Complete the code to convert the 'age' column to integer type.
df['age'] = df['age'].astype([1])
To convert a column to integer type, use astype("int").
Complete the code to convert the 'price' column to float type.
df['price'] = df['price'].astype([1])
Use astype("float") to convert a column to floating point numbers.
Fix the error in the code to convert the 'status' column to category type.
df['status'] = df['status'].astype([1])
The dtype name must be a string: "category".
Fill both blanks to create a dictionary that converts 'score' to float and 'passed' to bool.
dtype_dict = {'score': [1], 'passed': [2]Use "float" for 'score' and "bool" for 'passed' in the dtype dictionary.
Fill in the blank to convert columns using a dtype dictionary: 'height' to float, 'weight' to int, and 'active' to bool.
df = df.astype([1])The dictionary must map 'height' to 'float', 'weight' to 'int', and 'active' to 'bool' for correct dtype conversion.