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Change the structure of data to better fit specific analysis or modeling requirements.

Data reshaping definition:

Data reshaping is the process of changing the layout or structure of data from one form to another, in order to better suit the needs of an analysis or downstream processing. This can involve rearranging rows and columns, changing the data type, or converting between wide and long formats.

Data transformation example using Python:

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

In Python, the reshape() function in the NumPy library can be used for data reshaping. Let’s look at a simple example:

import numpy as np

# create a 3x4 array of data
data = np.array([[1, 2, 3, 4],
                 [5, 6, 7, 8],
                 [9, 10, 11, 12]])

# reshape the data into a 2x6 array
reshaped_data = data.reshape(2, 6)


This will print out the new array as follows:

[[ 1  2  3  4  5  6]
 [ 7  8  9 10 11 12]]

This example reshapes a 3x4 array into a 2x6 array. The reshape function takes the new dimensions of the array as arguments. Note that the total number of elements in the original array must equal the total number of elements in the reshaped array, or an error will occur.

Other data engineering terms related to
Data Transformation:


Aligning data can mean one of three things: aligning datasets, meeting business rules or arranging data elements in memory.

Big Data Processing

Process large volumes of data in parallel and distributed computing environments to improve performance.

Clean or Cleanse

Remove invalid or inconsistent data values, such as empty fields or outliers.


Group data points based on similarities or patterns to facilitate analysis and modeling.


Remove noise or artifacts from data to improve its accuracy and quality.


Optimize data for faster read access by reducing the number of joins needed to retrieve related data.


Transform continuous data into discrete categories or bins to simplify analysis.


Extract, transform, and load data between different systems.


Extract a subset of data based on specific criteria or conditions.


Convert data into a linear format for efficient storage and processing.


Fill in missing data values with estimated or imputed values to facilitate analysis.


See 'wrangle'.


Standardize data values to facilitate comparison and analysis. organize data into a consistent format.


Convert a large set of data into a smaller, more manageable form without significant loss of information.


Convert data into a linear format for efficient storage and processing.


Break down large datasets into smaller, more manageable pieces for easier processing and analysis.


An imbalance in the distribution or representation of data.


Transform data to a common unit or format to facilitate comparison and analysis.


Convert data into tokens or smaller units to simplify analysis or processing.


Convert data from one format or structure to another.


Convert unstructured data into a structured format.