Test Expected Got import pandas as pddataset_filename = 'penguins.csv'data_col = 'bill_depth_mm'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueFalseTrueimport pandas as pddataset_filename = 'mpg.csv'data_col = 'weight'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueFalseTrueimport pandas as pddataset_filename = 'iris.csv'data_col = 'petal_length'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)dataset_path = '/var/lib/seaborn-data/'filename = dataset_path + dataset_filenamedf = pd.read_csv(filename)sbefore =df.shapedf.dropna(inplace = True)safter =df.shapetotal_removed = sbefore[0]-safter[0]mean_val = df[data_col].mean()print(total_removed==tr)print(abs(mn-mean_val)/mn>=0 and abs(mn-mean_val)/mn<.1)TrueTrueTrueTrueYour code must pass all tests to earn any marks. Try again.

file_path = "assessment/" file_name = "coffee_sales.csv" index_column = "Date" coffee_sales_df = pd.read_csv(f"{file_path}{file_name}", index_col=index_column) print(coffee_sales_df.shape) # displays (rows,cols) FileNotFoundError: [Errno 2] No such file or directory: 'assessment/coffee_sales.csv' How to fix the error

10. import pandas as pdfrom sklearn.preprocessing import train_test_splitdf = pd.read_csv('insurance_claims.csv')xtrain, xtest, ytrain, ytest = train_test_split(df.drop("is_claim", axis=1), df.is_claim, test_size=0.3, random_state=42)Which of the following is true about the code above?The code reads a csv file named insurance_claims. It splits the data into train and test sets. The test split contains 30% of the data. The random state makes sure that the data is split at random to remove inherent order which may be in the data. When the code is run multiple times it produces the diffent splits since `train_test_split` with the parameter `random_state` splits data at random.None of the given answersThe code reads a csv file names insurance claims. The `train_test_split` function will give an error since the second position argument `df.is_claim` is referencing a column that has been drop on the first position argument `df.drop("is_claim", axis=1)The code reads a csv file named insurance_claims. It splits the data into train and test sets. The train split contains 70% of the data. The random state makes sure that when the code is run multiple times it produces the same identical splits since `train_test_split` splits data at random.

Write a function named clean_mean_df that will take as its input the name of a Seaborn dataset contained in a CSV file and the name of a dataset column containing numerical data. Your function will: read the dataset into a pandas dataframe;remove any missing values from the chosen column; andcompute the mean of the cleaned column. Finally, your function will return the total number of values removed from the column and the computed mean. You may assume import pandas as pd has been invoked.Function call syntax: tr, mn = clean_mean_df(name=dataset_name, col_name=data_col), where:dataset_name = a text string containing the name of the CSV file containing the datasetdata_col = a text string containing a column containing numerical datatr = the total number of values removedmn = column mean after cleaningExample usage:dataset_filename = 'penguins.csv'data_col = 'bill_depth_mm'tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)should return the values 11 and 17.1648.Note: The data path for the CSV file location is already set for you in the answer preload.Answer:(penalty regime: 10, 20, ... %)

If you want to load the "my-data.csv" file to Dataframe so that you can explore find out the number of data items in the data set, how should you import the required library?import numpy as dataframeimport seaborn as pdimport matplotlib.pyplot as pdimport pandas as pd

# prerequisite package imports#numpy,pandas, matplotlib and seaborn#YOUR CODEimport numpy as npimport pandas as pdimport seaborn as sbimport osimport matplotlib.pyplot as pltfrom google.colab import drivedrive.mount('/content/drive')