Pandas Read CSV in Python

CSV files are the Comma Separated Files. To access data from the CSV file, we require a function read_csv() from Pandas that retrieves data in the form of the data frame.

Syntax of read_csv() 

Here is the Pandas read CSV syntax with its parameters.

Syntax: pd.read_csv(filepath_or_buffer, sep=’ ,’ , header=’infer’,  index_col=None, usecols=None, engine=None, skiprows=None, nrows=None) 

Parameters: 

• filepath_or_buffer: Location of the csv file. It accepts any string path or URL of the file.
• sep: It stands for separator, default is ‘, ‘.
• header: It accepts int, a list of int, row numbers to use as the column names, and the start of the data. If no names are passed, i.e., header=None, then, it will display the first column as 0, the second as 1, and so on.
• usecols: Retrieves only selected columns from the CSV file.
• nrows: Number of rows to be displayed from the dataset.
• index_col: If None, there are no index numbers displayed along with records.  
• skiprows: Skips passed rows in the new data frame.

Read CSV File using Pandas read_csv

Before using this function, we must import the Pandas library, we will load the CSV file using Pandas.

Output:

  First Name Last Name     Sex                       Email Date of birth  Job Title        
0     Shelby   Terrell    Male        elijah57@example.net    1945-10-26  Games developer 
1    Phillip   Summers  Female       bethany14@example.com    1910-03-24  Phytotherapist  
2   Kristine    Travis    Male       bthompson@example.com    1992-07-02  Homeopath  
3    Yesenia  Martinez    Male   kaitlinkaiser@example.com    2017-08-03  Market researcher
4       Lori      Todd    Male  buchananmanuel@example.net    1938-12-01  Veterinary surgeon

Using sep in read_csv()

In this example, we will take a CSV file and then add some special characters to see how the sep parameter works.

Output:

        totalbill   tip Unnamed: 2   sex smoker Unnamed: 5     day    time  Unnamed: 8  size 
16.99         NaN  1.01     Female    No    NaN        Sun     NaN  Dinner         NaN     2
10.34         NaN  1.66        NaN  Male    NaN         No     Sun  Dinner         NaN     3
21.01        3.50  Male        NaN    No    Sun        NaN  Dinner     NaN         3.0  None
23.68         NaN  3.31        NaN  Male     No        NaN     Sun  Dinner         NaN     2
24.59        3.61   NaN     Female    No    NaN        Sun     NaN  Dinner         NaN     2
25.29         NaN  4.71       Male   NaN     No        Sun     NaN  Dinner         NaN     4

Using usecols in read_csv()

Here, we are specifying only 3 columns,i.e.[“First Name”, “Sex”, “Email”] to load and we use the header 0 as its default header.

Output:

  First Name     Sex                       Email
0     Shelby    Male        elijah57@example.net
1    Phillip  Female       bethany14@example.com
2   Kristine    Male       bthompson@example.com
3    Yesenia    Male   kaitlinkaiser@example.com
4       Lori    Male  buchananmanuel@example.net

Using index_col in read_csv()

Here, we use the “Sex” index first and then the “Job Title” index, we can simply reindex the header with index_col parameter.

Output:

                                                Email
Sex    Job Title                                     
Male   Games developer           elijah57@example.net
Female Phytotherapist           bethany14@example.com
Male   Homeopath                bthompson@example.com
       Market researcher    kaitlinkaiser@example.com
       Veterinary surgeon  buchananmanuel@example.net

Using nrows in read_csv()

Here, we just display only 5 rows using nrows parameter.

Output:

                                        Email
Sex    Job Title                             
Male   Games developer   elijah57@example.net
Female Phytotherapist   bethany14@example.com
Male   Homeopath        bthompson@example.com

Using skiprows in read_csv()

The skiprows help to skip some rows in CSV, i.e, here you will observe that the rows mentioned in skiprows have been skipped from the original dataset.

Output:

Previous Dataset:
  First Name Last Name     Sex                       Email Date of birth           Job Title 
0     Shelby   Terrell    Male        elijah57@example.net    1945-10-26     Games developer
1    Phillip   Summers  Female       bethany14@example.com    1910-03-24      Phytotherapist  
2   Kristine    Travis    Male       bthompson@example.com    1992-07-02           Homeopath  
3    Yesenia  Martinez    Male   kaitlinkaiser@example.com    2017-08-03   Market researcher
4       Lori      Todd    Male  buchananmanuel@example.net    1938-12-01  Veterinary surgeon 
5       Erin       Day    Male         tconner@example.org    2015-10-28  Management officer  
6  Katherine      Buck  Female     conniecowan@example.com    1989-01-22             Analyst
7    Ricardo    Hinton    Male     wyattbishop@example.com    1924-03-26      Hydrogeologist  
Dataset After skipping rows:               
  First Name Last Name     Sex                       Email Date of birth           Job Title 
0     Shelby   Terrell    Male        elijah57@example.net    1945-10-26     Games developer
1   Kristine    Travis    Male       bthompson@example.com    1992-07-02           Homeopath  
2    Yesenia  Martinez    Male   kaitlinkaiser@example.com    2017-08-03   Market researcher
3       Lori      Todd    Male  buchananmanuel@example.net    1938-12-01  Veterinary surgeon      
4  Katherine      Buck  Female     conniecowan@example.com    1989-01-22             Analyst
5    Ricardo    Hinton    Male     wyattbishop@example.com    1924-03-26      Hydrogeologist