Pandas Power-Up: Uncover Student Well-being Insights with Calculations & Stats

Issue 23: Your Data, Mental Health: Pandas Shines a Light 🔍✨

Welcome back, data champions!

You've mastered data transformations. Now, let's dive deeper into your student mental health dataset to uncover valuable insights using Pandas' calculation and statistical tools.

Loading Your Data:

First things first, let's load your dataset into a Pandas DataFrame:

We used student mental health data from Kaggle.

import pandas as pd

# Replace 'your_dataset.csv' with your actual file name or path
df = pd.read_csv('your_dataset.csv')

Why Analyze Student Mental Health Data?

Understanding the mental well-being of your students is crucial for creating a supportive and thriving academic environment. By analyzing this data, you can:

• Identify trends: Are certain years of study more prone to anxiety or depression?

• Spot correlations: Is there a relationship between CGPA and mental health?

• Evaluate interventions: Have your support programs been effective?

Your Data: A Closer Look

You have a treasure trove of information:

• Gender: Can we identify gender-specific trends?

• Age: Does mental health vary across age groups?

• Year of Course/Study: Are there certain academic pressures associated with specific years?

• CGPA: Does academic performance correlate with mental health?

• Marital Status: Does relationship status play a role?

• Depression/Anxiety/Panic Attack: Prevalence and potential connections to other factors.

Pandas Power Moves:

• Basic Calculations:

  Example: Calculate the average age of students:

average_age = df['Age'].mean()
print(f'Average student age: {average_age}')

• Aggregation Functions:

  Example: Compare the proportion of depression between genders:

# Convert 'Yes' and 'No' to 1 and 0 respectively in the 'Depression' column
df['Depression'] = df['Depression'].map({'Yes': 1, 'No': 0})

# Now calculate the mean of depression per gender group
depression_by_gender = df.groupby('Gender')['Depression'].mean()

print(depression_by_gender)

• Custom Functions:

  Example: Create a new column categorizing CGPA:

def categorize_cgpa(cgpa_str):
    """Categorizes CGPA based on ranges like 3.00-3.45."""
    try:
        lower, upper = map(float, cgpa_str.split('-'))
        if upper >= 3.5:
            return 'High'
        elif lower >= 2.0:
            return 'Average'
        else:
            return 'Low'
    except ValueError:
        return 'Unknown' 

# Apply the categorization to the CGPA column after converting to string
df['CGPA_Category'] = df['CGPA'].astype(str).apply(categorize_cgpa)

# Print the updated DataFrame
print(df)

• Statistical Tests (Chi-Squared Example):

  Example: Is there an association between marital status and anxiety?

from scipy.stats import chi2_contingency
contingency_table = pd.crosstab(df['Marital status'], df['Anxiety?'])
chi2, p, dof, expected = chi2_contingency(contingency_table)
print(f'P-value: {p}') 

• (A low p-value would suggest a statistically significant association)

Challenge Time!

• Data Exploration: Calculate the most common year of study among students with anxiety.

• Statistical Test: Perform a t-test to compare the average CGPA of students with and without depression.

Poll Time! 🗳️

Stay Tuned!

Next time, we'll tackle missing data, visualize your findings, and delve into more advanced statistical techniques. Your journey towards becoming a data-driven advocate for student well-being continues!

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Solutions to the previous newsletter (Issue 22)

Data Transformation Challenge: Reshaping with Melt or Pivot

We'll use the melt function to transform the dataset from a wide format (columns for depression, anxiety, panic attacks) to a long format. This makes it easier to analyze the frequency of each mental health condition:

# Melt the mental health columns into a long format
melted_df = df.melt(id_vars=['Gender', 'Age', 'Year', 'CGPA', 'Marital status'],
                   value_vars=['Depression', 'Anxiety', 'Panic_attack'],
                   var_name='Condition', value_name='Present')

print(melted_df.head())

Output (Sample):

     Gender   Age    Year         CGPA Marital status   Condition Present
0  Female  18.0  year 1  3.00 - 3.49             No  Depression       1
1    Male  21.0  year 2  3.00 - 3.49             No  Depression       0
2    Male  19.0  Year 1  3.00 - 3.49             No  Depression       1
3  Female  22.0  year 3  3.00 - 3.49            Yes  Depression       1
4    Male  23.0  year 4  3.00 - 3.49             No  Depression       0

Now, each row represents a single student and a single mental health condition, making it easier to count occurrences or perform analyses by condition.

Data Combining Challenge: Merging Related Datasets

For this example, let's assume you have a second dataset containing information about the students' participation in counseling sessions:

# Counseling sessions dataset
counseling_data = {'Student_ID': [1, 1, 2, 3],
                   'Date': ['2023-05-15', '2023-06-20', '2023-04-10', '2023-06-05']}
counseling_df = pd.DataFrame(counseling_data)

To combine this with your main dataset, we need a common identifier. Let's assume your main dataset also has a Student_ID column. We can then merge the datasets:

# Merge datasets on 'Student_ID'
merged_df = pd.merge(df, counseling_df, on='Student_ID', how='left')

print(merged_df.head())

Now you have a combined dataset where you can see which students have attended counseling sessions (those with values in the Date column) alongside their mental health information.

Important Note:

In reality, you'd likely use a more reliable unique identifier like a student ID number rather than relying on names.

Let me know if you'd like further clarification or have more questions!

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