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As shown above, the method read_csv() takes the path of the CSV file as input and returns a Pandas DataFrame
object. For example, if we have a CSV file named Grocery.csv located in the same directory as our Python script, we
can read it using the above mentioned code. By default, the read_csv() function assumes that the first row of the
CSV file contains the column headers. If the CSV file does not have column headers, we can set the header parameter
to None as follows:
>>> import pandas as pd
>>> groceryDF = pd.read_csv('Grocery.csv', header=None)
>>> print(groceryDF)
output:
0 1 2 3
0 Product Category Price Quantity
1 Bread Food 20 2
2 Milk Food 60 5
3 Biscuit Food 20 2
4 Bourn-Vita Food 70 1
5 Soap Hygiene 40 4
6 Brush Hygiene 30 2
7 Detergent Household 80 1
8 Tissues Hygiene 30 5
2.3.1 Specifying the Delimiter
Suppose, we now wish to read the content of the file grocery.txt as shown below:
Product;Category;Price;Quantity
Bread;Food;20;2
Milk;Food;60;5
Biscuit;Food;20;2
>>> import pandas as pd
>>> groceryDF = pd.read_csv('Grocery.txt')
>>> print(groceryDF)
output:
Product;Category;Price;Quantity
0 Bread;Food;20;2
1 Milk;Food;60;5
2 Biscuit;Food;20;2
Note that when reading a text file (grocery.txt), each line of text is considered a single object i.e. a single string.
The method pd.read_csv ignores the semicolons, because by default Pandas considers only a comma as a valid
delimiter. However, we can explicitly specify the delimiter using either the delimiter or sep keyword argument
while invoking the read_csv() function, as shown below:
>>> import pandas as pd
>>> groceryDF = pd.read_csv('Grocery.txt', delimiter=';')
>>> print(groceryDF)
output:
Product Category Price Quantity
0 Bread Food 20 2
1 Milk Food 60 5
2 Biscuit Food 20 2
>>> import pandas as pd
>>> groceryDF = pd.read_csv('Grocery.txt', sep=';')
>>> print(groceryDF)
output:
Product Category Price Quantity
0 Bread Food 20 2
1 Milk Food 60 5
2 Biscuit Food 20 2
34 Touchpad Informatics Practices-XII

