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Milk Food 60 5
Biscuit Food 20 2
Bourn-Vita Food 70 1
Soap Hygiene 40 4
Brush Hygiene 30 2
Detergent Household 80 1
Tissues Hygiene 30 5
>>> print(groceryDF.head(5))
Product Category Price Quantity
0 Bread Food 20 2
1 Milk Food 60 5
2 Biscuit Food 20 2
3 Bourn-Vita Food 70 1
4 Soap Hygiene 40 4
Note that the Product column is not appearing as row indexes when the DataFrame is accessed again. It is typical
of several DataFrame methods, to return a new DataFrame, leaving the original DataFrame unchanged. However, if
we want that the method should modify the original DataFrame, we must set keyword argument inplace to True:
>>> groceryDF.set_index('Product', inplace = True)
>>> print(groceryDF.head(5))
Category Price Quantity
Product
Bread Food 20 2
Milk Food 60 5
Biscuit Food 20 2
Bourn-Vita Food 70 1
Soap Hygiene 40 4
Note that the column Product has been set as the index in the dataframe groceryDF.
2.6.4 Indexing by Integer Indexing
The integer indexes are used to specify the integer location (iloc) of the rows and columns to be retrieved from a
Pandas DataFrame. Recall that the integer indexes begin with 0. The syntax for using the iloc attribute is:
DataFrame.iloc[intRowIndex, intColIndex].
Here, intRowIndex and intColIndex may be:
1. a single integer
2. a list or array of integers
3. a slice object (e.g., 1:4)
4. a boolean array
Thus, to retrieve details of first five purchases, we use the slice [0:5]:
>>> groceryDF = pd.read_csv('Grocery.csv')
>>> groceryDF.iloc[0:5]
output:
Product Category Price Quantity
0 Bread Food 20 2
1 Milk Food 60 5
2 Biscuit Food 20 2
3 Bourn-Vita Food 70 1
4 Soap Hygiene 40 4
Similarly, in the above example, to retrieve the columns Product (column 0) and Price (column 2), we specify these
columns as the list [0, 2]:
>>> groceryDF.iloc[0:5, [0, 2]]
44 Touchpad Informatics Practices-XII

