qiuyujx

import Foundation


func test(completion: (Int) -> ()) {
    let queue = dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0)
    //let queue = dispatch_queue_create("serial queue", DISPATCH_QUEUE_SERIAL)
    
    var counter = 0
    let grp = dispatch_group_create()
    

qiuyujx

Result: 1
Items {
  TemplateId: "BADGE_BATTLE_ATTACK_WON"
  Badge {
    BadgeType: BADGE_BATTLE_ATTACK_WON
    BadgeRanks: 4
    Targets: "\nd\350\007"
  }
}
Items {

qiuyujx

var debug = process.env.NODE_ENV !== "production";
var webpack = require('webpack');

module.exports = {
  context: __dirname,
  devtool: debug ? "inline-sourcemap" : null,
  entry: "./js/scripts.js",
  output: {
    path: __dirname + "/js",
    filename: "scripts.min.js"

qiuyujx

import logging

import azure.functions as func

import snowflake.connector


def get_connection():
    return snowflake.connector.connect(
        user='<your_user>',

qiuyujx

import pandas as pd
import numpy as np
import re
import random

# Prepare testing dataset
tags = np.array(['tag'+str(i) for i in np.random.randint(10, size=200)])  # randomly generate tag list
vals = np.random.randint(20, size=200)  # generate a list of random integers

raw_df = pd.DataFrame({

qiuyujx

def missing_pct(df):
    # Calculate percentage of missing for each column
    s_missing = df.isnull().sum() * 100 / df.shape[0]
    # Convert the series back to data frame
    df_missing = pd.DataFrame(s_missing).round(2)
    # Reset and rename the index
    df_missing = df_missing.reset_index().rename(
                    columns={
                            'index':'Column',
                            0:'Missing_Percentage (%)'

qiuyujx

def find_max_in_group(df, group_col, val_col, tie_for_first=False):
    # Decide ranking method
    if tie_for_first:
        rank_method = 'min'
    else:
        rank_method = 'first'
    # Add rank number for each group
    df["rank"] = df.groupby(group_col)[val_col].rank(method=rank_method, ascending=False)
    # Only return rank == 1
    return df[df['rank'] == 1].drop(['rank'], axis=1)

qiuyujx

def expand_collection_type_column(df, target_col, col_names=None):
    # Expand the list into multiple columns
    df_expand = df[target_col].apply(pd.Series)
    # Concatenate the expanded data frame with the original one
    df = pd.concat([df, df_expand], axis=1).drop(['values'], axis=1)
    # Rename columns if passed in
    if col_names:
        df = df.rename(columns={index:name for index, name in enumerate(col_names)})
    return df

qiuyujx

# Original Function
f = (lambda x: x((lambda x: x(lambda x: x))(x)))(lambda x: x)

# Reduced confusion
f = (
        lambda a: a(
            (
                lambda b: b(lambda c: c)
            )(a)
        )

qiuyujx

def max_vote_recursive(states, days_left, index):
    # Terminating conditions
    if len(states) == 0 or index >= len(states) or days_left <= 0:
        return 0

    # If we have enough days, go to this state
    votes_if_go = 0
    if states[index]['days'] <= days_left:
        votes_if_go = states[index]['votes'] + max_vote_recursive(states, days_left - states[index]['days'], index + 1)