Popcorn Hack #1 import pandas as pd
Sample student_data for testing
student_data = pd.DataFrame({ ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’], ‘Score’: [85, 92, 78, 88] })
Function to find students with scores in a specific range
def find_students_in_range(df, min_score, max_score): return df[(df[‘Score’] >= min_score) & (df[‘Score’] <= max_score)]
Test the function
print(find_students_in_range(student_data, 80, 90))
Name Score 0 Alice 85 3 David 88 Popcorn Hack #2 import pandas as pd
Sample student_data
student_data = pd.DataFrame({ ‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Eve’], ‘Score’: [85, 92, 78, 64, 55] })
Function to add a ‘Letter’ column
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
Test it
print(add_letter_grades(student_data))
Name Score Letter 0 Alice 85 B 1 Bob 92 A 2 Charlie 78 C 3 David 64 D 4 Eve 55 F Pocorn Hack #3 import pandas as pd
Function to find the mode (most common value) in a Series
def find_mode(series): return series.mode().iloc[0] # returns the first mode if multiple exist
Test it
print(find_mode(pd.Series([1, 2, 2, 3, 4, 2, 5])))
2 Homework Hack import pandas as pd
Load your dataset
datas = pd.read_csv(‘data_title.csv’) # Replace with your actual filename datas.head()
SQL Series – Average temperature and wind speed per vegetation type SELECT vegetation_type, AVG(temperature), AVG(wind_speed) FROM fire_incidents GROUP BY vegetation_type;
– High temperature and wind speed events SELECT * FROM fire_incidents WHERE temperature > 120 AND wind_speed > 15;
– Avg fire intensity per weather condition SELECT weather_condition, AVG(fire_intensity) FROM fire_incidents GROUP BY weather_condition;