Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Load a CSV file and filter rows where salary is greater than 40

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 40]
print(filtered_df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Read a CSV file and compute the average price grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['price'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Load a CSV file and filter rows where price is greater than 17

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 17]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and compute the average price grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['price'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 37

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 37]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Load a CSV file and filter rows where price is greater than 32

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 32]
print(filtered_df)


Instruction: Load a CSV file and filter rows where salary is greater than 46

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 46]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Read a CSV file and compute the average price grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['price'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average quantity grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['quantity'].mean()
print(result)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Load a CSV file and filter rows where salary is greater than 24

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 24]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where salary is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 27]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['salary'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and compute the average score grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['score'].mean()
print(result)


Instruction: Load a CSV file and filter rows where price is greater than 20

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 20]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 31

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 31]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 14

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 14]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 23

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 23]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 30

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 30]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Load a CSV file and filter rows where salary is greater than 11

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 11]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average salary grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['salary'].mean()
print(result)


Instruction: Load a CSV file and filter rows where salary is greater than 50

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 50]
print(filtered_df)


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Load a CSV file and filter rows where score is greater than 32

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 32]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Load a CSV file and filter rows where score is greater than 31

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 31]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Load a CSV file and filter rows where price is greater than 21

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 21]
print(filtered_df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where quantity is greater than 44

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 44]
print(filtered_df)


Instruction: Load a CSV file and filter rows where score is greater than 18

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 18]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 38

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 38]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 34

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 34]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 46

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 46]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Load a CSV file and filter rows where quantity is greater than 34

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 34]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where age is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 47]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where salary is greater than 29

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 29]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 25

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 25]
print(filtered_df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where quantity is greater than 12

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 12]
print(filtered_df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where score is greater than 39

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 39]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 37

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 37]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 47]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 22

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 22]
print(filtered_df)


Instruction: Read a CSV file and compute the average price grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['price'].mean()
print(result)


Instruction: Read a CSV file and compute the average score grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 14

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 14]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['quantity'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 48

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 48]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 47]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and compute the average score grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 34

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 34]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 47]
print(filtered_df)


Instruction: Load a CSV file and filter rows where quantity is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 27]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 20

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 20]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 42

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 42]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 32

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 32]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where salary is greater than 36

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 36]
print(filtered_df)


Instruction: Load a CSV file and filter rows where score is greater than 22

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 22]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 43

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 43]
print(filtered_df)


Instruction: Load a CSV file and filter rows where salary is greater than 22

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 22]
print(filtered_df)


Instruction: Load a CSV file and filter rows where quantity is greater than 19

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 19]
print(filtered_df)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Load a CSV file and filter rows where quantity is greater than 50

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 50]
print(filtered_df)


Instruction: Load a CSV file and filter rows where quantity is greater than 20

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 20]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 30

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 30]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 42

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 42]
print(filtered_df)


Instruction: Read a CSV file and compute the average price grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['price'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Load a CSV file and filter rows where quantity is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 27]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where salary is greater than 10

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 10]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average price grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['price'].mean()
print(result)


Instruction: Read a CSV file and compute the average price grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['price'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['quantity'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Load a CSV file and filter rows where score is greater than 50

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 50]
print(filtered_df)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 11

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 11]
print(filtered_df)


Instruction: Read a CSV file and compute the average quantity grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['age'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where score is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 47]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 41

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 41]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average price grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['price'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 28

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 28]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where score is greater than 15

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 15]
print(filtered_df)


Instruction: Read a CSV file and compute the average price grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['price'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Load a CSV file and filter rows where salary is greater than 39

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 39]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 37

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 37]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 27]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 12

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 12]
print(filtered_df)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 39

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 39]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where quantity is greater than 35

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 35]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 40

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 40]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Load a CSV file and filter rows where price is greater than 36

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 36]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where score is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 47]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where age is greater than 41

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 41]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average quantity grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average quantity grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['quantity'].mean()
print(result)


Instruction: Read a CSV file and compute the average score grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['score'].mean()
print(result)


Instruction: Load a CSV file and filter rows where price is greater than 32

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 32]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 21

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 21]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Load a CSV file and filter rows where salary is greater than 11

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 11]
print(filtered_df)


Instruction: Read a CSV file and compute the average price grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['price'].mean()
print(result)


Instruction: Read a CSV file and compute the average age grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['age'].mean()
print(result)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Load a CSV file and filter rows where score is greater than 42

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 42]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Load a CSV file and filter rows where age is greater than 42

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 42]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where age is greater than 33

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 33]
print(filtered_df)


Instruction: Load a CSV file and filter rows where quantity is greater than 32

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 32]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 38

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 38]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where age is greater than 30

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 30]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Load a CSV file and filter rows where salary is greater than 16

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 16]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where salary is greater than 37

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 37]
print(filtered_df)


Instruction: Read a CSV file and compute the average price grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['price'].mean()
print(result)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 11

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 11]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Load a CSV file and filter rows where quantity is greater than 29

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['quantity'] > 29]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['salary'].mean()
print(result)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 38

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 38]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where score is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 47]
print(filtered_df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where score is greater than 18

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 18]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Load a CSV file and filter rows where score is greater than 49

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 49]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 27]
print(filtered_df)


Instruction: Load a CSV file and filter rows where price is greater than 33

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 33]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 40

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 40]
print(filtered_df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 31

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 31]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 35

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 35]
print(filtered_df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['quantity'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and compute the average salary grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Load a CSV file and filter rows where age is greater than 47

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 47]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where price is greater than 30

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 30]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where score is greater than 36

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 36]
print(filtered_df)


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where price is greater than 16

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 16]
print(filtered_df)


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the quantity column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['quantity'] = np.where(df['quantity'] < 0, 0, df['quantity'])
print(df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Read a CSV file and compute the average age grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the age column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['age'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the score column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['score'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average salary grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average price grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['price'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 27

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 27]
print(filtered_df)


Instruction: Load a CSV file and filter rows where age is greater than 29

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 29]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 42

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 42]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Read a CSV file and compute the average price grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['price'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the quantity column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['quantity'].mean())


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average age grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['age'].mean()
print(result)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Read a CSV file and compute the average score grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['score'].mean()
print(result)


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average score grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['score'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Load a CSV file and filter rows where score is greater than 10

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 10]
print(filtered_df)


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where salary is greater than 34

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 34]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where price is greater than 50

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 50]
print(filtered_df)


Instruction: Read a CSV file and compute the average quantity grouped by region

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('region')['quantity'].mean()
print(result)


Instruction: Load a CSV file and filter rows where age is greater than 40

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['age'] > 40]
print(filtered_df)


Instruction: Read a CSV file and compute the average salary grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['salary'].mean()
print(result)


Instruction: Read a CSV file and compute the average price grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['price'].mean()
print(result)


Instruction: Load a CSV file and filter rows where price is greater than 19

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 19]
print(filtered_df)


Instruction: Read a CSV file and calculate the mean of the score column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['score'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 31

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 31]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and compute the average score grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['score'].mean()
print(result)


Instruction: Replace negative values with zero in the age column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['age'] = np.where(df['age'] < 0, 0, df['age'])
print(df)


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Read a CSV file and compute the average quantity grouped by category

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('category')['quantity'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Read a CSV file and calculate the mean of the price column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['price'].mean())


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Load a CSV file and filter rows where salary is greater than 15

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['salary'] > 15]
print(filtered_df)


Instruction: Replace negative values with zero in the price column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['price'] = np.where(df['price'] < 0, 0, df['price'])
print(df)


Instruction: Read a CSV file and compute the average age grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['age'].mean()
print(result)


Instruction: Read a CSV file and calculate the mean of the age column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['age'].mean())


Instruction: Load a CSV file and filter rows where price is greater than 24

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['price'] > 24]
print(filtered_df)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Read a CSV file and calculate the mean of the salary column

Output:
import pandas as pd

df = pd.read_csv('data.csv')
print(df['salary'].mean())


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Read a CSV file and compute the average quantity grouped by department

Output:
import pandas as pd

df = pd.read_csv('data.csv')
result = df.groupby('department')['quantity'].mean()
print(result)


Instruction: Replace negative values with zero in the score column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['score'] = np.where(df['score'] < 0, 0, df['score'])
print(df)


Instruction: Using numpy, calculate the mean of values in the quantity column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['quantity'].to_numpy()
print(np.mean(values))


Instruction: Using numpy, calculate the mean of values in the price column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['price'].to_numpy()
print(np.mean(values))


Instruction: Load a CSV file and filter rows where score is greater than 24

Output:
import pandas as pd

df = pd.read_csv('data.csv')
filtered_df = df[df['score'] > 24]
print(filtered_df)


Instruction: Using numpy, calculate the mean of values in the salary column from a CSV file

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
values = df['salary'].to_numpy()
print(np.mean(values))


Instruction: Replace negative values with zero in the salary column using numpy and pandas

Output:
import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
df['salary'] = np.where(df['salary'] < 0, 0, df['salary'])
print(df)