Reading data from CSV (comma separated values) may be a fundamental necessity in Data Science. Often, we get data from various sources which may get exported to CSV format in order that they will be employed by other systems. The Panadas library provides features using which we will read the CSV enter full also as in parts for less than a specific group of columns and rows.

Input as CSV File

The csv file may be a document during which the values within the columns are separated by a comma. Let’s consider the subsequent data present within the file named input.csv.

You can create this file using windows notepad by copying and pasting this data. Save the file as input.csv using the save As All files(*.*) option in notepad.

id,name,salary,start_date,dept

1,Rick,623.3,2012-01-01,IT

2,Dan,515.2,2013-09-23,Operations

3,Tusar,611,2014-11-15,IT

4,Ryan,729,2014-05-11,HR

5,Gary,843.25,2015-03-27,Finance

6,Rasmi,578,2013-05-21,IT

7,Pranab,632.8,2013-07-30,Operations

8,Guru,722.5,2014-06-17,Finance

Reading a CSV File

The read_csv function of the pandas library is employed read the content of a CSV file into the python environment as a pandas DataFrame. The function can read the files from the OS by using proper path to the file.

import pandas as pd

data = pd.read_csv('path/input.csv')

print (data)

When we execute the above code, it produces the subsequent result. Please note how a further column starting with zero as a index has been created by the function.

 id name salary start_date dept

0 1 Rick 623.30 2012-01-01 IT

1 2 Dan 515.20 2013-09-23 Operations

2 3 Tusar 611.00 2014-11-15 IT

3 4 Ryan 729.00 2014-05-11 HR

4 5 Gary 843.25 2015-03-27 Finance

5 6 Rasmi 578.00 2013-05-21 IT

6 7 Pranab 632.80 2013-07-30 Operations

7 8 Guru 722.50 2014-06-17 Finance

Reading Specific Rows

The read_csv function of the pandas library also can be wont to read some specific rows for a given column. We slice the result from the read_csv function using the code shown below for first 5 rows for the column named salary.

import pandas as pd

data = pd.read_csv('path/input.csv')

# Slice the result for first 5 rows

print (data[0:5]['salary'])

When we execute the above code, it produces the subsequent result.

0 623.30

1 515.20

2 611.00

3 729.00

4 843.25

Name: salary, dtype: float64

Reading Specific Columns

The read_csv function of the pandas library also can be wont to read some specific columns. We use the multi-axes indexing method called .loc() for this purpose. we elect to display the salary and name column for all the rows.

import pandas as pd
data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[:,['salary','name']])

When we execute the above code, it produces the subsequent result.

 salary name

0 623.30 Rick

1 515.20 Dan

2 611.00 Tusar

3 729.00 Ryan

4 843.25 Gary

5 578.00 Rasmi

6 632.80 Pranab

7 722.50 Guru

Reading Specific Columns and Rows

The read_csv function of the pandas library also can be wont to read some specific columns and specific rows. We use the multi-axes indexing method called .loc() for this purpose. we elect to display the salary and name column for a few of the rows.

import pandas as pd

data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[[1,3,5],['salary','name']])

When we execute the above code, it produces the subsequent result.

 salary name

1 515.2 Dan

3 729.0 Ryan

5 578.0 Rasmi

Reading Specific Columns for a variety of Rows

The read_csv function of the pandas library also can be wont to read some specific columns and a variety of rows. We use the multi-axes indexing method called .loc() for this purpose. we elect to display the salary and name column for a few of the rows.

import pandas as pd
data = pd.read_csv('path/input.csv')
# Use the multi-axes indexing funtion
print (data.loc[2:6,['salary','name']])

When we execute the above code, it produces the subsequent result.

 salary name

2 611.00 Tusar

3 729.00 Ryan

4 843.25 Gary

5 578.00 Rasmi

6 632.80 Pranab

So, this brings us to the end of blog. This Tecklearn ‘Processing CSV Data in Python’ blog helps you with commonly asked questions if you are looking out for a job in Python Programming. If you wish to learn Python and build a career in Data Science domain, then check out our interactive, Python with Data Science Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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