Complete the code to remove rows with missing values from the DataFrame.
cleaned_df = df.[1]()The dropna() method removes rows with missing values.
Complete the code to convert the 'date' column to datetime format.
df['date'] = pd.[1](df['date'])
The pd.to_datetime() function converts strings to datetime objects.
Fix the error in the code to rename the column 'old_name' to 'new_name'.
df = df.rename(columns=[1])The rename() method expects a dictionary mapping old names to new names.
Fill both blanks to create a new column 'length' with the length of strings in 'name' column, but only for names longer than 3 characters.
df['length'] = df['name'].apply(lambda x: [1] if len(x) [2] 3 else 0)
We use len(x) to get string length and check if length is greater than 3.
Fill all three blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 4 characters.
lengths = { [1]: [2] for [3] in words if len([3]) > 4 }The dictionary comprehension uses word as key and len(word) as value, iterating over word in words.