Snippets Collections
# Analyzing Clicks A Python Function for Extracting Weekly Stats

users_data = [
    {'day': 0, 'clicks': 100}, {'day': 1, 'clicks': 10},
    {'day': 2, 'clicks': 150}, {'day': 3, 'clicks': 1000},
    {'day': 4, 'clicks': 100}, {'day': 5, 'clicks': 190},
    {'day': 6, 'clicks': 150}, {'day': 7, 'clicks': 1000}
]

def get_stats(day):
    # -1 to start from day 0 because day in date is 1
    return users_data[day - 1: day + 7]

print(get_stats(1))
from random import randint
from faker import Faker
from datetime import date
import pandas as pd

f = Faker()

s_date = date(2018, 5, 1)
e_date = date(2018, 5, 30)

dict_data = {'date': [], 'email': [], 'money': []}

for _date in pd.date_range(start = s_date, end = e_date):
    dict_data['date'].append(_date)
    dict_data['email'].append(f.email())
    dict_data['money'].append(randint(1, 100) * 0.99)

df = pd.DataFrame.from_dict(dict_data)
df.to_csv('out.csv', index = 0)
from datetime import date
from pandas import date_range
from uuid import uuid4
from random import randint


s_date = date(2019, 1, 1)
e_date = date(2019, 1, 30)

stats = {}

for d in date_range(start=s_date, end=e_date):
    d = str(d.date())
    stats[d] = {
        'user_id': str(uuid4()),
        'clicks': randint(0, 1000)
    }


print(stats)
curl --request POST \
  --url 'https://api.apyhub.com/convert/rss-file/json?detailed=true' \
  --header 'apy-token: APY0BOODK2plpXgxRjezmBOXqID51DGpFq8QnHJeBQrrzuIBc25UIglN93bbwvnkBWlUia1' \
  --header 'content-type: multipart/form-data' \
  --form 'file=@"test.xml"'
curl --request POST \
  --url 'https://api.apyhub.com/convert/word-file/pdf-file?output=test-sample.pdf&landscape=false' \
  --header 'apy-token: APY0BOODK2plpXgxRjezmBOXqID51DGpFq8QnHJeBQrrzuIBc25UIglN93bbwvnkBWlUia1' \
  --header 'content-type: multipart/form-data' \
  --form 'file=@"test.doc"'
curl --request POST \
  --url 'https://api.apyhub.com/generate/charts/bar/file?output=sample.png' \
  --header 'Content-Type: application/json' \
  --header 'apy-token: APY0BOODK2plpXgxRjezmBOXqID51DGpFq8QnHJeBQrrzuIBc25UIglN93bbwvnkBWlUia1' \
  --data '{
    "title":"Simple Bar Chart",
    "theme":"Light",
    "data":[
        {
            "value":10,
            "label":"label a"
        },
        {
            "value":20,
            "label":"label b"
        },
        {
            "value":80,
            "label":"label c"
        },
        {
            "value":50,
            "label":"label d"
        },
        {
            "value":70,
            "label":"label e"
        },
        {
            "value":25,
            "label":"label f"
        },
        {
            "value":60,
            "label":"label g"
        }
    ]
}'
curl --request POST \
  --url 'https://api.apyhub.com/generate/charts/bar/file?output=sample.png' \
  --header 'Content-Type: application/json' \
  --header 'apy-token: APY0BOODK2plpXgxRjezmBOXqID51DGpFq8QnHJeBQrrzuIBc25UIglN93bbwvnkBWlUia1' \
  --data '{
    "title":"Simple Bar Chart",
    "theme":"Light",
    "data":[
        {
            "value":10,
            "label":"label a"
        },
        {
            "value":20,
            "label":"label b"
        },
        {
            "value":80,
            "label":"label c"
        },
        {
            "value":50,
            "label":"label d"
        },
        {
            "value":70,
            "label":"label e"
        },
        {
            "value":25,
            "label":"label f"
        },
        {
            "value":60,
            "label":"label g"
        }
    ]
}'
curl --request POST \
  --url 'https://api.apyhub.com/generate/qr-code/file?output=sample.png' \
  --header 'Content-Type: application/json' \
  --header 'apy-token: APY0BOODK2plpXgxRjezmBOXqID51DGpFq8QnHJeBQrrzuIBc25UIglN93bbwvnkBWlUia1' \
  --data '{
    "content": "https://apyhub.com",
    "logo": "https://apyhub.com/logo.svg",
    "background_color": "#000000",
    "foreground_color": ["#e8bf2a", "#e8732a"]
}'
// file data.json

{
"localItems": {
	"items": [
      {
		"title": "Halabut",
		"type": "string",
        "status": false
		},
			{
        "title": "Taco",
		"type": "string",
        "status": true
		},
      {
        "title": "Fish",
		"type": "string",
        "status": true
		},
      {
        "title": "Pork",
		"type": "string",
        "status": false
		}
		]
	}
}




//js file must be a module
// <script src="scripts.js" defer type="module"></script>

// JS file 
import data from "./data.json" assert { type: "json" };
const {localItems} = data;
const JSONDATA = localItems['items'];

const waitForSiblingElementToBeRemoved = (form) => {
    // This Element are where any errors would appear
    const input = form.querySelector(`input[type='submit']`);

    // This observer looks for the sibling of the input submit button that appears 
    // and then removes when the request is complete
    new MutationObserver((entries, observer) => {
      log('entries[0].removeNodes: ', entries[0].removedNodes);
      if (!entries[0].removedNodes[0]?.classList.contains('ajax-progress')) return; 
      observer.disconnect(); // should this be moved after sendEvent 
      window.setTimeout(() => {
        if (input.classList.contains('error')) return; 
        log('Email has been submitted');
        sendEvent('pjs_email_submitted');
      }, 0);
    }).observe(input.parentElement, { childList: true, subtree: true }); // should be actually have two separate observers for each input?
  };
window.optimizely.get('visitor')

// example of getting specific pjs segment (from tb440)
window.optimizely.get('visitor').custom['22521880007']
import logging

logging.basicConfig(filename='codelogging.log', level=logging.DEBUG,
                format='%(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')

a_variable = 200

# for loop with logging
for i in range(100):
    x = print(i)
    logging.info('Iteriating')

logging.info('Script completed')
import dtale
from pandas_profiling import ProfileReport
from dataprep.eda import create_report

class AutomateEDA:
    
    def __init__(self, df):
        self.df = df
     
    def show_dtale(self):
        d = dtale.show(self.df)
        d.open_browser()
        print('dtale opened in browser!')
        
    def show_pandas_profile_report(self):
        profile = ProfileReport(self.df, title="Pandas Profiling Report", explorative=True)
        profile.to_file("pandas-profiling-report.html")
        print('pandas-profile-report created and saved in the project folder!')
        
    def show_dataprep(self):
        create_report(self.df).show_browser()
        print('dataprep opened in browser!')
        
    def show_all(self):
        self.show_dtale()
        self.show_dataprep()
        self.show_pandas_profile_report();


eda = AutomateEDA(df)
eda.show_all()
import pandas as pd
data = pd.read_csv('../input/udemy-courses/udemy_courses.csv')

x = pd.Series([int(i[:4]) for i in data.published_timestamp])
xx = pd.DataFrame(x,columns=['published_year'])
z = pd.Series([int(i[5:7]) for i in data.published_timestamp])
zz = pd.DataFrame(z,columns=['published_day'])

data = pd.concat([data,xx,zz],axis=1)
data
   let User = Auth.auth().currentUser
        if let user = User{
            let db = Firestore.firestore()
            db.collection("Appointments").whereField("patient_id", isEqualTo: user.uid).getDocuments { [self] (document, error) in
                guard let data = document?.documents else {
                    return
                }
                      for d in data{
                    self.chatStatus = d.get("chat_status") as? String ?? ""
                    self.startTime = d.get("start_time") as? String ?? ""
                }
                      //updating data
                    let document = document!.documents.first
                    document?.reference.updateData([
                                   "chat_status": "Started"
                               ])
@IBAction func saveChangesPressed(_ sender: UIButton) {
        
        let user = Auth.auth().currentUser
        if let user = user {
            let db = Firestore.firestore()
            let docRef = db.collection("Users").document(user.uid)
            docRef.updateData(["Name": self.nameField.text ?? "User","Email": self.emailField.text ?? "a@gmail.com", "MobileNumber": self.phoneNumberField.text ?? "123"])
        }
    }
func getDataFromFireStore() {
        print("getting data")
        let activityView = activityIndicatorView()
        activityView.startAnimating()
        let db = Firestore.firestore()
        db.collection("Activities").getDocuments() { (querySnapshot, error) in
            if error != nil {
                print("Error getting documents: \(error!)")
            } else {
                self.activitiesArray.removeAll()
                for document in querySnapshot!.documents {
                    print("for loop")
                    let activities = ActivitiesModel()
                    let data = document.data()
                    activities.title = data["title"] as! String
                    activities.description = data["description"] as! String
                    activities.image_url = data["image_url"] as! String
                    self.activitiesArray.append(activities)
                }
                activityView.stopAnimating()
                self.activitiesTableView.reloadData()
            }
        }
        
    }
def log_scale(x):
    C = 1 / np.log(10)
    return np.sign(x) * np.log10(1 + np.abs(x / C))
df['Purchase'] = df['Purchase'].apply(lambda x: 1 if x=='Yes' else 0)
df2.loc[["one",'three'],['pop','state']]
df2.loc["one":'three'],['pop','state']]
df2.loc[:'three',['pop','state']]
df.apply(lambda x: x == ' ', axis = 1).mean()
df.loc[df.TotalCharges == ' ', :]
df.loc[df.TotalCharges == ' ', 'TotalCharges'] = 0
def detect_outliers(df,n,features): 
    outlier_indices = []
    for col in features:
        Q1 = np.percentile(df[col],25)
        Q3 = np.percentile(df[col],75)
        IQR = Q3 - Q1
        outlier_step = 1.5 * IQR 
        outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
        outlier_indices.extend(outlier_list_col)

    outlier_indices = Counter(outlier_indices)
    multiple_outliers = list(k for k, v in outlier_indices.items() if v>n)
    return multiple_outliers

Outliers_to_drop = detect_outliers(data1,2,['Age','Parch','Fare','SibSp'])
data1.iloc[Outliers_to_drop]
import tkinter as tk
from tkinter import ttk

root = tk.Tk()

# Pack a big frame so, it behaves like the window background
big_frame = ttk.Frame(root)
big_frame.pack(fill="both", expand=True)

# Set the initial theme
root.tk.call("source", "sun-valley.tcl")
root.tk.call("set_theme", "light")

def change_theme():
    # NOTE: The theme's real name is sun-valley-<mode>
    if root.tk.call("ttk::style", "theme", "use") == "sun-valley-dark":
        # Set light theme
        root.tk.call("set_theme", "light")
    else:
        # Set dark theme
        root.tk.call("set_theme", "dark")

# Remember, you have to use ttk widgets
button = ttk.Button(big_frame, text="Change theme!", command=change_theme)
button.pack()

root.mainloop()
from google.colab import drive
drive.mount('/content/drive/')
import sys
sys.path.insert(0,'/content/drive/MyDrive/')
train_df = pd.read_csv("/content/drive/MyDrive/train.csv")
var endExperiment = function() {

  prompt_resubmit = function() {
    replaceBody(error_message);
    $("#resubmit").click(resubmit);
  };

  resubmit = function() {
    replaceBody("<h1>Trying to resubmit...</h1>");
    reprompt = setTimeout(prompt_resubmit, 10000);
    
    psiTurk.saveData({
      success: function() {
          clearInterval(reprompt); 
      }, 
      error: prompt_resubmit
    });
  };
  // Load the debriefing page 
  psiTurk.showPage('debriefing.html');

  //code for bonus??
  $("#next").click(function () {
      record_responses();
      psiTurk.saveData({
            success: function(){
                psiTurk.computeBonus('compute_bonus', function() { 
                  psiTurk.completeHIT(); // when finished saving compute bonus, the quit
                }); 
            }, 
            error: prompt_resubmit});
  });
#importing Autoviz class
from autoviz.AutoViz_Class import AutoViz_Class#Instantiate the AutoViz class
AV = AutoViz_Class()

df = AV.AutoViz('car_design.csv')
# Discretization
df3["Total_Amt_Chng_Q4_Q1_qcut"]=pd.qcut(df3["Total_Amt_Chng_Q4_Q1"],4)
df3["Total_Trans_Amt_qcut"]=pd.qcut(df3["Total_Trans_Amt"],4)
df3["Total_Ct_Chng_Q4_Q1_qcut"]=pd.qcut(df3["Total_Ct_Chng_Q4_Q1"],4)
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#api #data
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#api #data
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#api #data
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#api #data
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Sun Jul 23 2023 00:55:56 GMT+0000 (Coordinated Universal Time)

#javascript #module #json #data #import
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#visitor #data #info #optimizely
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#optimizely #data
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#graphs #data #jupyter_notebooks
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#ios #swift #getandupdate #data #getandupdatedata
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#ios #swift #update #updatedata #data
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#getdata #data #firestore #getdatafrom firestore
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#python #scaling #data
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#data
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#data
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#data
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#data
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#python #data #visualisation #graphs #plots #charts #dashboards
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#colab #drive #data
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#data #javascript #jspsych
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#js #jspsych #data #saving
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##js #js #javascript #psiturk #data
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#python #data #visualisation #graphs #plots #charts #dashboards
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#python #data #visualisation #graphs #plots #charts #dashboards
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#numerical #data #discretization #datamining

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