A Window Function performs a Data Analysis calculation across a set of table rows that are somehow related to the current row. Address the comparable type of calculation can be done with an aggregate function that gives a single row or grouped by condition (refer to Figure 1).
Window function does not cause rows to become grouped into a single output row. Rows retain their separate identities also able to access more than just the current row of the query result. (refer to Figure 1).
Window Function Syntax: Window_Function([All] expression) OVER( [PARTITION BY expression_list] [ORDER BY order_list Row_or_ Range…
A window function performs a Data Analysis calculation across a set of table rows that are somehow related to the current row. Address the comparable type of calculation can be done with an aggregate function that gives a single row or grouped by condition (refer to Figure 1).
Window function does not cause rows to become grouped into a single output row. Rows retain their separate identities also able to access more than just the current row of the query result. (refer to Figure 1)
Python is a popular language that allows programmers to write elegant, easy-to-write and read code like plain English. The unique feature of Python is a different type of comprehensions.
In Python, there are three types of comprehensions viz. List, Dictionary and Set.
By the end of this blog, you’ll understand the full power of Python comprehensions and how to easily use its functionality.
List: List is a collection of data surrounded by square brackets and each element are separated by a comma.
List Comprehension: Is also surrounded by square brackets but instead of the list of elements…
Part 1: Basic Introduction About Time-Series Forecasting
Time Series Forecasting is the use of the statistical model to predict future value based on past observation/results. Use any variables tracked and collected over time. For example, annual population, daily stock prices, daily-weekly-quarterly sales, etc.
Time Series Data Characteristics & Objectives:
After handle missing values in the dataset, the next step was to handle categorical data. In this blog, I will explain different ways to handle categorical features/columns along with implementation using python.
Introduction: All Machine Learning models are some kind of mathematical model that needs numbers to work with. Categorical data have possible values (categories) and it can be in text form. For example, Gender: Male/Female/Others, Ranks: 1st/2nd/3rd, etc.
While working on a data science project after handling…
In my last blog Link, I explained different ways to handle Continuous column missing data and its implementation.
In this blog, I will explain how to handle missing values of the Categorical data column in the dataset with implementation using python.
Discrete/ Categorical Data: discrete data is quantitative data that can be counted and has a finite number of possible values or data which may be divided into groups e.g. days in a week, number of months in a year, sex (Male/Female/Others), Grades (High/Medium/Low), etc.
The dataset used to explain is Titanic (Kaggle dataset):
import pandas as pd import numpy…
In my last blog Link, I explained missing values and their types.
In this blog, I will explain how to handle missing values for the Continuous data column in the dataset with implementation.
Continous Data: Continuous data is quantitative data that can be measured, it has an infinite number of possible values within a selected range e.g. temperature range, height, weight, etc.
The dataset used to explain is Titanic (Kaggle dataset)
import pandas as pd
import numpy as np
Data = pd.read_csv("train.csv")
Abbott is headquartered in Abbott Park, Illinois, United States, an American multinational healthcare and medical equipment corporation. Business established initially in 1888 to formulate drugs; now they market medical equipment, diagnostics, generic branded drugs, and nutritional goods (Wikipedia Contributors, 2019). Abbott well-known market leader for glucose monitoring devices, blood and plasma screening, remote heart failure monitoring, chronic pain devices, and many others. Dr. David Spindell, Abbott’s vice president of medical and clinical affairs at the Tech Health conference for the Wall Street Journal in San Francisco, said, AI and data analysis help us gain insights into health care. It allows…
In the life cycle of the data science project, the data has been collected from various sources like internal databases, 3rd party API’s or by surveys. Data engineers usually take care of adding collected data into databases. But, when data come to data scientists or analysts most of the time data has some missing values or some unacceptable characters. It is found that 80 percent of overall project time has been utilized in data preparation in order to make data ready for analysis.
First, understand why there are missing values.
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