%%python
import pandas as pd
student_data = pd.DataFrame({
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Score': [95, 85, 78, 88]
})
def find_students_in_range(df, min_score, max_score):
return df[(df['Score'] >= min_score) & (df['Score'] <= max_score)]
print(find_students_in_range(student_data, 80, 90))
Name Score
1 Bob 85
3 David 88
import pandas as pd
def add_letter_grades(df):
# Define a function to assign letter grades based on score
def assign_grade(score):
if score >= 90:
return 'A'
elif score >= 80:
return 'B'
elif score >= 70:
return 'C'
elif score >= 60:
return 'D'
else:
return 'F'
# Apply the function to the 'Score' column and create the 'Letter' column
df['Letter'] = df['Score'].apply(assign_grade)
return df
student_data = pd.DataFrame({
'Name': ['Alice', 'Bob', 'Charlie', 'David'],
'Score': [95, 85, 78, 88]
})
# Test the function
print(add_letter_grades(student_data))
Name Score Letter
0 Alice 95 A
1 Bob 85 B
2 Charlie 78 C
3 David 88 B
import pandas as pd
def find_mode(series):
# Use the .mode() function to find the most common value(s)
return series.mode()[0] # Return the first mode in case of multiple modes
# Test the function with a sample Series
print(find_mode(pd.Series([1, 2, 2, 3, 4, 2, 5])))