Data is the most important element in the analysis and decision-making of organizations and large companies. Sometimes it happens that in the analysis of a huge amount of data, we encounter unexpected results and errors. For example, data with typographical errors or upper and lower case letters are not respected.
In this situation, how can we be sure that the data is accurate and reliable? How to turn data full of errors into useful data? What tools and techniques are there to clean up redundant data or fix data problems?
The Python programming language has many features, one of the most important of which is data cleaning.
In this article, Data Cleaning with Python is discussed.
Table of Contents
What does data cleaning mean in Python?
Data cleaning or data cleaning means identifying and correcting problems in the data. These problems include various errors in data entry, incomplete or incorrect information, discrepancies, and contradictions in data, or even duplicate information.
The main goal of data cleaning is to organize and optimize data in such a way that it can be used for analysis, prediction, and effective decision-making.
For example, suppose you have a database where people’s information is stored, including their names, ages, and addresses. But the name or address may have been entered incorrectly or the age may have been entered as a negative or irrational number. With data cleaning, these problems are easily identified and corrected.
Whose task is data cleaning?
Data analysts: These people look closely at data to ensure that the reports and results they produce are accurate and reliable. For this, data must be cleaned to remove errors and inconsistencies.
Data scientists: These people deal with machine learning models, that is, they train machine learning models based on data. If the data is not cleaned, the performance of these models may decrease.
Developers: Developers need clean data in many of their projects. For example, a web developer who uses data to display information to users may encounter problems in the application if this data is not sanitized.
The importance of data cleaning
- Increasing data accuracy
Inaccurate data affects statistics and results. With data cleansing, organizations make data-driven decisions with greater confidence.
- Increase data usability
Cleaned and reliable data can be used more widely and in different parts of the organization.
- Easier analysis
Data cleaning helps make data analysis easier.
- Better data storage
Data cleaning and removing unnecessary and duplicate data reduces the cost of data storage. As a result, organizations can optimize the use of data resources.
- Data quality
Improve data quality by removing errors such as misspellings, correcting date format, and fixing common problems.
- Performance
Data cleansing optimizes the data processing process, which helps save time and resources and makes the data analysis process faster and more efficient.
When is data cleaning done?
Data cleaning is a very important step in the data analysis process. Because at this stage, the accuracy and reliability of the data is checked.
1. Empty or missing data
In large data sets, there may be empty or missing values. To solve this problem, data scientists use data-cleaning techniques to populate these values with appropriate estimates. For example, if there is a data item called “location” that is empty, data scientists can replace it with the average “location” data from that dataset or find data from another source to fill it.
2. Outliers
Data sets may contain data that are outliers in terms of quantity or behavior compared to other data. Outliers cause errors in analysis that lead to wrong results or wrong decisions. For this reason, identifying outliers is very important. To solve this problem, data scientists use data-cleaning techniques to identify and remove outliers in datasets.
3. Data formatting
Data formatting means converting existing data into a specific format or type of data that is suitable for future analysis.
For example, if the data includes different textual, numerical, and categorical types, it may be necessary to convert all these data into a specific format to perform various analyses. In this case, batch data must be converted to numeric or textual data to be effectively usable.
Also, a dataset may be obtained from several different sources and each of them may have different structures. In this case, by using the data formatting process, these data can be converted into a unified structure so that their analysis can be done more effectively.
Steps of Data Cleaning with Python
The Python programming language provides a robust environment for data cleaning thanks to libraries such as PANDAS and NUMPY. Although other tools such as Excel are used for manual data cleaning, Python provides many possibilities for automating the data cleaning process, making it ideal for use with large data sets and routine tasks.
To clean the data and create a reliable data set, the following steps should be performed:
Step 1: Identify data bugs using tools
First, data analysts should use data visualization tools such as Monte Carlo or Anomalo to find anomalies in the data.
Step 2: Eliminate data conflicts
By identifying data bugs and evaluating them, data analysts attempt to remove these bugs from the existing data set.
Step 3: Data format standardization
After the data are resolved, their format is standardized to ensure consistency across the dataset. For example, a dataset may contain dates in different formats. Data analysts must ensure that all data is stored in the correct format, for example, YYYY/MM/DD or MM/DD/YYYY.
Step 4: Data integration
In this step, several different data sets are combined into a single data set, unless data privacy laws prohibit this. Often, this step requires breaking dependencies between datasets and merging them.
Step 5: Check the accuracy of the data
Data analysts must check the accuracy of data to ensure its accuracy, validity, and up-to-dateness. This is done by running data validity tests or data validity tests.
Step 6: Save data securely
Analysts must store data securely to prevent unauthorized access. This storage is done by encrypting data, using file transfer protocols, and regularly backing up data sets.