Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Working with csv files in Python Programming
The CSV file or comma separated values file are one of the most widely used flat files to store and hare data across platforms. The columns are separated by comma and there is optional header row also which will indicate the name of each column. Python can read the CSV files using many modules. In this article we will see how the CSV library in python can be used to read and write a CSV file. We can also see the pandas library onl to read the CSV file.
Reading CSV file using csv module
We can get the CSV file from (https://www.guru99.com/python-csv.html)
Example
import csv
with open('C:\iris.csv','rt')as file:
csv_rows = csv.reader(file)
for row in csv_rows:
print(row)Output
Running the above code gives us the following result −
['sepal.length', 'sepal.width', 'petal.length', 'petal.width', 'variety'] ['5.1', '3.5', '1.4', '.2', 'Setosa'] ['4.9', '3', '1.4', '.2', 'Setosa'] ['4.7', '3.2', '1.3', '.2', 'Setosa'] ['4.6', '3.1', '1.5', '.2', 'Setosa'] ['5', '3.6', '1.4', '.2', 'Setosa'] ['5.4', '3.9', '1.7', '.4', 'Setosa'] ['4.6', '3.4', '1.4', '.3', 'Setosa'] ……………… ……………
Reading CSV file using Pandas
Pandas library can also be used to read the csv files. It has the method for reading csv which can be directly applied bypassing the path and the file name. Once the file is read it becomes a data set and then we can print different parts of the data set as required.
Example
import pandas as pd
datainput = pd.read_csv('C:\iris.csv')
print("Given dataset values : \n", datainput)
#size of the dataset
print("\nThe size of the dataset is :\n",datainput.shape)
#printing few rows from the dataset
print("\n printing few rows from the dataset :\n",datainput[0:6])Output
Running the above code gives us the following resul −
Given dataset values : sepal.length sepal.width petal.length petal.width variety 0 5.1 3.5 1.4 0.2 Setosa 1 4.9 3.0 1.4 0.2 Setosa 2 4.7 3.2 1.3 0.2 Setosa 3 4.6 3.1 1.5 0.2 Setosa 4 5.0 3.6 1.4 0.2 Setosa .. ... ... ... ... ... 145 6.7 3.0 5.2 2.3 Virginica 146 6.3 2.5 5.0 1.9 Virginica 147 6.5 3.0 5.2 2.0 Virginica 148 6.2 3.4 5.4 2.3 Virginica 149 5.9 3.0 5.1 1.8 Virginica [150 rows x 5 columns] The size of the dataset is : (150, 5) printing few rows from the dataset : sepal.length sepal.width petal.length petal.width variety 0 5.1 3.5 1.4 0.2 Setosa 1 4.9 3.0 1.4 0.2 Setosa 2 4.7 3.2 1.3 0.2 Setosa 3 4.6 3.1 1.5 0.2 Setosa 4 5.0 3.6 1.4 0.2 Setosa 5 5.4 3.9 1.7 0.4 Setosa
Writing CSV file using csv module
To create a csv file we use Python lists we declare a data set containing each row as a list and all the rows are sublist in a big singer list. We also create another data set which just represents the header row. Then we use various methods like writerow() and csv.writer to finally write the file into the local system.
Example
import csv data = ["Month", "1958", "1959", "1960"] x = [ ["JAN", 340, 360, 417], ["FEB", 318, 342, 391], ["MAR", 362, 406, 419], ["APR", 348, 396, 461], ["MAY", 363, 420, 472], ["JUN", 435, 472, 535], ["JUL", 491, 548, 622], ["AUG", 505, 559, 606], ["SEP", 404, 463, 508], ["OCT", 359, 407, 461], ["NOV", 310, 362, 390], ["DEC", 337, 405, 432], ] y = "C:\years.csv" with open(y, 'w') as work: z = csv.writer(work) z.writerow(data) z.writerows(x)
Output
Running the above code gives us the following result −
Month,1958,1959,1960 JAN,340,360,417 FEB,318,342,391 MAR,362,406,419 APR,348,396,461 MAY,363,420,472 JUN,435,472,535 JUL,491,548,622 AUG,505,559,606 SEP,404,463,508 OCT,359,407,461 NOV,310,362,390 DEC,337,405,432
Writing CSV file using pandas
Uisng pandas is we create a data frame which country is the rows as well as the headers of the rows. Then we use the to_csv method which text the filename and path as parameters and drives the data to csv file.
Example
from pandas import DataFrame
C = {'Month': ['JAN','FEB', 'MAR'],
'1958': ['345', '435', '545'],
'1959': ['377', '135', '985'],
'1960': ['498', '354', '765'],
}
df = DataFrame(C, columns= ["Month", "1958", "1959", "1960"])
export_csv = df.to_csv (r'C:\years_p.csv', index = None, header=True) # here you have to write path, where result file will be stored
print (df)Output
Running the above code gives us the following result −
Month 1958 1959 1960 0 JAN 345 377 498 1 FEB 435 135 354 2 MAR 545 985 765
1K+ Views
