Have you ever wanted to analyze data but found that the variables you're working with have different criteria for grouping? For example, you might be curious about the relationship between age and income across different countries or exploring how temperature and humidity interact in different parts of a region. In such cases, using binning to group variables can help you draw meaningful comparisons from the data. In this guide, we'll walk through how to use Pandas and NumPy to bin and categorize data.

To illustrate this further, imagine you're a teacher and you're interested in finding out how your students are performing in different subjects. For instance, you might want to see how vocabulary, spelling, and reading comprehension skills vary across different age groups. By using binning techniques, you can categorize your students based on their test scores and gain valuable insights into their academic performance.

So, let's grab a coffee, put on some tunes, and dive into this sweet process!

Step 1: Define Criteria and Labels

First, we need to define the criteria and labels for each age category. We can do this by creating dictionaries that map age categories to the score levels for vocabulary, spelling, and reading.

For example, if we have three age categories ('Elementary', 'Middle School', and 'High School'), we might define the score levels for each category as follows:

vocabulary_criteria = {
    'Elementary': [0, 30, 50, 100],
    'Middle School': [0, 50, 70, 100],
    'High School': [0, 70, 90, 100]
}

spelling_criteria = {
    'Elementary': [0, 3, 5, 10],
    'Middle School': [0, 5, 7, 10],
    'High School': [0, 7, 9, 10]
}

reading_criteria = {
    'Elementary': [0, 10, 30, 50],
    'Middle School': [0, 15, 35, 50],
    'High School': [0, 20, 45, 50]
}

After we've defined these criteria, we need to assign score levels to each age category (''Beginner', 'Intermediate', and 'Advanced'). For this, we can use the following list:

score_levels = ['Beginner', 'Intermediate', 'Advanced']

Step 2: Bin the Data with pd.cut()

Once we have these criteria in place, we can use the cut() function from the Pandas library to create the cuts for each age category. This function is specifically designed to cut continuous variables into discrete bins based on predefined criteria.

In our case, we'll use the cut() function to create cuts for each age category based on their vocabulary, spelling, and reading scores. The code below demonstrates how to use the cut() function to create the necessary cuts:

vocabulary_cuts = { 
    age_category: pd.cut(df['Vocabulary Score'], bins=vocabulary_criteria[age_category], labels=score_levels) for age_category in vocabulary_criteria
}

spelling_cuts = {
    age_category: pd.cut(df['Spelling Score'], bins=spelling_criteria[age_category], labels=score_levels) for age_category in spelling_criteria
}

reading_cuts = {
    age_category: pd.cut(df['Reading Score'], bins=reading_criteria[age_category], labels=score_levels) for age_category in reading_criteria
}

You can see that we created three separate dictionaries, vocabulary_cuts, spelling_cuts, and reading_cuts, which each uses a dictionary comprehension to create the cuts for each age category. The resulting dictionaries map each age category to the list of score levels for each student's score.

Step 3: Conditional Columns with np.select()

Now that we have created three dictionaries that tell us what score levels correspond to different age groups for each subject (vocabulary, spelling, and reading), we want to create a new column for each subject in the data frame that tells us the accurate score level for each student. To do this, we use a function called np.select() which is like an if-else statement. We give it a list of conditions to check and a list of values to return if each condition is true.

For example, to create a new column for the vocabulary scores, we can use the following code:

df["Vocabulary Level"] = np.select(*zip(*[(df["Age Group"] == age_category, vocabulary_cuts[age_category]) for age_category in vocabulary_criteria]))

In this case, we want to check if the age group of each student matches a certain age group in our dictionaries, and if it does, we want to assign the corresponding score level for that age group.

So, we use a bit of code magic called list comprehension to create a list of tuples. Each tuple contains two elements - a condition to check (df["Age Group"] == age_category) and a value to return if that condition is true (vocabulary_cuts[age_category]).

Then, we use another bit of code magic called *zip() to "unzip" this list of tuples into two separate lists - one with all the conditions and one with all the values. Finally, we pass these two lists as arguments to np.select() using the * operator to unpack them, and assign the resulting values to new columns in our data frame (df["Vocabulary Level"], df["Spelling Level"], and df["Reading Level"]).

We can use a similar code to create the new columns for the spelling and reading scores:

df["Spelling Level"] = np.select(*zip(*[(df["Age Group"] == age_category, spelling_cuts[age_category]) for age_category in spelling_criteria]))

df["Reading Level"] = np.select(*zip(*[(df["Age Group"] == age_category, reading_cuts[age_category]) for age_category in reading_criteria]))

And just like that, we now have a new column for each subject that tells us the score level for each student based on their age group!

Conclusion

In conclusion, binning on multiple conditions is a useful technique for exploring the relationship between variables that don't have the same criteria for grouping. By defining criteria and labels for each variable and using the pd.cut() and np.select() functions from the Pandas and NumPy libraries, we can bin our data and make meaningful comparisons and draw insights from it.