#Please answer in Python Codeblock

Use the iloc method to select the 5th and 7th rows of cereals_data. Note that indexes start at 0, so the 5th row, for example, is at index 4.

"Select" simply means let the notebook run this command and then output the result to the screen to view the contents.

Now select the same two rows using the loc method, but additionally select the all the columns between Type through Carbo, inclusive.

Now make the exact same selection as above (with the columns from Type to Carbo), but using iloc once again.

Use the square bracket syntax (not using loc nor iloc) to select the first 10 rows and the columns Protein, Sodium, and Carbo. Use iloc to select the first five rows.

B) Use boolean indexing to select the rows with a value of Cal greater than 100.

Use boolean indexing again to select rows with calories greater than 120, but additionally only the rows that also meet the condition that the `Manuf` is 'K'.

Select the same rows as above (calories > 120 and manufacturer being 'K'), but using the query method.

cereals_data pd.read_csv('cereals_data.csv') cereals_data.head() 3 8 3 name mfr type calories protein fat sodium fiber carbo sugars potass vitamins shelf weight cups rating 0 100% Bran N 70 4 1 130 10.0 5.0 6 280 25 3 1.0 0.33 68.402973 1 100% Natural Bran Q 120 3 5 15 2.0 8.0 135 0 3 1.0 1.00 33.983679 2 All Bran K 70 4 1 260 9.0 7.0 5 320 25 3 1.0 0.33 59.425505 3 All-Bran with Extra Fiber K 4 0 140 14.0 8.0 0 330 25 3 1.0 0.50 93.704912 4 Almond Delight R 110 2 2 200 1.0 14.0 8 25 3 1.0 0.75 34.384843 50 c 2 2 -1