Data Manipulation and Cleaning Techniques: Some Pointers

Data scientists usually spend a considerable chunk, between 70% to 80%, of their work time tidying and organising data. This is a substantial amount of time that could have been utilised innovatively to develop new algorithms for machine learning. Nevertheless, computers can only comprehend structured and arranged data, which makes it integral that human involvement is required to clean and organise the information.

With the exponential growth of data collection and storage, efficient management, modification, and organisation of data have become crucial. This post delves deeper into the concept of data wrangling and modification, providing useful tips and guidance.

Data Manipulation

Data manipulation is the process of converting raw information into a form that is more easily comprehensible to humans. By selectively modifying the source data, fresh data can be created in addition to the original and saved for future use.

Data manipulation and data modification are two distinct concepts. Manipulation involves reorganising data, whereas modification involves making changes to either the values or the structure of data. Rectifying inaccuracies in frequently misstated information is indeed possible.

The choices and consequences of data manipulation are dependent on the dataset and the intended application.

Data Manipulation Language (DML) is a computer language that is specifically designed to aid in the cleaning, organisation and structuring of data within databases. By utilising SQL – a powerful language for database communication – data manipulation can lead to multiple outcomes, some of which include:

  • Select:

    The ‘SELECT’ command facilitates obtaining a specific database subset for further processing. The command enables data selection from a specific directory of the database.
  • Update:

    The ‘update’ function enables users to amend existing information saved in a database. This function allows for the addition of new data that can overwrite prior records or collections of records.
  • Insert:

    With the ‘Insert’ feature, it is possible to replicate data from one database to another.
  • Delete:

    The “delete” option allows for the permanent removal of data stored in a database. It is important to note that the user must specify the file location that requires the removal of the data.

Tips for Working with Data

We have collated essential data manipulation techniques for Excel to optimise your data handling experience:

  • Formulas and functions:

    Excel offers a multitude of mathematical operations that can be used to manipulate data. To perform addition, subtraction, multiplication, or division, simply insert the relevant functions into the corresponding cells.
  • Removing unwanted items and organising:

    When working with large datasets, sorting and filtering data can be extremely advantageous. You can carry out such granular analysis by using the Sort & Filter function.
  • Create new columns, merge or split existing ones:

    Rearranging the columns allows for the customisation of your database layout.
  • Autofill:

    By dragging and dropping an equation into multiple cells, the ‘Autofill’ feature saves time by automatically populating the results.
  • Eliminating repetitions:

    Duplicate information can reduce the accuracy of data. To ensure data accuracy, it is important to avoid the presence of duplications. This can be accomplished by using the ‘Remove Duplicates’ feature.

Data Processing

‘Data wrangling’, also known as ‘data munging’, involves the organisation and standardisation of disparate datasets to conduct analysis. Typically, this process requires the manual transformation and remapping of the data, providing customers with more versatile options for storage and analysis.

The process of ‘data wrangling’ involves transforming raw data from a source into a main stage table, preparing the data for subsequent analysis. This step is crucial to ensure that the data is suitable for use in a data warehouse pipeline and can be utilised to generate valuable visualisations such as dashboards.

Data wrangling is performed to ensure that

Tips for Data Management

The subsequent recommendations can aid in the manipulation of data:

  • Insufficient profile data:

    The gathering of data from several sources raises the likelihood of encountering incomplete or disparate information, significantly affecting the accuracy of the final findings. To counteract this issue, data profiling is a crucial initial step. Effective data profiling is recommended for detecting any potential discrepancies among data points, allowing for a more detailed understanding of the data in use.
  • Why drop Nulls?

    To truly evaluate data, it is critical to organise missing information into columns determined by various identified categories. Drawing inferences from only one category is insufficient; it is also necessary to make predictions about where the value belongs.
  • Utilising daily conditional checks:

    During the data pipeline, the data is loaded into tables at specific intervals for extensive quality assurance measures. Both automated and manual inspections are carried out to verify the accuracy of the data. The results of these checks can be used to validate the accuracy of the data, and the verified values can be used to estimate the effort required for the data wrangling process.
  • Accurate records:

    To ensure a successful start to analytics and machine learning, it is essential to record precise data during the initial stages. To obtain a comprehensive understanding of customers at the account level, capturing extensive information about how customers use the product at each tier is critical.
  • Track the role of each participant in your production process:

    When individuals from various departments are assigned to work with the same data, developing successful cross-functional collaboration can be difficult due to the use of different tools and varying levels of expertise. To ensure a favourable outcome, it is critical to determine the individuals responsible for each stage of the pipeline before creating a plan.
  • Automated Control Systems Auditing:

    Auditing automation necessitates monitoring investments and projects to ensure compliance with applicable regulations. After the data has been cleansed, it can be compared to existing information to determine whether modifications to existing measures are necessary or if a new approach is required.
  • If your model ignores outliers, it might not be effective.

    While valid models may appear unaffected by outliers, they can still be influenced by these extreme values. Smoothing historical data in a dataset may be advantageous at certain stages, but applying it to the entire pipeline may not always be advisable.
  • Scaling up:

    If your box’s capacity is insufficient to store the data, you can use MapReduce.
  • Module development:

    Breaking down a project into distinct parts or modules and assigning each one to a team member make it easier to identify and address potential issues. By investigating only the affected module rather than examining the entire project, troubleshooting time and overall task complexity can be significantly reduced.
  • Embrace encoding nuances:

    Encoding issues can arise when transferring data to a functioning model, which is the final step in the data processing pipeline. Organising values in this way can make it easier to analyse available options. Indicators can then be generated for each class and ordinal level.
  • Activating “Black Box” memory prompts

    Inadequate records can make it difficult to identify the source of any issues that may arise during a procedure. To avoid such problems, it is essential to maintain well-organised and confidential records. This can help ensure that all details are quickly accessible if needed.

Shortcuts in Scientometrics

Find the best data science tips here.

  1. Selecting Types in Pandas

    The ‘If’ condition can be used to differentiate between continuous and classified information to streamline data analysis processes. This can be an effective method of saving time and effort by eliminating the need to perform repetitive tasks.
  2. Master Pandas Melt Function

    Pandas’ melt function can be utilised to more effectively organise and structure data frames. This function can be used to convert a data frame to a long format, with one or more columns serving as keys. The data can also be “unmelted” using the pivot() method.
  3. Extract Email using Regular Expressions

    To quickly and easily obtain customer email addresses, employ the RegEx command.
  4. Using Glob to Read Data

    Reading data from multiple sources is a typical requirement, and using the Glob command can help adhere to Unix shell script standards by finding all paths that match a specified pattern.
  5. Resizing Images

    To create a precise data science-based system for image classification, it is crucial to ensure that all images have the same dimensions. As the images can come from different sources, their shapes may vary. Therefore, adjusting the image dimensions to meet the system requirements and ensure that all images are in a consistent format is necessary.
  6. Avoid Smileys!

    Preprocessing is critical to enhancing performance. Remove extraneous values, such as emojis, to achieve this objective.
  7. Breaking Down Tasks into Manageable Pieces

    Data errors might be unavoidable, but reducing the likelihood of mistakes and improving the quality of the final results is possible by dividing the task into smaller, more achievable portions.
  8. Benefitting from Parallel Processing with Pandas

    For large datasets, the regular Pandas library can be too slow. However, using the parallel library can effectively parallelize Pandas operations and greatly enhance speed and performance.
  9. Divide Data Frames using str.split() Method

    When working with a Pandas data frame, utilize the str.split() method to perform string functions. This can split names into separate tables.
  10. Enhance Existing Images Digitally

    Collecting large quantities of data for training deep learning models can be quite challenging. To address this, it is recommended to use image augmentation techniques instead of spending excessive time collecting data.

Streamlining and organising data, which is multifaceted and continually generated, can provide substantial benefits for organisations. Employing data wrangling and modification can aid in enhancing data accessibility, allowing decision makers to obtain meaningful insights. This process helps ensure that data is both accessible and useful for its intended purposes.

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