IllusiveMilkman

-- Window Function examples
-- PostgreSQL conference South Africa 2018
-- By Willem Booysen 
-- Youtube: https://www.youtube.com/watch?v=blHEnrYwySE


-- Create database and templates for demo
DROP DATABASE IF EXISTS WindowFunctions;
CREATE DATABASE WindowFunctions;

rain-1

Ventsislav-Yordanov

numbers = [23, 57, 10, 10, 12, 35, 2, 74, 302, 10]
# Repalce the "None" values with your solutions
# Use NumPy to calculate the values for each variable
mean = None
median = None
standard_deivation = None
sorted_numbers = None
numbers_sum = None

# Tests

supriya-gdptl

pip show tensorflow
pip uninstall tensorflow

# Tensorflow 1.4 works only with Python 3.5
# to install downgrade Python 3.6 to Python 3.5 in Anaconda
conda install python=3.5
# now install tensorflow 1.4
sudo apt-get install python3-pip python3-dev
pip install tensorflow

dre4success

{"lastUpload":"2019-07-22T17:21:44.605Z","extensionVersion":"v3.4.1"}

chriddyp

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import pandas as pd

app = dash.Dash()

df = pd.read_csv(
    'https://gist.githubusercontent.com/chriddyp/'

simecek

mahermalaeb

import zipfile
from surprise import Reader, Dataset, SVD, evaluate

# Unzip ml-100k.zip
zipfile = zipfile.ZipFile('ml-100k.zip', 'r')
zipfile.extractall()
zipfile.close()

# Read data into an array of strings
with open('./ml-100k/u.data') as f:

garywu

#Mining YouTube using Python & performing social media analysis (on ALS ice bucket challenge)
#https://www.analyticsvidhya.com/blog/2014/09/mining-youtube-python-social-media-analysis/

#complete Python script to mine YouTube data. Just replace your key and keyword you want to search

from apiclient.discovery import build #pip install google-api-python-client
from apiclient.errors import HttpError #pip install google-api-python-client
from oauth2client.tools import argparser #pip install oauth2client
import pandas as pd #pip install pandas
import matplotlib as plt

gbersac

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os

def computeCost(X, y, theta):
    inner = np.power(((X * theta.T) - y), 2)
    return np.sum(inner) / (2 * len(X))

def gradientDescent(X, y, theta, alpha, iters):