analysis

Question 1

Get summary statistics of a variable

Answer 1

df[‘var’].describe()

Question 2

Get the sum of revenue

Answer 2

df[‘revenue’].sum()

Question 3

Get the average

Answer 3

df[‘var’].mean()

Question 4

Get the minimum / maximum

Answer 4

df[‘var’].min()

df[‘var’].max()

Question 5

Get the standard deviation

Answer 5

df[‘var’].std()

Question 6

Get the average of multiple columns

Answer 6

df[[‘var1’, ‘var2’]].mean()

Question 7

Get both sum and mean from a variable

Answer 7

df[‘var’].agg([sum, mean])

Question 8

Count per value of a variable

Answer 8

df[‘var’].value_counts()

add sort = True to get them immediately in count descending order

Question 9

Count per value of a variable (but in percentage)

Answer 9

df[‘var’].value_counts(normalize = True)

Question 10

Get the average per value of a variable

Answer 10

df.groupby(‘category’)[‘var’].mean()

Question 11

Get the average, min, max per value of a variable

Answer 11

df.groupby(‘category’)[‘var’].agg([mean, min, max])

Question 12

Create a pivottable output

Answer 12

df.pivot_table(values = ‘var’, index = ‘rowheader’)

Question 13

Create a pivottable output for both mean and median

Answer 13

df.pivot_table(values = ‘var’, index = ‘rowheader’, aggfunc = [np.mean, np.median])

Question 14

Create pivottable output on both row and column variable

Answer 14

df.pivot_table(values = ‘var’, index = ‘col’, columns = ‘col’)