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Exploratory Data Analysis

Data is the most important input for any Data Science project. Exploratory Data Analysis (EDA) helps data scientists to understand the input data

Why EDA?

EDA is vital for Data Science projects since it enables data understanding (e.g. discovery of patterns in the data leading to hidden or deep-seated insights about a particular business, which were earlier untapped).

Specialized techniques are typically applied before formal modeling is launched and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about a real-life problem that can be addressed by adjoining data.

Thus, the best outcomes for projects can be determined with this approach, which includes summarizing, visualizing, and closely engaging with the decisive characteristics of a data set. For this, a robust system that allows organizing data efficiently and setting up data structures must be in place. Let’s look into the details below:

Data Organization

Enterprise data is characterized by wide variations in volume, velocity, variety, and veracity. To effectively analyze and make sense of this data, an effective EDA system at the outset must be able to:

  • accept or support various kinds of data
  • allow developers to run simple commands for analyzing and exploring data
  • distinguish decisively between the variety of data from different data sources such as images, audio, video structured data, documents, spreadsheets or relational data, JSON files, etc.
  • discern, for example, textual data from image data upon being presented with data and launch the relevant analysis
  • provide an intuitive and easy-to-use interface that allows developers to work without delving into intricacies extends all of these functionalities through Data Structures and libraries based on open-source Python libraries commonly used by data scientists, e.g. Pandas and NumPy. This enables data scientists to perform further exploration and analysis using their favourite tools.

How does it work?

The Data Exploration library supports structured, unstructured or semi-structured datasets. A developer can use the EDA libraries to analyze and understand these datasets, using standard interfaces for each. EDA libraries support the following operations:

  • investigating various types of data
  • exploring and cleaning data using standard techniques
  • analyzing independent variables with respect to a target variable and categorizing data attributes enables investigating data along two dimensions:

  1. based on volume – distributed vs non-distributed
  2. based on variety – structured vs unstructured

3 steps to EDA

For each of the above combinations, provides three steps for analysis:

  1. understand – inspect each variable in the data, and classify it into one of the following types:
    a) numeric – integer / floating point variables
    b) date
    c) categorical – strings with a finite set of values, e.g. gender
    d) string – non-categorical strings, e.g. first name, last name
    e) text – long strings on which various NLP algorithms can be run, e.g. comments
  2. explore – univariate and bivariate analysis of the data, enabling data scientists to determine data characteristics as well as understand relationships
  3. analyze – enables deeper analysis on the data based on a target (prediction) variable

Let’ see how it is done in


As a mandatory prerequisite for exploration here, we need to import data from various data sources. This can be achieved by providing a dict as parameter to import_dataset method of StructuredDataset class.

E.g., EDA library is also capable of handling unstructured data. Unstructured data here could be a file of any extension. Currently, supports .txt, .csv and .xlsx file types. This can be achieved by providing a dict as a parameter to import_dataset method of UnstructuredDataset class.


1. Understand

In this step, the types of each attribute in the data are identified. While the Pandas DataFrame object is able to handle the datatype of each attribute, this step identifies the higher-level attribute type, to populate the type member variable for each attribute of the dataset.

2. Explore

Univariate Analysis

In this step, each attribute of the dataset is explored in detail, and various metrics of the attribute are calculated.

Multivariate Analysis

In this step, various multivariate metrics associated with the dataset are calculated

3. Analyze

In this step, a deeper analysis of all analysis is performed with the target variable defined as the reference


These analyses enables data scientists to understand the input data, narrow down the correct attributes for analysis, and provide guidelines on which variables can be selected for analytics projects. They can also obtain a robust feature set needed for both supervised and unsupervised ML. While can work seamlessly in fetching and exploring data from ‘normal’ (i.e. not very large) data sources, most exploration functions are also supported on Apache Spark clusters, thus enabling Big Data exploration as well.


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