Data Manipulation and CSV Importing: A Complete Python Guide

To avoid losing valuable insights, proper data wrangling (or “mungling”) is a necessary step. This involves the cleaning and formatting of data into a specific structure prior to its storage in a database. Neglecting this crucial process can lead to the laborious and tedious task of manual data formatting. This article delves into data wrangling in Python, highlighting the use of key tools such as the Python CSV writer, and more.

Python’s ability for data manipulation is fundamental.

Data wrangling is often employed by companies and organisations as an aid in making informed decisions, developing effective solutions, and resolving issues related to data. However, if the data is not properly enhanced and finalised, the analysis and resulting insights can be greatly reduced in value.

Approaches to organising data

Since the quality of insights is unequivocally linked to the quality of data that underpins it, identifying the most pertinent data science skills can be difficult for business professionals. Therefore, it is vital to transform unstructured data into a more functional format. Data wrangling proves to be extremely advantageous in this pursuit as it enables the improvement and extension of data.

The activities involved in data wrangling will vary depending on the modifications made to render a dataset usable. The procedure typically involves:

Initiating the Process with Some Investigation

Identifying the type and quality of data held by a source is a crucial first step in the data analysis process. Conducting data exploration and discovery plays a significant role in this stage, enabling researchers to delve deeper into their data to unveil hidden patterns and insights. Subsequently, data wrangling ensues, comprising the organisation of data into distinct groups based on their reliability and accuracy.

Stage 2: Arrangement

Data, when formatted consistently, can be applied for multiple purposes. Regrettably, raw data is often disorganised and diverse in structure, making it challenging to extract any valuable insights. To realise its full potential, this data should be restructured and arranged in a format that enables data analysts to effectively decipher and analyse it.

Step 3: Clean Up

After undergoing a thorough investigation and data formatting, data cleaning becomes an essential stage to guarantee the quality of data in preparation for analysis. This entails eliminating any irrelevant or missing information and substituting any instances of “Null Values” with blank spaces or zeroes. The final outcome must be a dataset that is correctly formatted and primed for subsequent analysis.

Section 4: Enhancing Value

One aspect of the data enrichment process is determining whether we have adequate information or require additional data from internal or external sources. This phase of the process involves the transformation of data from its purified state to its formatted form. For optimal results, it’s recommended to start by increasing the data’s resolution, followed by downsampling it, and finally, generating a forecast based on the modified data.

Stage 5: Validation

Validation stage entails testing to identify any inconsistencies or other data quality issues. To ensure the precision of a dataset, data quality criteria are utilised by experts. Subsequently, after the processing of data, its quality and coherence are scrutinised. Establishing these parameters lays the groundwork for addressing security concerns. Various aspects that comply with the syntactic rules are employed in the tests.

Step 6: Publishing.

The aim of publishing is to provide users with purified data for subsequent operations. This is the final stage of the data refinement process, after which it is transformed into a format that is compatible with analytical procedures.

Advantages and Disadvantages of Python in Data Manipulation

Python offers the following data wrangling capabilities:

Data Exploration

This necessitates data visualisation. During this stage, the gathered information is analysed and interpreted.

Handling Missing Data.

Analysing vast datasets may necessitate addressing missing values. Various techniques, such as using Not-a-Number (NaN) values, computing the mean, or selecting the mode, may be utilised to fill these gaps in the data.

Data Restructuring

The data is converted from its initial format into a useful structure based on specific criteria.

Data Filtering

Data filtering is a technique in which each row and column of a dataset undergoes filtration to eliminate any unnecessary or irrelevant information. This procedure not only reduces the dataset’s size, freeing up valuable storage space, but also results in a more concise and organised dataset.


After data has been converted into a dataset, it is analysed for data visualisation, model training, and other purposes.

Now that we understand what data wrangling entails and why it is critical, let’s look at an example.

What is the meaning of “comma-separated values” or CSV?

When software is intended to process vast amounts of data, a Comma-Separated-Values (CSV) file is typically generated as an output. This mechanism provides a simple and effective means of exchanging information between databases and spreadsheets. Assembling and utilising a CSV file necessitates minimal effort. Furthermore, CSV files may be read and handled by any programming language that can read and write text files and manipulate strings.

So, can you explain what a CSV file is and how it works?

A CSV or comma-separated values file is a specific type of text file created to store tabular data following a pre-defined set of rules. Since it is just a text file, all the content inside it is plain text in ASCII characters. Its name indicates the structure of the CSV file and usually contains a distinctive numerical identifier.

Developers can now work with data sets and spreadsheets using the CSV module to read and write CSV-formatted tables. This module provides a convenient method for importing and exporting information from various programs and apps, as well as storing data in Excel. Therefore, the CSV module greatly aids developers in efficiently handling data.

Read more about the pros and cons of working remotely versus with a team in this blog post.

How do you intend to read a CSV file in Python?

The reader object helps in reading a Comma Separated Values (CSV) file. Python’s open() method treats the CSV file as a text file, and generates a file object that is then passed on to the reader.

Below is an illustration of an employee birthday calendar (employee birthday.text).

Department, Organisation, Month of Birth

Marketing – Jay – May

Recruitment – Svetlana – March

Customizable reader configurations

By adding particular additional options to the ‘reader’ object, you can read different kinds of CSV file formats. This post explains each of them:

  • A delimiter is a symbol used to specify the extent of a specific field within a dataset. The most widely used delimiter is the comma (,), which is typically utilized as the default delimiter.
  • You can use the ‘quotechar’ option to place a specific number of quotation marks around fields that include the delimiter character. The double quote (“) is the default quotechar.
  • Without quotation marks, the ‘escapechar’ option indicates the number of characters that are used to bypass the delimiter. This option, by default, does not operate as an escape character.

The following example demonstrates how to utilize these configurations accurately.

Text within a Comma-Separated Value

Name, Address, Date of Joining

To get in touch with Jay, kindly send your message to 1132 Anywhere Lane, Hoboken, New Jersey 07030 by May 26.

Mailing Address: Svetlana, 1234 Smith Lane, Hoboken, New Jersey 07030 Date: March 14th

The CSV file above contains three columns.

  • Name
  • Address
  • Date of Joining

The fields are separated by commas. Nevertheless, the zip code and address fields include commas, which causes an issue.

As seen earlier, there are multiple techniques to address data issues.

Implementing a New Delimiter

By employing this method, you can ensure the efficiency of the comma as a delimiter for your data. Furthermore, the argument presented is not mandatory, providing you with the freedom to set a different delimiter if you prefer.

Encapsulation of Data using Quotation Marks

Whenever anything is enclosed in quotes, the delimiter becomes irrelevant. In order to avoid this, use the quotechar parameter to specify the quotation character.

Handling Escaped Delimiter Characters in Data

The use of an escape character helps to avoid the interpretation of a string in any manner similar to format strings. When using an escape character, it is crucial to specify the ‘escapechar’ parameter.

Using Python’s CSV Writer for File Generation

Python’s ‘.writerow()’ method and writer object enable the creation of CSV files.

Data wrangling plays a critical role in the data analysis process as it has the potential to transform the way data is collected and analysed. Data wrangling must be undertaken before applying filters or processing the data to ensure that the data science outcomes are of the highest quality. By optimizing raw data, data wrangling enables researchers to leverage the available information to its fullest potential.


  1. When and why is “wrangling data” necessary?

    Automating Data Wrangling is achievable. Here are some examples of data manipulation.
    • Removing or deleting any irrelevant or unnecessary information for the current task at hand.
    • Identifying the specific data point and improving it to enable its use in further analyses.
    • Blank cells in the spreadsheet can be edited to fill in any missing pieces of information.
    • Merging data from multiple sources to create a unified database for analysis purposes.
  2. Is data cleansing included in data wrangling?

    Data Cleaning and Data Wrangling are two separate processes. Data Cleaning mainly entails removing unprocessed data, whereas Data Wrangling involves transforming raw data into a more organised and comprehensible format by applying filtering and enhancing techniques.
  3. What is the process of working with .csv files in Python?

    Each line of a CSV file represents a single tabular data record. These records contain one or more fields separated by commas.

    The CSV module and Pandas library are only two of the many choices available in Python to handle CSV files.
  4. The CSV Module:

    The CSV module is one of the most widely used modules in Python and offers a range of classes to read and write CSV files.

    The Pandas Library:

    The Pandas Toolkit is a powerful open-source library that provides many valuable data structures and analytical tools for working with Comma Separated Value (CSV) files in Python. Utilizing the Pandas Toolkit is an excellent approach to handle CSV files in Python. Pandas developers can be hired to help with the process.
  5. What is the reason for data cleanup?

    The aim of Data Wrangling is to convert raw data into a format that is suitable for the target system, thereby enhancing its usability and ensuring its quality. Utilizing this method simplifies and automates the data flow within a user interface, resulting in a more efficient overall process.
  6. Data Wrangling vs Data Integration & Transformation (ETL): What is the distinction?

    The ETL (Extract, Transfer, Load) process is a useful approach for transmitting data between databases. It can be employed to move structured data, such as that found in databases and operating systems, from one location to another. This method is particularly valuable when an organization is upgrading its data storage solution and requires transferring the current data to a new system. Conversely, less structured data necessitates different techniques for processing, such as Data Wrangling.

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