Pandas - days since date (time since event)

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Thu Nov 03 2022 04:23:59 GMT+0000 (Coordinated Universal Time)

Saved by @janduplessis883 #pandas #datetime

df['TIMESINCE'] = (pd.Timestamp.today() - df['REGDATE']).dt.days
df['YEARSSINCE'] = df['timesince'] / 365
df

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#apex #datetime

datetime converion of timezone in apex

Integer year = 2021, mon=02, day=12, hour=23, min=50;

Datetime dtGMT = Datetime.newInstanceGMT(year, mon, day, hour, min, 0);

String exTimezone = 'America/Los_Angeles';
String dateTimeForamat = 'yyyy-MM-dd HH:mm:ssz';
String schedulerFormat = 'mm:HH:dd:MM:yyyy';
String crnTemplate = '0 {0} {1} {2} {3} ? {4}';

String localFromatted = dtGMT.format(dateTimeForamat, UserInfo.getTimeZone().getId());
String exFormatted = dtGMT.format(dateTimeForamat, exTimezone);

Datetime localDTValueGMT = Datetime.valueOfGMT(localFromatted);
Datetime exDTVaueGMT = Datetime.valueOfGMT(exFormatted);

Integer minDiff = (Integer)(localDTValueGMT.getTime() - exDTVaueGMT.getTime())/60000;

Datetime scheduleDTGMT = dtGMT.addMinutes(minDiff + 15);

String schFormmated = scheduleDTGMT.formatGMT(schedulerFormat);

String cronExp = String.format(crnTemplate, schFormmated.split(':'));

System.debug('dtGMT : ' + JSON.serialize(dtGMT));
System.debug('localDTValueGMT : ' + JSON.serialize(localDTValueGMT));
System.debug('exDTVaueGMT : ' + JSON.serialize(exDTVaueGMT));
System.debug('scheduleDTGMT : ' + JSON.serialize(scheduleDTGMT));
System.debug('cronExp: ' + cronExp);

#python #datetime #pandas

Convert Excel Days Date to real date Pandas

df['SettlementDate'] = pd.TimedeltaIndex(df['SettlementDate'], unit='d') + dt.datetime(1900,1,1)

#python #pandas #data-cleaning

Forward fills Column names

def ffill_cols(df, cols_to_fill_name='Unn'):
    """
    Forward fills column names. Propagate last valid column name forward to next invalid column. Works similarly to pandas
    ffill().
    
    :param df: pandas Dataframe; Dataframe
    :param cols_to_fill_name: str; The name of the columns you would like forward filled. Default is 'Unn' as
    the default name pandas gives unnamed columns is 'Unnamed'
    
    :returns: list; List of new column names
    """
    cols = df.columns.to_list()
    for i, j in enumerate(cols):
        if j.startswith(cols_to_fill_name):
            cols[i] = cols[i-1]
    return cols

#python #datetime

get the current time

from time import gmtime, strftime

 strftime("%Y-%m-%d %H:%M:%S", gmtime())
'2009-01-05 22:14:39'

#python #datetime

now

now = datetime.datetime.now()
request_dict["time_now"] =  now.strftime("%Y-%m-%d %H:%M:%S")

#ios #swift #date #time #datetime

Convert timestamp to Date or Time or DateTime Both

    func getDateTime(_ timeStamp : String,_ format: String) -> String {
        var t1 = Int(timeStamp) ?? 1
         t1 = t1/1000
        let date = NSDate(timeIntervalSince1970: TimeInterval(t1))
        let dateFormatter = DateFormatter()
        dateFormatter.dateFormat = format
        let dateString = dateFormatter.string(from: date as Date)
        print("Date = \(dateString)")
        return dateString
    }

#python #datetime #cryptoprice

real time price of crypto with datetime on a loop

import requests
from datetime import datetime

URL = "https://min-api.cryptocompare.com/data/price?fsym={}&tsyms={}"

def get_price(coin, currency):
    try:
        response = requests.get(URL.format(coin, currency)).json()
        return response
    except:
        return False

while True:
    date_time = datetime.now()
    date_time = date_time.strftime("%d/%m/%Y %H:%M:%S")
    currentPrice = get_price("BTC", "USD")
    if currentPrice:
        print(date_time, "$", currentPrice["USD"])

#python #pandas #dataset #eda #extractyear #datetime

Extract the year from a date data In pandas

df['year'] = df['date'].dt.year if we put dt.month is for month etc..
df.head()

#pandas #datetime

Pandas - days since date (time since event)

df['TIMESINCE'] = (pd.Timestamp.today() - df['REGDATE']).dt.days
df['YEARSSINCE'] = df['timesince'] / 365
df

##python #coding #statistics #fake #generate #date #datetime #data

How to generate fake user statistics using Python

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)

#python #pandas #grouper

Pandas Grouper

df['column_name'] = pd.to_datetime(df['column_name'])
# new version
df.groupby(pd.Grouper(key='column_name', freq="M")).mean().plot()

#python #pandas

How to reorder columns in a Pandas DataFrame in Python

df = df[["C", "A", "B"]]

#pandas #duplicates #drop

Pandas.drop_duplicates

df.drop_duplicates()

#python #pandas #formatting

Format a float as currency in Python

'${:,.2f}'.format(dfCombined['Amount'].sum())

#python #pandas #format #currency

Style pandas value to currency

(df.groupby('name')['ext price']
 .agg(['mean', 'sum'])
 .style.format('${0:,.2f}'))

Pandas - days since date (time since event)

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