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Pandas Read CSV in Python

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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.

PYTHON3

# Import pandas
import pandas as pd
 
# reading csv file
df = pd.read_csv("people.csv")
print(df.head())

                    

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.

Python3

# sample = "totalbill_tip, sex:smoker, day_time, size
# 16.99, 1.01:Female|No, Sun, Dinner, 2
# 10.34, 1.66, Male, No|Sun:Dinner, 3
# 21.01:3.5_Male, No:Sun, Dinner, 3
#23.68, 3.31, Male|No, Sun_Dinner, 2
# 24.59:3.61, Female_No, Sun, Dinner, 4
# 25.29, 4.71|Male, No:Sun, Dinner, 4"
 
# Importing pandas library
import pandas as pd
 
# Load the data of csv
df = pd.read_csv('sample.csv',
                 sep='[:, |_]',
                 engine='python')
 
# Print the Dataframe
print(df)

                    

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.

Python3

df = pd.read_csv('people.csv',
        header=0,
        usecols=["First Name", "Sex", "Email"])
# printing dataframe
print(df.head())

                    

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.

Python3

df = pd.read_csv('people.csv',
        header=0,
        index_col=["Sex", "Job Title"],
        usecols=["Sex", "Job Title", "Email"])
 
print(df.head())

                    

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.

Python3

df = pd.read_csv('people.csv',
        header=0,
        index_col=["Sex", "Job Title"],
        usecols=["Sex", "Job Title", "Email"],
                nrows=3)
 
print(df)

                    

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.

Python3

df= pd.read_csv("people.csv")
print("Previous Dataset: ")
print(df)
# using skiprows
df = pd.read_csv("people.csv", skiprows = [1,5])
print("Dataset After skipping rows: ")
print(df)

                    

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



Last Updated : 24 Jul, 2023
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