dataframe = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_train_raw.csv') df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_test_raw.csv') Write a function that takes in as input a dataframe and a column name, and returns the mean for numerical columns and the mode for non-numerical columns. Function Specifications: The function should take two inputs: (df, column_name), where df is a pandas DataFrame, column_name is a str. If the column_name does not exist in df, raise a ValueError. Should return as output the mean if the specified column is numerical and return a list of the mode(s) otherwise. The mean should be rounded to 2 decimal places. If there is more than one mode for a given non-numerical column, the fuction should return a list of all modes. def calc_mean_mode(df, column_name): # your code here return calc_mean_mode(df,'Age') Expected Outputs: calc_mean_mode(df, 'Age') == 29.7 calc_mean_mode(df, 'Embarked') == ['S']
dataframe = pd.read_csv('https://raw.githubusercontent.com/Explore-
df = pd.read_csv('https://raw.githubusercontent.com/Explore-AI/Public-Data/master/Data/regression_sprint/titanic_test_raw.csv')
Write a function that takes in as input a dataframe and a column name, and returns the mean for numerical columns and the mode for non-numerical columns. Function Specifications: The function should take two inputs: (df, column_name), where df is a pandas DataFrame, column_name is a str. If the column_name does not exist in df, raise a ValueError. Should return as output the mean if the specified column is numerical and return a list of the mode(s) otherwise. The mean should be rounded to 2 decimal places. If there is more than one mode for a given non-numerical column, the fuction should return a list of all modes.
def calc_mean_mode(df, column_name): # your code here return
calc_mean_mode(df,'Age')
Expected Outputs:
calc_mean_mode(df, 'Age') == 29.7
calc_mean_mode(df, 'Embarked') == ['S']
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