pourmand1376

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Intro

I assembled a super quick cheatsheet of the most common Pandas, Numpy and Python tasks I tend to do. Let me know if I missed anything important in the comments below!

If you like this kernel, please give it an upvote. Thank you! :)

Table of Contents

1  Data Structures1.1  Lists[]1.1.1  List Comprehensions - (do.. for)

pourmand1376

#sudo wget https://github.com/shiftkey/desktop/releases/download/release-2.1.0-linux1/GitHubDesktop-linux-2.1.0-linux1.deb
#sudo gdebi GitHubDesktop-linux-2.1.0-linux1.deb

# UPDATE (2021-03-05): Thanks to PaoloRanzi81's comment, the updated code is as follows https://gist.github.com/PaoloRanzi81

sudo wget https://github.com/shiftkey/desktop/releases/download/release-2.6.3-linux1/GitHubDesktop-linux-2.6.3-linux1.deb
### Uncomment below line if you have not installed gdebi-core before
# sudo apt-get install gdebi-core 
sudo gdebi GitHubDesktop-linux-2.6.3-linux1.deb

pourmand1376

import numpy as np
import matplotlib.pyplot as plt
from skimage import color

def fft(length_x,width_y,v,y,image,inner=True):
    expVector=np.exp(-2j*np.pi/width_y*(v@y)) if inner else np.exp(-2j*np.pi/length_x*(v@y))
    if inner:
        return image @ expVector
    else:
        return expVector @ image

pourmand1376

import math
import random

def calculateCost(x,y):
    return -0.0001*((
        math.fabs(
            math.sin(x)*math.sin(y)*math.exp(
                math.fabs(
                    100 - math.sqrt(x**2+y**2)/math.pi

pourmand1376

import math
import random
import numpy


def calculateCost(x,y):
    return -0.0001*((
        math.fabs(
            math.sin(x)*math.sin(y)*math.exp(
                math.fabs(

pourmand1376

DECLARE @Name nvarchar(1000);
DECLARE @Sql nvarchar(1000);
DECLARE @Result int;

IF OBJECT_ID('tempdb..#myTable') IS NOT NULL
DROP TABLE #myTable

CREATE TABLE #myTable(
row INT,
query NVARCHAR(500))