what's this?

Question 1

.head()

Answer 1

printing the first few rows of the DF

Question 2

.info()

Answer 2

information about the columns

Question 3

.mean()

Answer 3

mean of a column

Question 4

.median()

Answer 4

median of a column

Question 5

.min()

Question 6

.max()

Question 7

sort column

Answer 7

.sort_values
(‘column’)

Question 8

get cumulative sum

Answer 8

.cumsum()

Question 9

get cumulative maximum

Answer 9

.cummax()

Question 10

Calculate the total Column over the whole dataset.

Answer 10

new_column = df[‘column’].sum()

Question 11

Subset for (column) ‘type’ (“A”- content under column) stores, and calculate their total weekly (‘column’) sales.

Answer 11

new_column = df[df[‘type’] == ‘A’][‘weekly_sales’].sum()

Question 12

Subset for (column) ‘type’ (“C”- content under column) stores, and calculate their total weekly (‘column’) sales.

Answer 12

new_column = df[df[‘type’]==’C’][‘weekly_sales’].sum()

Question 13

Get the min of the column, ‘weekly_sales’, for each store ‘type’ using .groupby() and .agg(). store as sales_stats

Answer 13

sales_stats = sales.groupby(‘type’)[‘weekly_sales’].agg([np.min])

Question 14

Get the min, max, and mean of the column, ‘weekly_sales’, for each store ‘type’ using .groupby() and .agg(). store as sales_stats

Answer 14

sales_stats = sales.groupby(‘type’)[‘weekly_sales’].agg([np.min, np.max, np.mean, np.median])

Question 15

Get the mean weekly_sales (column) by type(‘column’) using .pivot_table() and store as mean_sales_by_type.

Answer 15

mean_sales_by_type = sales.pivot_table(values=’weekly_sales’, index=’type’)

Question 16

Get the mean and median (using NumPy functions) of the column, weekly_sales, by column, type, using .pivot_table() and store as mean_med_sales.

Answer 16

mean_med_sales = sales.pivot_table(values=’weekly_sales’, index=
‘type’, aggfunc=[np.mean, np.median])

Question 17

Print the mean ‘weekly_sales’ by ‘department’ and ‘type’, using pivot_table(), and filling in any missing values with 0

Answer 17

print(sales.pivot_table
(values=’weekly_sales’, index=’department’, columns=’type’, fill_value=0))

Question 18

Get the mean of ‘weekly_sales’ by ‘type’ and ‘is_holiday’ using .pivot_table() and store as mean_sales

Answer 18

mean_sales = sales.pivot_table(values=’weekly_sales’, index=’type’, columns=’is_holiday’)