1610612456

“In statistics,exploratory data analysis(EDA) is an approach of analyzing datasets to summarize their main characteristics, often with visual methods.” — Wikipedia

There are a number of tools that are useful for EDA, but EDA is characterized more by the attitude taken than by particular techniques. Typical graphical techniques used in EDA are:

- Box plot
- Histogram
- Multi-vari chart
- Run chart
- Pareto chart
- Scatter plot
- Stem-and-leaf plot
- Parallel coordinates
- Odds ratio
- Targeted projection pursuit
- Glyph-based visualization methods such as PhenoPlot and Chernoff faces
- Projection methods such as grand tour, guided tour and manual tour
- Interactive versions of these plots

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable.

- Multidimensional scaling
- Principal component analysis (PCA)
- Multilinear PCA
- Nonlinear dimensionality reduction (NLDR)

Typical quantitative techniques are:

**In Data Analysis, we will analyze to find out the following:**

- Dataset’s shape and overview
- Missing values
- All numerical variables
- Distribution of the numerical variables
- Outliers
- Categorical variables
- Cardinality of categorical variables
- Relationship between independent and dependent feature (We will plot and check distributions in each section).

#python #data-science #matplotlib #data-analysis #data-visualization

1619518440

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

…

#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

1621635960

Exploratory Data Analysis (EDA) is a very common and important practice followed by all data scientists. It is the process of looking at tables and tables of data from different angles in order to understand it fully. Gaining a good understanding of data helps us to clean and summarize it, which then brings out the insights and trends which were otherwise unclear.

EDA has no hard-core set of rules which are to be followed like in ‘data analysis’, for example. People who are new to the field always tend to confuse between the two terms, which are mostly similar but different in their purpose. Unlike EDA, data analysis is more inclined towards the implementation of probabilities and statistical methods to reveal facts and relationships among different variants.

Coming back, there is no right or wrong way to perform EDA. It varies from person to person however, there are some major guidelines commonly followed which are listed below.

- Handling missing values: Null values can be seen when all the data may not have been available or recorded during collection.
- Removing duplicate data: It is important to prevent any overfitting or bias created during training the machine learning algorithm using repeated data records
- Handling outliers: Outliers are records that drastically differ from the rest of the data and don’t follow the trend. It can arise due to certain exceptions or inaccuracy during data collection
- Scaling and normalizing: This is only done for numerical data variables. Most of the time the variables greatly differ in their range and scale which makes it difficult to compare them and find correlations.
- Univariate and Bivariate analysis: Univariate analysis is usually done by seeing how one variable is affecting the target variable. Bivariate analysis is carried out between any 2 variables, it can either be numerical or categorical or both.

We will look at how some of these are implemented using a very famous ‘Home Credit Default Risk’ dataset available on Kaggle here. The data contains information about the loan applicant at the time of applying for the loan. It contains two types of scenarios:

- The client with payment difficulties: he/she had late payment more than X days

on at least one of the first Y instalments of the loan in our sample,

- All other cases: All other cases when the payment is paid on time.

We’ll be only working on the application data files for the sake of this article.

#data science #data analysis #data analysis in python #exploratory data analysis in python

1620466520

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

1610612456

“In statistics,exploratory data analysis(EDA) is an approach of analyzing datasets to summarize their main characteristics, often with visual methods.” — Wikipedia

There are a number of tools that are useful for EDA, but EDA is characterized more by the attitude taken than by particular techniques. Typical graphical techniques used in EDA are:

- Box plot
- Histogram
- Multi-vari chart
- Run chart
- Pareto chart
- Scatter plot
- Stem-and-leaf plot
- Parallel coordinates
- Odds ratio
- Targeted projection pursuit
- Glyph-based visualization methods such as PhenoPlot and Chernoff faces
- Projection methods such as grand tour, guided tour and manual tour
- Interactive versions of these plots

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable.

- Multidimensional scaling
- Principal component analysis (PCA)
- Multilinear PCA
- Nonlinear dimensionality reduction (NLDR)

Typical quantitative techniques are:

**In Data Analysis, we will analyze to find out the following:**

- Dataset’s shape and overview
- Missing values
- All numerical variables
- Distribution of the numerical variables
- Outliers
- Categorical variables
- Cardinality of categorical variables
- Relationship between independent and dependent feature (We will plot and check distributions in each section).

#python #data-science #matplotlib #data-analysis #data-visualization

1603270800

Exploratory Data Analysis (EDA) is one of the most important aspect in every data science or data analysis problem. It provides us greater understanding on our data and can possibly unravel hidden insights that aren’t that obvious to us. The first article I’ve wrote on Medium is also on performing EDA in R, you can check it out here. This post will focus more on graphical EDA in Python using matplotlib, regression line and even motion chart!

The dataset we are using for this article can be obtained from Gapminder, and drilling down into _Population, Gender Equality in Education _and *Income.*

The _Population _data contains yearly data regarding the estimated resident population, grouped by countries around the world between 1800 and 2018.

The *Gender Equality in Education* data contains yearly data between 1970 and 2015 on the ratio between female to male in schools, among 25 to 34 years old which includes primary, secondary and tertiary education across different countries

The _Income _data contains yearly data of income per person adjusted for differences in purchasing power (in international dollars) across different countries around the world, for the period between 1800 and 2018.

Let’s first plot the population data over time, and focus mainly on the three countries Singapore, United States and China. We will use `matplotlib `

library to plot 3 different line charts on the same figure.

```
import pandas as pd
import matplotlib.pylab as plt
%matplotlib inline
## read in data
population = pd.read_csv('./population.csv')
## plot for the 3 countries
plt.plot(population.Year,population.Singapore,label="Singapore")
plt.plot(population.Year,population.China,label="China")
plt.plot(population.Year,population["United States"],label="United States")
## add legends, labels and title
plt.legend(loc='best')
plt.xlabel('Year')
plt.ylabel('Population')
plt.title('Population Growth over time')
plt.show()
```

#exploratory-data-analysis #data-analysis #data-science #data-visualization #python