Importing a CSV file to a DataFrame

A CSV (Comma Separated Values) file stores tabular data as plain text, where each line is a row and values are separated by commas. Pandas provides the read_csv() function to load such a file directly into a DataFrame. This is the most common way to bring external data into your Python environment for analysis.

Consider a file named ResultData.csv stored at the path C:/NCERT. The file contains the following data:

To load this into a DataFrame named marks, you write:

>>> marks = pd.read_csv("C:/NCERT/ResultData.csv", sep=",", header=0)
>>> marks
   RollNo     Name  Eco  Maths
0       1    Arnab   18     57
1       2  Kritika   23     45
2       3   Divyam   51     37
3       4   Vivaan   40     60
4       5  Aaroosh   18     27

Notice that the DataFrame automatically assigns integer row indices (0, 1, 2, …) and uses the first row of the CSV as column names.

Parameters of read_csv()

• First parameter (file path): The string "C:/NCERT/ResultData.csv" gives the full location of the CSV file. You must include the path so Python can find the file on your system.

• sep: This parameter tells pandas what character separates the values in the file. Common separators are comma (,), semicolon (;), or tab (\t). The default value for sep is a space. In the example, sep="," is used because the file is comma-separated.

• header: This parameter specifies which row in the file should be used as the column names for the DataFrame. It also marks the row from which the actual data begins.

  • header=0 means the first line (row 0) of the file is taken as column names, and the data starts from the second line onward.
  • By default, header=0 is assumed, so you can omit it if the first row of your CSV contains the column headers.

Using the names parameter to supply custom column labels

If your CSV file does not have a header row, or if you want to assign different column names than those in the file, you can use the names parameter. When names is provided, pandas ignores any header in the file and uses the list you supply.

For example, suppose you have a file ResultData1.csv that contains the same data but without column names. You can create a DataFrame with your own labels:

>>> marks1 = pd.read_csv("C:/NCERT/ResultData1.csv", sep=",", names=['RNo', 'StudentName', 'Sub1', 'Sub2'])
>>> marks1
   RNo StudentName  Sub1  Sub2
0    1       Arnab    18    57
1    2     Kritika    23    45
2    3      Divyam    51    37
3    4      Vivaan    40    60 …