(BaseModel):
attr: str
model_attr: Model
model = AnotherModel(attr="test", model_attr=Model(attr="something"))
# field values are model objects
for field in model:
print(field)
# field values are dictionaries
for key, val in model.dict().items():
print((key, val))
</code></pre>
<p>Why is that and how can I change it?</p>
|
<python><pydantic>
|
2023-05-31 11:34:45
| 1
| 964
|
lupl
|
76,373,005
| 3,099,749
|
Accurate evaluation of function for negative x
|
<p>Is there a way to accurately evaluate <code>f(x)</code> as given below for moderate negative x (around -1e6 to -1e2), while sticking with float64 numbers?</p>
<pre class="lang-py prettyprint-override"><code>def f(x):
return 0.5 + arctan(x/sqrt(3)) / pi + sqrt(3)*x/(pi*(x*x+3))
</code></pre>
<p>For example:</p>
<pre class="lang-py prettyprint-override"><code>g(-10_000) == 1.1026577511479050e-12 # actual closest float64 (what I want)
f(-10_000) == 1.1026461116240595e-12 # current implementation
# ^^^^^^^^^^^^ <- wrong
</code></pre>
|
<python><precision><numeric>
|
2023-05-31 11:29:18
| 1
| 303
|
hbwales
|
76,372,780
| 10,755,782
|
Version conflict error while installing albumentation on python 3.6.8 with opencv-python
|
<p>I'm trying to install <code>albumentations</code> on python 3.6.8. However, the installation is failing with the following error</p>
<pre><code>Failed to build opencv-python
ERROR: Could not build wheels for opencv-python,
which is required to install pyproject.toml-based projects
</code></pre>
<p>This is happening because my Python version is 3.6.8 and the installed opencv-python version is 3.4.13.47 and albumentation is trying to install the latest version of opencv-python (4.7.x). As per the install instructions of albumentations as given <a href="https://albumentations.ai/docs/getting_started/installation/" rel="nofollow noreferrer">here</a>, there is a flag to force-use the existing opencv version</p>
<pre><code>pip install -U albumentations --no-binary qudida, albumentations
</code></pre>
<p>However, even after using these flags, albumentation is trying to install opencv-python-4.7.0.72, and this is failing.</p>
<p>How can we force this to use the existing opencv version?</p>
|
<python><opencv><pip><python-3.6><albumentations>
|
2023-05-31 11:00:00
| 0
| 660
|
brownser
|
76,372,689
| 4,451,521
|
tox cannot install numpy
|
<p>I have pyenv installed and these versions</p>
<pre><code>pyenv versions
system
3.8.0
3.9.16
* 3.10.1 (set by /home/me/.pyenv/version)
</code></pre>
<p>and I got this <code>tox.ini</code></p>
<pre><code>[tox]
envlist = unit_tests
skipsdist = True
[testenv]
install_command = pip install {opts} {packages}
deps =
-rrequirements.txt
commands=
py.test
[testenv:unit_tests]
envdir = {toxworkdir}/unit_tests
deps =
{[testenv]deps}
setenv =
PYTHONPATH=.
commands =
python gradient_boosting_model/train_pipeline.py
pytest \
-s \
-vv \
{posargs:tests/}
[testenv:train]
envdir = {toxworkdir}/train
deps =
{[testenv]deps}
setenv =
PYTHONPATH=.
commands =
python gradient_boosting_model/train_pipeline.py
</code></pre>
<p>the system python is 3.6.9</p>
<p>When I run tox , no matter in which version I get</p>
<pre><code>tox
unit_tests create: /media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests
unit_tests installdeps: -rrequirements.txt
ERROR: invocation failed (exit code 1), logfile: /media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/log/unit_tests-1.log
==================================================================== log start ====================================================================
ERROR: Could not find a version that satisfies the requirement numpy<1.21.0,>=1.20.0 (from versions: 1.3.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.6.1, 1.6.2, 1.7.0, 1.7.1, 1.7.2, 1.8.0, 1.8.1, 1.8.2, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.10.0.post2, 1.10.1, 1.10.2, 1.10.4, 1.11.0, 1.11.1, 1.11.2, 1.11.3, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 1.13.3, 1.14.0, 1.14.1, 1.14.2, 1.14.3, 1.14.4, 1.14.5, 1.14.6, 1.15.0, 1.15.1, 1.15.2, 1.15.3, 1.15.4, 1.16.0, 1.16.1, 1.16.2, 1.16.3, 1.16.4, 1.16.5, 1.16.6, 1.17.0, 1.17.1, 1.17.2, 1.17.3, 1.17.4, 1.17.5, 1.18.0, 1.18.1, 1.18.2, 1.18.3, 1.18.4, 1.18.5, 1.19.0, 1.19.1, 1.19.2, 1.19.3, 1.19.4, 1.19.5)
ERROR: No matching distribution found for numpy<1.21.0,>=1.20.0
===================================================================== log end =====================================================================
ERROR: could not install deps [-rrequirements.txt]; v = InvocationError('/media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/bin/pip install -rrequirements.txt', 1)
_____________________________________________________________________ summary _____________________________________________________________________
ERROR: unit_tests: could not install deps [-rrequirements.txt]; v = InvocationError('/media/me/cbe421fe-1303-4821-9392-a849bfdd00e2/MyStudy/Udemy/testing-and-monitoring/testing-and-monitoring-ml-deployments/packages/gradient_boosting_model/.tox/unit_tests/bin/pip install -rrequirements.txt', 1)
</code></pre>
<p>the <code>requirements.txt</code> file is</p>
<pre><code># ML requirements
numpy>=1.20.0,<1.21.0
pandas>=1.3.5,<1.4.0
scikit-learn>=1.0.2,<1.1.0
feature-engine>=1.0.2,<1.1.0
joblib>=1.0.1,<1.1.0
# config parsing
strictyaml>=1.3.2,<1.4.0
ruamel.yaml==0.16.12
pydantic>=1.8.1,<1.9.0
# validation
marshmallow>=3.2.2,<4.0
# packaging
setuptools>=41.4.0,<42.0.0
wheel>=0.33.6,<0.34.0
# testing requirements
pytest>=5.3.2,<6.0.0
</code></pre>
<p>At first I thought it was because the original python version is old but even when I run it with python 10 it does not work.</p>
<p>What can it be solved?</p>
|
<python><pyenv><tox>
|
2023-05-31 10:49:11
| 1
| 10,576
|
KansaiRobot
|
76,372,584
| 10,430,394
|
Can't make virtual environment using virtualenv with python 2.7 on Windows 10
|
<p>I need a python 2.7 install for some niche lib that runs into errors on 3.8.</p>
<p>So I tried to make a virtual environment using virtualenv using the following command:</p>
<p><code>virtualenv -p C:\Python27\python.exe pdf</code></p>
<p>This returns the error:</p>
<p><code>RuntimeError: failed to query C:\Python27\python.exe with code 1 err: ' File "C:\\Python\\Python38\\lib\\site-packages\\virtualenv\\discovery\\py_info.py", line 152\n os.path.join(base_dir, exe) for exe in (f"python{major}", f"python{major}.{minor}")\n ^\nSyntaxError: invalid syntax\n' </code></p>
<p>Looks like it wants to find the minor and major release info, but can't. I just installed Python 2.7.0 from <a href="https://www.python.org/download/releases/2.7/" rel="nofollow noreferrer">here</a>. Should I just use a different release instead?</p>
|
<python><virtualenv>
|
2023-05-31 10:35:54
| 1
| 534
|
J.Doe
|
76,372,544
| 4,231,821
|
Updated inkscape version stooped running my code
|
<p>This is my code using for converting svg to png and return it on browser.</p>
<pre><code>svgchart = chart_pygal.render()
inkscape_process = subprocess.Popen(['inkscape', '-z', '-e', '-', '-'], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
png_data, error = inkscape_process.communicate(input=svgchart)
png_io = BytesIO(png_data)
return send_file(png_io ,mimetype='image/png', download_name=image_name+'.png', as_attachment=True)
</code></pre>
<p>It was working fine with version of inkscape 0.92.1 but I have updated the version to 1.2.2 now it has stopped working as expected the image shows error when i open it .</p>
<p>sorry photos can not open this file because the format is currently unsupported, or the file is curropt</p>
|
<python><docker><inkscape><svgtopng>
|
2023-05-31 10:30:59
| 0
| 527
|
Faizan Naeem
|
76,372,367
| 7,848,740
|
Add a select field on Django form without saving on database
|
<p>I have a form that refers to a model into the database so, each time is filled and a user click SUBMIT, the form is saved on the database.</p>
<p>The form has a series of fields took directly from the Django Model. See below.</p>
<p>My problem is that, I want to add a new field, a select field, that have as option inside data I've processed from the backend via the home view and, when the form is submitted, it passes the data selected to the same view so I can elaborate from there. I don't want this field to be saved on the database with the <code>form.save()</code></p>
<p>Is there a way to have this kind of "hybrid" form where part of the data/field are taken from the database and others are just added by me?</p>
<h2>models.py</h2>
<pre><code>class Test(models.Model):
testTitle = models.CharField(max_length=50, blank=False, null=False)
testDescription = models.TextField()
sequenzaTest = models.ForeignKey("SequenzaMovimento", null=True, on_delete=models.SET_NULL)
numeroCicli = models.IntegerField(blank=False, null=False)
dataInizio = models.DateTimeField(auto_now_add=True)
dataFine = models.DateTimeField(blank=False, null=True)
testFinito = models.BooleanField(default=False)
testInPausa = models.BooleanField(default=False)
dataUltimaPausa = models.DateTimeField(null=True, blank=True)
workerID = models.CharField(max_length=200, blank=True, null=True)
def __str__(self):
return self.testTitle
class StartWorker(ModelForm):
class Meta:
model = Test
fields = ('testTitle', 'testDescription', 'sequenzaTest', 'numeroCicli')
widgets = {
'testTitle': forms.TextInput(attrs={'class':'form-control'}),
'testDescription': forms.Textarea(attrs={'class':'form-control'}),
'sequenzaTest': forms.Select(attrs={'class':'form-control'}),
'numeroCicli': forms.NumberInput(attrs={'class':'form-control'}),
}
</code></pre>
<h2>views.py</h2>
<pre><code>def home(request):
if request.method == "POST":
form = StartWorker(request.POST)
if form.is_valid():
form_info = form.save()
print("ID oggetto creato", form_info.id)
# print(form.cleaned_data['dataInizio'])
print("Ultima data aggiunta", Test.objects.get(pk=form_info.id).dataInizio.isoformat("T","seconds"))
return redirect("home")
else:
form = StartWorker()
return render(
request,
'dashboard/index.html',
{
"form": form,
}
)
</code></pre>
<h2>index.html</h2>
<pre><code> <form method="post">
{% csrf_token %}
{{ form }}
<input type="submit" value="Submit">
</form>
</code></pre>
|
<python><django><forms>
|
2023-05-31 10:09:30
| 2
| 1,679
|
NicoCaldo
|
76,372,225
| 9,986,657
|
Use LlamaIndex with different embeddings model
|
<p>OpenAI's GPT embedding models are used across all LlamaIndex examples, even though they seem to be the most expensive and worst performing embedding models compared to T5 and sentence-transformers models (<a href="https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9" rel="noreferrer">see comparison below</a>).</p>
<p>How do I use <a href="https://huggingface.co/sentence-transformers/all-roberta-large-v1" rel="noreferrer">all-roberta-large-v1</a> as embedding model, in combination with OpenAI's GPT3 as "response builder"? I'm not even sure if I can use one model for creating/retrieving embedding tokens and another model to generate the response based on the retrieved embeddings.</p>
<h2>Example</h2>
<p>Following is an example of what I'm looking for:</p>
<pre class="lang-py prettyprint-override"><code>documents = SimpleDirectoryReader('data').load_data()
# Use Roberta or any other open-source model to generate embeddings
index = ???????.from_documents(documents)
# Use GPT3 here
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
print(response)
</code></pre>
<h2>Model Comparison</h2>
<p><a href="https://i.sstatic.net/FqrUf.png" rel="noreferrer"><img src="https://i.sstatic.net/FqrUf.png" alt="Embedding Models" /></a></p>
<p><a href="https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9" rel="noreferrer">Source</a></p>
|
<python><llama-index>
|
2023-05-31 09:53:20
| 6
| 2,079
|
Jay
|
76,372,216
| 17,192,324
|
How to translate a request from overpass turbo format to a raw request and make it work?
|
<h1>Reason to ask: unexpected change in behavior between working overpass turbo request and a request made using Python to the API</h1>
<p>I need to get an outline of a region bases off its name and import it into geopandas</p>
<p>This request works in overpass turbo (<a href="https://overpass-turbo.eu/s/1vxy" rel="nofollow noreferrer">link to results</a>):</p>
<pre><code>[out:json][timeout:25];
{{geocodeArea:Велижский район}}[type="boundary"]->.a;
(
relation["boundary"="administrative"]["admin_level"="6"](area.a);
);
(
way(r);
node(w);
);
out skel qt;
</code></pre>
<p>This <a href="https://overpass-turbo.eu/s/1vxB" rel="nofollow noreferrer">request</a> also works:</p>
<pre><code>[out:json];
relation(3342303)[type="boundary"];
out geom;
</code></pre>
<p>I can then download them into GeoJSON and plot the results using geopandas:</p>
<pre class="lang-py prettyprint-override"><code>import geopandas as gpd
df = gpd.read_file('export.geojson')
df.geometry = df.geometry.to_crs(epsg=32636)
df.plot()
</code></pre>
<h1>What I have tried</h1>
<pre class="lang-py prettyprint-override"><code>import overpass
api = overpass.API()
query = """
relation(3342303)[type="boundary"];
out geom;
"""
res = api.Get(query)
</code></pre>
<h1>What I expected</h1>
<p>To get back a json</p>
<h1>What happened</h1>
<pre><code>---------------------------------------------------------------------------
UnknownOverpassError Traceback (most recent call last)
<ipython-input-10-2eef0e720784> in <cell line: 9>()
7 out geom;
8 """
----> 9 res = api.Get(query)
1 frames
/usr/local/lib/python3.10/dist-packages/overpass/api.py in _as_geojson(self, elements)
212 polygons.append([points])
213 else:
--> 214 raise UnknownOverpassError("Received corrupt data from Overpass (incomplete polygon).")
215 # Then get the inner polygons
216 for member in elem.get("members", []):
UnknownOverpassError: Received corrupt data from Overpass (incomplete polygon).
</code></pre>
<h2>update 1</h2>
<p>I was able to get something to Python, but this does not solve it as these lines have in total zero area:</p>
<pre class="lang-py prettyprint-override"><code>import requests
import geopandas as gpd
from shapely.geometry import LineString, Polygon
def extract_geometries(element, geometries):
if 'members' in element:
members = element['members']
for member in members:
if member['type'] == 'way' and 'geometry' in member:
geometry = member['geometry']
coordinates = [(point['lon'], point['lat']) for point in geometry]
line = LineString(coordinates)
geometries.append(line)
elif member['type'] == 'relation':
extract_geometries(member, geometries)
def close_polygon(geometry):
if geometry.geom_type == 'Polygon':
coordinates = list(geometry.exterior.coords)
if coordinates[0] != coordinates[-1]:
coordinates.append(coordinates[0])
return Polygon(coordinates)
return geometry
def get_relation_geometry(relation_id):
# Define the Overpass API URL
url = "https://overpass-api.de/api/interpreter"
# Define the Overpass query for the relation ID
query = f"""
[out:json];
rel({relation_id});
out geom;
"""
# Send the GET request to the Overpass API
response = requests.get(url, params={"data": query})
# Check if the request was successful
if response.status_code == 200:
# Convert the response to GeoPandas dataframe
data = response.json()
# Extract the 'elements' list
elements = data['elements']
# Create an empty list to store geometries
geometries = []
# Iterate over the elements and extract geometries
for element in elements:
extract_geometries(element, geometries)
# Create a GeoPandas dataframe from the geometries
gdf = gpd.GeoDataFrame(geometry=geometries)
# Forcefully close the polygons
gdf['geometry'] = gdf['geometry'].apply(close_polygon)
return gdf
else:
print(f"Request failed with status code {response.status_code}")
return None
# Example usage
relation_id = 3342303
gdf = get_relation_geometry(relation_id)
if gdf is not None:
print(gdf.head())
</code></pre>
<p><a href="https://i.sstatic.net/BpRiQ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/BpRiQ.png" alt="enter image description here" /></a></p>
|
<python><openstreetmap><overpass-api>
|
2023-05-31 09:52:23
| 1
| 331
|
Kirill Setdekov
|
76,372,125
| 10,889,650
|
Masking 4D numpy array with 3D mask
|
<p>I have a 4D numpy array D with shape nx,ny,nz,nt and a 3D numpy mask M with shape nx,ny,nz. I wish to set each element of D with index ix,iy,iz,: to 0 if M[ix,iy,iz] is False. How do I do this?</p>
|
<python><numpy>
|
2023-05-31 09:42:21
| 1
| 1,176
|
Omroth
|
76,372,115
| 8,248,194
|
Pandas scatterplot with coloring using a string
|
<p>I want to plot x and y using a scatterplot, and z as colors.</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
df = pd.DataFrame({
"x": [1,2 ,3],
"y": [4, 5, 6],
"z": ["A", "B", "C"],
})
df.plot(
x="x",
y="y",
kind="scatter",
c="z",
)
</code></pre>
<p>How can I do this using pandas plotting functionality?</p>
<p>This is throwing:</p>
<pre><code>ValueError: 'c' argument must be a color, a sequence of colors, or a sequence of numbers, not ['A' 'B' 'C']
</code></pre>
|
<python><pandas>
|
2023-05-31 09:41:24
| 2
| 2,581
|
David Masip
|
76,371,991
| 13,146,022
|
How to add tensorflow dependency to poetry project without limiting the range of supported python versions?
|
<p>I have a <strong>package</strong> built with poetry. The package requires <code>tensorflow</code> (most recent version to date is 2.12.0). To be able to run <code>poetry add tensorflow</code> I need to change the supported python version to <code>python = ">=3.8,<3.12"</code> as suggested in <a href="https://stackoverflow.com/questions/70356867/solverproblemerror-on-install-tensorfow-with-poetry">this post</a>. However, if I then try to add my package to a freshly initalized poetry project (with python version 3.8) I receive an error. I would like to have a rather user-friendly package, where a simple <code>poetry add <package></code> will not fail.</p>
<h3>Steps to reproduce</h3>
<p>I have a package with <code>python = "^3.8"</code>.Trying to add <code>tensorflow</code> to my package, getting the following error:</p>
<pre><code>...
For tensorflow-io-gcs-filesystem, a possible solution would be to set the `python` property to ">=3.8,<3.12"
...
</code></pre>
<p>Following <a href="https://stackoverflow.com/questions/70356867/solverproblemerror-on-install-tensorfow-with-poetry">this post</a>, I change the python version to <code>python = ">=3.8,<3.12"</code>. Then I am able to add <code>tensorflow</code></p>
<pre class="lang-bash prettyprint-override"><code>poetry add tensorflow
</code></pre>
<p>I initalize a new project with poetry (with default values and no dependencies) with python3.8.</p>
<pre class="lang-bash prettyprint-override"><code>poetry init
</code></pre>
<p>When trying to add my package</p>
<pre class="lang-bash prettyprint-override"><code>poetry add <package>
</code></pre>
<p>I receive following error</p>
<pre><code>The current project's Python requirement (>=3.8,<4.0) is not compatible with some of the required packages Python requirement:
- <package> requires Python >=3.8,<3.12, so it will not be satisfied for Python >=3.12,<4.0
Because <project> depends on <package> <version> which requires Python >=3.8,<3.12, version solving failed.
• Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
For <package>, a possible solution would be to set the `python` property to ">=3.8,<3.12"
</code></pre>
<p>I would like to have add my package to the freshly initialized project without any errors. How do I achieve this?</p>
|
<python><tensorflow><python-poetry>
|
2023-05-31 09:24:09
| 0
| 443
|
EricT
|
76,371,806
| 21,859,039
|
How to properly concatenate str_values from dataframe rows with NaN values in Pandas?
|
<p>Smash rows from dataframe with NaN values</p>
<p>I have this dataframe:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>descritpion</th>
<th>quantity</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<td>This is the description</td>
<td>2</td>
<td>100,00</td>
</tr>
<tr>
<td>for the first row</td>
<td></td>
<td></td>
</tr>
<tr>
<td>Row number 2</td>
<td>10</td>
<td>150,00</td>
</tr>
<tr>
<td>Row number 3</td>
<td>15</td>
<td>200,00</td>
</tr>
</tbody>
</table>
</div>
<p>As we can see, the description for the first row takes 2 lines, but I have no way to know that description is that long.
I have tried to iterate over rows, and if those rows have <code>NaN.sum() == len(df.shape[1]) -1</code> then I take the <code>temp_description_col = dataframe.description.iloc[row_index_iterator]</code> and join the descriptions to the last row with <code>join(dataframe.description.iloc[row_index_iterator - 1].join(temp_description_col)</code>. I also have tried <code>out = (df.bfill().groupby(['Importe'], as_index=False).agg({'Concepto': ' '.join}))</code> but it creates the first row with the smashed rows.</p>
<p>The problem I found is, that ofcourse seems very rudimentary to check ever row and overwrite the description from the last row if I find nans in all columns except one and the possibility that the description with nans belongs to the rows after. For example:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>descritpion</th>
<th>quantity</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<td>This is the description</td>
<td></td>
<td></td>
</tr>
<tr>
<td>for the first row</td>
<td>2</td>
<td>100,00</td>
</tr>
<tr>
<td>Row 2 description</td>
<td></td>
<td></td>
</tr>
<tr>
<td>and Row2 continuation</td>
<td>15</td>
<td>200,00</td>
</tr>
</tbody>
</table>
</div>
<p>In this dataframe we can check that the first row descrption belongs to the second row, since the rest of the columns have NaN, and the third description belongs to the 4th row, which by smashing them, it is the second row.</p>
<p>Wanted output:</p>
<p>df1</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>descritpion</th>
<th>quantity</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<td>This is the description for the first row</td>
<td>2</td>
<td>100,00</td>
</tr>
<tr>
<td>Row number 2</td>
<td>10</td>
<td>150,00</td>
</tr>
<tr>
<td>Row number 3</td>
<td>15</td>
<td>200,00</td>
</tr>
</tbody>
</table>
</div>
<p>df2</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>descritpion</th>
<th>quantity</th>
<th>value</th>
</tr>
</thead>
<tbody>
<tr>
<td>This is the description for the first row</td>
<td>2</td>
<td>100,00</td>
</tr>
<tr>
<td>Row 2 descriptionand Row2 continuation</td>
<td>15</td>
<td>200,00</td>
</tr>
</tbody>
</table>
</div>
|
<python><pandas>
|
2023-05-31 09:02:19
| 1
| 301
|
Jose M. González
|
76,371,507
| 10,722,169
|
Sort values of a pandas Dataframe based on date column but also take into consideration duplicated values of 3 other columns
|
<p>I want to sort values of a pandas dataframe based on date column in descending order but also take into consideration duplicated values of name, product and release version columns such that the sorting contains the rows based on duplicated name, product and release version columns in consecutive rows. But I'm not able to get the desired result.</p>
<p>This is the sample code that I wrote:-</p>
<pre><code>data = {
'Name': ['John', 'Alice', 'John', 'Bob', 'Alice', 'Bob', 'Alice', 'Bob'],
'Product': ['A', 'B', 'A', 'C', 'B', 'C', 'B', 'C'],
'Release Version': ['1.6', '2.0', '1.5', '3.0', '2.5', '3.2', '2.6', '2.8'],
'Date': ['2022-05-15', '2022-04-20', '2022-05-10', '2022-05-01', '2022-04-25', '2022-05-05', '2022-04-29', '2022-04-27']
}
# Create a DataFrame
df = pd.DataFrame(data)
# Convert the 'Date' column to datetime
df['Date'] = pd.to_datetime(df['Date'])
# Sort values based on 'Date' in descending order and 'Name', 'Product', 'Release Version' columns
df = df.sort_values(['Date', 'Name', 'Product', 'Release Version'], ascending=[False, True, True, False])
</code></pre>
<p>This gives me the below result:-</p>
<pre><code> Name Product Release Version Date
John A 1.6 2022-05-15
John A 1.5 2022-05-10
Bob C 3.2 2022-05-05
Bob C 3.0 2022-05-01
Alice B 2.6 2022-04-29
Bob C 2.8 2022-04-27
Alice B 2.5 2022-04-25
Alice B 2.0 2022-04-20
</code></pre>
<p>The desired result is something like this:-</p>
<pre><code> Name Product Release Version Date
John A 1.6 2022-05-15
John A 1.5 2022-05-10
Bob C 3.2 2022-05-05
Bob C 3.0 2022-05-01
Bob C 2.8 2022-04-27
Alice B 2.6 2022-04-29
Alice B 2.5 2022-04-25
Alice B 2.0 2022-04-20
</code></pre>
<p>Would be great if someone can help me with this.</p>
|
<python><pandas><dataframe>
|
2023-05-31 08:20:26
| 1
| 435
|
vesuvius
|
76,371,466
| 13,955,154
|
Find Table of Contents from a pdf file
|
<p>I want to get the table of content of each of my document and based on previous questions I have this code:</p>
<pre><code>from typing import Dict
import os
import fitz # pip install pymupdf
def get_bookmarks(filepath: str) -> Dict[int, str]:
# WARNING! One page can have multiple bookmarks!
bookmarks = {}
with fitz.open(filepath) as doc:
toc = doc.get_toc() # [[lvl, title, page, …], …]
for level, title, page in toc:
bookmarks[page] = title
return bookmarks
for root, directories, files in os.walk("doc/"):
for file in files:
file_path = os.path.join(root, file)
to_print = get_bookmarks(file_path)
if to_print != {}:
print(file_path)
print(get_bookmarks(file_path))
</code></pre>
<p>So I iterate through all my documents and perform get_bookmarks(). But this method doesn't work for most of my documents (actually I am able to find the toc of only 1 document out of 50). Why does that happen and how can I solve? The structure of my pdf's are quite classical and the toc can be in various forms but it's clearly recognisable. For example:
<a href="https://i.sstatic.net/U9MjM.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/U9MjM.png" alt="enter image description here" /></a>
<a href="https://i.sstatic.net/8PYKu.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8PYKu.png" alt="enter image description here" /></a></p>
<p>Is there some way I can solve? Do you need more information to understand what's wrong?</p>
|
<python><pdf><tableofcontents>
|
2023-05-31 08:15:24
| 1
| 720
|
Lorenzo Cutrupi
|
76,371,381
| 6,649,485
|
VS code pytest finding test directory but not files
|
<p>I'm trying to setup pytest for VS code. My project structure is the following.</p>
<pre><code>root-
|-projectA
|-__init__.py
|-tests
|-__init__.py
|-test_A1.py
|-test_A2.py
|-projectB
</code></pre>
<p>I can run pytest from the terminal:</p>
<pre><code>cd root
pytest */tests
...
========================== 15 passed, 1 warning in 8.97s===========================================
</code></pre>
<p>These are my settings in VS code:</p>
<pre><code>"python.testing.pytestArgs": [
"*/tests"
],
"python.testing.unittestEnabled": false,
"python.testing.nosetestsEnabled": false,
"python.testing.cwd": "root",
"python.testing.pytestEnabled": true,
</code></pre>
<p>In VS code, it looks as if the test directory is found, but not the test files inside. If I click to run the test, nothing happens.</p>
<p><a href="https://i.sstatic.net/efxyA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/efxyA.png" alt="VS code pytest snapshot" /></a></p>
|
<python><visual-studio-code><pytest>
|
2023-05-31 08:02:52
| 1
| 333
|
ta8
|
76,371,334
| 1,319,998
|
zlib difference in size for level=0 between Python 3.9 and 3.10
|
<p>In this code that uses zlib to encode some data, but with level=0 so it's not actually compressed:</p>
<pre class="lang-py prettyprint-override"><code>import zlib
print('zlib.ZLIB_VERSION', zlib.ZLIB_VERSION)
total = 0
print('Total 1', total)
compress_obj = zlib.compressobj(level=0, memLevel=9, wbits=-zlib.MAX_WBITS)
total += len(compress_obj.compress(b'-' * 1000000))
print('Total 2', total)
total += len(compress_obj.flush())
print('Total 3', total)
</code></pre>
<p>Python 3.9.12 outputs</p>
<pre><code>zlib.ZLIB_VERSION 1.2.12
Total 1 0
Total 2 983068
Total 3 1000080
</code></pre>
<p>but Python 3.10.6 (and Python 3.11.0) outputs</p>
<pre><code>zlib.ZLIB_VERSION 1.2.13
Total 1 0
Total 2 1000080
Total 3 1000085
</code></pre>
<p>so both a different final size, and a different size along the way.</p>
<p>Why? And how can I get them to be identical? (I'm writing a library where I would prefer identical behaviour between Python versions)</p>
|
<python><zlib><deflate>
|
2023-05-31 07:57:34
| 1
| 27,302
|
Michal Charemza
|
76,371,314
| 8,248,194
|
Pandas assign with comprehension after query
|
<p>I want to use assign in pandas passing a dictionary comprehension after a pandas query.</p>
<p>Reproducible example:</p>
<pre class="lang-py prettyprint-override"><code>
import pandas as pd
df = pd.DataFrame({
"a": [1, 2, 3],
"b": [4, 5, 6],
"weight": [0.1, 0.2, 0.3]
})
metrics = ["a", "b"]
df = df.query("b > 4").assign(
**{
f"weighted_{metric}": lambda df: df[metric] * df["weight"]
for metric in metrics
}
)
print(df)
</code></pre>
<p>Results:</p>
<pre class="lang-py prettyprint-override"><code> a b weight weighted_a weighted_b
1 2 5 0.2 1.0 1.0
2 3 6 0.3 1.8 1.8
</code></pre>
<p>I don't get the expected results, for a, I should get 0.2, 0.3.</p>
<p>Do you know how can I get weighted_a correctly?</p>
<p>I asked a similar question in <a href="https://stackoverflow.com/questions/76362880/pandas-assign-with-comprehension-giving-unexpected-results">here</a>, the solutions in there worked fine, but not with a query before. Any thoughts on how to adapt the answers in the other question in a pipeline-ish manner?</p>
<p>Keep in mind may use a more complex function than just a multiplication for my use case.</p>
|
<python><pandas>
|
2023-05-31 07:55:33
| 1
| 2,581
|
David Masip
|
76,371,188
| 3,993,405
|
How to Pandas fillna by method bfill with first occurence of data for the same ID
|
<p>i have a CSV pandas <a href="https://www.dropbox.com/s/phnbeklg09ze6t0/test.csv?dl=0" rel="nofollow noreferrer">https://www.dropbox.com/s/phnbeklg09ze6t0/test.csv?dl=0</a></p>
<p>i am trying to fillna with bfill method on column catH based on values of first seen value in column A.
e.g. in row 382, column A ID is 271 with value M, its the first occurence of ID 271 (later occurence may have different values so i couldnt use group by), so previous nan values should be fill with value 'm'</p>
<p>further example if row386, col A has ID 1286, and has value 'b', so row 746, 726, 704, and so on backwards with 1286 should be bfill with value 'b'</p>
<p>anyone know a pythonic way to do this?</p>
|
<python><pandas>
|
2023-05-31 07:39:40
| 1
| 2,101
|
desmond
|
76,370,880
| 14,135,840
|
Shortcut to manually defining every dunder method
|
<p>I'm defining a <code>Sqrt</code> class to help with some complications in my code, since it's going to be used a lot. The class has a value <code>val</code>, which is the actual value of the square root being computed.</p>
<p>Code:</p>
<pre><code># Square root class (helps w accuracy and readability)
class Sqrt(object):
def __init__(self, x: float) -> None:
self.sqr_val = x
self.val = math.sqrt(x)
def __str__(self) -> str:
return f"sqrt({self.sqr_val})"
def __pow__(self, n: float) -> float:
return self.sqr_val ** n / 2
</code></pre>
<p>The issue I'm facing is the need to define every dunder method ("magic method") to handle arithmetic operations like <code>+</code>, <code>-</code>, <code>*</code>, <code>/</code>, etc.:</p>
<pre><code>
class Sqrt(object):
def __init__(self, x: float) -> None:
self.sqr_val = x
self.val = math.sqrt(x)
def __str__(self) -> str:
return f"sqrt({self.sqr_val})"
def __pow__(self, n: float) -> float:
return self.sqr_val ** n / 2
def __add__(self, x):
return self.val + x
def __radd__(self, x):
return self.val + x
def __sub__(self, x):
return self.val - x
def __rsub__(self, x):
return x - self.val
def __mul__(self, x):
return self.val * x
def __rmul__(self, x):
return self.val * x
def __truediv__(self, x):
return self.val / x
</code></pre>
<p>What I'd like is to be able to define these dunders implicitly, telling Python to use the normal int/float dunders for my class, but using <code>self.val</code> instead of <code>self</code>.</p>
<p>I have tried inheriting from <code>int</code> and using <code>super()</code> but that doesn't decrease repetition whatsoever.
Is there a way to do this (maybe some fancy descriptor)? Or do I write every applicable dunder out manually?</p>
|
<python>
|
2023-05-31 06:57:15
| 1
| 507
|
Codeman
|
76,370,861
| 2,810,187
|
Common method for DRF model Viewset
|
<p>I have multiple model viewsets that overrides perform_create as below.</p>
<pre><code> def perform_create(self, serializer):
user=self.request.user
instance = serializer.save(user=self.request.user)
</code></pre>
<p>I am inserting this codes for every modelviewsets I am creating. Is there any way to make this more structured instead of inserting codes everytime?</p>
|
<python><django><django-rest-framework>
|
2023-05-31 06:55:42
| 1
| 443
|
andylee
|
76,370,559
| 1,982,032
|
Why can't install yfinance in custom directory?
|
<p>Many python packages were installed in my <code>.local/lib/python3.9/site-packages</code>,and i had already tried that</p>
<pre><code>pip install git+https://github.com/ranaroussi/yfinance
</code></pre>
<p><code>yfinance</code> can be installed in <code>.local/lib/python3.9/site-packages</code>.Now i am trying to install with downloaded package.<br />
Download a specified yfinance version on <code>https://github.com/ranaroussi/yfinance</code> and extract it.</p>
<pre><code>cd /home/debian/Downloads/yfinance-hotfix-proxy
ls
build LICENSE.txt mkdocs.yml setup.cfg test_yfinance.py
CHANGELOG.rst MANIFEST.in README.md setup.py yfinance
dist meta.yaml requirements.txt tests yfinance.egg-info
</code></pre>
<p>Install with <code>--prefix</code>:</p>
<pre><code>debian@debian:~/Downloads/yfinance-hotfix-proxy$ python3 setup.py install --prefix /home/debian/.local
running install
/home/debian/.local/lib/python3.9/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
/home/debian/.local/lib/python3.9/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.
warnings.warn(
running bdist_egg
running egg_info
error: Cannot update time stamp of directory 'yfinance.egg-info'
</code></pre>
<p>Why can't install it with <code>python3 setup.py install --prefix /home/debian/.local</code>?</p>
|
<python><installation><package>
|
2023-05-31 05:57:42
| 0
| 355
|
showkey
|
76,370,428
| 11,098,908
|
Preventing an object disappears from the screen in pygame
|
<p>This is the code from the book <em>Beginning Python Games Development With PyGame</em>:</p>
<pre><code>import pygame
from pygame.locals import *
from sys import exit
from math import *
class Vector2:
def __init__(self, x=0, y=0):
self.x = x
self.y = y
if hasattr(x, "__getitem__"):
x, y = x
self._v = [float(x), float(y)]
else:
self._v = [float(x), float(y)]
def __getitem__(self, index):
return self._v[index]
def __setitem__(self, index, value):
self._v[index] = 1.0 * value
def __str__(self):
return "(%s, %s)"%(self.x, self.y)
def from_points(P1, P2):
return Vector2(P2[0] - P1[0], P2[1] - P1[1])
# return np.array(P2) - np.array(P1) # using numpy method
def get_magnitude(self):
return math.sqrt( self.x**2 + self.y**2 )
def normalize(self):
magnitude = self.get_magnitude()
try:
self.x /= magnitude
self.y /= magnitude
except ZeroDivisionError:
self.x = 0
self.y = 0
# rhs stands for Right Hand Side
def __add__(self, rhs):
return Vector2(self.x + rhs.x, self.y + rhs.y)
def __sub__(self, rhs):
return Vector2(self.x - rhs.x, self.y - rhs.y)
def __neg__(self):
return Vector2(-self.x, -self.y)
def __mul__(self, scalar):
return Vector2(self.x * scalar, self.y * scalar)
def __truediv__(self, scalar):
return Vector2(self.x / scalar, self.y / scalar)
background_image_filename = 'sushiplate.jpg'
sprite_image_filename = 'fugu.png'
pygame.init()
screen = pygame.display.set_mode((640, 480), 0, 32)
background = pygame.image.load(background_image_filename).convert()
sprite = pygame.image.load(sprite_image_filename).convert_alpha()
clock = pygame.time.Clock()
pygame.mouse.set_visible(False)
pygame.event.set_grab(True)
sprite_pos = Vector2(200, 150)
sprite_speed = 300.
sprite_rotation = 0.
sprite_rotation_speed = 360. # Degrees per second
while True:
for event in pygame.event.get():
if event.type == QUIT:
pygame.quit()
exit()
if event.type == KEYDOWN:
if event.key == K_ESCAPE:
pygame.quit()
exit()
pressed_keys = pygame.key.get_pressed()
pressed_mouse = pygame.mouse.get_pressed()
rotation_direction = 0.
movement_direction = 0.
rotation_direction = pygame.mouse.get_rel()[0] / 3.
if pressed_keys[K_LEFT]:
rotation_direction = +1.
if pressed_keys[K_RIGHT]:
rotation_direction = -1.
if pressed_keys[K_UP] or pressed_mouse[0]:
movement_direction = +1.
if pressed_keys[K_DOWN] or pressed_mouse[2]:
movement_direction = -1.
screen.blit(background, (0,0))
rotated_sprite = pygame.transform.rotate(sprite, sprite_rotation)
w, h = rotated_sprite.get_size()
sprite_draw_pos = Vector2(sprite_pos.x-w/2, sprite_pos.y-h/2)
screen.blit(rotated_sprite, (sprite_draw_pos.x, sprite_draw_pos.y))
time_passed = clock.tick()
time_passed_seconds = time_passed / 1000.0
sprite_rotation += rotation_direction * sprite_rotation_speed * time_passed_seconds
heading_x = sin(sprite_rotation*pi/180.)
heading_y = cos(sprite_rotation*pi/180.)
heading = Vector2(heading_x, heading_y)
heading *= movement_direction
sprite_pos+= heading * sprite_speed * time_passed_seconds
pygame.display.update()
</code></pre>
<p>The problem with this script is that the <code>sprite</code> can <em>disappear</em> from the display screen. I tried to fix that problem by adding the following code right above the last line <code>pygame.display.update()</code></p>
<pre><code>if sprite_pos.x > screen.get_width() or sprite_pos.x < 0:
sprite_draw_pos.x = 0
if sprite_pos.y > screen.get_height() or sprite_pos.y < 0:
sprite_draw_pos.y = 0
</code></pre>
<p>However, that fix didn't work because the <code>sprite</code> still disappeared? Could someone please explain what I did wrong and show me how to achieve that goal? Thanks.</p>
|
<python><class><oop><pygame>
|
2023-05-31 05:34:13
| 1
| 1,306
|
Nemo
|
76,370,353
| 5,169,785
|
convert pydantic dataclass with extra arguments to dict
|
<p>I want to convert a pydantic dataclass to a dict but the method I'm using doesn't work (using python 3.8.10 and pydantic==1.10.8)</p>
<pre class="lang-py prettyprint-override"><code>import dataclasses
import pydantic
class configExtraArgsAllow:
extra = pydantic.Extra.allow
@pydantic.dataclasses.dataclass(config=configExtraArgsAllow)
class MyDataModel:
foo: int
data = {
"foo": 1,
"bar": 2,
}
dc = MyDataModel.__pydantic_model__.parse_obj(data)
assert dc.bar == 2
dc_as_dict = dataclasses.asdict(dc)
</code></pre>
<p>It results in</p>
<pre><code> dc_as_dict = dataclasses.asdict(dc)
File "/usr/lib/python3.8/dataclasses.py", line 1072, in asdict
raise TypeError("asdict() should be called on dataclass instances")
TypeError: asdict() should be called on dataclass instances
</code></pre>
<p>The method above works for pydantic dataclass without extra arguments though.
Is there any other way to convert the pydantic dataclass into a dict when extra arguments are 'allowed'?</p>
|
<python><pydantic><python-dataclasses>
|
2023-05-31 05:20:05
| 1
| 351
|
Uwe Brandt
|
76,370,351
| 1,155,409
|
Memory leak when using multiprocessing in python
|
<p>I have memory leak problem when running the following code. I removed unnecessary parts of code as much as I can. When I running this, the usage of memory for cpu and swap is getting bigger and it reaches the maximum of it and ended up killing the program with the exit code 9. I changed the process save its result with saving csv file and release all variables with del command. But, I do not know the further solution. Thanks in advance!!</p>
<pre><code>@njit
def haversine_np(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2
c = 2 * np.arcsin(np.sqrt(a))
km = 6367 * c
return km
def get_similiar_candidates_lambda(i, r, df_emd, dists):
KM_MAX_DISTANCE = 1.0
TOP_K = 5
similiar_min_candidates, similiar_max_candidates = [], []
viz_candidates = []
dists = {k: v for k, v in dists.items() if v <= KM_MAX_DISTANCE}
for c in df_emd.itertuples():
if i != c.Index:
dc = c._asdict()
if r['용도지역1'] == dc['용도지역1'] and r['지목'] == dc['지목']:
try:
euclidean_distance = dists[(r.name, dc['Index'])]
if euclidean_distance <= KM_MAX_DISTANCE:
if dc['도로접면'] in pair_road_cond[r['도로접면']] and dc['토지이용상황'] in pair_land_usage[r['토지이용상황']]:
if r['종류'] != '토지' and r['건물가격_추정'] != 0.: # 총액, 단가 기준 추가
total_price_prop = (dc['건물가격_추정'] + dc['공시가격_2022'] * dc['Mutiple_Pred'] ) / ( r['건물가격_추정'] + r['공시가격_2022'] * r['Mutiple_Pred'] )
else:
total_price_prop = None
unit_price_prop = (dc['공시가격_2022'] * dc['Mutiple_Pred']) / (r['공시가격_2022'] * r['Mutiple_Pred'])
lb_total_price_prop, ub_total_price_prop = 0.5, 1.5
lb_unit_price_prop, ub_unit_price_prop = 0.5, 1.5
check1 = total_price_prop is not None and (lb_total_price_prop <= total_price_prop and total_price_prop <= ub_total_price_prop)
check2 = lb_unit_price_prop <= unit_price_prop and unit_price_prop <= ub_unit_price_prop
if check1:
similiar_min_candidates.append((c.Index, euclidean_distance))
if check1 and check2:
similiar_max_candidates.append((c.Index, euclidean_distance))
except KeyError as e:
continue
viz_candidates.append((c.Index, euclidean_distance)) # 반경 1km 내의 용도지역1과 지목이 같은 케이스
generals = [t for t in viz_candidates]
min_candidates, max_candidates = None, None
if len(similiar_min_candidates) > TOP_K:
similiar_min_candidates.sort(key=lambda x:x[1])
min_candidates = [t for t in similiar_min_candidates][:5]
elif len(similiar_min_candidates) >=1 and len(similiar_min_candidates) <= 5:
min_candidates = [t for t in similiar_min_candidates]
if len(similiar_min_candidates) > TOP_K:
similiar_min_candidates.sort(key=lambda x:x[1])
max_candidates = [t for t in similiar_max_candidates][:5]
elif len(similiar_min_candidates) >=1 and len(similiar_max_candidates) <= 5:
max_candidates = [t for t in similiar_max_candidates]
return min_candidates, max_candidates, generals
def process_emd(args, tuple_emd):
sd, sgg, emd = tuple_emd
df_land_ai = get_land_ai(src_url, sql_land_ai)
df_land = get_land(dm_url, sql_land)
df_final = pd.merge(df_land_ai, df_land, on='pnu', how='right')
df_final['Mutiple_Pred'] = df_final['predicted_land_price'] / df_final['공시가격_2022']
df_final = df_final.loc[:, columns]
df_final = df_final.set_index('pnu')
df_final = df_final[~df_final['Mutiple_Pred'].isna()]
df_emd = df_final[~df_final.index.duplicated(keep='first')]
candidate = list(df_emd.index)
geo = df_emd[['lon_x', 'lat_y']].to_numpy()
geos = np.asarray(list(it.product(geo, geo))).reshape(-1, 4)
euclideans = haversine_np(geos[:,0], geos[:,1], geos[:,2], geos[:,3])
KM_MAX_DISTANCE = 1.0
TOP_K = 5
df_emd.reset_index(inplace=True)
df_emd_cp = pd.merge(df_emd, df_emd, how='cross', suffixes=('_x', '_y')) # cartesian product for df_emd for speed up
df_emd_cp['euclidean_distance'] = euclideans
df_emd_cp = df_emd_cp[df_emd_cp['euclidean_distance'] < KM_MAX_DISTANCE] # remove euclidean distance > 1km in advance for speed up
df_emd_cp = df_emd_cp[df_emd_cp['pnu_x'] != df_emd_cp['pnu_y']]
df_emd_cp = df_emd_cp[df_emd_cp.apply(lambda x: True if x['도로접면_y'] in pair_road_cond[x['도로접면_x']] else False, axis=1)] # 236
df_emd_cp = df_emd_cp[df_emd_cp.apply(lambda x: True if x['토지이용상황_y'] in x['토지이용상황_x'] in pair_land_usage.keys() and pair_land_usage[x['토지이용상황_x']] else False, axis=1)] # 193
df_emd_cp['unit_price_prop'] = (df_emd_cp['공시가격_2022_y'] * df_emd_cp['Mutiple_Pred_y']) / (df_emd_cp['공시가격_2022_x'] * df_emd_cp['Mutiple_Pred_x'])
lb_total_price_prop, ub_total_price_prop = 0.5, 1.5
lb_unit_price_prop, ub_unit_price_prop = 0.5, 1.5
df_emd_cp['check1'] = df_emd_cp['total_price_prop'].apply(lambda x : np.isnan(x) or (x is not None and (lb_total_price_prop <= x and x <= ub_total_price_prop)))
df_emd_cp['check2'] = df_emd_cp['unit_price_prop'].apply(lambda x : x is not None and (lb_unit_price_prop <= x and x <= ub_unit_price_prop))
if len(df_emd_cp) > 1:
df_similiar_min_candidates = df_emd_cp.groupby(by=['pnu_x'], group_keys=True).apply(lambda x:
(sorted(
zip(x.loc[x['check1'], 'pnu_y'].tolist(),
x.loc[x['check1'], 'euclidean_distance'].tolist()),
key=lambda x:x[1])[:min(TOP_K, x['check1'].sum())])
).reset_index().rename(columns={0:'similiar_min_candidates'})
df_similiar_max_candidates = df_emd_cp.groupby(by=['pnu_x'], group_keys=True).apply(lambda x:
sorted(
zip(x.loc[(x['check1'] & x['check2']), 'pnu_y'].tolist(),
x.loc[(x['check1'] & x['check2']), 'euclidean_distance'].tolist()),
key=lambda x:x[1])[: min( TOP_K, (x['check1'] & x['check2']).sum())]
).reset_index().rename(columns={0:'similiar_max_candidates'})
if len(df_similiar_min_candidates) != 0:
df_similiar_min_candidates = df_similiar_min_candidates[['pnu_x', 'similiar_min_candidates']]
df_similiar_min_candidates.rename(columns={'pnu_x':'pnu'}, inplace=True, errors='ignore') # euclidean_distance
df_similiar_min_candidates = df_similiar_min_candidates[df_similiar_min_candidates['similiar_min_candidates'].apply(lambda x: x is not None and len(x) > 0)]
df_emd_final = df_similiar_min_candidates.merge(df_emd, how='left', on='pnu')
df_neighbors = df_emd_final[['pnu']].copy()
df_land_neighbors = df_neighbors.merge(df_emd_final, how='left', on='pnu')
df_land_neighbors = df_land_neighbors.groupby(by=['pnu'], group_keys=True).apply(lambda x: pd.DataFrame.from_records(x['similiar_min_candidates'].iloc[0], columns=['neighbor_pnu', 'neighbor_distance'])).reset_index()
df_land_neighbors = df_land_neighbors.rename(columns={'level_1':'neighbor_seq'})
df_land_neighbors = df_land_neighbors.loc[:, ['criteria_id', 'criteria_version', 'pnu', 'neighbor_seq', 'neighbor_pnu', 'neighbor_distance', 'sd', 'sgg', 'emd', 'createdat', 'updatedat']]
df_building = pd.read_sql(sql_building, con=get_db_connection('dm'))
df_neighbors = df_land_neighbors[['pnu', 'neighbor_pnu', 'neighbor_seq', 'neighbor_distance', 'sd', 'sgg', 'emd']]
df_neighbors = df_neighbors.rename(columns={'pnu': 'base_pnu'})
df_right = pd.merge(df_neighbors, df_building.loc[:, df_building.columns.isin(['pnu','building_master_id'])], left_on='neighbor_pnu', right_on='pnu')
df_right.rename(columns={'building_master_id': 'neighbor_building_master_id'}, inplace=True)
result = pd.merge(df_building.loc[:, df_building.columns.isin(['pnu','building_master_id'])], df_right, left_on='pnu', right_on='base_pnu')
df_ai_building_neighbors = result[['criteria_id', 'criteria_version', 'building_master_id', 'neighbor_building_master_id', 'neighbor_distance', 'sd', 'sgg', 'emd']]
os.makedirs(f'data/{sd}/{sgg}/{emd}', exist_ok=True)
df_ai_building_neighbors.to_csv(f'data/{sd}/{sgg}/{emd}/ai_building_neighbors.csv', index=False)
df_land_neighbors.to_csv(f'data/{sd}/{sgg}/{emd}/ai_land_neighbors.csv', index=False)
del result, df_ai_building_neighbors, df_neighbors, df_right, df_building, df_similiar_min_candidates, df_similiar_max_candidates, df_emd_final, df_land_neighbors
del df_emd_cp, df_land_ai, df_land, df_final, df_emd, geo, geos, euclideans
else:
del df_emd_cp, df_land_ai, df_land, df_final, df_emd, geo, geos, euclideans
t.toc(msg=f'Elapsed time for getting similiar properties of {emd} : ', restart=True)
def get_args():
parser = argparse.ArgumentParser(description='lightgbm to evaluate land price')
parser.add_argument('--model_id', type=str, default='lightgbm', help='model id')
parser.add_argument('--model_version', type=str, default='3.0', help='model version')
parser.add_argument('--data_version', type=str, default='230525')
return parser.parse_args()
if __name__ == '__main__':
args = get_args()
NUM_CORES = 5
df_emd = pd.read_sql(sql_emd, con=get_db_connection('src'))
pool = Pool(NUM_CORES)
pool.starmap(process_emd, itertools.product([args], list(zip(df_emd.sd2, df_emd.sgg, df_emd.emd))))
pool.close()
pool.join()
</code></pre>
|
<python><pandas>
|
2023-05-31 05:19:35
| 0
| 3,939
|
verystrongjoe
|
76,370,279
| 1,260,682
|
setting python path in virtual env
|
<p>I have the following directory structure:</p>
<pre><code>~
|-- test
|-- foo.py
</code></pre>
<p>I was able to run <code>foo.py</code> as <code>python test/foo.py</code> or <code>python -m test.foo</code> in the normal command line. But once I started a venv in <code>~</code> the former still works but the latter no longer works (python complains about "No module named test.foo"). Did I set my python path incorrectly?</p>
|
<python><python-venv>
|
2023-05-31 04:59:20
| 1
| 6,230
|
JRR
|
76,370,238
| 7,023,590
|
Product of 2 numbers exclusively in Kivy language (.kv file) - why the order is important?
|
<p>I want to multiply 2 numbers, performing it exclusively in the .kv file.</p>
<p>So I created this minimalistic <code>main.py</code> file:</p>
<pre><code>from kivy.app import App
class Test(App):
pass
Test().run()
</code></pre>
<p>and this minimalistic <code>test.kv</code> file:</p>
<p> </p>
<ol>
<li><p>My 1<sup>st</sup> approach:</p>
<pre><code>BoxLayout:
TextInput:
id: num_1
text: "8"
TextInput:
id: num_2
text: "4"
Label:
text: f"Product: {int(num_1.text) * int(num_2.text)}"
</code></pre>
<p>After launching the application, I obtained the error</p>
<blockquote>
<pre><code> >> 9: text: f"Procuct: {int(num_1.text) * int(num_2.text)}"
...
ValueError: invalid literal for int() with base 10: ''
</code></pre>
</blockquote>
</li>
</ol>
<p> </p>
<ol start="2">
<li><p>My 2<sup>nd</sup> approach – I changed only <em>the order</em> of widgets, putting the Label before TextInputs:</p>
<pre><code>BoxLayout:
Label:
text: f"Procuct: {int(num_1.text) * int(num_2.text)}"
TextInput:
id: num_1
text: "8"
TextInput:
id: num_2
text: "4"
</code></pre>
<p>and I obtained the expected result</p>
<p><a href="https://i.sstatic.net/6JGrE.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/6JGrE.jpg" alt="enter image description here" /></a></p>
<p>(which correctly reflects changes in TextInputs).</p>
</li>
</ol>
<p><strong>So the question is: Why my first approach didn't work?</strong></p>
<hr />
<p><em>Note:</em></p>
<p>I'm not interested in a better solution or some workaround, I want only understand, why my first <code>test.kv</code> file don't work.</p>
<hr />
<p><strong>EDIT:</strong></p>
<p>If in my 1<sup>st</sup>, not working approach, I change the text of Label to <code>num_1.text + num_2.text</code> (concatenation of strings), it works!</p>
|
<python><kivy><kivy-language>
|
2023-05-31 04:46:23
| 1
| 14,341
|
MarianD
|
76,370,208
| 3,521,180
|
Why is the mock data unit test failing?
|
<p>I am writing a unit test with mock data for one of my functions.</p>
<p>Previously the response of my function looked like this:</p>
<pre><code>{
"data": [
{
"a": 2264,
"b": "Comp PT"
}
],
"success": True
}
</code></pre>
<p>The unit test was written for this, and the test was passing as well. But due to some requirements the response is a little different from the previous one as shown below:</p>
<pre><code>{
"data": {
"data": [
{
"a": 2264,
"b": "Comp PT"
}
],
"status": "In Progress",
"c": "5"
},
"success": true
}
</code></pre>
<p>Now my test is failing with this error:</p>
<pre><code>FAILED (failures=1)
200 != 500
Expected :500
Actual :200
</code></pre>
<p>The mock data is passed as <code>self.get_nano</code> inside the test file as shown below:</p>
<pre class="lang-py prettyprint-override"><code>from unittest.mock import patch
from src.rest_apis.web_app import app
from src.v1.tests.test_main import MockTestBase
class TestSimulationConfig(MockTestBase):
@patch('<path_to>.SimulationResult')
@patch('<path_to>.execute_query')
def test_get_abc(self, mock_query_execut, mock_sim_result):
mock_query_execut.return_value = {'result': self.get_nano['data']}
mock_sim_result.get_selected_components.return_value = self.get_nano
api_url = f'<path_to_api>'
response = self.app.get(api_url, content_type='application/json', follow_redirects=True)
self.assertEqual(response.status_code, 200)
</code></pre>
|
<python><python-3.x><flask><python-unittest>
|
2023-05-31 04:35:28
| 0
| 1,150
|
user3521180
|
76,370,183
| 8,553,795
|
Why does Lime need training data to compute local explanations
|
<p>I am using Lime to compute local explanation, however I do not understand why do I have to pass training data <code>X_train</code> in the below line of code</p>
<pre><code>explainer = lime_tabular.LimeTabularExplainer(X_train, mode="regression", feature_names= boston.feature_names)
</code></pre>
<p>Below is an excerpt around how Lime operates from this great book named <a href="https://christophm.github.io/interpretable-ml-book/lime.html#lime" rel="nofollow noreferrer">Interpretable Machine Learning</a> by Christoph Molnar around XAI -</p>
<blockquote>
<p>The recipe for training local surrogate models:</p>
<ul>
<li>Select your instance of interest for which you want to have an explanation of its black box prediction.</li>
<li>Perturb your dataset and get the black box predictions for these new points.</li>
<li>Weight the new samples according to their proximity to the instance of interest.</li>
<li>Train a weighted, interpretable model on the dataset with the variations.</li>
<li>Explain the prediction by interpreting the local model.</li>
</ul>
</blockquote>
<p>If I understand correctly, Lime trains a weighted interpretable model for each instance of interest by sampling points from its neighbourhood. The weights assigned to the features in this model serve as the local explanation for that particular instance.
And this is exactly what we do in the below lines of code -</p>
<p><code>exp = explainer.explain_instance(X_test.values[3], model.predict, num_features=6)</code></p>
<p>We pass an instance, using this instance it would compute the neighbours for which it would fit an interpretable model. So why did we pass <code>X_train</code> in the first line of code? How would Lime make use of it is what I don't understand.</p>
|
<python><machine-learning><shap><lime>
|
2023-05-31 04:28:17
| 1
| 393
|
learnToCode
|
76,370,158
| 17,685,806
|
How to encapsulate running a process in Python with realtime + to-variable output capture and return code capture?
|
<blockquote>
<p>Note: I have read <a href="https://stackoverflow.com/questions/14043030/checking-status-of-process-with-subprocess-popen-in-python">this</a>, <a href="https://stackoverflow.com/questions/25750468/displaying-subprocess-output-to-stdout-and-redirecting-it">this</a>, <a href="https://stackoverflow.com/questions/803265/getting-realtime-output-using-subprocess">this</a>, and <a href="https://stackoverflow.com/questions/18344932/python-subprocess-call-stdout-to-file-stderr-to-file-display-stderr-on-scree">this</a> question. While there is much useful info, none give an exact answer. My knowledge of the language is limited, so I cannot put together pieces from those answers in a way that fit the needed use case (especially point (4) below).</p>
</blockquote>
<p>I am looking for a way to run a process with a given set of arguments in current Python (latest version atm is 3.11), with the following requirements:</p>
<ol>
<li>The stderr and stdout of the process are displayed in real-time, as they would be if run directly (or through a script) in almost any shell, i.e. <code>bash</code> or <code>PowerShell</code></li>
<li>Both streams are also separately captured into a string or byte array for accessing later</li>
<li>The return code is captured on process finish</li>
<li>This is encapsualted in a function that simply requires the argument list, and returns an object containing both streams and the return code</li>
</ol>
<p><em>All points except for (1)</em> are covered by <code>subprocess.run(args, capture_output=True)</code>. So, I need to define a function</p>
<pre class="lang-py prettyprint-override"><code>def run_and_output_realtime:
# ???
return result
</code></pre>
<p>Which would allow to change <em>just the first line</em> of code like</p>
<pre class="lang-py prettyprint-override"><code>run_tool_result = subproccess.run(args, capture_output=True)
if run_tool_result.returncode != 0:
if "Unauthorized" in run_tool_result.stderr.decode():
print("Please check authorization")
else:
if "Duration" in run_tool_result.stdout.decode():
# parse to get whatever duration was in the output
</code></pre>
<p>To <code>run_tool_result = run_and_output_realtime(args)</code>, and have all the remaining lines unchanged and working.</p>
|
<python><terminal><command-line><automation><subprocess>
|
2023-05-31 04:22:24
| 1
| 1,615
|
goose_lake
|
76,370,121
| 11,098,908
|
Why an object can be split into 2 components
|
<p>I came across this class <code>Vector2</code> which was constructed like this</p>
<pre><code>class Vector2:
def __init__(self, x=0, y=0):
self.x = x
self.y = y
if hasattr(x, "__getitem__"):
x, y = x
self._v = [float(x), float(y)]
else:
self._v = [float(x), float(y)]
def __getitem__(self, index):
return self._v[index]
def __setitem__(self, index, value):
self._v[index] = 1.0 * value
def __str__(self):
return "(%s, %s)"%(self.x, self.y)
</code></pre>
<p>As I don't have much knowledge in Python, I couldn't understand the first line of the block <code>if hasattr(x, "__getitem__"):</code></p>
<pre><code>if hasattr(x, "__getitem__"):
x, y = x # why can we do this?
</code></pre>
<p>I meant how could <code>x</code> be split into <code>x</code> and <code>y</code> because <code>x</code> is already itself (that is, <code>x == x</code>)?</p>
<p>Also, what is the objective/purpose of <code>hasattr(x, "__getitem__")</code>?</p>
<p><strong>EDIT</strong>: If I instantiated a vector <code>v1 = Vector2(100, 200)</code> to represent the movement of an object in <code>pygame</code>, I found out that I couldn't use <code>v1[0]</code> to specify the location of the object on the x-axis for drawing (with <code>pygame.blit()</code>). Why was that, given <code>v1[0] == v1.x</code>?</p>
<p><strong>EDIT 2</strong>: correction of the <strong>edit</strong> above. I was wrong, <code>v1[0]</code> (same as <code>v1.x</code>) can be used to specify the location of the object.</p>
|
<python><class><oop><hasattr>
|
2023-05-31 04:11:20
| 1
| 1,306
|
Nemo
|
76,370,054
| 10,200,497
|
Creating a new column by subtracting two other columns that one of them is shifted
|
<p>This is my dataframe:</p>
<pre><code>df = pd.DataFrame({'a': [20, np.nan, np.nan], 'b': [np.nan, 3, 2]})
a b
0 20.0 NaN
1 NaN 3.0
2 NaN 2.0
</code></pre>
<p>And this is the output that I want:</p>
<pre><code> a b c
0 20.0 NaN NaN
1 NaN 3.0 17.0
2 NaN 2.0 15.0
</code></pre>
<p>I want to subtract column <code>a</code> and <code>b</code>. For example the second row is 20 - 3 and the last row is 17 - 2. Basically I want to shift one value. That is, I want to subtract by the result of the previous one.</p>
<p>This is the code that I have tried:</p>
<pre><code>df['c'] = df['a'].shift(1) - df.b
</code></pre>
|
<python><pandas>
|
2023-05-31 03:53:41
| 1
| 2,679
|
AmirX
|
76,369,917
| 1,603,480
|
With Python and `win32com`, how to open a RTF file without the pop-up asking to convert?
|
<p>When opening a RTF document in MS Word, Word is usually showing a pop-up to ask for converting (with different format including RTF).</p>
<p>Therefore, when opening the same file with <code>win32com</code>, you need to manually click on the icon of MS Word or switch to MS Word if this is open (to show this pop up) and click on <strong>OK</strong>. That breaks the automation flow.</p>
<p>The code I have currently is this one:</p>
<pre class="lang-py prettyprint-override"><code>from pathlib import Path
from win32com import client, __gen_path__
rtf_file = Path(...)
word = client.gencache.EnsureDispatch("Word.Application")
doc = word.Documents.Open(str(rtf_file.absolute()))
doc.Activate()
</code></pre>
<p>How to by-pass this pop-up to automate processing of RTF files?</p>
|
<python><ms-word><pywin32><win32com><rtf>
|
2023-05-31 03:10:48
| 1
| 13,204
|
Jean-Francois T.
|
76,369,916
| 8,702,633
|
Add new column in Dataframe based on value in the array from another column
|
<p>I have a dataframework where one of column is a array. I am trying to get value from some elements in the array in this column and append this value as a new column</p>
<p>Here is the dataframe:</p>
<pre><code> Key Version
0 ABC-1 [{'name': 'v1', 'releaseDate': '2023-01-01'}]
1 ABC-2 [{'name': 'v2', 'releaseDate': '2023-02-01'}]
2 ABC-3 [{'name': 'v3', 'releaseDate': '2023-03-01'}]
</code></pre>
<p>Here is my code:</p>
<pre><code>df["NewColumn"] = df["Version"][0][0]['name']
</code></pre>
<p>The result is like below. It always pull the 1st value in row in the dataframe.</p>
<pre><code> Key Version NewColumn
0 ABC-1 [{'name': 'v1', 'releaseDate': '2023-01-01'}] v1
1 ABC-2 [{'name': 'v2', 'releaseDate': '2023-02-01'}] v1
2 ABC-3 [{'name': 'v3', 'releaseDate': '2023-03-01'}] v1
</code></pre>
<p>I tried to use dataframe apply with lambda and it did not work. It says "KeyError: 0"</p>
<pre><code>df["NewColumn"] = df.apply(lambda row: row.Version[0][0]['name'], axis=1)
</code></pre>
|
<python><dataframe>
|
2023-05-31 03:10:47
| 2
| 331
|
Max
|
76,369,896
| 4,872,065
|
Creating a two level x-axis that groups the first axis
|
<p>I'm am trying to recreate a variant of this chart: <a href="https://i.sstatic.net/fhI3F.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/fhI3F.png" alt="chart generated using excel" /></a></p>
<p>The main difference being that I am creating a bar chart with x-axis being date time (in 3 hour increments), and the bar being coloured based on another series called intensity (low - green, high - red).</p>
<p>That said, one axis should be the the hours in a day, the second axis below it should group those times into the day they belong to.</p>
<p>What I have so far:</p>
<pre class="lang-py prettyprint-override"><code>import matplotlib as mplt
import matplotlib.pyplot as plt
from matplotlib import dates
import numpy as np
import datetime as pdt
from datetime import datetime, timedelta
import seaborn as sns
start_dt = np.datetime64('today').astype(np.datetime64)
end_dt = np.datetime64('today') + np.timedelta64(6, 'D')
x = np.arange(start_dt,
end_dt, np.timedelta64(180, 'm'))
x = [i.astype(datetime) for i in x]
intensity = np.random.uniform(0, 10, len(x))
y = np.ones(shape=len(x))
plt.rcParams["figure.figsize"] = [17, 1.2]
plt.rcParams["figure.autolayout"] = True
sns.set_style("whitegrid")
sns.despine(bottom = True, left = True, top = True)
fig, ax = plt.subplots()
colors = []
for i in intensity:
if 0 <= i <= 5:
colors.append('#75FF71')
elif 6 <= i < 8:
colors.append('#FFC53D')
else:
colors.append('#FF5C5C')
graph = sns.barplot(x=x, y=y, palette=colors, width=1.0, linewidth=0)
graph.grid(False)
graph.set(yticklabels=[])
x_tick_label = []
for val in x:
min_ts = min(x)
diff_days = (val - min_ts).days
diff_hours = (val - min_ts).seconds/3600
total = diff_days*24 + int(diff_hours)
if val.time() == pdt.time(0,0):
# x_tick_label.append(val.strftime("%m/%d"))
x_tick_label.append("")
elif val.time() == pdt.time(6,0) or val.time() == pdt.time(12,0) or val.time() == pdt.time(18,0) :
# elif val.time() == pdt.time(12,0):
x_tick_label.append(f"{val.strftime('%-H')}:00")
else:
x_tick_label.append('')
graph.set(xticklabels=x_tick_label)
for ticklabel in graph.axes.get_xticklabels():
ticklabel.set_color("#FFC53D")
ax2 = ax.axes.twiny()
ax2.spines['top'].set_position(('axes', -0.15))
ax2.spines['top'].set_visible(False)
# ax2.xaxis.set_major_formatter(day_locator)
plt.xticks(fontweight='light',ha='right', rotation=90)
plt.box(on=None)
plt.show()
</code></pre>
<p><a href="https://i.sstatic.net/WNsqd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WNsqd.png" alt="output of the code above" /></a></p>
|
<python><matplotlib><seaborn><x-axis>
|
2023-05-31 03:04:27
| 1
| 427
|
AGS
|
76,369,726
| 5,861,256
|
Get max value rows then min value rows, or if a subset has all Nan, keep everything
|
<p>I have a pandas dataframe</p>
<pre class="lang-py prettyprint-override"><code>x = pd.DataFrame({
"t": ["A", "A", "A", "B", "B", "B", "B", "A", "C", "C"],
"m": ["k", "m", "m", "k", "m", "m", "b", "k", "f", "d"],
"f1": [1.2, np.nan, 0.8, 1, 1, 1.5, 1, np.nan, np.nan, np.nan],
"f2": [100, 200, 200, 100, 100, 100, 50, 200, 300, 400]})
</code></pre>
<pre><code> t m f1 f2
0 A k 1.2 100
1 A m NaN 200
2 A m 0.8 200
3 B k 1.0 100
4 B m 1.0 100
5 B m 1.5 100
6 B b 1.0 50
7 A k NaN 200
8 C f NaN 300
9 C d NaN 400
</code></pre>
<p>On <code>f1</code> I want to pick min value of each subgroup <code>t</code>. If a subgroup has Nan values, we need to pick all the values. On <code>f2</code> I want to pick max values. In case of Nan values, we need to keep all the rows. So I want an output something similar to</p>
<pre><code> t m f1 f2
0 A m 0.8 200
1 B k 1.0 100
2 B m 1.0 100
3 C d NaN 400
</code></pre>
<p>I was able to achieve this using</p>
<pre class="lang-py prettyprint-override"><code>def keep_rows(k, col, op):
# if all values are Nan
if np.isnan(k[col].values).all():
return k
return k[k[col] == getattr(np, f"nan{op}")(k[col])]
</code></pre>
<pre class="lang-py prettyprint-override"><code>tt = x.groupby("t", as_index=False).apply(lambda x: keep_rows(x, "f1", "min")).reset_index(drop=True)
tt = tt.groupby("t", as_index=False).apply(lambda x: keep_rows(x, "f2", "max")).reset_index(drop=True)
tt
</code></pre>
<p>But is there a better way? Especially in pandas v2?</p>
|
<python><pandas>
|
2023-05-31 02:11:12
| 1
| 715
|
Prakash Vanapalli
|
76,369,707
| 653,397
|
Flask Application on Azure App Service throwing "400 Bad Request" error
|
<p>I have deployed a simple <code>Flask app</code> on <code>Azure App Service</code> using Azure DevOps Build and Release pipeline. Flask app just takes user input and returns it back, on local deployment & testing its running without any issue, I can see the expected output but when the app is deployed on the App service and request is sent from either Postman or Python then I am getting the below error.</p>
<pre><code><!doctype html>
<html lang=en>
<title>400 Bad Request</title>
<h1>Bad Request</h1>
<p>The browser (or proxy) sent a request that this server could not understand.</p>
</code></pre>
<p>Below are relevant data</p>
<p><strong>app.py</strong></p>
<pre><code>from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/chat', methods=['GET', 'POST'])
def chat():
data = request.json
return jsonify(data)
if __name__ == '__main__':
app.run(debug=True)
</code></pre>
<p><strong>send_request.py</strong></p>
<pre><code>import requests
import json
url = "http://<app>.azurewebsites.net/chat"
payload = json.dumps({
"name": "Atinesh Singh"
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
</code></pre>
<p><strong>Azure DevOps Release pipeline configuration</strong>
<a href="https://i.sstatic.net/EkNqp.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/EkNqp.png" alt="enter image description here" /></a></p>
<p><strong>App Service Access Restrictions settings</strong>
<a href="https://i.sstatic.net/ItBK9.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ItBK9.png" alt="enter image description here" /></a></p>
|
<python><azure><flask><azure-web-app-service><azure-pipelines>
|
2023-05-31 02:06:36
| 1
| 1,930
|
Atinesh Singh
|
76,369,510
| 1,968,829
|
Is there a vectorization method to match a df column to a dictionary
|
<p>I have the following (abbreviated) dictionary:</p>
<pre><code>_d = {
"pain":
["C0030193",
"C0150055",
"C0151825",
"C0184567"],
"anxiety":
["C0003467",
"C0003469",
"C0027769",
"C0154587",
"C0231397",
"C0231401",
"C0231402"],
"depression":
["C0001539",
"C0005587",
"C0011579",
"C0011581",
"C0024517",
"C0086132"],
"fatigue":
["C0015672"]
}
</code></pre>
<p>and the following dataframe:</p>
<pre><code>df =
cui
1 "C0015672"
2 "C0015634"
3 "C0011579"
4 "C0030193"
5 "C0031193"
6 "C0030193"
</code></pre>
<p>I want to match up the column df["cui"], such that if the value of df["cui"] is in any of the value lists in then dictionary then I want a new column "symptom" based on the dictionary key, otherwise it would remain null.</p>
<p>This is the desired output:</p>
<pre><code>df =
cui symptom
1 C0015672 fatigue
2 C0015634 NaN
3 C0011579 depression
4 C0030193 pain
5 C0031193 NaN
6 C0030193 pain
</code></pre>
<p>I can do this by iterating over each row in the dataframe, but since there are 10s of millions of rows, it's super slow. I'm looking for a way to vectorize this.</p>
|
<python><pandas><dictionary><vectorization>
|
2023-05-31 01:06:04
| 2
| 2,191
|
horcle_buzz
|
76,369,462
| 11,116,696
|
EntryPoints attribute error when trying to combine files using xarray
|
<p><strong>Background</strong></p>
<p>As recently as two months ago, I've had a working python script which i used regularly to combine netcdf4 weather files together.</p>
<p>However, ever since I recently updated my laptop, and reinstalled python (3.7 as per I.T. policy) and the latest python libraries, and the script has stopped working, it appears something has been deprecated, this has caused my code to stop working.</p>
<p><strong>Problem</strong></p>
<p>I am getting the error message: AttributeError: 'EntryPoints' object has no attribute 'get'</p>
<p><strong>What I've tried</strong></p>
<p>I've referred to these other posts on SO (<a href="https://stackoverflow.com/questions/73990243/import-fsspec-throws-error-attributeerror-entrypoints-object-has-no-attribut">link</a>, <a href="https://stackoverflow.com/questions/73929564/entrypoints-object-has-no-attribute-get-digital-ocean">link</a>). From that I can see the issue has arisen from importlib-metadata (v5.0.0. +), Which comes as part of the standard python install (including 3.7) (I have importlib-metadata-6.6.0 on my machine)</p>
<p>I tried downgrading to an older version (importlib-metadata 4.0.0), but that resulted in a different problem.</p>
<p>I also asked I.T. to update my python to higher version (3.9, 3.10, etc). But apparently that's an entire process which could take ages to do.</p>
<p><strong>Help Requested</strong></p>
<p>Anyone know how I can resolve this issue.</p>
<p><strong>Example Error message</strong></p>
<pre><code>Traceback (most recent call last):
File "C:/Users/User/PycharmProjects/project/python_script.py", line 59, in get_era5
merge_netcdf4 = xr.open_mfdataset(list, combine='by_coords')
File "C:/Users/User/PycharmProjects/project\venv\lib\site-packages\xarray\backends\api.py", line 908, in open_mfdataset
datasets = [open_(p, **open_kwargs) for p in paths]
File "C:/Users/User/PycharmProjects/project\venv\lib\site-packages\xarray\backends\api.py", line 908, in <listcomp>
datasets = [open_(p, **open_kwargs) for p in paths]
File "C:/Users/User/PycharmProjects/project\venv\lib\site-packages\xarray\backends\api.py", line 479, in open_dataset
engine = plugins.guess_engine(filename_or_obj)
File "C:/Users/User/PycharmProjects/project\venv\lib\site-packages\xarray\backends\plugins.py", line 110, in guess_engine
engines = list_engines()
File "C:/Users/User/PycharmProjects/project\venv\lib\site-packages\xarray\backends\plugins.py", line 105, in list_engines
entrypoints = entry_points().get("xarray.backends", ())
AttributeError: 'EntryPoints' object has no attribute 'get'
</code></pre>
<p><strong>Example code</strong></p>
<pre><code>for single_date in daterange(start_date, end_date):
YYYY = single_date.strftime("%Y")
MM = single_date.strftime("%m")
DD = single_date.strftime("%d")
fname = fpath + YYYY + MM + DD + '_era5.nc'
list.append(fname)
# Details
lat_toplot = np.arange(25.25, 25.50, 0.25) # last number is exclusive
lon_toplot = np.arange(140.25, 140.50, 0.25) # last number is exclusive
merge_netcdf4 = xr.open_mfdataset(list, combine='by_coords').sel(longitude=lon_toplot, latitude=lat_toplot)
</code></pre>
|
<python><python-xarray><python-importlib>
|
2023-05-31 00:49:06
| 1
| 601
|
Bobby Heyer
|
76,369,404
| 9,435,771
|
Extract and update DataFrame column names
|
<p>Reading data into DataFrame, expect certain column names but often they are mixed with a random string either before or after the name. There are also other columns and the column order is not guaranteed. I want to rename the applicable columns to the correct names.</p>
<pre><code>import pandas as pd
import numpy as np
df = pd.DataFrame(
np.random.randn(4, 5),
columns=['betarandstr1.823491', 'alpha randstr123', 'other', 'delta', 'randstr-1.281999 gamma']
)
keys = ['alpha', 'beta', 'gamma', 'delta'] # expected names
</code></pre>
<p>I can extract the desired name for each column.</p>
<pre><code>>>> df.columns.str.extract('(%s)' % '|'.join(keys))
0
0 beta
1 alpha
2 NaN
3 delta
4 gamma
</code></pre>
<p>My question is how to update the names, but if NaN to keep the original name. So the desired result in this case <code>'other'</code> is retained instead of NaN.</p>
<pre><code>>>> df.columns
Index(['beta', 'alpha', 'other', 'delta', 'gamma'],
dtype='object')
</code></pre>
|
<python><pandas>
|
2023-05-31 00:27:09
| 1
| 6,122
|
alec
|
76,369,395
| 5,942,100
|
Reindex dataframe based on column values using Pandas
|
<p><strong>Data</strong></p>
<pre><code>ID Q1 Q2 Q3
AA 1 2 4
BB 5 5 5
CC 6 7 7
</code></pre>
<p><strong>Desired</strong></p>
<pre><code>ID Q1 Q2 Q3
BB 5 5 5
CC 6 7 7
AA 1 2 4
</code></pre>
<p><strong>Doing</strong></p>
<pre><code>df = pd.DataFrame(df, index = ["BB","CC","AA"])
</code></pre>
<p>This is simply adding the row name, I wish to rea arrange row order based on name. Any suggestion is appreciated.</p>
|
<python><pandas><numpy>
|
2023-05-31 00:25:43
| 0
| 4,428
|
Lynn
|
76,369,340
| 3,008,221
|
What is the pandas equivalent to a sql count window function with a filter?
|
<p>I am trying to find the pandas equivalent to a sql count window function with a filter. Here is the sql query:</p>
<pre><code>select t.*,
count(*) filter(where grp = 'new') over(partition by usr order by id) rn
from mytable t
order by usr, id
</code></pre>
<p>I tried the below after sorting by id:</p>
<pre><code>mytable['rn'] = mytable.groupby('usr')['grp'].transform('count')
</code></pre>
<p>But it is wrong as I am not filtering on grp as I should and I don't know how to do such filter. So, what is the correct pandas equivalent (a vectorized solution)?</p>
<p>Note: For more context, if needed, you can refer to this question, but you don't have to:
<a href="https://stackoverflow.com/questions/76364851/how-to-create-a-row-number-that-infer-the-group-it-belongs-to-from-another-colum">link</a></p>
|
<python><pandas>
|
2023-05-31 00:09:52
| 2
| 433
|
Aly
|
76,369,298
| 15,542,245
|
How to limit Python regex 'greedyness' when asking for all chars before negative lookup
|
<p>I have 4 matches with my pattern:</p>
<pre><code>\d+\/?\d+\s[A-z]+.(?!\d)
</code></pre>
<p><a href="https://regex101.com/r/mPt19w/1" rel="nofollow noreferrer">Regex demo</a></p>
<p><a href="https://i.sstatic.net/8bTA0.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/8bTA0.png" alt="regex demo" /></a></p>
<p>Require parsing of 4 strings:</p>
<pre><code>17 Howard Rd Howard. Stdnt
11/169 Wall Road, Wontown, Wkr
105 AGNEW, Marilyn Barbara
106 AGNEW, Mavis Rosina
</code></pre>
<p>If I add <code>*</code> or <code>+</code> after <code>.</code> The match goes to the end of the string. So I lose the matches and the negative lookup. How do I reconfigure this regex to extend the matches so I get 4 complete strings?</p>
|
<python><regex><regex-lookarounds>
|
2023-05-30 23:53:59
| 1
| 903
|
Dave
|
76,369,286
| 2,673,149
|
How to run different pytest commands on windows vs Linux with Tox?
|
<p>I figured the following would work but only if I pass <code>tox -e linux</code> or <code>tox -e win32</code> are any tests ran at all.</p>
<pre class="lang-ini prettyprint-override"><code>[testenv]
commands =
linux: py.test {posargs}
win32: py.test -m "not fails_on_windows" {posargs}
deps =
pyflakes
pytest
</code></pre>
|
<python><pytest><tox>
|
2023-05-30 23:51:30
| 1
| 400
|
Spitfire19
|
76,369,225
| 5,942,100
|
Tricky shift values from specific rows to another row using Pandas with multi-index columns
|
<p>I am looking to shift values from specific rows to another row using Pandas.</p>
<p><strong>Data</strong></p>
<pre><code> cn_positions cn_positions cn_positions
Date Q1.22 Q1.23 Q1.24
ID
AA 73 87 104
BB 0 0 13
CC 0 20 62
CC 0 0 11
</code></pre>
<p><strong>Desired</strong></p>
<pre><code> cn_positions_Q1.22 cn_positions_Q1.23 cn_positions_Q1.24
Date
ID
AA 73 87 104
BB 0 0 13
CC 0 20 62
CC 0 0 11
</code></pre>
<p><strong>Doing</strong></p>
<pre><code>df_pivot = pd.pivot_table(df.unstack().reset_index(), values=0, index='ID', columns='Date').rename_axis(None, axis=1)
</code></pre>
<p>However, the above seems to eliminate the Q1.22,Q1.23 values. Any suggestion is appreciated.</p>
|
<python><pandas><multi-index>
|
2023-05-30 23:31:33
| 1
| 4,428
|
Lynn
|
76,369,147
| 6,087,667
|
re substitute with the first group
|
<p>I have an expression that I want to extract the first part before the <code>=</code> sign. However it doesn't work:</p>
<pre><code>import re
re.sub('(.*?)=(.*?)', r'\1', 'F0=None')
</code></pre>
<p>This returns 'F0None'. Shouldn't it return the first captured group, i.e. 'F0' only?</p>
|
<python><python-re>
|
2023-05-30 23:06:05
| 1
| 571
|
guyguyguy12345
|
76,369,119
| 512,480
|
VSCode: import of pip3-installed modules suddenly quit working
|
<p>I have been developing in a certain folder for months, undre python 3.10. Today, suddenly, VS code thinks that my imported modules don't exist. In particular, any that were installed by pip3. The same is true of an actual python process running in that context. But pip thinks they're installed!</p>
<p>Possible culprit: a few days ago a package wouldn't install, and in a bit of haste and frustration I said "sudo pip3 install ..." Apparently a bad idea. Today I uninstalled all those pieces that I had installed, and made sure that everything under my site-packages folder belongs to my user. Didn't help.</p>
<p>Deleting <strong>pycache</strong> from my working folder didn't help.</p>
<p>Now here's the stumper: I created a new folder and into it wrote the following tiny program:</p>
<pre><code>import numpy, cv2, PIL
print("success")
</code></pre>
<p>It works great, with or without the intervention of VScode!</p>
<p>Here's the relevant part of my launch.json, in case it could be somehow creating a strange environment. The environment variable MOCK is recognized by my code, shouldn't be an issue anywhere else, right? It certainly isn't new.</p>
<pre><code>{
"name": "UIServer",
"type": "python",
"request": "launch",
"program": "${workspaceFolder}/UIServer.py",
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"MOCK": "true"
},
},
</code></pre>
<p><a href="https://i.sstatic.net/lHKgc.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/lHKgc.png" alt="enter image description here" /></a></p>
|
<python><visual-studio-code><pip><python-import>
|
2023-05-30 22:55:45
| 1
| 1,624
|
Joymaker
|
76,369,055
| 1,282,160
|
Using throttler and joblib for heavy jobs and getting "cannot unpack non-iterable function object"
|
<p>I have a lot of heavy jobs which need to send throttled HTTP requests (2 requests/sec) and calculate the results based on the response values.</p>
<p>I use <a href="https://github.com/uburuntu/throttler" rel="nofollow noreferrer">throttler</a> to control the request rate, and use <a href="https://github.com/joblib/joblib" rel="nofollow noreferrer">joblib</a> to calculate the results on multi-processes:</p>
<pre><code>import asyncio
from math import sqrt
import time
from joblib import Parallel, delayed
from throttler import throttle
@throttle(rate_limit=1, period=0.5)
async def request(i):
"""
Do nothing. Just sleep to mimic HTTP requests.
"""
time.sleep(0.3)
print(f"get response: {i} {time.time()}")
return i
def heavy_calculate(i):
"""
Just sqrt and square the number i for 30_000_000 times to mimik heavy job.
It takes about 2.1 sec for each call.
"""
n = i
for _ in range(30_000_000):
n = sqrt(n)
n = n ** 2
print(f"heavy_calculate: {n} {time.time()}")
return n
async def many_tasks(count: int):
print(f"=== START === {time.time()}")
with Parallel(n_jobs=-1) as parallel:
a = []
coros = [request(i) for i in range(count)]
for coro in asyncio.as_completed(coros):
i = await coro
#! Note: this heavy_calculate will block the request rate too
results = heavy_calculate(i)
#! Try to using the following call instead, but it raises error "cannot unpack non-iterable function object"
# results = parallel(delayed(heavy_calculate)(i))
a.append(results)
print(f"=== END === {time.time()}")
print(a)
# Start run
asyncio.run(many_tasks(10))
</code></pre>
<p>The above code shows two functions should be run:</p>
<ul>
<li><code>request</code>: mimic HTTP requests which is throttled with 0.5 sec.</li>
<li><code>heavy_calculate</code>: mimic CPU-bound calculation which will block other jobs.</li>
</ul>
<p>I want to call <code>request</code> with throttling and run <code>heavy_calculate</code> on multi-processes, but it shows error:</p>
<pre><code>...
TypeError: cannot unpack non-iterable function object
</code></pre>
<p>Any suggestion?</p>
|
<python><multiprocessing><throttling><joblib>
|
2023-05-30 22:39:58
| 1
| 3,407
|
Xaree Lee
|
76,369,026
| 5,130,078
|
How to access US Census TIGER shapefiles in geopandas?
|
<p>Context: I have a range of ACS data I work with, and I was looking to plot it at the block group level. However, I am struggling to find the relevant <code>geometry</code> objects to actually make such plots. TIGER files from census (which I think I used back in the early 2010s with geopandas) can't be loaded to <code>gpd</code>, and nowhere in the <a href="https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2022/TGRSHP2022_TechDoc.pdf" rel="nofollow noreferrer">current TIGER documentation</a> do they mention a data table with a geometry column.</p>
<p>This stands in contrast to numerous example codes online (eg. <a href="https://pygis.io/docs/d_access_census.html" rel="nofollow noreferrer">here</a> and <a href="https://n8henrie.com/uploads/2017/11/plotting-us-census-data-with-python-and-geopandas.html" rel="nofollow noreferrer">here</a>) that just load TIGER uris into geopandas and implicitly have geometry in the loaded object.</p>
<p>Example code:</p>
<pre><code>import geopandas as gpd
# input vars
state_id = 78
year = 2020
file_type='bg'
# load TIGER from URI
uri = f"https://www2.census.gov/geo/tiger/TIGER{year}/{file_type.upper()}/tl_{year}_{state_id}_{file_type.lower()}.zip"
example_blockgroups = gpd.read_file(uri)
</code></pre>
<p>gives <code>TypeError: __init__() missing 1 required keyword-only argument: 'geometry'</code></p>
<p>As do other very basic loads of TIGER files like</p>
<pre><code>gpd.read_file("https://www2.census.gov/geo/tiger/TIGER2020/STATE/tl_2020_us_state.zip")
gpd.read_file("https://www2.census.gov/geo/tiger/TIGER2017/STATE/tl_2017_us_state.zip")
gpd.read_file("https://www2.census.gov/geo/tiger/TIGER2019/TABBLOCK/tl_2019_01_tabblock10.zip")
</code></pre>
|
<python><geopandas><tiger-census>
|
2023-05-30 22:30:51
| 0
| 1,344
|
Mark_Anderson
|
76,368,967
| 14,820,295
|
Crosstab (contingency table, or similar)
|
<p>I have a dataset like this</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>id</th>
<th>january</th>
<th>february</th>
<th>march</th>
<th>april</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>scheduled</td>
<td>done</td>
<td>null</td>
<td>done</td>
</tr>
<tr>
<td>2</td>
<td>scheduled</td>
<td>scheduled</td>
<td>done</td>
<td></td>
</tr>
<tr>
<td>3</td>
<td>ongoing</td>
<td>canceled</td>
<td>scheduled</td>
<td></td>
</tr>
</tbody>
</table>
</div>
<p>I desire to transform this dataset in a matrix like this in output, where each cell are the occurrences of the exact intersection (keeping Null Values).</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th>event</th>
<th>january</th>
<th>february</th>
<th>march</th>
<th>april</th>
</tr>
</thead>
<tbody>
<tr>
<td>scheduled</td>
<td>2</td>
<td>1</td>
<td>1</td>
<td>0</td>
</tr>
<tr>
<td>done</td>
<td>0</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<td>ongoing</td>
<td>1</td>
<td>0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<td>null</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>2</td>
</tr>
</tbody>
</table>
</div>
|
<python><group-by><pivot-table>
|
2023-05-30 22:10:14
| 1
| 347
|
Jresearcher
|
76,368,843
| 610,569
|
How to load a .py script directly with evaluate.load?
|
<p>If I have a script like this <a href="https://huggingface.co/spaces/evaluate-metric/frugalscore/blob/main/frugalscore.py" rel="nofollow noreferrer">https://huggingface.co/spaces/evaluate-metric/frugalscore/blob/main/frugalscore.py</a> and save it as <code>fgscore.py</code> with a directory locally like:</p>
<pre><code>./
my_script.py
fgscore/
fgscore.py
</code></pre>
<p>And in my_script.py, I can do something like:</p>
<pre><code>import evaluate
mt_metrics = evaluate.load("fgscore")
sources = ["안녕하세요 저는 당신의 아버지입니다", "일반 케노비"]
predictions = ["hello here I am your father", "general kenobi"]
references = ["hello there I am your father", "general yoda"]
results = mt_metrics.compute(predictions=predictions,
references=references)
print(results)
</code></pre>
<p>Looking at the <code>evaluate.load()</code> function, <a href="https://github.com/huggingface/evaluate/blob/main/src/evaluate/loading.py#L688" rel="nofollow noreferrer">https://github.com/huggingface/evaluate/blob/main/src/evaluate/loading.py#L688</a>, it states:</p>
<pre><code>path (`str`):
Path to the evaluation processing script with the evaluation builder. Can be either:
- a local path to processing script or the directory containing the script (if the script has the same name as the directory),
e.g. `'./metrics/rouge'` or `'./metrics/rouge/rouge.py'`
- a evaluation module identifier on the HuggingFace evaluate repo e.g. `'rouge'` or `'bleu'` that are in either `'metrics/'`,
`'comparisons/'`, or `'measurements/'` depending on the provided `module_type`
</code></pre>
<h3>Is there a reason to do <code>{name}/{name}.py</code> for using the path arguments in <code>evaluate.load()</code>?</h3>
<h3>Is there a way to override such that I can point the evaluate directly to the <code>.py</code> file? E.g. <code>evaluate.load("fgscore.py")</code></h3>
|
<python><metrics><huggingface><huggingface-evaluate>
|
2023-05-30 21:41:23
| 1
| 123,325
|
alvas
|
76,368,834
| 2,100,039
|
Add Suffix to Dataframe with Specific Repeating Column Name
|
<p>i have data in a dataframe such as the following columns: week, SITE, LAL, SITE, LAL. I need to assign a suffix to the col name == 'SITE' such that the final df will look like: week, SITE_1, LAL, SITE_2, LAL.</p>
<p>Thank you,</p>
<p>dataframe example:</p>
<pre><code> week SITE LAL SITE LAL
0 1 BARTON CHAPEL 1.1 PENASCAL I 1.0
1 2 BARTON CHAPEL 1.1 PENASCAL I 1.0
2 3 BARTON CHAPEL 1.1 PENASCAL I 1.0
3 4 BARTON CHAPEL 1.1 PENASCAL I 1.0
4 5 BARTON CHAPEL 1.1 PENASCAL I 1.0
5 6 BARTON CHAPEL 1.4 PENASCAL I 1.0
</code></pre>
|
<python><pandas><suffix>
|
2023-05-30 21:38:43
| 3
| 1,366
|
user2100039
|
76,368,791
| 11,760,357
|
python regex to remove all text not between '<' and '>'
|
<p>I want the following string</p>
<pre><code>Doe, John PGM GUY FOOBARINC MD (USA) <john.doe@email.mail>
</code></pre>
<p>to become</p>
<pre><code>john.doe@email.mail
</code></pre>
<p>while using the <code>series.str.replace()</code> function</p>
<p>I have code like the following</p>
<pre class="lang-py prettyprint-override"><code>email= email.squeeze()
if '<' in email[0] and '>' in email[0]: # Checking to see if strings in this series are formatted with <>'s. Not all are, hence the check
email.str.replace(r"[^<]*\<|\>[^>]*", "")
</code></pre>
<p>which seems to work <a href="https://pythex.org/?regex=%5B%5E%3C%5D*%5C%3C%7C%5C%3E%5B%5E%3E%5D*&test_string=Doe%2C%20John%20PGM%20GUY%20FOOBARINC%20MD%20(USA)%20%3Cjohn.doe%40email.mail%3E&ignorecase=0&multiline=0&dotall=0&verbose=0" rel="nofollow noreferrer">here</a>, but doesn't work when I run the code. I simply get back the same strings, no edits made to them at all.</p>
|
<python><pandas><regex>
|
2023-05-30 21:31:23
| 2
| 375
|
Caleb Renfroe
|
76,368,779
| 2,302,262
|
Keep frequency when selecting with datetimeindex in pandas
|
<h1>Sample</h1>
<p>I have a <code>DatetimeIndex</code> with a yearly frequency, and 2 series that together cover all its values:</p>
<pre class="lang-py prettyprint-override"><code>import pandas as pd
i = pd.date_range('2023', freq='AS', periods=3)
s1 = pd.Series([1,2], [i[0], i[2]])
s2 = pd.Series([-30], [i[1]])
concat = pd.concat([s1, s2])
</code></pre>
<p>In the final line I concatenate these series to a longer series, in which the index values are not sorted.</p>
<p>I can select the values in the original index order with <code>result = concat.loc[i]</code>.</p>
<h1>Problem</h1>
<p><strong>The problem is that the <strong><code>freq</code></strong> property is lost.</strong></p>
<p>The original index has frequency <code>i.freq</code> (<code><YearBegin: month=1></code>), but in <code>result.index.freq</code> is <code>None</code>.</p>
<p>I don't understand why; I understand the <code>.loc[]</code> to select the corresponding value in <code>concat</code> and return a Series with <code>i</code> as its index. So I would assume <code>result</code>'s index to have retained the frequency.</p>
<p><strong>Is there a way to keep the frequency?</strong></p>
<h1>Remarks</h1>
<ul>
<li><p>Here, I can manually set the frequency with <code>result.index.freq = 'AS'</code>, or have pandas devise it with <code>result.index.freq = pd.infer_freq(result.index)</code>.</p>
</li>
<li><p>However, I cannot expect <code>i</code> to always be a <code>DatetimeIndex</code>.</p>
</li>
<li><p>I would like to avoid having to do extensive typechecking and error-catching, like so...</p>
<pre class="lang-py prettyprint-override"><code>if isinstance(concat.index, pd.DatetimeIndex) and i.freq:
try:
concat.index.freq = i.freq
except ValueError:
pass
</code></pre>
<p>...if I can possibly avoid it.</p>
</li>
</ul>
|
<python><pandas>
|
2023-05-30 21:29:29
| 0
| 2,294
|
ElRudi
|
76,368,723
| 3,420,371
|
Insert list of dictionaries into Postgres table where each dict has different keys
|
<p>I have a list of Python dictionaries that look something like below where each dictionary can have slightly different keys.</p>
<pre><code>data = [
{'name': 'Bob', 'age': 32},
{'name': 'Sara', 'city': 'Dallas'},
{'name': 'John', 'age': 45, 'city': 'Atlanta'}
]
</code></pre>
<p>I also have a Postgres table that contains all of the possible keys that are seen within this list of dictionaries (e.g.: <code>name</code>, <code>age</code>, <code>city</code>).</p>
<p>I am looking for an elegant solution to efficiently insert this data into my database. While I could iterate over <code>data</code> line by line, and insert each line individually, that doesn't scale so well to my actual dataset including millions of records.</p>
<p>I attempted to use the <code>execute_values</code> function from <code>psycopg2</code>, as seen in the example below, but that expected all of the dictionaries to have the same keys.</p>
<p><strong>How can I edit my process below to insert multiple dictionaries at once, where each dictionary can contain different keys?</strong></p>
<pre><code>import psycopg2
from psycopg2.extras import execute_values
# connect to the database
conn = psycopg2.connect(
host="localhost",
database="db_name",
user="psql_user",
password="psql_password",
)
conn.autocommit = True
cur = conn.cursor()
# get the columns from first dictionary
columns = data[0].keys()
# write the SQL query to insert the records
query = """INSERT INTO schema.table
({}) VALUES %s
ON CONFLICT (name) DO NOTHING""".format(
",".join(columns)
)
# extract the values from each dictionary into as list of lists
values = [[value for value in line.values()] for line in data]
# execute the SQL query with the associated values
execute_values(cur, query, values)
</code></pre>
|
<python><postgresql><psycopg2>
|
2023-05-30 21:20:36
| 3
| 2,447
|
CurtLH
|
76,368,691
| 8,229,534
|
How to filter on STRING column using BETWEEN clause in pandas
|
<p>Consider a sample pandas dataframe as below -</p>
<pre><code>import pandas as pd
money = [100,200,300,400,500,600,700,800,900,1000,1100,1200]
batch_code = ['B1_2023','B2_2023','B3_2023','B4_2023','B1_2024','B2_2024','B3_2024','B4_2024','B1_2025','B2_2025','B3_2025','B4_2025']
test_df = pd.DataFrame([money,batch_code]).T
test_df.columns = ['money','batch_code']
test_df.money = test_df.money.astype(int)
</code></pre>
<p>Here is the how the data looks like -</p>
<p><a href="https://i.sstatic.net/MBgSd.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MBgSd.png" alt="enter image description here" /></a></p>
<p>I want to <code>filter</code> this data on column batch_code using 2 parameters - from_batch and to_batch.</p>
<p>For example -</p>
<pre><code>from_batch = B3_2023
to_batch = B4_2024
</code></pre>
<p>Then my output data frame should consist of all the records that are in between B3_2024 and B3_2024. The batches represents quarters but are coded with prefix B. How can I achieve this in pandas?</p>
<p>I tried writing</p>
<pre><code>test_df[test_df.batch_code.between('B3_2023','B4_2024')]
</code></pre>
<p>but I got this as output, which seems to be incorrect -</p>
<p><a href="https://i.sstatic.net/ufTxV.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ufTxV.png" alt="enter image description here" /></a></p>
<p>The correct output would be -</p>
<p><a href="https://i.sstatic.net/7fnc9.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/7fnc9.png" alt="enter image description here" /></a></p>
<p><strong>NOTE</strong> - The maximum range of prefix quarters is B1 to B4 only.</p>
|
<python><pandas>
|
2023-05-30 21:14:03
| 1
| 1,973
|
Regressor
|
76,368,612
| 16,462,878
|
Subprocess - pip: pass package names dynamically
|
<p>I am trying to download without installation some Python packages using <code>pip</code> with the shell command</p>
<pre><code>$ python -m pip download --destination-directory path -r requirements.txt
</code></pre>
<p>What I'd like to do is to bypass the step of having a file, <em>requirements.txt</em>, which contains the list of packages to download and just pass the package names as parameters.
Here my attempt</p>
<pre><code>import sys
import subprocess
from tempfile import NamedTemporaryFile
PIP = (sys.executable, '-m', 'pip')
def download(to_dir, *pkgs):
with NamedTemporaryFile(mode="w+", suffix=".txt") as tmp:
tmp.write('\n'.join(pkgs))
# debugging info
tmp.seek(0)
print(tmp.read(), tmp.name)
tmp.seek(0)
return PIP + ('download', '--destination-directory', to_dir, '-r', tmp.name)
pkg = "sphinx" # example of package
to_dir = "valid path" # download directory
cmd = download(to_dir, pkg)
print(cmd)
p = subprocess.call(cmd)
print(p)
</code></pre>
<p>which raises the error</p>
<pre><code>sphinx
/tmp/tmpmg3inbtu.txt
('path to python3.11', '-m', 'pip', 'download', '--destination-directory', 'valid path', '-r', '/tmp/tmpmg3inbtu.txt')
ERROR: Could not open requirements file: [Errno 2] No such file or directory: '/tmp/tmpmg3inbtu.txt'
</code></pre>
|
<python><unix><pip><subprocess>
|
2023-05-30 21:02:51
| 1
| 5,264
|
cards
|
76,368,573
| 6,567,319
|
Is there a way to compress a .npy file more tightly than by using the LZMA algorithm?
|
<p>I am trying to compress some .npy files as tightly as possible. What I have read is that typically to do this you use the LZMA algorithm.</p>
<p>So far I have tried xz tar compression level 9, and python lzma compression. This seems effective but I was wondering if anybody had tried something better? Is LZMA really the best algorithm or is there something better? I am optimizing SOLELY for compression, time to compress is a non-issue. I also recognize that .npy is already more compressed than, for example, an image so there is a limit to the opimality of the result.</p>
<p>I am dealing with both folders of npy files and single npy files alone.</p>
<p>Edit: The .npy files contain hyperspectral images from the Harvard Real World Hyperspectral Image Dataset stacked together</p>
|
<python><numpy><compression><lzma><pylzma>
|
2023-05-30 20:56:55
| 1
| 449
|
Mira Welner
|
76,368,516
| 7,903,749
|
Python unit test got error on INSTALLED_APPS settings not configured
|
<p>With PyCharm IDE, we have a unit test importing the <code>models</code> module of the Django project for testing. While expecting a happy pass, the test always hit en error saying:</p>
<p><code>django.core.exceptions.ImproperlyConfigured: Requested setting INSTALLED_APPS, but settings are not configured. You must either define the environment variable DJANGO_SETTINGS_MODULE or call settings. Configure() before accessing settings.</code>.</p>
<p>We are new to this part of the Python unit test, so we highly appreciate any hints or suggestions.</p>
<p><strong>The unit test:</strong></p>
<pre class="lang-py prettyprint-override"><code>from applicationapp import models
...
class TestElastic(TestCase):
def test_parse_request(self):
# expecting happy pass
#
# self. Fail()
return
</code></pre>
<p><strong>The full error trace:</strong></p>
<pre><code>/data/app-py3/venv3.7/bin/python /var/lib/snapd/snap/pycharm-professional/327/plugins/python/helpers/pycharm/_jb_unittest_runner.py --target test_elastic.TestElastic
Testing started at 2:07 PM ...
Launching unittests with arguments python -m unittest test_elastic.TestElastic in /data/app-py3/APPLICATION/tests
Traceback (most recent call last):
File "/var/lib/snapd/snap/pycharm-professional/327/plugins/python/helpers/pycharm/_jb_unittest_runner.py", line 35, in <module>
sys.exit(main(argv=args, module=None, testRunner=unittestpy.TeamcityTestRunner, buffer=not JB_DISABLE_BUFFERING))
File "/usr/local/lib/python3.7/unittest/main.py", line 100, in __init__
self.parseArgs(argv)
File "/usr/local/lib/python3.7/unittest/main.py", line 147, in parseArgs
self.createTests()
File "/usr/local/lib/python3.7/unittest/main.py", line 159, in createTests
self.module)
File "/usr/local/lib/python3.7/unittest/loader.py", line 220, in loadTestsFromNames
suites = [self.loadTestsFromName(name, module) for name in names]
File "/usr/local/lib/python3.7/unittest/loader.py", line 220, in <listcomp>
suites = [self.loadTestsFromName(name, module) for name in names]
File "/usr/local/lib/python3.7/unittest/loader.py", line 154, in loadTestsFromName
module = __import__(module_name)
File "/data/app-py3/APPLICATION/tests/test_elastic.py", line 6, in <module>
from applicationapp.soap.elastic import Elastic
File "/data/app-py3/APPLICATION/applicationapp/soap/elastic.py", line 7, in <module>
from applicationapp import models
File "/data/app-py3/APPLICATION/applicationapp/models.py", line 3, in <module>
from django.contrib.auth.models import User
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/contrib/auth/models.py", line 2, in <module>
from django.contrib.auth.base_user import AbstractBaseUser, BaseUserManager
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/contrib/auth/base_user.py", line 48, in <module>
class AbstractBaseUser(models.Model):
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/db/models/base.py", line 108, in __new__
app_config = apps.get_containing_app_config(module)
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/apps/registry.py", line 253, in get_containing_app_config
self.check_apps_ready()
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/apps/registry.py", line 135, in check_apps_ready
settings.INSTALLED_APPS
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/conf/__init__.py", line 83, in __getattr__
self._setup(name)
File "/data/app-py3/venv3.7/lib/python3.7/site-packages/django/conf/__init__.py", line 68, in _setup
% (desc, ENVIRONMENT_VARIABLE))
django.core.exceptions.ImproperlyConfigured: Requested setting INSTALLED_APPS, but settings are not configured. You must either define the environment variable DJANGO_SETTINGS_MODULE or call settings.configure() before accessing settings.
Process finished with exit code 1
Empty suite
</code></pre>
|
<python><django><unit-testing><pycharm>
|
2023-05-30 20:46:58
| 2
| 2,243
|
James
|
76,368,511
| 1,473,517
|
How to get changelog info for pip upgrade-able modules
|
<p>If I do</p>
<pre><code> pip list --outdated
</code></pre>
<p>I get a list of upgrade-able modules. For example:</p>
<pre><code>Package Version Latest Type
------------------ ---------- -------- -----
aiofiles 22.1.0 23.1.0 wheel
anyio 3.6.2 3.7.0 wheel
attrs 22.2.0 23.1.0 wheel
beautifulsoup4 4.11.2 4.12.2 wheel
comm 0.1.2 0.1.3 wheel
Cython 0.29.33 0.29.35 wheel
[...]
</code></pre>
<p>Is there any way to get the changelog or relevant release notes for the new versions that would be installed? I would like to get these programmatically, ideally from the command line if possible.</p>
|
<python><pip><python-module>
|
2023-05-30 20:46:22
| 2
| 21,513
|
Simd
|
76,368,493
| 1,700,890
|
rpy2 how to check column data type
|
<p>I created pandas dataframe and converted it to R object using rpy2. Now I would like to check data type of a column. Here is my code:</p>
<pre><code>import pandas as pd
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
utils = importr('utils')
base = importr('base')
pd_df = pd.DataFrame({'col_1': [1,2,3], 'col_2': [4,5,6]})
with (ro.default_converter + pandas2ri.converter).context():
r_df = ro.conversion.get_conversion().py2rpy(pd_df)
</code></pre>
<p>Normally in R it would be</p>
<pre><code>str(r_df$col_1)
</code></pre>
<p>I tried the following (nothing worked)</p>
<pre><code>r_df$col_1
r_df['col_1']
r_df[1,1]
robjects.r['r_df']
robjects.r('''r_df''')
</code></pre>
<p>Any suggestions?</p>
|
<python><r><types><rpy2>
|
2023-05-30 20:43:27
| 2
| 7,802
|
user1700890
|
76,368,415
| 2,631,300
|
Catch CTRL+C and exit gracefully in Python with a multithreaded controller on Windows
|
<p>I'm trying to create a <strong>Windows</strong> SMTP server in Python that uses the <code>aiosmtpd</code> library, which provides a multithreaded controller that listens on a TCP port for connections.</p>
<p>This code works perfectly:</p>
<pre class="lang-py prettyprint-override"><code>from aiosmtpd.controller import Controller
class CustomSMTPServer:
# Proof of concept
async def handle_DATA(self, server, session, envelope):
print(f'> Incoming mail from \'{envelope.mail_from}\'')
return '250 OK'
if __name__ == '__main__':
controller = Controller(CustomSMTPServer(), hostname='127.0.0.1', port=25)
controller.start()
# I would like to wait for CTRL+C instead of ENTER
input(f'SMTP listening on port {controller.port}. Press ENTER to exit.\n')
# ... and stopping the controller after the key has been pressed
controller.stop()
</code></pre>
<p>I would like to modify it such as it doesn't wait for <kbd>ENTER</kbd> but for <kbd>CTRL</kbd>+<kbd>C</kbd> instead, without using a <code>while True</code> endless loop because this would eat CPU. It would be great to not use a lot of libraries imports, if possible.</p>
<p>Is there anything better than using <code>time.sleep(1)</code> inside the loop, as the <code>aiosmtpd.controller</code> is already running in its thread and the main thread is not doing anything useful, apart from waiting for <kbd>CTRL</kbd>+<kbd>C</kbd>?</p>
|
<python><windows><multithreading><keyboardinterrupt>
|
2023-05-30 20:28:00
| 2
| 509
|
virtualdj
|
76,368,304
| 12,436,050
|
Oracle error message: DPY-4004: invalid number
|
<p>I am creating a table in Oracle db with following lines in python script.</p>
<pre><code>cur.execute('''BEGIN EXECUTE IMMEDIATE 'DROP TABLE MRCONSO';
EXCEPTION WHEN OTHERS THEN NULL;
END;
''')
try:
cur.execute('''
CREATE TABLE MRCONSO (
CUI char(8) NOT NULL,
LAT char(3) NOT NULL,
TS char(1) NOT NULL,
LUI varchar2(10) NOT NULL,
STT varchar2(3) NOT NULL,
SUI varchar2(10) NOT NULL,
ISPREF char(1) NOT NULL,
AUI varchar2(9) NOT NULL,
SAUI integer,
SCUI varchar2(100),
SDUI varchar2(100),
SAB varchar2(40) NOT NULL,
TTY varchar2(40) NOT NULL,
CODE varchar2(100) NOT NULL,
STR varchar2(3000) NOT NULL,
SRL integer NOT NULL,
SUPPRESS char(1) NOT NULL,
CVF integer)
PCTFREE 10 PCTUSED 80
'''
)
print("MRCONSO Table Created")
except Exception as e:
print("Error: ",str(e))
</code></pre>
<p>When I insert the data using csv file, I get following error.</p>
<pre><code>Oracle error message: DPY-4004: invalid number
</code></pre>
<pre><code>df_umls = pd.read_csv("../umls_files/umls-2023AA-metathesaurus-full/2023AA/META/MRCONSO.RRF", sep = '|', low_memory=False)
df_umls.columns=["CUI", "LAT", "TS", "LUI", "STT", "SUI", "ISPREF", "AUI","SAUI", "SCUI", "SDUI", "SAB", "TTY", "CODE", "STR", "SRL", "SUPPRESS", "CVF", "JUNK"]
df_umls = df_umls.drop(columns=['JUNK'])
df_umls['CVF'] = df_umls['CVF'].fillna(0)
df_umls['CVF'] = df_umls['CVF'].astype("Int64")
df_umls['SAUI'] = df_umls['SAUI'].fillna(0)
df_umls['SAUI'] = df_umls['SAUI'].astype("Int64")
try:
if conn:
print("Oracle version:", oracledb.version)
print("Database version:", conn.version)
#print("Client version:", oracledb.clientversion())
print('Inserting data into table....')
for i,row in tqdm(df_umls.iterrows(), total=df_umls.shape[0]):
sql_conso = "insert into MRCONSO (CUI,LAT,TS,LUI,STT,SUI,ISPREF, AUI, SAUI, SCUI, SDUI, SAB, TTY, CODE, STR, SRL, SUPPRESS, CVF) values (:0,:1,:2,:3,:4,:5,:6,:7,:8,:9,:10,:11,:12,:13,:14,:15,:16,:17)"
cur.execute(sql_conso, tuple(row))
# the connection is not autocommitted by default, so we must commit to save our changes
conn.commit()
print("Record inserted succesfully")
except DatabaseError as e:
err, = e.args
print("Oracle-Error-Code:", err.code)
print("Oracle-Error-Message:", err.message)
finally:
cur.close()
conn.close()
</code></pre>
<p>The dataframe datatype is:</p>
<pre><code>CUI object
LAT object
TS object
LUI object
STT object
SUI object
ISPREF object
AUI object
SAUI Int64
SCUI object
SDUI object
SAB object
TTY object
CODE object
STR object
SRL int64
SUPPRESS object
CVF Int64
dtype: object
</code></pre>
<p>How should I modify the dataframe to insert the data into table successfully.</p>
|
<python><oracle-database>
|
2023-05-30 20:10:23
| 0
| 1,495
|
rshar
|
76,368,302
| 5,212,614
|
How can we loop through all text files in a folder, copy the first N rows from each one, and append the file name of each?
|
<p>I am trying to loop through a bunch of text files, copy the first N-rows from each, and append the file name of each. This is the code that I am testing.</p>
<pre><code># import necessary libraries
import pandas as pd
import csv
import os
import glob
# use glob to get all the txt files
# in the folder
path = 'C:\\Users\\ryans\\Desktop\\all_files\\'
txt_files = glob.glob(os.path.join(path, "*.txt"))
df_headers = pd.DataFrame()
# loop over the list of files
for f in txt_files:
with open(f) as myfile:
print(f)
firstNlines=myfile.readlines()[0:5]
df = pd.DataFrame(firstNlines)
fname = pd.DataFrame({f})
df_headers = pd.concat([df, fname], axis=1)
df_headers.to_csv('C:\\Users\\ryans\\Desktop\\out.csv')
</code></pre>
<p>When I run the code, I get this.</p>
<p><a href="https://i.sstatic.net/9gTL2.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/9gTL2.png" alt="enter image description here" /></a></p>
<p>So, the problem is as follows.</p>
<ol>
<li>I only get one file, which presumably is the last file</li>
<li>The data is all tab-delimited, but everything is compressed into one single cell, not split out</li>
<li>I'm not sure how to copy the file name down N-rows</li>
</ol>
<p>What I'd like to end up with is something like this, but with all the files read, not just the last file.</p>
<p><a href="https://i.sstatic.net/fWXh3.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/fWXh3.png" alt="enter image description here" /></a></p>
|
<python><python-3.x><pandas><dataframe>
|
2023-05-30 20:10:18
| 1
| 20,492
|
ASH
|
76,368,297
| 682,466
|
reading data compressed by NSMutableData using the lz4 library in python
|
<p>I'm compressing random data using NSMutableData (in Swift) as follows:</p>
<pre class="lang-swift prettyprint-override"><code>import Foundation
let n = 1024
let numbers = (0..<n).map { _ in UInt8.random(in: 0...1) }
print("generated random data: \(numbers)")
let data = Data(numbers)
let mutable = NSMutableData(data: data)
try! mutable.compress(using: .lz4)
print("compressed size: \(mutable.count)")'
try! mutable.write(toFile: "compressed.dat", options: [])
</code></pre>
<p>And reading into python using:</p>
<pre class="lang-py prettyprint-override"><code>import os
import lz4.frame
fh = open('compressed.dat', 'rb')
ba = bytearray(fh.read())
print("read %d bytes" % len(ba))
decompressed = lz4.frame.decompress(ba)
print("decompressed to %d bytes" % len(decompressed))
</code></pre>
<p>When calling <code>lz4.frame.decompress</code>, I get:</p>
<pre><code>RuntimeError: LZ4F_getFrameInfo failed with code: ERROR_frameType_unknown
</code></pre>
<p>trying <code>lz4.block.decompress</code>, I get:</p>
<pre><code>_block.LZ4BlockError: Decompression failed: corrupt input or insufficient space in destination buffer. Error code: 4
</code></pre>
<p>What am I doing wrong?</p>
|
<python><swift><lz4><nsmutabledata>
|
2023-05-30 20:09:56
| 0
| 6,498
|
Taylor
|
76,368,145
| 21,420,742
|
Filling in for missing values by name. pandas
|
<p>I have a dataset that I need to fill in the blanks of the ID.</p>
<pre><code>ID Name
Adam
101 Adam
Adam
101 Adam
102 Ben
102 Ben
Cathy
Cathy
103 Cathy
</code></pre>
<p>What I need:</p>
<pre><code>ID Name
101 Adam
101 Adam
101 Adam
101 Adam
102 Ben
102 Ben
103 Cathy
103 Cathy
103 Cathy
</code></pre>
<p>I tried using <code>df['Name'].ffill()</code> but does not work when trying on multiple names.</p>
<p>Any other suggestions?</p>
|
<python><python-3.x><pandas><dataframe>
|
2023-05-30 19:51:28
| 5
| 473
|
Coding_Nubie
|
76,368,086
| 869,598
|
Search for imports which could be TYPE_CHECKING
|
<p>I make heavy use of mypy static type checking. I have a large lib where I know I have many imports which are being done just for typehints that could be protected with an <code>if TYPE_CHECKING</code> to speed things up.</p>
<p>But searching for them all is proving difficult.</p>
<p>Is there a way to identify these "unused" imports automatically so I can fix them?</p>
|
<python><mypy>
|
2023-05-30 19:38:56
| 1
| 303
|
JonFitt
|
76,367,953
| 5,091,720
|
pandas Auto increment based on date condition
|
<p>I would like to auto increment a new column I have the below code.</p>
<pre><code>df = pd.DataFrame({'Date': ['2020-02-29', '2020-03-01', '2020-10-01', '2020-10-02', '2020-10-03', '2020-10-04', '2021-10-01', '2021-10-02', '2021-10-03', '2021-10-04']})
conditions = [((df['Date'].dt.day == 1) & (df['Date'].dt.month == 10)), ((df['Date'].dt.day == 29) & (df['Date'].dt.month == 2))]
r_choice = [1, 151]
df['Oct_year_day'] = np.select(conditions, r_choice, np.nan)
df['Oct_year_day'] = df['Oct_year_day'].fillna(1).cumsum()
# the below code does not work...
reset_condition = (df['Date'].dt.day == 1) & (df['Date'].dt.month == 10)
df.loc[reset_condition, 'Oct_year_day'] = 1
</code></pre>
<p>What I want returned is:</p>
<pre><code> Date Oct_year_day
0 2020-02-29 151.0
1 2020-03-01 152.0
2 2020-10-01 1.0
3 2020-10-02 2.0
4 2020-10-03 3.0
5 2020-10-04 4.0
6 2021-10-01 1.0
7 2021-10-02 2.0
8 2021-10-03 3.0
9 2021-10-04 4.0
</code></pre>
<p><em>Edit The incrementation.</em></p>
<p>So the incrementation would start over every October 1st with 1 and increase by 1 each proceeding date afterword. Think of it like the <code>df['Date'].dt.dayofyear</code>. The other part is that the leap day would be 151 and each proceeding date afterword would increase by 1.</p>
|
<python><pandas>
|
2023-05-30 19:15:18
| 1
| 2,363
|
Shane S
|
76,367,729
| 10,266,106
|
NumPy all Function Across Third Dimension of ndarray
|
<p>Consider the ndarray with the following dimensions <code>(1200,2600,200)</code></p>
<p>I am looking to assess whether all the values along the third dimension, or axis=2, at point [i,j] in this array are all the same value. For example, I'd use the following to assess the presence of all zeroes. I'm using the following code:</p>
<pre><code>import numpy as np
array = np.random.randint(0.00, 75.00, size=(1200, 2600, 200))
logical = array.all(axis=2) == 0
</code></pre>
<p>This executes, however returns the boolean True at all array points, even where zeroes do not populate the entire third dimension. What alterations are required to yield the desired result?</p>
|
<python><numpy><numpy-ndarray><logical-operators>
|
2023-05-30 18:40:44
| 1
| 431
|
TornadoEric
|
76,367,557
| 2,916,639
|
Unable to list blobs from Azure VM with Python using system assigned managed identity
|
<p>I am try to list all blobs available in azure storage container. Below is the python code</p>
<pre><code>import io
import os
from azure.core.exceptions import HttpResponseError, ResourceExistsError
from azure.identity import DefaultAzureCredential
from msrestazure.azure_active_directory import MSIAuthentication
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient, BlobLeaseClient, BlobPrefix, ContentSettings
class BlobSamples(object):
# <Snippet_list_blobs_flat>
def list_blobs_flat(self, blob_service_client: BlobServiceClient, container_name):
container_client = blob_service_client.get_container_client(container=container_name)
blob_list = container_client.list_blobs()
for blob in blob_list:
print(f"Name: {blob.name}")
# </Snippet_list_blobs_flat>
if __name__ == '__main__':
# TODO: Replace <storage-account-name> with your actual storage account name
account_url = "https://testinglist.blob.core.windows.net"
credential = DefaultAzureCredential()
# Create the BlobServiceClient object
blob_service_client = BlobServiceClient(account_url, credential=credential)
sample = BlobSamples()
sample.list_blobs_flat(blob_service_client, "testing")
</code></pre>
<p>I am running this code from virtual machine which has the below system assigned managed identity.</p>
<p><a href="https://i.sstatic.net/alkcn.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/alkcn.png" alt="enter image description here" /></a></p>
<p>WHen i execute code getting below error.</p>
<pre><code>Traceback (most recent call last):
File "listblob.py", line 28, in <module>
sample.list_blobs_flat(blob_service_client, "testing")
File "listblob.py", line 15, in list_blobs_flat
for blob in blob_list:
File "/usr/local/lib/python3.6/site-packages/azure/core/paging.py", line 128, in __next__
return next(self._page_iterator)
File "/usr/local/lib/python3.6/site-packages/azure/core/paging.py", line 76, in __next__
self._response = self._get_next(self.continuation_token)
File "/usr/local/lib/python3.6/site-packages/azure/storage/blob/_list_blobs_helper.py", line 83, in _get_next_cb
process_storage_error(error)
File "/usr/local/lib/python3.6/site-packages/azure/storage/blob/_shared/response_handlers.py", line 181, in process_storage_error
exec("raise error from None") # pylint: disable=exec-used # nosec
File "<string>", line 1, in <module>
azure.core.exceptions.HttpResponseError: This request is not authorized to perform this operation using this permission.
RequestId:32c35c2b-101e-0066-801f-932ff0000000
Time:2023-05-30T17:55:50.1950002Z
ErrorCode:AuthorizationPermissionMismatch
Content: <?xml version="1.0" encoding="utf-8"?><Error><Code>AuthorizationPermissionMismatch</Code><Message>This request is not authorized to perform this operation using this permission.
RequestId:32c35c2b-101e-0066-801f-932ff0000000
Time:2023-05-30T17:55:50.1950002Z</Message></Error>
</code></pre>
<p>The container is private but accessible from all networks. I am unable to catch the issue. Kindly help.</p>
|
<python><azure><azure-blob-storage>
|
2023-05-30 18:12:56
| 1
| 428
|
user2916639
|
76,367,483
| 2,426,635
|
Identifying Read-Only SQL Statements
|
<p>I'm using SQLAlchemy to execute SQL statements on a variety of different databases and am trying to find a way to determine whether or not an SQL statement (passed to the connector as a python string). What is the best way to do this reliably for multiple flavours of SQL?</p>
<pre><code>raw_query = "SELECT * FROM table"
engine = get_engine(username=db_dict['username'],
password=db_dict['password'],
db=database,
hostname=db_dict['hostname'],
port=port,
db_type=db_dict['type'])
with engine.connect() as db_conn:
# query database
result = db_conn.execute(sqlalchemy.text(raw_query))
</code></pre>
<p>In the above, what is the best way to check whether the SQL in raw_query will require more than read-only permissions? Or in other words, whether or not it will make changes to the database.</p>
|
<python><sql>
|
2023-05-30 18:00:53
| 1
| 626
|
pwwolff
|
76,367,218
| 13,217,286
|
How do I make a time delta column in Polars from two datetime columns
|
<p>How would I make a column with the delta (in days) of two date columns. I thought I could just subtract the date objects, but I'm obviously missing something</p>
<pre class="lang-py prettyprint-override"><code>(pl.from_records([{'start': '2021-01-01', 'end': '2022-01-01'}])
.with_columns(pl.col(['start', 'end']).str.to_date('%Y-%m-%d'))
.with_columns(delta = pl.col('end') - pl.col('start'))
)
</code></pre>
|
<python><duration><timedelta><python-polars>
|
2023-05-30 17:23:04
| 1
| 320
|
Thomas
|
76,367,016
| 16,912,844
|
Getting `Abort trap: 6` Error During `import cv2` on Python 3.10 with macOS ARM64
|
<p>Getting the following <code>Abort trap: 6</code> error while doing <code>import cv2</code> on Python 3.10 with macOS ARM64.</p>
<p>I tried using lower version of opencv-python (4.6.0.66) and latest version but still doesn't work. I've also tried some of the fixes such as using different Terminal and symlink <code>libssl.dylib</code> and <code>libcrypto.dylib</code> to <code>/usr/local/lib</code>.</p>
<pre><code>> python
Python 3.10.11 (main, May 22 2023, 00:42:58) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
Modules/gcmodule.c:115: gc_decref: Assertion "gc_get_refs(g) > 0" failed: refcount is too small
Enable tracemalloc to get the memory block allocation traceback
object address : 0x105a54010
object refcount : 8
object type : 0x1056e3ab8
object type name: type
object repr : <class 'cv2.utils.nested.ExportClassName'>
Fatal Python error: _PyObject_AssertFailed: _PyObject_AssertFailed
Python runtime state: initialized
Current thread 0x00000001fce51e00 (most recent call first):
Garbage-collecting
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 1176 in create_module
File "<frozen importlib._bootstrap>", line 571 in module_from_spec
File "<frozen importlib._bootstrap>", line 674 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1006 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1027 in _find_and_load
File "<frozen importlib._bootstrap>", line 1050 in _gcd_import
File "/opt/python/3.10.11/lib/python3.10/importlib/__init__.py", line 126 in import_module
File "/Users/alpha/Python/pv310-3di/lib/python3.10/site-packages/cv2/__init__.py", line 153 in bootstrap
File "/Users/alpha/Python/pv310-3di/lib/python3.10/site-packages/cv2/__init__.py", line 181 in <module>
File "<frozen importlib._bootstrap>", line 241 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 883 in exec_module
File "<frozen importlib._bootstrap>", line 688 in _load_unlocked
File "<frozen importlib._bootstrap>", line 1006 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 1027 in _find_and_load
File "<stdin>", line 1 in <module>
Extension modules: numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator (total: 13)
Abort trap: 6
>
</code></pre>
|
<python><macos><opencv>
|
2023-05-30 16:52:57
| 2
| 317
|
YTKme
|
76,366,955
| 13,763,436
|
wxPython install, attrdict dependency ModuleNotFound error
|
<p><strong>System Details:</strong></p>
<ul>
<li>Machine: Raspberry Pi Model 4B - 4GB</li>
<li>Operating System: Debian 11 (Bullseye)</li>
<li>wxPython version: 4.2.0 from pypi</li>
<li>Python version: 3.11.3 built from source</li>
</ul>
<p><strong>Problem Description:</strong></p>
<p>When installing wxPython (latest version 4.2.0) I am getting an error that the <code>attrdict</code> dependency is not found, even though it is installed.</p>
<pre><code>(.venv) user@raspberrypi:~/Documents $ python --version
Python 3.11.3
(.venv) user@raspberrypi:~/Documents $ python -m pip freeze
attrdict==2.0.1
attrdict3==2.0.2
dbus-fast==1.86.0
pytz==2023.3
six==1.16.0
tzdata==2023.3
(.venv) user@raspberrypi:~/Documents $ python -m pip install wxPython
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting wxPython
Using cached wxPython-4.2.0.tar.gz (71.0 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> [23 lines of output]
Traceback (most recent call last):
File "/home/user/Documents/.venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 353, in <module>
main()
File "/home/user/Documents/.venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/Documents/.venv/lib/python3.11/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
^^^^^^^^^^^^^^^^^^^^^
File "/tmp/pip-build-env-fs9i9itq/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 341, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/pip-build-env-fs9i9itq/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 323, in _get_build_requires
self.run_setup()
File "/tmp/pip-build-env-fs9i9itq/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 488, in run_setup
self).run_setup(setup_script=setup_script)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/pip-build-env-fs9i9itq/overlay/lib/python3.11/site-packages/setuptools/build_meta.py", line 338, in run_setup
exec(code, locals())
File "<string>", line 27, in <module>
File "/tmp/pip-install-6s5ym1hn/wxpython_0afd12e3eb4741b4854a998e85194d91/buildtools/config.py", line 30, in <module>
from attrdict import AttrDict
ModuleNotFoundError: No module named 'attrdict'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
(.venv) user@raspberrypi:~/Documents $
</code></pre>
<p>Why is it saying that the dependency cannot be found when it is clearly there? Is there any way to correct this error and install wxPython on my system?</p>
<p>I have also opened an issue in the wxPython GitHub page: <a href="https://github.com/wxWidgets/Phoenix/issues/2401" rel="nofollow noreferrer">https://github.com/wxWidgets/Phoenix/issues/2401</a></p>
|
<python><raspberry-pi><debian><wxpython><python-3.11>
|
2023-05-30 16:42:59
| 2
| 403
|
stackoverflowing321
|
76,366,875
| 5,080,858
|
Seaborn is jumbling x-axis tick labels, no longer referring to bars
|
<p>I'm trying to write a little suite of regular seaborn plots, along with styling, to allow for regular production of similarly styled plots. I'm working on bar plots. Mostly this works fine, but if I have too many values on the x axis, and specifically, if the names are too long and plentiful, the axis tick labels get completely jumbled, meaning that the plot becomes useless.</p>
<p>I'd really appreciate any advice that could help me with making the ticks on the x axis always informative when I have a scenario where I have many value labels for the data I'm trying to visualise. I realise that I could stick the labels within the bars, but I'd like to keep this as x axis ticks.</p>
<p>See here for an example of the x axis becoming uninformative. The data I used is taken from here <a href="https://footystats.org/england/premier-league/xg" rel="nofollow noreferrer">https://footystats.org/england/premier-league/xg</a>:</p>
<p><a href="https://i.sstatic.net/AwMaS.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/AwMaS.png" alt="enter image description here" /></a></p>
<p>Here's my example code:</p>
<pre><code>def bar_plot(self,
df: pd.DataFrame,
x_var: str,
y_var: str,
x_label: str,
y_label: str,
y_line: float,
rotate_x_labels: bool = False,
export: bool = False,
filepath: str = None):
'''
produces a bar plot.
'''
plt.figure(figsize=self.figsize)
ax = sns.barplot(data=df,
x=x_var,
y=y_var,
palette=self.plot_elems_palette)
ax.spines['bottom'].set_linewidth(0.75)
ax.spines['left'].set_linewidth(0.75)
ax.tick_params(axis='x', which='major', width=0.5)
ax.tick_params(axis='x', which='minor', width=0.5)
ax.tick_params(axis='y', which='major', width=0.5)
ax.tick_params(axis='y', which='minor', width=0.5)
if x_label:
ax.set_xlabel(x_label, fontweight='bold')
if y_label:
ax.set_ylabel(y_label, fontweight='bold')
if y_line is not None:
ax.axhline(y_line,
color=self.plot_elems_colour,
linestyle='--',
linewidth=0.5)
if rotate_x_labels:
ax.tick_params(axis='x', rotation=45)
ax.tick_params(axis='both', labelsize=(self.font_size*0.5), rotation=45)
if export:
if filepath:
plt.savefig(filepath,
dpi=DPI,
bbox_inches=BBOX)
else:
raise ValueError('filepath must be specified if export is True')
plt.show()
</code></pre>
<p>this all exists within a class that initialises defaults for all my plotting methods, that looks like this:</p>
<pre><code>class PlotBuilder:
def __init__(self,
figsize: tuple = FIGSIZE,
background: str = BACKGROUND,
body_colour: str = BODY_COLOUR,
plot_elems_colour: str = PLOT_ELEMS_COLOUR,
plot_elems_palette: list = generate_colour_palette(BODY_COLOUR),
grid_colour: str = GRID_COLOUR,
font: str = FONT,
font_size: int = FONT_SIZE):
self.figsize = figsize
self.background = background
self.body_colour = body_colour
self.plot_elems_colour = plot_elems_colour
self.plot_elems_palette = plot_elems_palette
self.grid_colour = grid_colour
self.font = font
self.font_size = font_size
</code></pre>
|
<python><matplotlib><seaborn>
|
2023-05-30 16:30:41
| 1
| 679
|
nikUoM
|
76,366,860
| 6,758,739
|
Load file to an array and make necessary changes to array as per input and save it back to file using python
|
<p>The code below reads the file and stores it to an array
it searches in array for specific username and changes password inside the array
and save the array to a File.</p>
<blockquote>
<ol>
<li>The first column of the file can be a * or DB name</li>
<li>I would like to change the password depending on the input</li>
</ol>
</blockquote>
<pre><code>* DB_USR QWERTY
* DB_MGR QWERTY
TESTDB10 DB_USR PQRSTUV
TESTDB20 DB_USR QWERTY
TESTDB30 DB_USR QWERTY
</code></pre>
<p>Below is the code snippet that reads the file into array and searches the array</p>
<pre><code>import sys
import logging
def file_to_array():
file_info="/application/files/Password.txt"
with open (file_info,'rt') ass fh:
pw_lines = [x.strip() for x in fh.readlines() if not x.startswith('#')]
username="DB_USR"
pwd="PQRSTUV"
pw_lines = [re.sub(fr'^(\s*{username}\S+)\s+', fr'\1{pwd}', line) for line in pw_lines] # This line searches for a value DBFS_USR in 2nd column and changes the password in 3rd column
def main():
file_to_array()
</code></pre>
<p>I would like the below regex to work in two ways ,</p>
<pre><code>pw_lines = [re.sub(fr'^(\b{username}\S+)\s+', fr'\1{pwd}', line) for line in pw_lines] # This line searches for a value DB_USR in 2nd column and changes the password in 3rd column
</code></pre>
<blockquote>
<p>if the input is ./update_pwd TESTDB10 -> This has to change the
password in the line which starts with TESTDB10</p>
</blockquote>
<p>New File:</p>
<pre><code> * DB_USR QWERTY
* DB_MGR QWERTY
TESTDB10 DB_USR PQRSTUV. -> This value should change
TESTDB20 DB_USR QWERTY
TESTDB30 DB_USR QWERTY
</code></pre>
<blockquote>
<p>if the input is just ./update_pwd -> it should change the password in
the line that starts with *</p>
</blockquote>
<p>New File:</p>
<pre><code> * DB_USR PQRSTUV -> This value should change
* DB_MGR QWERTY
TESTDB10 DB_USR QWERTY
TESTDB20 DB_USR QWERTY
TESTDB30 DB_USR QWERTY
</code></pre>
<blockquote>
<p>As of now, the regex is only working for the wildcard line , but doesn't work If I input the db name.</p>
</blockquote>
<blockquote>
<p>I would also like to know if storing the file data in array and interpolating it and changing is good approach or is there any better approach than this ?
I am new to python and please excuse my mistakes</p>
</blockquote>
|
<python><python-3.x>
|
2023-05-30 16:28:28
| 1
| 992
|
LearningCpp
|
76,366,589
| 9,658,149
|
How to select the correct tool in a specific order for an agent using Langchain?
|
<p>I think I don't understand how an <strong>agent</strong> chooses a tool. I have a vector database (<strong>Chroma</strong>) with all the embedding of my <strong>internal knowledge</strong> that I want that the agent looks at first in it. Then, if the answer is not in the Chroma database, it should answer the question using the information that OpenAI used to train (external knowledge). In the case that the question is a "natural conversation" I want that the agent takes a role in answering it. This is the code that I tried, but It just uses the <strong>Knowledge External Base</strong> tool. I want that it decides the best tool.</p>
<pre><code>from langchain.agents import Tool
from langchain.chat_models import ChatOpenAI
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.chains import RetrievalQA
from langchain.agents import initialize_agent
from chroma_database import ChromaDatabase
from langchain.embeddings import OpenAIEmbeddings
from parameters import EMBEDDING_MODEL, BUCKET_NAME, COLLECTION_NAME
embeddings = OpenAIEmbeddings(model=EMBEDDING_MODEL)
chroma = ChromaDatabase(embedding_function=embeddings,
persist_directory='database/vectors/',
bucket_name=BUCKET_NAME,
collection_name=COLLECTION_NAME)
# chat completion llm
llm = ChatOpenAI(
model_name='gpt-3.5-turbo',
temperature=0.0
)
# conversational memory
conversational_memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=0,
return_messages=True
)
# retrieval qa chain
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=chroma.db.as_retriever()
)
tools = [
Tool(
name='Knowledge Internal Base',
func=qa.run,
description=(
'use this tool when answering internal knowledge queries. Search in the internal database retriever'
)
),
Tool(
name='Knowledge External Base',
func=qa.run,
description=(
'use this tool when the answer is not retrieved in the Knowledge Internal Base tool'
)
),
Tool(
name='Natural Conversation',
func=qa.run,
description=(
'use this tool when the answer is related to a natural conversation, act as friendly person'
)
)
]
agent = initialize_agent(
agent='chat-conversational-react-description',
tools=tools,
llm=llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
memory=conversational_memory
)
agent.run("What Pepito said?") #Pepito conversation is stored as embedding in Chroma
agent.run("What Tom Cruise said in the movie Impossible Mission 1?") #I don't have anything about Tom Cruise in Chroma
agent.run("Hello, how are you?") #I want the answer looks like: "I'm pretty fine, how about you?"
</code></pre>
<p>What should I do to have a correct plan-execute/orchestrator agent that takes the correct tool in the right order?</p>
|
<python><python-3.x><chatgpt-api><langchain>
|
2023-05-30 15:53:22
| 2
| 2,097
|
Eric Bellet
|
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