Python Tutorial

A csv stands for "comma separated values", which is defined as a simple file format that uses specific structuring to arrange tabular data. It stores tabular data such as spreadsheet or database in plain text and has a common format for data interchange. A csv file opens into the excel sheet, and the rows and columns data define the standard format.

Reading CSV files

Python provides various functions to read csv file. Few of them are discussed below as. To see examples, we must have a csv file. Let's consider an example data in the python.csv file as

Name	Age	City
Ram	27	Mumbai
Bheem	29	Pune
Sita	23	Delhi

1. Using csv.reader() function

In Python, the csv.reader() module is used to read the csv file. It takes each row of the file and makes a list of all the columns.

Example:

Code

import csv
def main():
    # To Open the CSV file
    with open(' python.csv ', newline = '') as csv_file:
        csv_read = csv.reader( csv_file, delimiter = ',')

        # To Read and display each row
        for row in csv_read:
            print(row)
if __name__ == '__main__':
    main()

Output:

[ 'Name', 'Age', 'City' ]
[ 'Ram', '27', 'Mumbai' ]
[ 'Bheem', '29', 'Pune' ]
[ 'Sita', '23', 'Delhi' ]

In the above code, we have opened 'python.csv' using the open() function. We used csv.reader() function to read the file, that returns an iterable reader object. The reader object have consisted the data and we iterated using for loop to print the content of each row

2. Read a CSV into a Dictionary

We can also use DictReader() function to read the csv file directly into a dictionary rather than deal with a list of individual string elements.

Example:

Code

import csv
with open('python.csv', mode='r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    line_count = 0

    for row in csv_reader:
        if line_count == 0:
            print( f'The column names are as follows : {", ".join(row)}' )
            line_count += 1

        print(  f'\t{row[ " Name " ]} lives in {row[" City "]} department and is {row[" Age "]} years old. ')
        line_count += 1

    print(f'Processed {line_count} lines.') 

Output:

The column names are as follows : Name, Age, City
    Ram lives in Mumbai department and is 27 years old.
    Bheem lives in Pune department and is 29 years old.
    Sita lives in Delhi department and is 23 years old.
Processed 4 lines.

Reading csv files with Pandas

3. Reading csv files with Pandas

The Pandas is defined as an open-source library which is built on the top of the NumPy library. It provides fast analysis, data cleaning, and preparation of the data for the user. Reading the csv file into a pandas DataFrame is quick and straight forward. We don't need to write enough lines of code to open, analyze, and read the csv file in pandas and it stores the data in DataFrame. Here, we are taking a slightly more complicated file to read, called hrdata.csv, which contains data of company employees.

Name, Hire Date, Salary, Leaves Remaining

John Idle, 08/15/14, 50000.00, 10

Smith Gilliam, 04/07/15, 65000.00, 8

Parker Chapman, 02/21/14,45000.00, 10

Jones Palin, 10/14/13, 70000.00, 3

Terry Gilliam, 07/22/14, 48000.00, 7

Michael Palin, 06/28/13, 66000.00, 8

Example:

Code

import pandas as pd
# Read the CSV file into a DataFrame
df = pd.read_csv(' hrdata.csv ')

# Print the DataFrame
print(df)

In the above code, the three lines are enough to read the file, and only one of them is doing the actual work, i.e., pandas.read_csv()

Output:

         Name                Hire Date     Salary      Leaves Remaining
0     John Idle              03/15/14      50000.0       10
1     Smith Gilliam          06/01/15      65000.0       8
2     Parker Chapman         05/12/14      45000.0       10
3     Jones Palin            11/01/13      70000.0       3
4     Terry Gilliam          08/12/14      48000.0       7
5     Michael Palin          05/23/13      66000.0       8

Python CSV Module Functions

The CSV module work is used to handle the CSV files to read/write and get data from specified columns. There are different types of CSV functions, which are as follows:

csv.reader	It reads the data from a csv file
csv.writer	It writes the data to a csv file
csv.field_size_limit	It returns the current maximum field size allowed by the parser.
csv.get_dialect	It returns the dialect associated with a name.
csv.list_dialects	It returns the names of all registered dialects.
csv.register_dialect	It associates dialect with a name. The name must be a string or a Unicode object.
csv.unregister_dialect	It deletes the dialect which is associated with the name from the dialect registry. If a name is not a registered dialect name, then an error is being raised.
csv.QUOTE_ALL	It instructs the writer objects to quote all fields.
csv.QUOTE_MINIMAL	It instructs the writer objects to quote only those fields which contain special characters such as quote char, delimiter, etc.
csv.QUOTE_NONNUMERIC	It instructs the writer objects to quote all the non-numeric fields.
csv.QUOTE_NONE	It instructs the writer object never to quote the fields.

Conclusion

While working with CSV records, it is essential to guarantee that the document is appropriately organized and that the fitting libraries are imported. You might have to deal with potential issues, for example, missing qualities, conflicting information types, and extraordinary characters. Understanding the design of the CSV record, including the header and information sections, is pivotal for powerful information handling.

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