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Time Series Analysis & Visualization in Python
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Basic DateTime Operations in Python

Last Updated : 20 Oct, 2021
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Python has an in-built module named DateTime to deal with dates and times in numerous ways. In this article, we are going to see basic DateTime operations in Python.

There are six main object classes with their respective components in the datetime module mentioned below:

  1. datetime.date
  2. datetime.time
  3. datetime.datetime
  4. datetime.tzinfo
  5. datetime.timedelta
  6. datetime.timezone

Now we will see the program for each of the functions under datetime module mentioned above. 

datetime.date():

We can generate date objects from the date class. A date object represents a date having a year, month, and day.

Syntax:datetime.date( year, month, day)

strftime to print day, month, and year in various formats. Here are some of them are: 

  • current.strftime(“%m/%d/%y”) that prints in month(Numeric)/date/year format
  • current.strftime(“%b-%d-%Y”) that prints in month(abbreviation)-date-year format
  • current.strftime(“%d/%m/%Y”) that prints in date/month/year format
  • current.strftime(“%B %d, %Y”) that prints in month(words) date, year format

Python3

from datetime import date
 
# You can create a date object containing
# the current date
# by using a classmethod named today()
current = date.today()
 
# print current year, month, and year individually
print("Current Day is :", current.day)
print("Current Month is :", current.month)
print("Current Year is :", current.year)
 
# strftime() creates string representing date in
# various formats
print("\n")
print("Let's print date, month and year in different-different ways")
format1 = current.strftime("%m/%d/%y")
 
# prints in month/date/year format
print("format1 =", format1)
     
format2 =  current.strftime("%b-%d-%Y")
# prints in month(abbreviation)-date-year format
print("format2 =", format2)
 
format3 = current.strftime("%d/%m/%Y")
 
# prints in date/month/year format
print("format3 =", format3)
     
format4 =  current.strftime("%B %d, %Y")
 
# prints in month(words) date, year format
print("format4 =", format4)
                      
                       

Output:

Current Day is : 23 Current Month is : 3 Current Year is : 2021   Let's print date, month and year in different-different ways format1 = 03/23/21 format2 = Mar-23-2021 format3 = 23/03/2021 format4 = March 23, 2021

datetime.time():

 A time object generated from the time class represents the local time.

Components: 

  • hour
  • minute
  • second
  • microsecond
  • tzinfo

Syntax: datetime.time(hour, minute, second, microsecond)

Code:

Python3

from datetime import time
 
# time() takes hour, minutes, second,
# microsecond respectively in order
# if no parameter is passed in time() by default
# it takes 0
defaultTime = time()
 
print("default_hour =", defaultTime.hour)
print("default_minute =", defaultTime.minute)
print("default_second =", defaultTime.second)
print("default_microsecond =", defaultTime.microsecond)
 
# passing parameter in different-different ways
# hour, minute and second respectively is a default
# order
time1= time(10, 5, 25)
print("time_1 =", time1)
 
# assigning hour, minute and second to respective
# variables
time2= time(hour = 10, minute = 5, second = 25)
print("time_2 =", time2)
 
# assigning hour, minute, second and microsecond to
# respective variables
time3= time(hour=10, minute= 5, second=25, microsecond=55)
print("time_3 =", time3)
                      
                       

Output:

default_hour = 0 default_minute = 0 default_second = 0 default_microsecond = 0 time_1 = 10:05:25 time_2 = 10:05:25 time_3 = 10:05:25.000055

datetime.datetime():

datetime.datetime() module shows the combination of a date and a time. 

Components: 

  • year
  • month
  • day
  • hour
  • minute
  • second,
  • microsecond
  • tzinfo

Syntax: datetime.datetime( year, month, day )

                           or

datetime.datetime(year, month, day, hour, minute, second, microsecond)

Current date and time using the strftime() method in different ways:

  • strftime(“%d”) gives current day
  • strftime(“%m”) gives current month
  • strftime(“%Y”) gives current year
  • strftime(“%H:%M:%S”) gives current time in an hour, minute, and second format
  • strftime(“%m/%d/%Y, %H:%M:%S”) gives date and time together

Code:

Python3

from datetime import datetime
 
# now() gives current date and time
current = datetime.now()
 
# print combinedly
print(current)
print("\n")
print("print each term individually")
 
day = current.strftime("%d")
 
# print day
print("day:", day)
 
month = current.strftime("%m")
 
# print month
print("month:", month)
 
year = current.strftime("%Y")
 
# print year
print("year:", year)
 
time = current.strftime("%H:%M:%S")
 
# time in hour, minute and second
print("time:", time)
 
print("\n")
print("printing date and time together")
date_time = current.strftime("%m/%d/%Y, %H:%M:%S")
print("date and time:", date_time)
print("\n")
 
# fetching details from timestamp
timestamp = 1615797322
date_time = datetime.fromtimestamp(timestamp)
 
# %c, %x and %X are used for locale's proper date and time representation
time_1 = date_time.strftime("%c")
print("first_output:", time_1)
 
time_2 = date_time.strftime("%x")
print("second_output:", time_2)
 
time_3 = date_time.strftime("%X")
print("third_output:", time_3)
 
print("\n")
 
# assigning each term manually
manual = datetime(2021, 3, 28, 23, 55, 59, 342380)
print("year =", manual.year)
print("month =", manual.month)
print("hour =", manual.hour)
print("minute =", manual.minute)
print("timestamp =", manual.timestamp())
                      
                       

Output:

2021-03-23 19:00:20.726833   print each term individually day: 23 month: 03 year: 2021 time: 19:00:20   printing date and time together date and time: 03/23/2021, 19:00:20   first_output: Mon Mar 15 14:05:22 2021 second_output: 03/15/21 third_output: 14:05:22   year = 2021 month = 3 hour = 23 minute = 55 timestamp = 1616955959.34238

datetime.timedelta():

It shows a duration that expresses the difference between two date, time, or datetime instances to microsecond resolution.

Here we implemented some basic functions and printed past and future days. Also, we will print some other attributes of timedelta max, min, and resolution that show maximum days and time, minimum date and time, and the smallest possible difference between non-equal timedelta objects respectively. Here we will also apply some arithmetic operations on two different dates and times.

Python3

from datetime import timedelta, datetime
 
present_date_with_time = datetime.now()
 
print("Present Date :", present_date_with_time)
 
# coming date after 10 days
ten_days_after= present_date_with_time + timedelta(days = 10)
print('Date after 10 days :',ten_days_after)
 
# date before 10 days
ten_days_before= present_date_with_time - timedelta(days = 10)
print('Date before 10 days :',ten_days_before)
 
# date before one year ago
one_year_before_today= present_date_with_time + timedelta(days = 365)
print('One year before present Date :', one_year_before_today)
 
#date before one year ago
one_year_after_today= present_date_with_time - timedelta(days = 365)
print('One year before present Date :', one_year_after_today)
 
print("\n")
print("print some other attributes of timedelta\n")
 
# maximum days and time
print("Max : ",timedelta.max)
 
# minimum days and time
print("Min : ",timedelta.min)
 
# The smallest possible difference between non-equal
# timedelta objects, timedelta(microseconds=1)
print("Resolution: ",timedelta.resolution)
 
print('Total number of seconds in an year :',
      timedelta(days = 365).total_seconds())
 
print("\nApply some operations on timedelta function\n")
time_after_one_min = present_date_with_time + timedelta(seconds=10) * 6
print('Time after one minute :', time_after_one_min)
 
print('Timedelta absolute value :', abs(timedelta(days = +20)))
 
print('Timedelta string representation :', str(timedelta(days = 5,
                       seconds = 40, hours = 20, milliseconds = 355)))
 
print('Timedelta object representation :', repr(timedelta(days = 5,
                       seconds = 40, hours = 20, milliseconds = 355)))
                      
                       

Output:

Present Date : 2021-03-25 22:34:27.651128

Date after 10 days : 2021-04-04 22:34:27.651128

Date before 10 days : 2021-03-15 22:34:27.651128

One year before present Date : 2022-03-25 22:34:27.651128

One year before present Date : 2020-03-25 22:34:27.651128

print some other attributes of timedelta

Max :  999999999 days, 23:59:59.999999

Min :  -999999999 days, 0:00:00

Resolution:  0:00:00.000001

Total number of seconds in an year : 31536000.0

Apply some operations on timedelta function

Time after one minute : 2021-03-25 22:35:27.651128

Timedelta absolute value : 20 days, 0:00:00

Timedelta string representation : 5 days, 20:00:40.355000

Timedelta object representation : datetime.timedelta(days=5, seconds=72040, microseconds=355000)

datetime.tzinfo():

It is an abstract base class for time zone information objects. They are used by the datetime and time classes to provide a customizable notion of time adjustment. 

There are the following four methods available for tzinfo base class:

  • utcoffset(self, dt): returns the offset of the datetime instance passed as an argument
  • dst(self, dt): dst stands for Daylight Saving Time. dst denotes advancing the clock 1 hour in summer so that darkness falls later according to the clock.  It is set to on or off. It is checked on the basis of the following elements:

(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.weekday(), 0, 0)

  • tzname(self, dt): It returns a Python String object. It is used to find the time zone name of the datetime object passed.
  • fromutc(self, dt) : This function returns the equivalent local time and takes up the date and time of the object in UTC. It is mostly used to adjust the date and time. It is called from default datetime.astimezone() implementation. The dt.tzinfo will be passed as self, dst date and time data will be returned as an equivalent local time.

Note: It raises ValueError if dt.tzinfo is not self or/and dst() is None.

Python3

# code
from datetime import datetime, timedelta
from pytz import timezone
import pytz
 
time_zone = timezone('Asia/Calcutta')
 
normal = datetime(2021, 3, 16)
ambiguous = datetime(2021, 4, 16, 23, 30)
 
# is_dst parameter is ignored for most of the
# timstamps.It is only used during DST
# transition ambiguous periods to resolve that
# ambiguity
print("Operations on normal datetime")
print(time_zone.utcoffset(normal, is_dst=True))
print(time_zone.dst(normal, is_dst=True))
print(time_zone.tzname(normal, is_dst=True))
 
# put is_dst=False
print(time_zone.utcoffset(normal, is_dst=False))
print(time_zone.dst(normal, is_dst=False))
print(time_zone.tzname(normal, is_dst=False))
 
print("\n")
print("Operations on ambiguous datetime")
print(time_zone.utcoffset(ambiguous, is_dst=True))
print(time_zone.dst(ambiguous, is_dst=True))
print(time_zone.tzname(ambiguous, is_dst=True))
 
# is_dst=False
print(time_zone.utcoffset(ambiguous, is_dst=False))
print(time_zone.dst(ambiguous, is_dst=False))
print(time_zone.tzname(ambiguous, is_dst=False))
                      
                       

Output
Operations on normal datetime 5:30:00 0:00:00 IST 5:30:00 0:00:00 IST   Operations on ambiguous datetime 5:30:00 0:00:00 IST 5:30:00 0:00:00 IST

Output:

Operations on normal datetime 5:30:00 0:00:00 IST 5:30:00 0:00:00 IST   Operations on ambiguous datetime 5:30:00 0:00:00 IST 5:30:00 0:00:00 IST

datetime.timezone():

Description: It is a class that implements the tzinfo abstract base class as a fixed offset from the UTC.

Syntax: datetime.timezone()

Python3

from datetime import datetime, timedelta
from pytz import timezone
import pytz
 
utc = pytz.utc
print(utc.zone)
 
india = timezone('Asia/Calcutta')
print(india.zone)
 
eastern = timezone('US/Eastern')
print(eastern.zone)
 
time_format = '%Y-%m-%d %H:%M:%S %Z%z'
 
# localize() is used to localize
# datetime with no timezone information
loc_dt = india.localize(datetime(2021, 3, 16, 6, 0, 0))
loc_dt = india.localize(datetime(2021, 3, 16, 6, 0, 0))
print(loc_dt.strftime(time_format))
 
# another way of building a localized time is by converting
# an existing localized time
# using the standard astimezone() method
eastern_dt = loc_dt.astimezone(eastern)
print(eastern_dt.strftime(time_format))
 
print(datetime(2021, 3, 16, 12, 0, 0, tzinfo=pytz.utc).strftime(time_format))
 
# 10 minutes before
before_dt = loc_dt - timedelta(minutes=10)
print(before_dt.strftime(time_format))
print(india.normalize(before_dt).strftime(time_format))
 
# 20 mins later
after_dt = india.normalize(before_dt + timedelta(minutes=20))
print(after_dt.strftime(time_format))
                      
                       

Output:

UTC Asia/Calcutta US/Eastern 2021-03-16 06:00:00 IST+0530 2021-03-15 20:30:00 EDT-0400 2021-03-16 12:00:00 UTC+0000 2021-03-16 05:50:00 IST+0530 2021-03-16 05:50:00 IST+0530 2021-03-16 06:10:00 IST+0530

Let’s see different Functions with description under time module :-

    Function

                          Description

       time( )        Returns the time in floating point number in seconds                   
       ctime( )   Returns the current date and time
       sleep( )   Stops execution of a thread for the given duration       
       localtime( )      Returns the date and time in time.struct_time format      
      gmtime( )   Returns time.struct_time in UTC format
      mktime( )   Returns the seconds passed since epochs are output
      asctime( )   Returns a string representing the same

Now we will see the program and output for each of the above-mentioned functions in the table.

1: time( ) method: The time() method returns the time as a floating-point number expressed in seconds since the epoch, in UTC.

Syntax: time.time([ ])

NOTE: It does not have any parameter

Python3

# import time
import time
 
#prints total number of seconds passed since epoch
print(time.time())
                      
                       

Output:

1616692391.3081982

2: ctime( ) method 

ctime() method converts a time expressed in seconds since the epoch to a string representing local time. The current time as returned by time() is used If secs is not provided or None. This method is equivalent to asctime(localtime(secs)). Locale information is not used by ctime() method.

Syntax: time.ctime([ sec ])

Where sec passed as an argument is the number of seconds to be converted Into string representation.

Python3

import time
 
number_of_seconds=1625925769.9618232
 
# function takes seconds passed since epoch as an argument and returns
# a string representing local time
print(time.ctime(number_of_seconds))
                      
                       

Output
Sat Jul 10 14:02:49 2021

3: sleep( ) method

Python time method sleep() stops execution for the given number of seconds. The floating-point the number can be passed as an argument to get more precise sleep time.

Syntax: time.sleep([ sec ])

where sec passed as an argument is the number of seconds for which

the process is to be stopped.

Python3

import time
 
# prints GEEKSFORGEEKS immediately
print("GEEKSFORGEEKS")
 
time.sleep(1.23)
 
# prints GEEKSFORGEEKS after 1.23 seconds
# as it stops execution for that time interval
print("GEEKSFORGEEKS")
                      
                       

Output
GEEKSFORGEEKS GEEKSFORGEEKS

4: localtime( ) method

localtime() method converts number of seconds to local time. If secs is not provided or None, the current time as returned by time() is used. The dst flag is set to 1 when DST applies to the given time.

Syntax: time.localtime([ sec ])

Where sec passed as an argument is the number of seconds to be converted into struct_time representation.

Python3

import time
 
# returns a time.struct_time
# object with a named tuple interface
print(time.localtime())
                      
                       

Output

time.struct_time(tm_year=2021, tm_mon=3, tm_mday=30, tm_hour=8, tm_min=48, tm_sec=58, tm_wday=1, tm_yday=89, tm_isdst=0)

5: gmtime( ) method.

gmtime() method converts a time expressed in seconds since the Epoch to a struct_time in UTC in which the dst flag is always zero. If secs is not provided or None, the current time as returned by time() is used.

Syntax: time.gmtime([ sec ])

Where sec passed as an argument is the number of seconds to be converted into structure struct_time representation.

Python3

# code
import time
# returns a time.struct_time object with a named tuple interface
# If secs is not provided or None,
# the current time as returned by time() is used
print(time.gmtime())
                      
                       

Output:

time.struct_time(tm_year=2021, tm_mon=3, tm_mday=30, tm_hour=8, tm_min=49, tm_sec=18, tm_wday=1, tm_yday=89, tm_isdst=0)

6: mktime( ) method

It is the inverse function of localtime() method. It takes an argument as struct_time or full 9-tuple and it returns a floating-point number.  If the input value is not represented as a valid time, then either OverflowError or ValueError is raised.

Syntax: time.mktime([t])

Where t passed as an argument is a time.struct_time object or a tuple containing 9 elements corresponding to time.struct_time object

Python3

# code
import time
 
# method mktime() is the inverse function of localtime()
# Its argument is the struct_time or full 9-tuple and
# it returns a floating point number, for compatibility with time().
 
t = (2016, 2, 15, 10, 13, 38, 1, 48, 0)
d = time.mktime(t)
print ("time.mktime(t) : %f" %  d)
print ("asctime(localtime(secs)): %s" % time.asctime(time.localtime(d)))
                      
                       

Output
time.mktime(t) : 1455531218.000000 asctime(localtime(secs)): Mon Feb 15 10:13:38 2016

7: asctime( ) method

Python time method asctime() converts a struct_time representing a time as returned by gmtime() or localtime() to a 24-character string of the following form: ‘Tue Mar 23 23:21:05 2021’.

Syntax: time.asctime([t])

Where t passed as an argument is a tuple of 9 elements or struct_time representing a time as returned by gmtime() or localtime() function.

Python3

import time
# method returns 24-character string of
# the following form − 'Mon March 15 23:21:05 2021'
 
local_time = time.localtime()
print ("asctime : ",time.asctime(local_time))
                      
                       

Output
asctime :  Tue Mar 16 06:02:42 2021


Next Article
Time Series Analysis & Visualization in Python

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      Skewness is a statistical term and it is a way to estimate or measure the shape of a distribution.  It is an important statistical methodology that is used to estimate the asymmetrical behavior rather than computing frequency distribution. Skewness can be two types: Symmetrical: A distribution can b
      3 min read

    • Difference Between Skewness and Kurtosis
      What is Skewness? Skewness is an important statistical technique that helps to determine the asymmetrical behavior of the frequency distribution, or more precisely, the lack of symmetry of tails both left and right of the frequency curve. A distribution or dataset is symmetric if it looks the same t
      4 min read

    • Histogram | Meaning, Example, Types and Steps to Draw
      What is Histogram?A histogram is a graphical representation of the frequency distribution of continuous series using rectangles. The x-axis of the graph represents the class interval, and the y-axis shows the various frequencies corresponding to different class intervals. A histogram is a two-dimens
      5 min read

    • Interpretations of Histogram
      Histograms helps visualizing and comprehending the data distribution. The article aims to provide comprehensive overview of histogram and its interpretation. What is Histogram?Histograms are graphical representations of data distributions. They consist of bars, each representing the frequency or cou
      7 min read

    • Box Plot
      Box Plot is a graphical method to visualize data distribution for gaining insights and making informed decisions. Box plot is a type of chart that depicts a group of numerical data through their quartiles. In this article, we are going to discuss components of a box plot, how to create a box plot, u
      7 min read

    • Quantile Quantile plots
      The quantile-quantile( q-q plot) plot is a graphical method for determining if a dataset follows a certain probability distribution or whether two samples of data came from the same population or not. Q-Q plots are particularly useful for assessing whether a dataset is normally distributed or if it
      8 min read

    • What is Univariate, Bivariate & Multivariate Analysis in Data Visualisation?
      Data Visualisation is a graphical representation of information and data. By using different visual elements such as charts, graphs, and maps data visualization tools provide us with an accessible way to find and understand hidden trends and patterns in data. In this article, we are going to see abo
      3 min read

    • Using pandas crosstab to create a bar plot
      In this article, we will discuss how to create a bar plot by using pandas crosstab in Python. First Lets us know more about the crosstab, It is a simple cross-tabulation of two or more variables. What is cross-tabulation? It is a simple cross-tabulation that help us to understand the relationship be
      3 min read

    • Exploring Correlation in Python
      This article aims to give a better understanding of a very important technique of multivariate exploration. A correlation Matrix is basically a covariance matrix. Also known as the auto-covariance matrix, dispersion matrix, variance matrix, or variance-covariance matrix. It is a matrix in which the
      4 min read

    • Covariance and Correlation
      Covariance and correlation are the two key concepts in Statistics that help us analyze the relationship between two variables. Covariance measures how two variables change together, indicating whether they move in the same or opposite directions. In this article, we will learn about the differences
      5 min read

    • Factor Analysis | Data Analysis
      Factor analysis is a statistical method used to analyze the relationships among a set of observed variables by explaining the correlations or covariances between them in terms of a smaller number of unobserved variables called factors. Table of Content What is Factor Analysis?What does Factor mean i
      13 min read

    • Data Mining - Cluster Analysis
      Data mining is the process of finding patterns, relationships and trends to gain useful insights from large datasets. It includes techniques like classification, regression, association rule mining and clustering. In this article, we will learn about clustering analysis in data mining. Understanding
      6 min read

    • MANOVA Test in R Programming
      Multivariate analysis of variance (MANOVA) is simply an ANOVA (Analysis of variance) with several dependent variables. It is a continuation of the ANOVA. In an ANOVA, we test for statistical differences on one continuous dependent variable by an independent grouping variable. The MANOVA continues th
      3 min read

    • MANOVA Test in R Programming
      Multivariate analysis of variance (MANOVA) is simply an ANOVA (Analysis of variance) with several dependent variables. It is a continuation of the ANOVA. In an ANOVA, we test for statistical differences on one continuous dependent variable by an independent grouping variable. The MANOVA continues th
      3 min read

    • Python - Central Limit Theorem
      Central Limit Theorem (CLT) is a foundational principle in statistics, and implementing it using Python can significantly enhance data analysis capabilities. Statistics is an important part of data science projects. We use statistical tools whenever we want to make any inference about the population
      7 min read

    • Probability Distribution Function
      Probability Distribution refers to the function that gives the probability of all possible values of a random variable.It shows how the probabilities are assigned to the different possible values of the random variable.Common types of probability distributions Include: Binomial Distribution.Bernoull
      9 min read

    • Probability Density Estimation & Maximum Likelihood Estimation
      Probability density and maximum likelihood estimation (MLE) are key ideas in statistics that help us make sense of data. Probability Density Function (PDF) tells us how likely different outcomes are for a continuous variable, while Maximum Likelihood Estimation helps us find the best-fitting model f
      8 min read

    • Exponential Distribution in R Programming - dexp(), pexp(), qexp(), and rexp() Functions
      The exponential distribution in R Language is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. It is a particular case of the gamma distribution. In R Programming Langu
      2 min read

    • Mathematics | Probability Distributions Set 4 (Binomial Distribution)
      The previous articles talked about some of the Continuous Probability Distributions. This article covers one of the distributions which are not continuous but discrete, namely the Binomial Distribution. Introduction - To understand the Binomial distribution, we must first understand what a Bernoulli
      5 min read

    • Poisson Distribution | Definition, Formula, Table and Examples
      The Poisson distribution is a discrete probability distribution that calculates the likelihood of a certain number of events happening in a fixed time or space, assuming the events occur independently and at a constant rate. It is characterized by a single parameter, λ (lambda), which represents the
      11 min read

    • P-Value: Comprehensive Guide to Understand, Apply, and Interpret
      A p-value is a statistical metric used to assess a hypothesis by comparing it with observed data. This article delves into the concept of p-value, its calculation, interpretation, and significance. It also explores the factors that influence p-value and highlights its limitations. Table of Content W
      12 min read

    • Z-Score in Statistics | Definition, Formula, Calculation and Uses
      Z-Score in statistics is a measurement of how many standard deviations away a data point is from the mean of a distribution. A z-score of 0 indicates that the data point's score is the same as the mean score. A positive z-score indicates that the data point is above average, while a negative z-score
      15+ min read

    • How to Calculate Point Estimates in R?
      Point estimation is a technique used to find the estimate or approximate value of population parameters from a given data sample of the population. The point estimate is calculated for the following two measuring parameters: Measuring parameterPopulation ParameterPoint EstimateProportionπp Meanμx̄ T
      3 min read

    • Confidence Interval
      Confidence Interval (CI) is a range of values that estimates where the true population value is likely to fall. Instead of just saying The average height of students is 165 cm a confidence interval allow us to say We are 95% confident that the true average height is between 160 cm and 170 cm. Before
      9 min read

    • Chi-square test in Machine Learning
      Chi-Square test helps us determine if there is a significant relationship between two categorical variables and the target variable. It is a non-parametric statistical test meaning it doesn’t follow normal distribution. It checks whether there’s a significant difference between expected and observed
      9 min read

    • Understanding Hypothesis Testing
      Hypothesis method compares two opposite statements about a population and uses sample data to decide which one is more likely to be correct.To test this assumption we first take a sample from the population and analyze it and use the results of the analysis to decide if the claim is valid or not. Su
      14 min read

    Data Preprocessing

    • ML | Data Preprocessing in Python
      Data preprocessing is a important step in the data science transforming raw data into a clean structured format for analysis. It involves tasks like handling missing values, normalizing data and encoding variables. Mastering preprocessing in Python ensures reliable insights for accurate predictions
      7 min read

    • ML | Overview of Data Cleaning
      Data cleaning is a important step in the machine learning (ML) pipeline as it involves identifying and removing any missing duplicate or irrelevant data. The goal of data cleaning is to ensure that the data is accurate, consistent and free of errors as raw data is often noisy, incomplete and inconsi
      14 min read

    • ML | Handling Missing Values
      Missing values are a common issue in machine learning. This occurs when a particular variable lacks data points, resulting in incomplete information and potentially harming the accuracy and dependability of your models. It is essential to address missing values efficiently to ensure strong and impar
      12 min read

    • Detect and Remove the Outliers using Python
      Outliers, deviating significantly from the norm, can distort measures of central tendency and affect statistical analyses. The piece explores common causes of outliers, from errors to intentional introduction, and highlights their relevance in outlier mining during data analysis. The article delves
      10 min read

    Data Transformation

    • Data Normalization Machine Learning
      Normalization is an essential step in the preprocessing of data for machine learning models, and it is a feature scaling technique. Normalization is especially crucial for data manipulation, scaling down, or up the range of data before it is utilized for subsequent stages in the fields of soft compu
      9 min read

    • Sampling distribution Using Python
      There are different types of distributions that we study in statistics like normal/gaussian distribution, exponential distribution, binomial distribution, and many others. We will study one such distribution today which is Sampling Distribution. Let's say we have some data then if we sample some fin
      3 min read

    Time Series Data Analysis

    • Data Mining - Time-Series, Symbolic and Biological Sequences Data
      Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns. Theoreticians and practitioners are continually seeking improved tech
      3 min read

    • Basic DateTime Operations in Python
      Python has an in-built module named DateTime to deal with dates and times in numerous ways. In this article, we are going to see basic DateTime operations in Python. There are six main object classes with their respective components in the datetime module mentioned below: datetime.datedatetime.timed
      12 min read

    • Time Series Analysis & Visualization in Python
      Every dataset has distinct qualities that function as essential aspects in the field of data analytics, providing insightful information about the underlying data. Time series data is one kind of dataset that is especially important. This article delves into the complexities of time series datasets,
      11 min read

    • How to deal with missing values in a Timeseries in Python?
      It is common to come across missing values when working with real-world data. Time series data is different from traditional machine learning datasets because it is collected under varying conditions over time. As a result, different mechanisms can be responsible for missing records at different tim
      10 min read

    • How to calculate MOVING AVERAGE in a Pandas DataFrame?
      Calculating the moving average in a Pandas DataFrame is used for smoothing time series data and identifying trends. The moving average, also known as the rolling mean, helps reduce noise and highlight significant patterns by averaging data points over a specific window. In Pandas, this can be achiev
      7 min read

    • What is a trend in time series?
      Time series data is a sequence of data points that measure some variable over ordered period of time. It is the fastest-growing category of databases as it is widely used in a variety of industries to understand and forecast data patterns. So while preparing this time series data for modeling it's i
      3 min read

    • How to Perform an Augmented Dickey-Fuller Test in R
      Augmented Dickey-Fuller Test: It is a common test in statistics and is used to check whether a given time series is at rest. A given time series can be called stationary or at rest if it doesn't have any trend and depicts a constant variance over time and follows autocorrelation structure over a per
      3 min read

    • AutoCorrelation
      Autocorrelation is a fundamental concept in time series analysis. Autocorrelation is a statistical concept that assesses the degree of correlation between the values of variable at different time points. The article aims to discuss the fundamentals and working of Autocorrelation. Table of Content Wh
      10 min read

    Case Studies and Projects

    • Step by Step Predictive Analysis - Machine Learning
      Predictive analytics involves certain manipulations on data from existing data sets with the goal of identifying some new trends and patterns. These trends and patterns are then used to predict future outcomes and trends. By performing predictive analysis, we can predict future trends and performanc
      3 min read

    • 6 Tips for Creating Effective Data Visualizations
      The reality of things has completely changed, making data visualization a necessary aspect when you intend to make any decision that impacts your business growth. Data is no longer for data professionals; it now serves as the center of all decisions you make on your daily operations. It's vital to e
      6 min read

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