Jamila Daniel

Jamila Daniel


How to Use Python and MissForest Algorithm to Impute Missing Data

Missing value imputation is an ever-old question in data science and machine learning. Techniques go from the simple mean/median imputation to more sophisticated methods based on machine learning. How much of an impact approach selection has on the final results? As it turns out, a lot.

Let’s get a couple of things straight — missing value imputation is domain-specific more often than not. For example, a dataset might contain missing values because a customer isn’t using some service, so imputation would be the wrong thing to do.

Further, simple techniques like mean/median/mode imputation often don’t work well. And it’s easy to reason why. Extremes can influence average values in the dataset, the mean in particular. Also, filling 10% or more of the data with the same value doesn’t sound too peachy, at least for the continuous variables.

The article is structured as follows:

  • Problems with KNN imputation
  • What is MissForest?
  • MissForest in practice
  • MissForest evaluation
  • Conclusion

#data-science #machine-learning #python

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How to Use Python and MissForest Algorithm to Impute Missing Data
Ray  Patel

Ray Patel


top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners

Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Arvel  Parker

Arvel Parker


Basic Data Types in Python | Python Web Development For Beginners

At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Table of Contents  hide

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

The Size and declared value and its sequence of the object can able to be modified called mutable objects.

Mutable Data Types are list, dict, set, byte array

Immutable objects

The Size and declared value and its sequence of the object can able to be modified.

Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.

id() and type() is used to know the Identity and data type of the object







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




Python supports 3 types of numeric data.

int (signed integers like 20, 2, 225, etc.)

float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)

complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)

A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).


The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.

# String Handling

‘Hello Python’

#single (') Quoted String

“Hello Python”

# Double (") Quoted String

“”“Hello Python”“”

‘’‘Hello Python’‘’

# triple (‘’') (“”") Quoted String

In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.

The operator “+” is used to concatenate strings and “*” is used to repeat the string.


output**:****‘Hello python’**

"python "*****2

'Output : Python python ’

#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type

Getting Started With Data Imputation Using Autoimpute

A large majority of datasets in the real world contain missing data. This leads to an issue since most Python machine learning models only work with clean datasets. As a result, analysts need to figure out how to deal with the missing data before proceeding on to the modeling step. Unfortunately, most data professionals are mainly focused on the modeling aspect and they do not pay much attention to the missing values. They usually either just drop the rows with missing values or rely on simple data imputation (replacement) techniques such as mean/median imputation. Such techniques can negatively impact model performance. This is where the Autoimpute library comes in — it provides you a framework for the proper handling of missing data.

Types of imputation

  1. Univariate imputation: Impute values using only the target variable itself, for example, mean imputation.
  2. Multivariate imputation: Impute values based on other variables, such as, using linear regression to estimate the missing values based on other variables.
  3. Single imputation: Impute any missing values within the dataset only once to create a single imputed dataset.
  4. Multiple imputation: Impute the same missing values within the dataset multiple times. This basically involves running the single imputation multiple times to get multiple imputed datasets (explained with a detailed example in the next section).

Using Autoimpute

Now let’s demonstrate how to tackle the issue of missingness using the Autoimpute library. This library provides a framework for handling missing data from the exploration phase up until the modeling phase. The image below shows a basic flowchart of how this process works on regression using multiple imputation.

Image for post

Flowchart demonstrating how multiple imputation works with linear regression.

In the above image, the raw dataset is imputed three times to create three new datasets, each one having its own new imputed values. Separate regressions are run on each of the new datasets and the parameters obtained from these regressions are pooled to form a single model. This process can be generalized to other values of ‘n’ (number of imputed datasets) and various other models.

In order to understand one major advantage of obtaining multiple datasets, we must keep in mind that the missing values are actually unknown and we are not looking to obtain the exact point estimates for them. Instead, we are trying to capture the fact that we do not know the true value and that the value could vary. This technique of having multiple imputed datasets containing different values helps in capturing this variability.

Importing Libraries

We’ll start off by importing the required libraries.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import norm, binom
import seaborn as sns
from autoimpute.utils import md_pattern, proportions
from autoimpute.visuals import plot_md_locations, plot_md_percent
from autoimpute.visuals import plot_imp_dists, plot_imp_boxplots
from autoimpute.visuals import plot_imp_swarm
from autoimpute.imputations import MultipleImputer

The complete code for this article can be downloaded from this repository: https://github.com/haiderwaseem12/Autoimpute

Creating Dummy Dataset

For demonstration purposes, we create a dummy dataset with 1000 observations. The dataset contains two variables; predictor ‘x’ and response ‘_y’. _Forty percent of the observations in ‘_y’ _are randomly replaced by missing values while ‘_x’ _is fully observed. The correlation between ‘_x’ _and ‘_y’ is approximately0.8. _A scatter plot of the data is shown below.

Image for post

#python #data-visualization #data-science #data-imputation #missing-data #data analysis

Ray  Patel

Ray Patel


Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map