Python Data Processing

Python Data CSV Processing

In this tutorial we will discuss reading the csv(comma separated values) it is the fundamental necessary topic we will learn about.Pandas Libraries Provides the full features of reading the csv with full control over the file .

Input as CSV file

CSV is the text file which contain the raw material of raw data ,with help of pandas we create the useful information

Name,Phone,Address,RollNo,Class
Rohan,8765435687,India,001,5
Sohan,4565465466,India,002,5
Reema,4987654765,India,003,5
Archana,987654556,India,004,5

Save as this file with the name of info.csv,you can write on notepad by copy and paste the data.

Lets import in Program

Reading CSV file

read_csv function is the library of pandas with the help of this function we can read the file.

Program

import pandas as pd    #importing pandas library 
abc = pd.read_csv('path/info.csv')   #read info.csv file
print (abc)            #print abc file

Output

when we execute this program the following output .

Name         Phone         Address        RollNo        Class
Rohan    8765435687         India          001           5
Sohan    4565465466         India          002           5
Reema    4987654765         India          003           5
Archana  987654556          India          004           5

Reading Specific Rows

Pandas library also used for printing the specific data, We slice the result from the read_csv function using code lets understand by the demonstration.

Program

#import pandas library
import pandas as pd
abc = pd.read_csv('path/info.csv')

# Slice the result for first 2 rows

print (abc[0:2]['Name'])

Output

0
Rohan 
1 
Sohan

Reading Specific Column

Pandas also provides the facility of read any specific column to process the data.

Program

import pandas as pd
#importing  info.csv file
data = pd.read_csv('path/info.csv')

# Use more then one column
print (data.loc[:,['Name','Address']])

When using the loc() function meaning that we use the more than dimension.

Output

0 Rohan     India       
1 Sohan     India         
2 Reema     India         
3 Archana   India  
Subscribe Now