Ritchie Vink ritchie46

## example.py
import polars as pl
import numpy as np

df = (pl.DataFrame({
    "a": np.random.randint(0, 100, int(1e8))
}).groupby("a").agg(
    pl.col("a").alias("list")
))

# prints

## question.py
size = 10 ** 2

df = pl.DataFrame({
    "groupid": [floor(i*0.1)for i in range(size)],
    "vectors": [[i,i+1,i-1] for i in range(size)],
    "numbers": [i for i in range(size)]
})

print(df)

## pokemon.csv

          
            #
            Name
            Type 1
            Type 2
            Total
            HP
            Attack
            Defense
            Sp. Atk
            Sp. Def
            Speed
            Generation
            Legendary

            
              1
              Bulbasaur
              Grass
              Poison
              318
              45
              49
              49
              65
              65
              45
              1
              False

            
              2
              Ivysaur
              Grass
              Poison
              405
              60
              62
              63
              80
              80
              60
              1
              False

            
              3
              Venusaur
              Grass
              Poison
              525
              80
              82
              83
              100
              100
              80
              1
              False

            
              3
              VenusaurMega Venusaur
              Grass
              Poison
              625
              80
              100
              123
              122
              120
              80
              1
              False

            
              4
              Charmander
              Fire
              
              309
              39
              52
              43
              60
              50
              65
              1
              False

            
              5
              Charmeleon
              Fire
              
              405
              58
              64
              58
              80
              65
              80
              1
              False

            
              6
              Charizard
              Fire
              Flying
              534
              78
              84
              78
              109
              85
              100
              1
              False

            
              6
              CharizardMega Charizard X
              Fire
              Dragon
              634
              78
              130
              111
              130
              85
              100
              1
              False

            
              6
              CharizardMega Charizard Y
              Fire
              Flying
              634
              78
              104
              78
              159
              115
              100
              1
              False

## example.py
# $ pip install requests
import requests
username = "my-username"
password = "my-little-secret"

r = requests.post(
    "https://crux-nuclei.com/login",
    json={"username": username, "password": password},
)
ACCESS_TOKEN = r.json()["access_token"]

## gist:4366135d5bdc61bdf69307001a249a99
nodename,batch,timestamp,task,data,in_rows,question,out_rows,out_cols,solution,version,git,fun,run,time_sec,mem_gb,cache,chk,chk_time_sec,comment,on_disk
instance-2,,1610130993.9517767,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,1,0.296,0.855,TRUE,30001016,0.0,,FALSE
instance-2,,1610130994.2619028,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,2,0.287,0.847,TRUE,30001016,0.0,,FALSE
instance-2,,1610130994.8431137,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,1,0.575,0.92,TRUE,30001016,0.0,,FALSE
instance-2,,1610130995.4009216,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,2,0.552,0.924,TRUE,30001016,0.0,,FALSE
instance-2,,1610130995.930853,groupby,G1_1e7_1e2_0_0,10000000,sum v1 mean v3 by id3,100000,3,polars,0.4.0,unknown,.groupby,1,0.522,0.832,TRUE,[30001016.          5001212.36305564],0.001,,FALSE
instance-2,,1610130996.471266,groupby,G1_1e7_1e2_0_0,10000

## gist:e81ff8816af3dd06a1515cf23f79b805
git filter-branch --force --index-filter \
  "git rm --cached --ignore-unmatch PATH-TO-YOUR-FILE-WITH-SENSITIVE-DATA" \
  --prune-empty --tag-name-filter cat -- --all

## rnn_minibatch.py
"""
How to do minibatches for RNNs in pytorch

Assume we feed characters to the model and predict the language of the words.
"""


def prepare_batch(x, y):
    # determine the maximum word length per batch and zero pad the tensors
    n_max = max([a.shape[0] for a in x])

## index.html
<navigator :navs="[
        {name: 'page 1', id: 'link-page1'},
        {name: 'page 2', id: 'link-page2'},
        {name: 'page 3', id: 'link-page3'},
        ]" active="link-page1">
</navigator>

<div v-show="activePage === 'link-page1'">
<p>Some content</p>
</div>

## index.js
import navigator from './components/navigator.vue'

let vm = new Vue({
    el: "#app",
    components: {
        navigator
    },
    data: {
        activePage: 'link-page1',
    },

## navigator.vue
<template>
    <nav>
        <ul class="pagination">
            <li v-for="page in navs" class="page-item"
                 v-bind:class="{active: activeId === page.id}" v-bind:id="page.id">
                <a class="page-link" v-on:click="toggle">
                    {{ page.name }}
                </a>
            </li>
        </ul>
	import polars as pl
	import numpy as np

	df = (pl.DataFrame({
	"a": np.random.randint(0, 100, int(1e8))
	}).groupby("a").agg(
	pl.col("a").alias("list")
	))

	# prints
	size = 10 ** 2

	df = pl.DataFrame({
	"groupid": [floor(i*0.1)for i in range(size)],
	"vectors": [[i,i+1,i-1] for i in range(size)],
	"numbers": [i for i in range(size)]
	})

	print(df)
#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
4	Charmander	Fire		309	39	52	43	60	50	65	1	False
5	Charmeleon	Fire		405	58	64	58	80	65	80	1	False
6	Charizard	Fire	Flying	534	78	84	78	109	85	100	1	False
6	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	130	85	100	1	False
6	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	159	115	100	1	False
	# $ pip install requests
	import requests
	username = "my-username"
	password = "my-little-secret"

	r = requests.post(
	"https://crux-nuclei.com/login",
	json={"username": username, "password": password},
	)
	ACCESS_TOKEN = r.json()["access_token"]
	nodename,batch,timestamp,task,data,in_rows,question,out_rows,out_cols,solution,version,git,fun,run,time_sec,mem_gb,cache,chk,chk_time_sec,comment,on_disk
	instance-2,,1610130993.9517767,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,1,0.296,0.855,TRUE,30001016,0.0,,FALSE
	instance-2,,1610130994.2619028,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1,100,2,polars,0.4.0,unknown,.groupby,2,0.287,0.847,TRUE,30001016,0.0,,FALSE
	instance-2,,1610130994.8431137,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,1,0.575,0.92,TRUE,30001016,0.0,,FALSE
	instance-2,,1610130995.4009216,groupby,G1_1e7_1e2_0_0,10000000,sum v1 by id1:id2,10000,3,polars,0.4.0,unknown,.groupby,2,0.552,0.924,TRUE,30001016,0.0,,FALSE
	instance-2,,1610130995.930853,groupby,G1_1e7_1e2_0_0,10000000,sum v1 mean v3 by id3,100000,3,polars,0.4.0,unknown,.groupby,1,0.522,0.832,TRUE,[30001016. 5001212.36305564],0.001,,FALSE
	instance-2,,1610130996.471266,groupby,G1_1e7_1e2_0_0,10000
	git filter-branch --force --index-filter \
	"git rm --cached --ignore-unmatch PATH-TO-YOUR-FILE-WITH-SENSITIVE-DATA" \
	--prune-empty --tag-name-filter cat -- --all
	"""
	How to do minibatches for RNNs in pytorch

	Assume we feed characters to the model and predict the language of the words.
	"""


	def prepare_batch(x, y):
	# determine the maximum word length per batch and zero pad the tensors
	n_max = max([a.shape[0] for a in x])
	<navigator :navs="[
	{name: 'page 1', id: 'link-page1'},
	{name: 'page 2', id: 'link-page2'},
	{name: 'page 3', id: 'link-page3'},
	]" active="link-page1">
	</navigator>

	<div v-show="activePage === 'link-page1'">
	<p>Some content</p>
	</div>
	import navigator from './components/navigator.vue'

	let vm = new Vue({
	el: "#app",
	components: {
	navigator
	},
	data: {
	activePage: 'link-page1',
	},
	<template>
	<nav>
	<ul class="pagination">
	<li v-for="page in navs" class="page-item"
	v-bind:class="{active: activeId === page.id}" v-bind:id="page.id">
	<a class="page-link" v-on:click="toggle">
	{{ page.name }}
	</a>
	</li>
	</ul>