List & Filtering Homework

Lists, Searching Algorithms, and Data Analysis with Pandas

PopCorn Hack 1: Find Students with Scores in a Range

This function filters a DataFrame to return only the rows where the ‘Score’ falls between min_score and max_score, inclusive.

def find_students_in_range(df, min_score, max_score):
    return df[(df['Score'] >= min_score) & (df['Score'] <= max_score)]

# Example usage:
find_students_in_range(student_data, 80, 90)

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PopCorn Hack 2: Calculate Letter Grades

This function adds a new column called 'Letter' to the DataFrame based on each student’s score using standard grading brackets.

def add_letter_grades(df):
    def get_letter(score):
        if score >= 90:
            return 'A'
        elif score >= 80:
            return 'B'
        elif score >= 70:
            return 'C'
        elif score >= 60:
            return 'D'
        else:
            return 'F'
    df['Letter'] = df['Score'].apply(get_letter)
    return df

# Example usage:
add_letter_grades(student_data)

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PopCorn Hack 3: Find the Mode in a Series

This function returns the most common (mode) value in a Pandas Series. If no mode is found, it returns None.

def find_mode(series):
    return series.mode().iloc[0] if not series.mode().empty else None

# Example usage:
find_mode(pd.Series([1, 2, 2, 3, 4, 2, 5]))

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Load Dataset

This code loads a CSV file into a Pandas DataFrame for further analysis.

import pandas as pd

fire_data = pd.read_csv('data_title.csv')

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Find Min/Max Average Temperature

This identifies the minimum and maximum values in the 'Temperature' column.

min_avg_temp = fire_data['Temperature'].min()
max_avg_temp = fire_data['Temperature'].max()

print(f"Min Avg Temp: {min_avg_temp}")
print(f"Max Avg Temp: {max_avg_temp}")

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Difference Between Max and Min Temp per Incident

This calculates the temperature range for each fire incident by subtracting 'Min Temp' from 'Max Temp'.

fire_data['Temp_Diff'] = fire_data['Max Temp'] - fire_data['Min Temp']
fire_data[['Incident ID', 'Temp_Diff']].head()

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Incidents Exceeding Average Temperature

Filters the dataset to show only incidents where the temperature is higher than the average temperature.

avg_temp = fire_data['Temperature'].mean()
hotter_incidents = fire_data[fire_data['Temperature'] > avg_temp]
hotter_incidents.head()

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Group by Vegetation and Weather - Average Temp and Wind

Groups the data by vegetation type and weather condition, then calculates the average temperature and wind speed for each group.

grouped_stats = fire_data.groupby(['Vegetation Type', 'Weather Condition'])[['Temperature', 'Wind Speed']].mean().reset_index()
grouped_stats.head()

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Correlation Between Vegetation Type and Fire Intensity

This calculates the correlation between vegetation type and fire intensity. Since correlation needs numerical values, vegetation type is converted into categorical codes.

fire_data['VegCode'] = fire_data['Vegetation Type'].astype('category').cat.codes
correlation = fire_data[['VegCode', 'Fire Intensity']].corr().iloc[0,1]
print(f"Correlation: {correlation}")

Visualize the fire intensity across different vegetation types using a boxplot.

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 6))
sns.boxplot(x='Vegetation Type', y='Fire Intensity', data=fire_data)
plt.title('Fire Intensity by Vegetation Type')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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Weather Condition with Highest Average Fire Intensity

This computes and visualizes the average fire intensity for each weather condition.

intensity_by_weather = fire_data.groupby('Weather Condition')['Fire Intensity'].mean().sort_values(ascending=False)
print(intensity_by_weather)

Visualize with a bar chart.

sns.barplot(x=intensity_by_weather.index, y=intensity_by_weather.values)
plt.title('Average Fire Intensity by Weather Condition')
plt.xticks(rotation=45)
plt.ylabel('Avg Intensity')
plt.tight_layout()
plt.show()

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Percentage of Incidents Above 100°F

Calculates the percentage of fire incidents where the temperature was greater than 100°F.

total_incidents = len(fire_data)
over_100 = fire_data[fire_data['Temperature'] > 100]
percentage = (len(over_100) / total_incidents) * 100
print(f"Percentage of Incidents > 100°F: {percentage:.2f}%")

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Store Fire Data in SQLite

Creates an in-memory SQLite database and stores the fire data in a table named fire_incidents.

import sqlite3

conn = sqlite3.connect(':memory:')
fire_data.to_sql('fire_incidents', conn, index=False)

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SQL Query: Avg Temp and Wind Speed by Vegetation Type

This SQL query retrieves average temperature and wind speed for each vegetation type.

query = """
SELECT "Vegetation Type", AVG("Temperature") as AvgTemp, AVG("Wind Speed") as AvgWind
FROM fire_incidents
GROUP BY "Vegetation Type"
"""
pd.read_sql_query(query, conn)

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SQL Query: Incidents with Extreme Conditions

Finds fire incidents with temperatures over 120°F and wind speed over 15 mph.

query = """
SELECT * FROM fire_incidents
WHERE "Temperature" > 120 AND "Wind Speed" > 15
"""
pd.read_sql_query(query, conn)

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SQL Query: Avg Fire Intensity by Weather Condition

Groups fire incidents by weather condition and calculates the average fire intensity for each group.

query = """
SELECT "Weather Condition", AVG("Fire Intensity") as AvgIntensity
FROM fire_incidents
GROUP BY "Weather Condition"
"""
pd.read_sql_query(query, conn)

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Pandas vs SQL: Comparison Table

Feature	Pandas	SQL
Ease of Use	Pythonic, fluent for data ops	Structured, ideal for queries
Performance	Fast in-memory for medium data	Efficient on large datasets
Readability	Intuitive chaining	Easy for structured logic
Flexibility	Great for complex ops + plotting	Best for aggregation/joins

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Data Visualizations

Boxplot: Temperature by Vegetation Type

plt.figure(figsize=(12, 6))
sns.boxplot(x='Vegetation Type', y='Temperature', data=fire_data)
plt.title('Temperature Distribution by Vegetation Type')
plt.ylabel('Temperature (°F)')
plt.xlabel('Vegetation Type')
plt.xticks(rotation=45)
plt.show()

Barplot: Avg Fire Intensity by Weather Condition

weather_effect = fire_data.groupby('Weather Condition')['Fire Intensity'].mean().reset_index()

sns.barplot(x='Weather Condition', y='Fire Intensity', data=weather_effect)
plt.title('Effect of Weather Condition on Fire Intensity')
plt.ylabel('Average Fire Intensity')
plt.xlabel('Weather Condition')
plt.xticks(rotation=45)
plt.show()

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