Data manipulation is an integral part of data preparation. This is especially so since data collection leaves room for duplication and data omission. There are various ways and coding languages for cleaning and manipulating data, but Python is arguably one of the best languages for this.
Cleaning and manipulation of data ensure that the data is of high quality and suitable for use. This process can be tricky, but this article aims at simplifying it for you. But first, what is data manipulation/cleaning, and what makes Python special for this?
Not all the data that you get from the web perfectly suits your requirements. Data cleaning is the process of identifying the incomplete, inaccurate, incorrect, irrelevant, or missing parts of the data and then replacing, altering or deleting them according to your needs. It is considered a foundational element of basic data science.
Garbage in, garbage out is the foundation of data cleaning. Incorrect data ultimately leads to wrong outcomes leaving algorithms unreliable even though these discrepancies may go unnoticed. The process of cleaning data varies according to the dataset you’re working with. It is, however, important to have a data cleaning process template to avoid reinventing the wheel every time.
For a good reason, Python is the preferred coding language in data science. It provides a wide range of libraries for scientists to clean and manipulate data. That’s why in this article, we’ll focus on using the Python Data Analysis Library (Pandas) to clean and manipulate data.
As we have mentioned, the techniques for data cleaning may vary according to the datasets you have and the data you require. Here are the basic steps for manipulating data with Python.
1. Handling Missing Data
Missing data is one of the most common problems you’ll come across in newly extracted raw data. It is something you can’t simply ignore, as most algorithms will not accept any data with missing values. Most data scientists may opt to drop observations with missing data. However, this means that you’ll lose crucial information.
You can use frequent category imputation to impute missing values. This allows you to replace the missing data value with the highest frequency of that column. This makes implementation easy, and you can obtain a complete dataset in the shortest time possible. It is suitable when small values of data are missing at random.
When combining large datasets from multiple places, scraping data, or receiving data from multiple departments, it’s easy to end up with duplicate data. Removing this data plays a key role in the cleansing process, as it takes up extra space and slows down calculations. By using Pandas duplicate removal, you can drop, remove, or delete duplicate data from your dataset.
You can also remove irrelevant observations from datasets. These are irrelevant pieces of data that do not fit the criteria that you are looking for to analyze and solve a problem. For example, if you were compiling the prices of apartments in an estate, you wouldn’t need to know the number of occupants of each house. Collecting data from other data sources leads to the occurrence of such irrelevant observations. Removing such irrelevant observations makes data analysis accurate and more efficient while reducing the deviation from your primary target.
There are often one-off observations that do not appear to fit within the dataset that you’re cleaning. Removing an outlier or an improper data entry enhances the legitimacy of the data you are working with. When using Python for cleansing and manipulation, Isolation Forest is the algorithm used to check the anomaly score of the data that you have. It selects a feature, isolates all observations, and then creates paths representing the value’s normality. The shorter the paths, the more anomalies are revealed.
However, the appearance of an outlier can also prove a theory that you’re working on, and just because it is present doesn’t mean that it is incorrect, hence the need to validate that number. If you can deduce that an outlier adds no value to the analysis, then you should consider removing it. There are two other methods, One-Class SVM and Local Outlier Factor, for detecting and eliminating outliers. It is crucial to use these methods to double-check and ensure that the anomalies are not simply data clusters.
Structural errors include strange naming conventions, typos, or incorrect capitalization, which happen especially when transferring or measuring data. These may occur during data transfer or due to the incompetency of entry personnel. Typographical errors and grammatical blunders are some of the errors to watch out for when fixing data structures.
The inconsistencies caused by structural errors often lead to mislabeled categories and classes. Using Python, you can correct misspelled words and summarize category headings that may be too long, as they may not fit fully into the dataset. After eliminating the inconsistency in the data structures, the data becomes more readable and usable.
Python provides a wide range of methods and functions for manipulating data, including merging data frames. This function allows you to create a new data frame with or without modifying or altering the original data source. However, sometimes data may have some inconsistencies, especially if it comes from different sources. You have various merging options according to how you want to merge your data.
The first two methods are concatenation and merging. Concatenation is the top-down method of joining data frames, whereas merging involves merging data from left to right. The last method of merging data frames is by using the join function. This is used to connect data where there is a common keyword in both data frames. However, you may experience inconsistencies between data within the same column caused by typos or spelling disagreements. For example, you may have the data written as New York and NY, which is the same thing but with different spellings. There are many Python libraries, such as fuzzy-wuzzy, that come in handy for merging these inconsistent keywords.
Data cleaning is essential to derive the most value from datasets. Here are some of the advantages of data cleaning and manipulation.
Data cleansing eliminates inaccurate information that may lead to poor decision-making when analyzing data. With up-to-date information from datasets, you can make informed decisions on whether or not to take certain courses of action.
When you have accurate datasets, it is easier to process them and get the information that you require. For better or worse, it may take you some time to track down a piece of data that you’d like to use.
Besides saving time, cleaning data helps to increase personnel productivity as they don’t spend time chasing data only to reach a dead end. For example, in a company, updated data means that employees don’t have to call expired contacts or give feedback to the wrong clients.
For companies that specialize in sharing data with their customers, cleansing data ensures that they present accurate and ‘clean’ data. This boosts the company’s reputation as a reliable data bank for its customers.
With the increase in digitization, data is one of the most important things now. Having irrelevant, incomplete, incorrect, or disorganized data is a major challenge, and there is a dire need to take time to clean it. It is one of the most crucial steps towards achieving impressive data analysis results.