 import numpy as np

s = '412 202 299 181 490 306 327 197 252 413 219 188 455 169 472 198 478 143 473 448 76 387 200 372 308 497 366 179 485 396 119 410 160 147 463 156 202 368 57 221 83 429 385 226 343 70 400 99 455 433 256 78 425 104  50 242 324 422 497 215 234 490 215 303 224 83 163 51 391 167 425 285 430 149 269 450 386 210 450 324 381 233 402 273 191 471 464 98 495 339 358 292 52 206 86 396 282 222 226 365 232 141 327 163 362 154 345 335 210 226 59 83 494 266 361 317 262 444 418 258 126 374 350 94 325 450 170 276 217 228 499 85 223 292 102 307 324 497  52 265 198 112 72 185 491 477 82 302 142 366 450 333 359 413 58 326 314 202 126 69 150 443  76 228 110 183 262 401 156 387 59 82 191 94 121 398 342 431 476 250'
orders = np.array(s.split(), dtype=int).reshape((3, 10, 6))

print(np.median(a=orders[0, :, 2]))
print(np.median(a=orders[:, :, -1], axis=1).argmax())
print(np.median(a=orders, axis=1))

created 1 year ago

Python Online Compiler

Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.

Taking inputs (stdin)

OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.

import sys
name = sys.stdin.readline()
print("Hello "+ name)

About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

Name	Description
NumPy	NumPy python library helps users to work on arrays with ease
SciPy	SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learn	Scikit-learn or Scikit-learn is the most useful library for machine learning in Python
Pandas	Pandas is the most efficient Python library for data manipulation and analysis
DOcplex	DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling