Python related published Questions

Scheduling tasks in python

from datetime import datetime

​

dt_obj = datetime.strptime('20.12.2016 09:38:42,76',

                           '%d.%m.%Y %H:%M:%S,%f')

millisec = dt_obj.timestamp() * 1000

​

print(millisec)

>>> pd.date_range(start='1/1/2018', periods=5, freq='3M')

DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',

               '2019-01-31'],

              dtype='datetime64[ns]', freq='3M')

​

from graphviz import *

df[column_name].sum()

df = pd.concat(pd.read_excel(workbook_url, sheet_name=None), ignore_index=True)

suff_A = ['_on_A_match_1', '_on_A_match_2']

suff_B = ['_on_B_match_1', '_on_B_match_2']

​

pd.concat([df1.merge(df2, on='A', suffixes=suff_A), 

           df1.merge(df2, on='B', suffixes=suff_B)])

​

     A  A_on_B_match_1  A_on_B_match_2    B  B_on_A_match_1  B_on_A_match_2

0  1.0             NaN             NaN  NaN             9.0             4.0

1  4.0             NaN             NaN  NaN             5.0             5.0

0  NaN             2.0             6.0  8.0             NaN             NaN

1  NaN             4.0             4.0  5.0             NaN             NaN

train_set_x_flatten shape: (12288, 209)

train_set_y shape: (1, 209)

test_set_x_flatten shape: (12288, 50)

test_set_y shape: (1, 50)

sanity check after reshaping: [17 31 56 22 33]

​

$ celery -A proj beat

​

ax = plt.axes(projection='3d')

ax.plot_surface(X, Y, Z, rstride=1, cstride=1,

                cmap='viridis', edgecolor='none')

ax.set_title('surface');

​

>>> genexpr = ('Hello' for i in range(3))

>>> list(genexpr)

['Hello', 'Hello', 'Hello']

​

If you are a beginner python is the best because. Javascript has complex statements.

import datetime

datetime.datetime.timestamp(datetime.timedelta(hours=1, days=2, seconds=1000)).total_seconds()

for i in range(10000):

    doTaskA()

    doTaskB()

df = pandas.DataFrame({'A': [1, 3, 5], 'B': [7, 6, 2]})

df[A'] = df.apply(lambda x: 0 if x['A'] > x['B'] else x['A'], axis = 1)

import ctypes

import os

import platform

import sys

​

def get_free_space_mb(dirname):

    "Return folder/drive free space (in megabytes)."

    if platform.system() == 'Windows':

        free_bytes = ctypes.c_ulonglong(0)

        ctypes.windll.kernel32.GetDiskFreeSpaceExW(ctypes.c_wchar_p(dirname), None, None, ctypes.pointer(free_bytes))

        return free_bytes.value / 1024 / 1024

    else:

        st = os.statvfs(dirname)

        return st.f_bavail * st.f_frsize / 1024 / 1024

​

results.append=get_average(student)

del dic[key]

pip install django-filter

Set-ExecutionPolicy -ExecutionPolicy Unrestricted

​

def bit_length(self):

    s = bin(self)       # binary representation:  bin(-37) --> '-0b100101'

    s = s.lstrip('-0b') # remove leading zeros and minus sign

    return len(s)       # len('100101') --> 6

​

tkinter

​

df.groupby(['col1', 'col2']).size()

CREATE TABLE friends (

   id INTEGER,

   name TEXT,

   birthday DATE

);

​

INSERT INTO friends (id, name, birthday) 

VALUES (1, 'Jane Doe', '1990-05-30');

​

​

INSERT INTO friends(id , name , birthday)

VALUES(2 , 'APOORV' , '2000-2-2');

​

​

​

UPDATE friends

SET name = 'Jane Srivastava'

WHERE id = 1;

​

ALTER TABLE friends 

ADD COLUMN email TEXT; 

​

​

UPDATE friends 

SET email =  '203029@klsafjls'

where id  = 1; 

​

UPDATE friends 

SET email =  '203029@klsafjls'

where id  = 2;

​

DELETE FROM friends 

WHERE id = 1;

​

​

SELECT * 

FROM friends;

scala> val regex =  ([0-9\\-TZ\\.:]+) (\\{.*)

regex: String = ([0-9\-TZ\.:]+) (\{.*)

​

scala> val dff = df.withColumn(tstamp, regexp_extract('json_content, regex, 1)).withColumn(json, regexp_extract('json_content, regex, 2)).drop(json_content)

dff: org.apache.spark.sql.DataFrame = [country: string, city: string ... 2 more fields]

​

scala> dff.show(false)

+-------+-------+------------------------+----------------------------------+

|country|city   |tstamp                  |json                              |

+-------+-------+------------------------+----------------------------------+

|america|chicago|2019-06-28T00:00:00.000Z|{ a: 123, b: 456, c: 789 }|

|india  |mumbai |2019-06-28T00:00:00.000Z|{ a: 123, b: 456, c: 789 }|

+-------+-------+------------------------+----------------------------------+

​

import pandas as pd

df = pd.DataFrame({pear: [1,2,3], apple: [2,3,4], orange: [3,4,5]})

​

len(df.columns)

3

The magic is real

32b5e15f473afb17beafdc3f0ba847cbfdb2b7de37763fb50b147bc8e6c93450

df['Weekday'] = df['Date'].dt.day_name()

# Import pandas library

import pandas as pd

# initialize list of lists

data = [['tom', 10], ['nick', 15], ['juli', 14]]

# Create the pandas DataFrame

df = pd.DataFrame(data, columns = ['Name', 'Age'])

# print dataframe.

df

​

this doesen't work ( especially for me )

from typing import List

Vector = List[float]

​

def scale(scalar: float, vector: Vector) -> Vector:

    return [scalar * num for num in vector]

​

# typechecks; a list of floats qualifies as a Vector.

new_vector = scale(2.0, [1.0, -4.2, 5.4])

​

import matplotlib.pyplot as mp, numpy as np

# Primitive

origin = lambda x: 2 * x - x ** 2

x = np.linspace(0, 2, 9999)

mp.plot(x, origin(x), c='black')  # Visualization

# Derivative of the original function

derivative = lambda x: 2 - 2 * x

# Gradient rising demand

extreme_point = 0  # Initial value

alpha = 0.1  # Step, that is the learning rate

presision = 0.001  # Range of tolerance

while True:

    mp.scatter(extreme_point, origin(extreme_point))  # Visualization

    error = alpha * derivative(extreme_point)  # Climbing pace

    extreme_point += error  Climbing #

    if abs(error) < presision:

        break  # Exit iterative error is small

mp.show()123456789101112131415161718

# For macs... 

#  Apparently you don't want to upgrade 2.* just install 3.*

​

% brew install python3

import discord

​

class MyClient(discord.Client):

    async def on_ready(self):

        print('Logged on as', self.user)

​

    async def on_message(self, message):

        # don't respond to ourselves

        if message.author == self.user:

            return

​

        if message.content == 'ping':

            await message.channel.send('pong')

​

client = MyClient()

client.run('token')

​

params = {'param0': 'param0', 'param1': 'param1'}

arr = np.random.rand(10, 10)

data = {'params': params, 'arr': arr.tostring()}

response = requests.post(url, json=data)

​

i = 1

while i < 100:

  print(i)

  i += 1

author = models.ForeignKey(auth.User, limit_choices_to={'groups__name': bloggers})

​

# pip install table2ascii (https://pypi.org/project/table2ascii)

from table2ascii import table2ascii

​

output = table2ascii(

    header=["#", "G", "H", "R", "S"],

    body=[["1", "30", "40", "35", "30"], ["2", "30", "40", "35", "30"]],

    footer=["SUM", "130", "140", "135", "130"],

)

​

print(output)

​

"""

╔═════════════════════════════╗

║  #     G     H     R     S  ║

╟─────────────────────────────╢

║  1    30    40    35    30  ║

║  2    30    40    35    30  ║

╟─────────────────────────────╢

║ SUM   130   140   135   130 ║

╚═════════════════════════════╝

"""

def download_file(url):

    local_filename = url.split('/')[-1]

    # NOTE the stream=True parameter below

    with requests.get(url, stream=True) as r:

        r.raise_for_status()

        with open(local_filename, 'wb') as f:

            for chunk in r.iter_content(chunk_size=8192): 

                # If you have chunk encoded response uncomment if

                # and set chunk_size parameter to None.

                #if chunk: 

                f.write(chunk)

    return local_filename

df.rename(columns={'old_name': 'new_name'}, inplace=True)

# + is an overloaded operater, meaning it has more than one meaning

firstStr = "This is an "

secondStr = "example."

# + can join two strings end to end 

example = firstStr + secondStr

print(example)

# Output -> This is an example. 

​

#other things you can do

other1 = firstStr + "apple"

other2 = "Stri" + "ng"

idx = 0

new_col = [7, 8, 9]  # can be a list, a Series, an array or a scalar   

df.insert(loc=idx, column='Col_name', value=new_col)

# 1. Download and install java

# 2. Install python library 'tabular-py' using pip

pip install tabula-py

# If this is the first time installing java and tabula-py 

# add your Java installation folder to the PATH variable.

# if you don't, this is the error message you'll get.

tabula.errors.JavaNotFoundError: `java` command is not found from this 

Python process.Please ensure Java is installed and PATH is set for `java`

# 3. Import and run the tabula function on the desire page on the pdf file.

import tabula

df = tabula.read_pdf('data.pdf', pages = 3, lattice = True)[1]

string = "[Message]"

string = string.replace("[", "")#Removes all [

string = string.replace("]", "")#Removes all ]

​

print(string)

Here is a list of the different modes of opening a file:

r

Opens a file for reading only. The file pointer is placed at the beginning of the file. This is the default mode.

​

rb

Opens a file for reading only in binary format. The file pointer is placed at the beginning of the file. This is the default mode.

​

r+

Opens a file for both reading and writing. The file pointer will be at the beginning of the file.

​

rb+

Opens a file for both reading and writing in binary format. The file pointer will be at the beginning of the file.

​

w

Opens a file for writing only. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing.

​

wb

Opens a file for writing only in binary format. Overwrites the file if the file exists. If the file does not exist, creates a new file for writing.

​

w+

Opens a file for both writing and reading. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.

​

wb+

Opens a file for both writing and reading in binary format. Overwrites the existing file if the file exists. If the file does not exist, creates a new file for reading and writing.

​

a

Opens a file for appending. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing.

​

ab

Opens a file for appending in binary format. The file pointer is at the end of the file if the file exists. That is, the file is in the append mode. If the file does not exist, it creates a new file for writing.

​

a+

Opens a file for both appending and reading. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.

​

ab+

Opens a file for both appending and reading in binary format. The file pointer is at the end of the file if the file exists. The file opens in the append mode. If the file does not exist, it creates a new file for reading and writing.

pip install translate

from django.db.models import Sum

​

ItemPrice.objects.aggregate(Sum('price'))

# returns {'price__sum': 1000} for example

​

dict(tuple(df.groupby('a')))

L = [{V:S001}, {V: S002}, {VI: S001}, {VI: S005}, {VII:S005}, {V:S009},{VIII:S007}]

print(Original List: ,L)

u_value = set( val for dic in L for val in dic.values())

print(Unique Values: ,u_value)

import datetime

print(Now:, datetime.datetime.now())

#PIL image

img2 = img.convert('L')

import urllib

from BeautifulSoup import *

​

URL = raw_input("Enter the URL:") #Enter main URL

link_line = int(raw_input("Enter position:")) - 1 #The position of link relative to first link

count = int(raw_input("Enter count:")) #The number of times to be repeated

​

while count >= 0:

    html = urllib.urlopen(URL).read()

    soup = BeautifulSoup(html)

    tags = soup('a')

    print URL

    URL = tags[link_line].get("href", None)

    count = count - 1

mylist = ['XYZ-78.txt', 'XYZ-8.txt', 'XYZ-18.txt'] 

print(sorted(mylist, key=lambda x: int(x.split(-)[-1].split(.)[0])))

isinstance(string, bytes)

for n in range(2,7000):

    if not is_prime(n):

        carmichael = True

        for a in range(1,n):

            if Mod(a,n)^n != Mod(a,n):

                carmichael = False

                break

        if carmichael:

            print n 

{k: v for k, v in points.items() if v[0] < 5 and v[1] < 5}

string[0].upper() + string[1:]

​

a_dict = {}

​

if a in a_dict:

​

  a_dict[a].append(kite)

​

print(a_dict)

​

​

for i in range(1,2,3): #you can change 1,2,3

  print(i)

language: python

deploy:

  provider: heroku

  buildpack: python

  api_key:

    secure: very_long_encrypted_token

  strategy: git

  app: glucosetracker-demo

  on:

    repo: jcalazan/glucose-tracker

  run:

    - python glucosetracker/manage.py syncdb --noinput --settings=settings.heroku

    - python glucosetracker/manage.py migrate --all --settings=settings.heroku

    - python glucosetracker/manage.py load_random_glucose_data jsmith --settings=settings.heroku

    - restart

script: coverage run --source=. glucosetracker/manage.py test -v 2

def sort_list(l):

    l=list(l)

    for i in l:

        if l[i]<=l[i+1]:

            return True

        else:

            return False

>>> x = torch.randn(2, 3)

>>> x

tensor([[ 0.6580, -1.0969, -0.4614],

        [-0.1034, -0.5790,  0.1497]])

>>> torch.cat((x, x, x), 0)

tensor([[ 0.6580, -1.0969, -0.4614],

        [-0.1034, -0.5790,  0.1497],

        [ 0.6580, -1.0969, -0.4614],

        [-0.1034, -0.5790,  0.1497],

        [ 0.6580, -1.0969, -0.4614],

        [-0.1034, -0.5790,  0.1497]])

>>> torch.cat((x, x, x), 1)

tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,

         -1.0969, -0.4614],

        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,

         -0.5790,  0.1497]])

​

# calculate mean

m = sum(results) / len(results)

​

# calculate variance using a list comprehension

var_res = sum((xi - m) ** 2 for xi in results) / len(results)

​

import matplotlib.pyplot as plt

fig = plt.figure(1) #identifies the figure 

plt.title("Y vs X", fontsize='16')  #title

plt.plot([1, 2, 3, 4], [6,2,8,4])   #plot the points

plt.xlabel("X",fontsize='13')   #adds a label in the x axis

plt.ylabel("Y",fontsize='13')   #adds a label in the y axis

plt.legend(('YvsX'),loc='best') #creates a legend to identify the plot

plt.savefig('Y_X.png')  #saves the figure in the present directory

plt.grid()  #shows a grid under the plot

plt.show()

>>> if len(a)==0:

    print (list is empty)

else:

    print (list is not empty)

s = Is This It? Nope, that's fine

​

'?' in s[:-1].replace('? ','')

Label().place(x=0, y=0)

pip install pandas

numpy. corrcoef (x, y=None, rowvar=True, bias=<no value>, ddof=<no ... The

from django.core.validators import MaxValueValidator, MinValueValidator

​

...

weight = models.FloatField(

    validators=[MinValueValidator(0.9), MaxValueValidator(58)],

)

​

import sys

from os.path import dirname, abspath

sys.path.insert(0, dirname(dirname(abspath(__file__))))

​

path1 = dirname(abspath(__file__))  # current folder

path2 = dirname(path1)  # parent folder

​

print(path1)

print(path2)

​

Will create a select box for this in the model form. Use can select from the

options.The first element in each tuple is the actual value to be set on the model, 

and the second element is the human-readable name. For example:

YEAR_IN_SCHOOL_CHOICES = [

        (FRESHMAN, 'Freshman'),

        (SOPHOMORE, 'Sophomore'),

        (JUNIOR, 'Junior'),

        (SENIOR, 'Senior'),

        (GRADUATE, 'Graduate'),

    ]

    year_in_school = models.CharField(

        max_length=2,

        choices=YEAR_IN_SCHOOL_CHOICES,

        default=FRESHMAN,

    )

from multiprocessing import Process

​

def f(name):

    print('hello', name)

​

if __name__ == '__main__':

    p = Process(target=f, args=('bob',))

    p.start()

    p.join()

# initialization

app = Flask(__name__)

app.config['SECRET_KEY'] = 'the quick brown fox jumps over the lazy   dog'

app.config['CORS_HEADERS'] = 'Content-Type'

​

cors = CORS(app, resources={r"/foo": {"origins": "http://localhost:port"}})

​

@app.route('/foo', methods=['POST'])

@cross_origin(origin='localhost',headers=['Content- Type','Authorization'])

def foo():

    return request.json['inputVar']

​

if __name__ == '__main__':

   app.run()

​

def tips_unfold(fn, seed):

  def fn_generator(val):

    while True: 

      val = fn(val[1])

      if val == False: break

      yield val[0]

  return [i for i in fn_generator([None, seed])]

​

f = lambda n: False if n > 50 else [-n, n + 10]

print(tips_unfold(f, 10))

​

# n integer to chech

# a = multipole

​

# find the near a-multipole to n

n + (a - n % a)

​

​

>>> int(3.1415)

3

@pytest.yield_fixture

def http_pretty_mock():

    httpretty.enable()

    yield

    httpretty.disable()

​

​

def test_write_file_from_datasource_failing(http_pretty_mock, tmpdir):

    tmpdir = str(tmpdir)

    # mock the connection

sns.lmplot(x, y, data=df, hue=hue)

ax = plt.gca()

ax.set_title(Graph (a))

​

#! /usr/bin/python3

import re

​

def makeNice(s):

    return re.subn('(#U[0-9a-f]{4})', lambda cp: chr(int(cp.groups()[0][2:],16)), s) [0]

​

a = '-#U2605-#U79c1-'

print(a, makeNice(a))

​

import datetime as DT

import io

import numpy as np

import pandas as pd

​

pd.options.mode.chained_assignment = 'warn'

​

content = '''     ssno        lname         fname    pos_title             ser  gender  dob 

0    23456789    PLILEY     JODY        BUDG ANAL             0560  F      031871 

1    987654321   NOEL       HEATHER     PRTG SRVCS SPECLST    1654  F      120852

2    234567891   SONJU      LAURIE      SUPVY CONTR SPECLST   1102  F      010999

3    345678912   MANNING    CYNTHIA     SOC SCNTST            0101  F      081692

4    456789123   NAUERTZ    ELIZABETH   OFF AUTOMATION ASST   0326  F      031387'''

​

df = pd.read_csv(io.StringIO(content), sep='\s{2,}')

df['dob'] = df['dob'].apply('{:06}'.format)

​

now = pd.Timestamp('now')

df['dob'] = pd.to_datetime(df['dob'], format='%m%d%y')    # 1

df['dob'] = df['dob'].where(df['dob'] < now, df['dob'] -  np.timedelta64(100, 'Y'))   # 2

df['age'] = (now - df['dob']).astype(')    # 3

print(df)

pd.read_csv(file_name, index_col=0)

# It's kinda hard to explain this just by code.

# So I'll provide a link to a pretty good explanation of it.

https://www.pythonforbeginners.com/super/working-python-super-function

SHUT UP TANAYOBRO STFU

# open both files 

with open('first.txt','r') as firstfile, open('second.txt','a') as secondfile: 

    # read content from first file 

    for line in firstfile: 

             # write content to second file 

             secondfile.write(line)

​

import numpy as np

​

x = np.array([1,2,4,3,5,1,4,2])#create an array...

​

x[n]#where n is any value from the size of the array 

    #e.g. x[1] is 2, x[5] is 1.

import asyncio, socket

​

async def handle_client(reader, writer):

    request = None

    while request != 'quit':

        request = (await reader.read(255)).decode('utf8')

        response = str(eval(request)) + '\n'

        writer.write(response.encode('utf8'))

        await writer.drain()

    writer.close()

​

async def run_server():

    server = await asyncio.start_server(handle_client, 'localhost', 15555)

    async with server:

        await server.serve_forever()

​

asyncio.run(run_server())

# importing pandas module  

import pandas as pd 

​

# new data frame with split value columns 

data["Team"]= data["Team"].str.split(" ", n = 1, expand = True) 

​

# df display 

data 

df.shape

df.info()

df.dtypes

des = df.describe()                        # Summary

des_num = df.describe(include=[np.number]) # Include only numerical columns

des_obj = df.describe(include=[np.object]) # Include only string columns

des_obj.transpose()

​

# Show list of variable / columns in DF

df.columns

​

# Count number of variables / columns in DF

len(df.columns)

​

# Count variable

df["myvar"].value_counts()

df.reset_index(inplace=True)

pd.read_csv(directory, heading = None)

from bs4 import BeautifulSoup

# works very well with Requests or urllib module 

​

# Quick Start

from bs4 import BeautifulSoup

soup = BeautifulSoup("
SomebadHTML")

print(soup.prettify())

#OUTPUT: 

<html>

 <body>

  <p>

   Some

   <b>

    bad

    <i>