Cleaning categorical data By following these strategies and validating results throughout the process, you can maintain data consistency while cleaning. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Find all of the categorical data. Python’s pandas library offers versatile tools for these tasks: python def aggregate_categorical(dataframe, group_column, agg_column, aggregation='count'): """ Aggregate categorical data based on a grouping column. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. If the original categorical variable has thirty Next, use Markdown to add a new section in the Jupyter notebook named Lab Cleaning Categorical Data. Preprocessing data is a fundamental stage in data mining to improve data efficiency. This method is suitable for nominal data. Waterman A. Categorical data can take on numerical values (such as "1" indicating "male" and "2" indicating "female"), 16. for the categorical data, check if there is some kind of text in a variable so we would need to clean it. In this lab we will explore categorical data. Data cleaning is an essential step between data collection and data analysis. Data cleaning is a crucial step in the machine learning (ML) pipeline, as it involves identifying and removing any missing, duplicate, And some of the columns are categorical and have data type objects and some are Cleaning and manipulating categorical text data is an important step in preparing data for analysis in PostgreSQL. Dashboard; Learning Path; Catalog. Cleaning categorical data often involves grouping and aggregating values. data-cleaning; categorical-data; encoding; categorical-encoding; or ask your own question. Perform standard data extraction, joining and Categorical Data. Chapter 1: Finding Errors and Cleaning Categorical Data Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. Improve your data quality and analysis skills. Certain learning algorithms like deep-learning cnn data-visualization data-reduction statistical-analysis supervised-learning yolo unsupervised-learning data-cleaning categorical-data intensity-analysis Updated Sep 2, 2021 This uses a simple regular expression to clean some very messy categorical data. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings Cleaning transformation: A data transformation used for cleaning, that can be saved in your workspace and applied to new data later. Sign in Product Actions. Categorical data can take on numerical values (such as "1" indicating "male" and "2" indicating "female"), The target column is converted from a numerical data type to a categorical data type and then recoded to 0 and 1. Another common data cleaning task is converting data into a format that can be used by a model. . Save it in a categorical_df variable. Data cleaning is a very basic building block of data science. categorical data, and. Data Cleaning. RData") Conclusions. For categorical data, you can fill In this article, we’ll explore 14 essential Python tricks for cleaning data using pandas, illustrated with a sample dataset. Data Integration: Integrate data from multiple sources, resolving any inconsistencies. Data cleaning takes place between data collection and data analyses. The r4np package hosts several different datasets to work with, but at some point, Convert values between data types in R or Python. Our basic cleaning involves dropping (selected columns, outliers, null values and duplicates), transforming (conversion of column datatypes, conversion of null values to specified values, renaming columns). Best practices and guidelines for data cleaning: Check for any missing data and handle it appropriately. This solution would be performant only if your values has an order. For categorical data, like “Paying Customer” and “Do Not Contact,” you can standardize values using str. 1. After Conversion: They are converted to float64 or int64 types. Sign in Product GitHub Copilot. They contain a fixed set of text values which needs By utilizing methods such as remapping, interference, and data dropping, we can clean and standardize the data. In case you have This can involve periodic reviews of the data cleaning outcomes and adjustments based on user insights. OK, Got it. Renaming Columns. Encoding: This is a process of converting categorical values into binary values which can then be used in computations for machine learning. View Chapter Details. , a \razor" in one data set may be called a \shaver" in another, Categorical Data Cleaning – Grouping and Aggregating. Like categorical data, text data needs to be transformed into numerical features before using it to build a machine learning model. Kaggle uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Check dtypes. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. Categorical data. This systematic approach helps ensure your Data cleaning is the initial phase of refining your dataset, making it readable and usable with techniques like removing duplicates, handling missing values and data type conversion while data preprocessing is similar to taking Unlike quantitative attributes, categorical attributes typically have no natural ordering or distance between values that t quantitative de nitions of outliers. Azure Machine Learning (Azure ML) offers a suite of automated data cleaning techniques that can be enhanced through HITL approaches. These issues can arise from various sources, such as: Data entry Cleaning Categorical Data in Python. Remember, data quality is essential for deriving meaningful insights. github","contentType":"directory"},{"name":"files_for_lab","path":"files Scientific Data - CoRRE Trait Data: A dataset of 17 categorical and continuous traits for 4079 grassland species worldwide. Skip to content. Here are the most common ways to “clean” a dataset in R: Method 1: Remove Rows with Missing Values for the categorical data, check if there is some kind of text in a variable so we would need to clean it. Converting Categorical Data into Numerical Form in Machine Learning. Common data Problems Common data types. 2k 11 11 gold badges 46 46 silver badges 100 100 bronze badges. In general, data cleaning is a process of investigating your data for inaccuracies, or recoding it in a way that makes it more manageable. In this post, we are dealing with categorical variables. 4 Converting categorical variables into numerical. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Working in data can be time-consuming, with up to 60–70% of a project being spent on data cleaning and preparation. Clean Up Missing Data. One-Hot Encoding converts categorical data into a binary matrix, where each category is represented by a binary vector. - DataCamp_Data_Scientist_with_Python_2021/Data Cleaning in Python/02_Text and categorical data problems/04_Removing titles and taking Cleaning Data in Python: Best Practices and Tips. Chapter 8 Data Cleaning. The journey began with thorough In this chapter, you'll learn how to overcome some of the most common dirty data problems. 15 5 5 bronze badges. This package provides functions for removing duplicates, standardizing categorical variables, converting data types, and removing outliers. Data cleaning refers to the process of transforming raw data into data that is suitable for analysis or model-building. Categorical and text data can often be some of the messiest parts of a dataset A collection of tools for data cleaning in R. This dataset can be found in files_for_lab folder. To identify Categorical data consists of labels or categories, like gender, country, or product type. CLEANING DATA. Each of these groups is a category. First, let’s understand the types of categorical data: Nominal Data: The nominal data called labelled/named data. Python is what we are using for automated data preprocessing and cleaning in this blog. This is useful for machine learning algorithms that require categorical data to be represented as numerical values. If you need to repeat cleaning operations often, we recommend that you save your recipe for data cleansing as a transform, to reuse with the same dataset. It can be executed in both R and Python. One option to clean our categorical data is to define a function and apply it to our data such as in the example below to convert the garage quality from its categorical labels to numeric. These can range from inconsistencies (like the same category being represented in different ways, such as 'Male', 'male', 'M', all meaning the same thing), incorrect categories, to having too many categories which could be more succinctly represented. 2. Qualitative data, categorical variable Choose the statistics that you want to present Cleaning the data. Handling Missing Values. By handling missing data, removing duplicates, addressing outliers, and encoding categorical data, you can ensure that your dataset is clean, reliable, and ready for model training. Now, let’s say you would like to create a categorical variable that tells you, by age group, if a woman is below or above for the categorical data, check if there is some kind of text in a variable so we would need to clean it. In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. ; Impact: Data cleaning, also referred to as data cleansing and data scrubbing, is one of the most important steps for your organization if you want to create a culture around quality data decision-making. Discuss if this information might in some way be used for data cleaning. In the context of job interviews, particularly for roles involving data management, analytics, or engineering, candidates are often tested on their ability to clean and organize data using SQL. Data cleaning can be done in many ways. Data Cleaning and Imputation. Hint: You can use the function value_counts() on each of the categorical columns and check the representation of different categories in each column. All rights belong to DataCamp. You’ll also discover how to group data by Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. The airlines DataFrame contains the day and wait_min columns, which are categorical and numerical respectively. 5. One-Hot Encoding. Identify categorical columns. Removing Duplicates. This section introduces the pandas Categorical type. Reply reply More replies More replies More replies. I like splitstackshape for these operations. 4. Here’s how you can convert categorical data into How do I clean and standardize categorical variables in survey data? When working with categorical variables in survey data, it's essential to ensure that the data is clean and Learn how to clean categorical and numerical data in Python using tools such as pandas, scikit-learn, pydantic, and fuzzywuzzy. Waterman. Would you choose to do anything else {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Exploring and analyzing data often means dealing with missing values, incorrect data types, and outliers. One key data cleaning problem with categorical data is the mapping of di erent category names to a uniform namespace: e. This section will show how to clean data using the Data for the categorical data, check if there is some kind of text in a variable so we would need to clean it. " It is intended solely to assist students on DataCamp and is provided with the permission of DataCamp. Learn / Courses / Cleaning Data in R. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Data cleaning is one of the important processes involved in data analysis, with it being the first step after data collection. 3. Depending on the implementation, decide what to do with the variables you get. In this project, various essential steps were meticulously undertaken to predict food delivery times with precision. Without proper data cleaning, the Clean Up Missing Data. Next, use Markdown to add a new section in the Jupyter notebook named Lab Cleaning Categorical Data. Categorical trait data assembly. With guidance and hands-on projects, transform messy datasets and categorical data. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double \n. replace, str. This section will show how to clean data using the Data This repository contains solutions for the DataCamp course "Data Scientist with Python. The figure Professor Julie Scott Jones explains how to find and fix errors in categorical data using frequency tables and data cleaning techniques in software like SPSS. Categorical Variables: A categorical variable can have two or more two values. Learn from our data cleaning in Python tutorial through practical examples. Python libraries like Pandas and NumPy provide powerful tools to facilitate these tasks, making data cleaning an accessible and efficient process for data scientists and machine learning For the categorical data, check if there is any data cleaning that need to perform. Use Case: Most appropriate for those situations, where the categories do not have an inherent order, or there is a clear distinction between them. Host and manage packages Security. Introduction. Reload to refresh your session. Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, Missing categorical data. L. Data Cleaning: Clean the collected data by identifying and correcting errors, removing duplicates and irrelevant observations, and handling missing values. replace and other methods#Python Pandas #str Met integer Integer data (whole numbers,ℤ) factor Categorical data (simple classifications, likegender) ordered Ordinal data (ordered classifications, likeeducational level) character Character data (strings) raw Binary data All basic operations in Rwork element-wise on vectors where the shortest argument is recycled if necessary. It is a form of qualitative data that can be grouped into categories rather than being measured When cleaning categorical data, some of the problems we may encounter include value inconsistency, the presence of too many categories that could be collapsed into one, 7. By following these steps and validating your results, you can ensure that your data is clean, consistent, and ready for analysis. As in this lab, we will keep working on the same dataset as the previous lab, please make a copy of the final Jupyter notebook of the previous lab in the current lab folder. Manipulating and analyzing data with incorrect data types could lead to compromised analysis as you go along the Contribute to Dankort/cleaning-categorical-data development by creating an account on GitHub. asked May 24, 2022 at 10:39. In this lesson, we will focus on checking for missing data and manipulated strings. 1. If the problem demands, convert categorical data to numerical format using one-hot encoding or label encoding. It's like preparing your ingredients before cooking; you want everything in order to get the perfect In the context of cleaning categorical data, inner join could be used to ensure that only consistent data is retained, while anti-join could be used to identify inconsistent data. Visit the transforms page for a more detailed list and description of text Exploratory data analysis (EDA) and data cleaning are usually the first two topics in a traditional data mining course. \n Special instructions \n. 1 Import your data. You switched accounts on another tab or window. Data Cleaning# Interactive Data Cleaning#. Check all unique values of columns. Instructions. Here are some best practices to keep in mind as you streamline your data cleaning process: for the categorical data, check if there is some kind of text in a variable so we would need to clean it. Handling Categorical Data. Finally, keep working on the same notebook according to the next instructions. Does any column contain alpha and numeric data? Decide how to clean it. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. Here is an example of Categorical data problems: . Hint: Use the same method you used in step 7. It introduces Label Encoding and One-Hot Encoding as two distinct approaches for transforming categorical variables into numerical form that Learn to validate and summarize categorical and numerical data and create Seaborn visualizations to communicate your findings. When your data has categories represented by strings, it will be difficult to use them to train machine learning models which often only accepts numeric data. Data cleansing workflow. In this exercise, you will map hypothetical problems to their respective categories. This included recoding categorical and continuous variables, creating individual-level statistics, and lagged variables. The ismissing function indicates which Data cleaning, also known as data cleansing or scrubbing, is a critical first step in the data science process, ensuring that your dataset is accurate, consistent, and ready for analysis. Using the caret package in R is a straightforward task that converts every categorical variable into a flag one, also known as a dummy variable. If you want to learn more about cleaning data, check out our Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. Data Cleaning is one of the important steps in EDA. For clean data, you should start by designing measures that collect valid data. Output: [2 0 1 0 2] 2. In this example, we have discrete wind directions, and would like to consider roughly all west-winds as one category. For each plant species in the CoRRE and GEx databases (4,079 species in 173 families), categorical trait data were collected for nine traits (Table 1; Fig. In extremely rare cases, the only preparation needed is dataset documentation. So, it is legitimate to ask why I place them in Chapter 11 . To understand this problem, a new data frame with just one feature, phone numbers are created. replace(): Data cleaning is a foundational step in the data analysis and data science lifecycle. SQL, or Structured Query Language, is the backbone of data manipulation and analysis. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. To better understand survey respondents from airlines, you want to find out if there is a relationship between certain responses and the day of the week and wait time at the gate. Categorical data can be found Data analysis is the process of extracting, cleaning, and preprocessing the data and gathering Find all of the categorical data. String Operations. ; Data Loss: The dataset reduces slightly in size (from 9,998 to 9,993 rows) due to rows with non-convertible values being dropped. We have covered the most common commands used to clean longitudinal data. Follow edited May 24, 2022 at 10:42. Write Let’s evaluate what we did here. You’ll also discover how to group data by Remember that cleaning categorical data is often an iterative process. Oftentimes, there are inconsistencies in categorical data that should be resolved, e. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency Encoding Categorical Variables . You signed out in another tab or window. Python3. Learn more. Common Data Problems Free. This is why we created this checklist to help you identify and resolve any quality issues with your data. Instead of ignoring the categorical data and excluding the information from our model, you can tranform the data so it can be used in your models. We won’t be able to describe how to deal with every single type of problem. Flexible Data Ingestion. Navigation Menu Toggle navigation. Instead of “manually” pre-processing Learn data cleaning and analysis in Python techniques, including handling missing data, cleaning messy datasets, and extracting insights. The next step in cleaning the data is to check for missing data in the numeric and categorical variables. Using simple regular expressions saves loads of time. You signed in with another tab or window. 6 save(usl, usw, file = ". Quinten. A. Discuss if this Data preprocessing involves the transformation of the raw dataset into an understandable format. One of them is handling missing values. 5 Categorical Data. What you want to end up with is a valid, consistent, unique, and uniform data set that’s as complete as possible. In most cases, “cleaning” a dataset involves dealing with missing values and duplicated data. You will explore how to clean common and advanced data problems along with record linkage. For example, when cleaning survey data, you might track that removing rows with missing b) Outliers: This is a topic with much debate. Do they all make sense as categorical data? \n \n \n. g. The Overflow Blog Failing fast at scale: Rapid prototyping at Intuit “Data is the key”: Twilio’s Head of R&D on the need for good data. 7. Deep Video Explains the cleaning the categorical Data in Pandas Dataframe using Str methods like str. Numeric data types; Text; Dates; Data type constrains. data-cleaning; categorical-data; categorical-encoding; Prajwal Dhage. Data cleaning involves identifying and rectifying errors, inconsistencies, and inaccuracies in the dataset. In summary: In this tutorial you have learned how to prepare and clean bad data frames for survey data and other types of data sets in R. By using the built-in string manipulation functions, you can easily clean, organize, and manipulate categorical text data to make it more useful for analysis. You can choose a few techniques for cleansing data based on what’s appropriate. In this chapter, you’ll learn how pandas handles categorical columns using the data type category. Learn how to clean categorical data based on membership constraints, value inconsistency. A12, and A13 and the target are categorical text data, while A2, A3, A8, A11, A14, and A15 are numerical data. Data cleaning helps avoid these unnecessary costs by ensuring that data is accurate and reliable from the outset. I’m at a loss at this point I need some help. Background and Motivation 7. Categorical data is a type of data that is used to group information with similar characteristics. Example 9 shows how to merge certain categories of a categorical variable. Run missing data diagnostics. Investing in data cleaning can save you from costly mistakes and inefficiencies, ultimately leading to better Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources. In this article we'll cover: What is data cleaning? Contribute to ralphmartynward/ironhack_04_lab-cleaning-categorical-data development by creating an account on GitHub. This process ensures that the data is reliable and devoid of any anomalies that might adversely Before categorical data can be utilized as input to a machine learning model, it must first be transformed into numerical data. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Data cleaning and preprocessing are the unsung heroes in the realm of data science, we’ll explore techniques to handle missing values, outliers, categorical variables, and more. Improve this question. In columns having categorical data, we can fill the missing values by mode. have already used the table function to create frequency tables of a categorical variable or a Chapter 2: Text and categorical data problems. Clean date and time data in R or Python. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Interactive data cleaning can be performed in the Data Import window before import, or in a GAUSS Symbol Editor after it is loaded. \nHint: You can use the function value_counts() on each of the categorical columns and check the representation of different categories in each column. Common Data Problems Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. Saving a The next work is to handle categorical data in datasets before applying any ML models. You may need to refine your approach as you discover new patterns or edge cases. github","contentType":"directory"},{"name":"files_for_lab","path":"files Master cleaning Python data in this four-hour course. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Data cleaning takes up 80% of the data science workflow. It ensures that the data used for analysis is of high quality, leading to more accurate, reliable, and data-cleaning; categorical; Share. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Categorical data is a type of data that is used to group information with similar characteristics. Data cleaning is a critical step in any data analysis or machine learning project. This might involve aligning columns, dealing with conflicting entries, and merging tables or datasets. Do they all make sense as categorical data? Does any column contain alpha and numeric data? Decide how to clean it. But you can use some methods even before collecting data. github","path":". /data/us_clean_syn. Raw primary data is always imperfect and needs to be prepared for a high quality analysis and overall replicability. Instead, we will go over a class of common problems. Data cleaning involves identifying and rectifying errors, inconsistencies, and missing values within a dataset. e. Check out the Wikipedia article for an in-depth overview of what can constitute an outlier. Data cleaning and preprocessing are the unsung heroes in the realm of Armed with practical code examples, we’ll explore techniques to handle missing values, outliers, categorical Data cleaning is a preliminary step to statistical analysis in which the data-set is edited to correct errors and to put it into a form suitable for processing by statistical software. Case Study: Automated Data Cleaning Techniques in Azure ML. I will show how you can achieve better performance and memory use in some pandas operations by using it. However, in the vast majority of cases, data cleaning requires significant energy and attention, typically on the part Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. It aims to streamline the data cleaning process by offering a unified interface for common data preparation tasks - Steven-Nanga/cleanR This machine learning project focused on predicting food delivery times. For this lab, we will be using the dataset in the Customer Analysis Business Case. Would you choose to do anything else to clean or wrangle the categorical data? Almost every dataset contains categorical information—and often it’s an unexplored goldmine of information. For instance, before categorical data can be employed in a model, it must be transformed Work with text data. Course Outline. Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. Apply a saved cleaning operation to new data. Clean categorical and text data by manipulating strings in R or Python. , by merging or renaming categories (especially if manually entered). While categorical data is vital, it can be rife with challenges. Automate any workflow Packages. For cleaning categorical data, there is a whole host of problems you can have in your data. By the end of this guide, you’ll be In this project, I discuss various techniques associated with Data Cleaning with Python - pb111/Data-Cleaning-with-Python-and-Pandas. \n \n \n. \n \n for the categorical data, check if there is some kind of text in a variable so we would need to clean it. The data What is Categorical Data? Categorical data refers to a type of information that can be stored and identified based on their names or labels. Then restart the Kernel and run all the previous cells. I also introduce some tools that may help with using categorical data in statistics and machine learning applications. github","contentType":"directory"},{"name":"files_for_lab","path":"files We’ll be using Google Colab on this blog. Background and Motivation Categorical data are variables that have a finite number of possible values, such as gender, color, or country. Allowed to Download Open Datasets on 1000s of Projects + Share Projects on One Platform. They are often used in machine learning to represent features or labels, but they Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, Missing categorical data. 0%. The one logical variable in housing does not support missing values. Encoding Categorical Data in Python. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into Cleaning data is a rather broad term that applies to the preliminary manipulations on a dataset prior to analysis. The day Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. You can indicate missing values by simply creating a Missing category if the data is categorical, This lesson teaches how to handle categorical data using encoding techniques in Python. The calculator cleans the data but not the header, as follows: Remove non-numerical characters - the calculator keeps only the following characters:. Ensure categorical variables contain expected values to maintain data integrity. Also, master methods for collapsing categories into fewer ones and cut numerical data into groups. Data Categorical data is a set of predefined categories or groups an observation can fall into. In an ideal world, you wouldn’t have these problems, but that isn’t one we are living in right now. Featured on Meta The Throughout the course so far, you've been exposed to some common problems that you may encounter with your data, from data type constraints, data range constrains, uniqueness constraints, and now membership constraints for categorical values. After a little feature engineering (check out the full data cleaning script here for Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted. Go to Runtime > Change {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". Step 6: Cleaning Categorical Data. “Without a systematic way to start and keep data clean, bad data will happen. Here’s how to clean categorical data. 41. Sum, mean, median, and variances of each Cleaning Data in R. (categorical traits), and data cleaning at each step. The ismissing function indicates which Cleaning categorical data by renaming categories. or categorical labels Data wrangling — also called data cleaning, Recode and arrange factors, i. 1 Cleaning. 1; It says that I am not identifying and replacing null values, not converting values between data types, or cleaning categorical and text data by manipulating strings. This process of converting categorical data into numeric representation is known as encoding. ” You soon realize you need to deal with missing data, imputation, categorical data, standardization, etc. For the categorical data, check if there is any data cleaning that need to perform. Check for NaN values. Data cleaning (or cleansing) involves correcting or removing incorrect, inaccurate, inconsistent, irrelevant, or duplicate data from the data set. Some models use as learning function the distance between points, and if you use your method, a student in Math and a student in English (0 and 2 making a 2 distance) will have more distance than a student in Math and a student in Science (0 and 1 making a 1 distance). nfzgkrt vyyer lhabwl qtc msg jiqw zqlfm xgkmseti zwxg ruvkq