<jupyter_start><jupyter_text>Question 1 How do you create an identity matrix in python?<jupyter_code>a = np.identity(3)
a<jupyter_output><empty_output><jupyter_text>Question 2 For the fuel type coal, what is the percentage change in the fuel cost per unit burned in 1998 compared to 1994?<jupyter_code>coal_ = pd.DataFrame(df[df.fuel_type_code_pudl == 'coal'])
coal = coal_.loc[coal_['report_year'] == 1994]
coal = coal.append(coal_.loc[coal_['report_year'] == 1998])
pct_change = (coal.groupby('report_year')['fuel_cost_per_unit_burned'].sum()).pct_change()
print(pct_change[1998]*100)<jupyter_output>-20.567654518260493<jupyter_text>Question 3 Which of the features has the second and third lowest correlation with the Fuel Cost Per Unit Burned?<jupyter_code>corr = df.corr()
sns.heatmap(corr, annot=True)<jupyter_output><empty_output><jupyter_text>Question 4 The feature with missing values falls under what category? What missing value imputation technique would you use?  Question 5 Which year has the highest average fuel cost per unit delivered?<jupyter_code>import operator
yy = dict(df.groupby(['report_year'])['fuel_cost_per_unit_delivered'].mean())
max(yy.items(), key=operator.itemgetter(1))<jupyter_output><empty_output><jupyter_text>Question 6 Which feature has missing values and what is the total number of missing value and percentage of the missing rows as a factor of the total number of rows in three decimal places? (Enter answer in the format: Feature: xxx, Total: xxx, Percent: xxx)<jupyter_code>df.isnull().sum()
percent = (180/df.shape[0])*100
percent<jupyter_output><empty_output><jupyter_text>Question 7 What is the skewness and kurtosis for the fuel quantity burned in two decimal places?<jupyter_code>s = df.skew()

k = df.kurt()
print('skewness: ', s['fuel_qty_burned'])
print('kurtosis: ', k['fuel_qty_burned'])<jupyter_output>skewness:  15.851495469109468
kurtosis:  651.3694501337802<jupyter_text>Question 8 What is the standard deviation and 75th percentile of the measure of energy per unit (Fuel_mmbtu_per_unit) in two decimal places?<jupyter_code>df['fuel_mmbtu_per_unit'].describe()<jupyter_output><empty_output><jupyter_text>Question 9 Which of the following fuel type code has the lowest average fuel cost per unit burned?<jupyter_code>fuel_type = pd.DataFrame(df.groupby(['fuel_type_code_pudl']).mean())
fuel_type['fuel_cost_per_unit_burned']<jupyter_output><empty_output><jupyter_text>Question 10 If you’re given two lists:A = [1,2,3,4,5,6], B = [13, 21, 34]The task is to create a list with the elements of A and B in a single dimension with output:A_B =  [1,2,3,4,5,6,13, 21, 34]Which of the following option is the best way to create this list?<jupyter_code>A = [1,2,3,4,5,6]
B = [13, 21, 34]
A.extend(B)
print(A)<jupyter_output>[1, 2, 3, 4, 5, 6, 13, 21, 34]
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