April 18, 2022

Tutorial: How to Easily Read Files in Python (Text, CSV, JSON)

Read Files in Python

Reading Files with Python

Files are everywhere: on computers, mobile devices, and across the cloud. Working with files is essential for every programmer, regardless of which programming language you’re using.

File handling is a mechanism for creating a file, writing data, and reading data from it. The good news is that Python is enriched with packages for handling different file types.

In this tutorial, we’ll learn how to handle files of different types. However, we’ll focus more on reading files with Python.

After you finish this tutorial, you’ll know how to do the following:

  • Open files and use the with context manager
  • File modes in Python
  • Read text
  • Read CSV files
  • Read JSON files

Let’s dive in.

Opening a File

Before accessing the contents of a file, we need to open the file. Python provides a built-in function that helps us open files in different modes. The open() function accepts two essential parameters: the file name and the mode; the default mode is 'r', which opens the file for reading only. The modes define how we can access a file and how we can manipulate its content. The open() function provides a few different modes that we’ll discuss later in this tutorial.

First, let’s try the function by opening a text file. Download the text file containing the Zen of Python, and store it in the same path as your code.

f = open('zen_of_python.txt', 'r')
print(f.read())
f.close()
    The Zen of Python, by Tim Peters

    Beautiful is better than ugly.
    Explicit is better than implicit.
    Simple is better than complex.
    Complex is better than complicated.
    Flat is better than nested.
    Sparse is better than dense.
    Readability counts.
    Special cases aren't special enough to break the rules.
    Although practicality beats purity.
    Errors should never pass silently.
    Unless explicitly silenced.
    In the face of ambiguity, refuse the temptation to guess.
    There should be one-- and preferably only one --obvious way to do it.
    Although that way may not be obvious at first unless you're Dutch.
    Now is better than never.
    Although never is often better than *right* now.
    If the implementation is hard to explain, it's a bad idea.
    If the implementation is easy to explain, it may be a good idea.
    Namespaces are one honking great idea -- let's do more of those!

In the code above, the open() function opens the text file in the reading mode, allowing us to grab information from the file without accidentally changing it. In the first line, the output of the open() function is assigned to the f variable, an object representing the text file. In the second line of the code above, we use the read() method to read the entire file and print its content. The close() method closes the file in the last line. We must always close the opened files after we’re done with them to release our computer resources and avoid raising exceptions.

In Python, we can use the with context manager to ensure a program releases the resources used after the file was closed, even if an exception has occurred. Let’s try it:

with open('zen_of_python.txt') as f:
    print(f.read())
    The Zen of Python, by Tim Peters

    Beautiful is better than ugly.
    Explicit is better than implicit.
    Simple is better than complex.
    Complex is better than complicated.
    Flat is better than nested.
    Sparse is better than dense.
    Readability counts.
    Special cases aren't special enough to break the rules.
    Although practicality beats purity.
    Errors should never pass silently.
    Unless explicitly silenced.
    In the face of ambiguity, refuse the temptation to guess.
    There should be one-- and preferably only one --obvious way to do it.
    Although that way may not be obvious at first unless you're Dutch.
    Now is better than never.
    Although never is often better than *right* now.
    If the implementation is hard to explain, it's a bad idea.
    If the implementation is easy to explain, it may be a good idea.
    Namespaces are one honking great idea -- let's do more of those!

The code above creates a context using the with statement that the file object is no longer open out of the context. The bound variable, f, represents the file object that all the file object methods are accessible through the variable. The read() method reads the entire file in the second line, and then the print() function outputs the file content.

When the program reaches the end of the with statement block context, it closes the file to release the resources and ensures that other programs can use them. In general, using the with statement is a highly recommended practice when you’re working with objects that need to be closed as soon as they’re no longer required, such as files, databases, and network connections.

Notice that we have access to the f variable even after exiting the with context manager block; however, the file is closed. Let’s try some of the file object properties to see if the variable is still alive and accessible:

print("Filename is '{}'.".format(f.name))
if f.closed:
    print("File is closed.")
else:
    print("File isn't closed.")
    Filename is 'zen_of_python.txt'.
    File is closed.

However, it’s impossible to read from the file or write to the file. When a file is closed, any attempt to access its content leads to the following error:

f.read()
    ---------------------------------------------------------------------------

    ValueError                                Traceback (most recent call last)

    ~\AppData\Local\Temp/ipykernel_9828/3059900045.py in <module>
    ----> 1 f.read()

    ValueError: I/O operation on closed file.

File Modes in Python

As we mentioned in the previous section, we need to specify the mode while opening a file. The following table shows the different file modes in Python:

Mode Description
'r' It opens a file for reading only.
'w' It opens a file for writing. If the file exists, it overwrites it, otherwise, it creates a new file.
'a' It opens a file for appending only. If the file doesn’t exist, it creates the file.
'x' It creates a new file. If the file exists, it fails.
'+' It opens a file for updating.

We can also specify opening a file in text mode, 't', the default mode, or binary mode, 'b'. Let’s see how we can copy an image file, dataquest_logo.png, using simple statements:

with open('dataquest_logo.png', 'rb') as rf:
    with open('data_quest_logo_copy.png', 'wb') as wf:
        for b in rf:
            wf.write(b)

The code above copies the Dataquest logo image and stores it in the same path. The 'rb' mode opens the file for reading in binary mode, and the 'wb' mode opens the file for writing in text mode.

Reading Text Files

There are different ways to read text files. This section will review some of the useful methods for reading the content of text files.

So far, we’ve learned the entire content of a file can be read with the read() method. What if we only want to read a few bytes from a text file. To do that, specify the number of bytes in the read() method. Let’s try it:

with open('zen_of_python.txt') as f:
    print(f.read(17))
The Zen of Python

The simple code above reads the first 17 bytes of the zen_of_python.txt file and prints them out.

Sometimes, it makes more sense to read the content of a text file one line at a time. In this case, we can use the readline() method. Let’s do it:

with open('zen_of_python.txt') as f:
    print(f.readline())
The Zen of Python, by Tim Peters

The code above returns the first line of the file. If we call the method one more time, it will return the second line in the file, etc., as follows:

with open('zen_of_python.txt') as f:
    print(f.readline())
    print(f.readline())
    print(f.readline())
    print(f.readline())
The Zen of Python, by Tim Peters  

Beautiful is better than ugly.

Explicit is better than implicit.

This useful method helps us to read the entire file incrementally. The following code outputs the entire file by iterating over it line by line until the file pointer that keeps track of where we’re reading or writing the file reaches the end of the file. When the readline() method reaches the end of the file, it returns an empty string, ''.with open(‘zen_of_python.txt’) as f:

with open('zen_of_python.txt') as f:
    line = f.readline()
    while line:
        print(line, end='')
        line = f.readline()        
    The Zen of Python, by Tim Peters

    Beautiful is better than ugly.
    Explicit is better than implicit.
    Simple is better than complex.
    Complex is better than complicated.
    Flat is better than nested.
    Sparse is better than dense.
    Readability counts.
    Special cases aren't special enough to break the rules.
    Although practicality beats purity.
    Errors should never pass silently.
    Unless explicitly silenced.
    In the face of ambiguity, refuse the temptation to guess.
    There should be one-- and preferably only one --obvious way to do it.
    Although that way may not be obvious at first unless you're Dutch.
    Now is better than never.
    Although never is often better than *right* now.
    If the implementation is hard to explain, it's a bad idea.
    If the implementation is easy to explain, it may be a good idea.
    Namespaces are one honking great idea -- let's do more of those!

The code above reads the first line of the file outside the while loop and assigns it to the line variable. Inside the while loop, it prints the string stored in the line variable, then reads the next line of the file. The while loop iterates the process until the readline() method returns an empty string. The empty string evaluates to False in the while loop, so the iteration process terminates.

The other helpful method for reading text files is the readlines() method. Applying this method on a file object returns a list of strings containing each line of the file. Let’s see how it works:

with open('zen_of_python.txt') as f:
    lines = f.readlines()

Let’s check the data type of the lines variable and then print it:

print(type(lines))
print(lines)
<class 'list'>
['The Zen of Python, by Tim Peters\n', '\n', 'Beautiful is better than ugly.\n', 'Explicit is better than implicit.\n', 'Simple is better than complex.\n', 'Complex is better than complicated.\n', 'Flat is better than nested.\n', 'Sparse is better than dense.\n', 'Readability counts.\n', "Special cases aren't special enough to break the rules.\n", 'Although practicality beats purity.\n', 'Errors should never pass silently.\n', 'Unless explicitly silenced.\n', 'In the face of ambiguity, refuse the temptation to guess.\n', 'There should be one-- and preferably only one --obvious way to do it.\n', "Although that way may not be obvious at first unless you're Dutch.\n", 'Now is better than never.\n', 'Although never is often better than *right* now.\n', "If the implementation is hard to explain, it's a bad idea.\n", 'If the implementation is easy to explain, it may be a good idea.\n', "Namespaces are one honking great idea -- let's do more of those!"]

It’s a list of strings wherein each item in the list is one line of the text file. The \n escape character represents a new line in the file. Also, we can access every item in the list by indexing or slicing operations:

print(lines)
print(lines[3:5])
print(lines[-1])
['The Zen of Python, by Tim Peters\n', '\n', 'Beautiful is better than ugly.\n', 'Explicit is better than implicit.\n', 'Simple is better than complex.\n', 'Complex is better than complicated.\n', 'Flat is better than nested.\n', 'Sparse is better than dense.\n', 'Readability counts.\n', "Special cases aren't special enough to break the rules.\n", 'Although practicality beats purity.\n', 'Errors should never pass silently.\n', 'Unless explicitly silenced.\n', 'In the face of ambiguity, refuse the temptation to guess.\n', 'There should be one-- and preferably only one --obvious way to do it.\n', "Although that way may not be obvious at first unless you're Dutch.\n", 'Now is better than never.\n', 'Although never is often better than *right* now.\n', "If the implementation is hard to explain, it's a bad idea.\n", 'If the implementation is easy to explain, it may be a good idea.\n', "Namespaces are one honking great idea -- let's do more of those!"]
['Explicit is better than implicit.\n', 'Simple is better than complex.\n']
Namespaces are one honking great idea -- let's do more of those!

Reading CSV Files

So far, we’ve learned how to work with regular text files. However, sometimes data comes in a CSV format, and it’s common for data professionals to retrieve required information and manipulate the content of CSV files.

We’ll use the CSV module in this section. The CSV module provides helpful methods to read the comma-separated values stored in a CSV file. We’ll try it right now, but first, you need to download the chocolate.csv file and store it in the current working directory:

import csv
with open('chocolate.csv') as f:
    reader = csv.reader(f, delimiter=',')
    for row in reader:
        print(row)
    ['Company', 'Bean Origin or Bar Name', 'REF', 'Review Date', 'Cocoa Percent', 'Company Location', 'Rating', 'Bean Type', 'Country of Origin']
    ['A. Morin', 'Agua Grande', '1876', '2016', '63%', 'France', '3.75', 'Â\xa0', 'Sao Tome']
    ['A. Morin', 'Kpime', '1676', '2015', '70%', 'France', '2.75', 'Â\xa0', 'Togo']
    ['A. Morin', 'Atsane', '1676', '2015', '70%', 'France', '3', 'Â\xa0', 'Togo']
    ['A. Morin', 'Akata', '1680', '2015', '70%', 'France', '3.5', 'Â\xa0', 'Togo']
    ['Acalli', 'Chulucanas, El Platanal', '1462', '2015', '70%', 'U.S.A.', '3.75', 'Â\xa0', 'Peru']
    ['Acalli', 'Tumbes, Norandino', '1470', '2015', '70%', 'U.S.A.', '3.75', 'Criollo', 'Peru']
    ['Adi', 'Vanua Levu', '705', '2011', '60%', 'Fiji', '2.75', 'Trinitario', 'Fiji']
    ['Adi', 'Vanua Levu, Toto-A', '705', '2011', '80%', 'Fiji', '3.25', 'Trinitario', 'Fiji']
    ['Adi', 'Vanua Levu', '705', '2011', '88%', 'Fiji', '3.5', 'Trinitario', 'Fiji']
    ['Adi', 'Vanua Levu, Ami-Ami-CA', '705', '2011', '72%', 'Fiji', '3.5', 'Trinitario', 'Fiji']
    ['Aequare (Gianduja)', 'Los Rios, Quevedo, Arriba', '370', '2009', '55%', 'Ecuador', '2.75', 'Forastero (Arriba)', 'Ecuador']
    ['Aequare (Gianduja)', 'Los Rios, Quevedo, Arriba', '370', '2009', '70%', 'Ecuador', '3', 'Forastero (Arriba)', 'Ecuador']
    ['Ah Cacao', 'Tabasco', '316', '2009', '70%', 'Mexico', '3', 'Criollo', 'Mexico']
    ["Akesson's (Pralus)", 'Bali (west), Sukrama Family, Melaya area', '636', '2011', '75%', 'Switzerland', '3.75', 'Trinitario', 'Indonesia']
    ["Akesson's (Pralus)", 'Madagascar, Ambolikapiky P.', '502', '2010', '75%', 'Switzerland', '2.75', 'Criollo', 'Madagascar']
    ["Akesson's (Pralus)", 'Monte Alegre, D. Badero', '508', '2010', '75%', 'Switzerland', '2.75', 'Forastero', 'Brazil']
    ['Alain Ducasse', 'Trinite', '1215', '2014', '65%', 'France', '2.75', 'Trinitario', 'Trinidad']
    ['Alain Ducasse', 'Vietnam', '1215', '2014', '75%', 'France', '2.75', 'Trinitario', 'Vietnam']
    ['Alain Ducasse', 'Madagascar', '1215', '2014', '75%', 'France', '3', 'Trinitario', 'Madagascar']
    ['Alain Ducasse', 'Chuao', '1061', '2013', '75%', 'France', '2.5', 'Trinitario', 'Venezuela']
    ['Alain Ducasse', 'Piura, Perou', '1173', '2013', '75%', 'France', '2.5', 'Â\xa0', 'Peru']
    ['Alexandre', 'Winak Coop, Napo', '1944', '2017', '70%', 'Netherlands', '3.5', 'Forastero (Nacional)', 'Ecuador']
    ['Alexandre', 'La Dalia, Matagalpa', '1944', '2017', '70%', 'Netherlands', '3.5', 'Criollo, Trinitario', 'Nicaragua']
    ['Alexandre', 'Tien Giang', '1944', '2017', '70%', 'Netherlands', '3.5', 'Trinitario', 'Vietnam']
    ['Alexandre', 'Makwale Village, Kyela', '1944', '2017', '70%', 'Netherlands', '3.5', 'Forastero', 'Tanzania']
    ['Altus aka Cao Artisan', 'Momotombo', '1728', '2016', '60%', 'U.S.A.', '2.75', 'Â\xa0', 'Nicaragua']
    ['Altus aka Cao Artisan', 'Bolivia', '1133', '2013', '60%', 'U.S.A.', '3', 'Â\xa0', 'Bolivia']
    ['Altus aka Cao Artisan', 'Peru', '1133', '2013', '60%', 'U.S.A.', '3.25', 'Â\xa0', 'Peru']
    ['Amano', 'Morobe', '725', '2011', '70%', 'U.S.A.', '4', 'Â\xa0', 'Papua New Guinea']
    ['Amano', 'Dos Rios', '470', '2010', '70%', 'U.S.A.', '3.75', 'Â\xa0', 'Dominican Republic']
    ['Amano', 'Guayas', '470', '2010', '70%', 'U.S.A.', '4', 'Â\xa0', 'Ecuador']
    ['Amano', 'Chuao', '544', '2010', '70%', 'U.S.A.', '3', 'Trinitario', 'Venezuela']
    ['Amano', 'Montanya', '363', '2009', '70%', 'U.S.A.', '3', 'Â\xa0', 'Venezuela']
    ['Amano', 'Bali, Jembrana', '304', '2008', '70%', 'U.S.A.', '2.75', 'Â\xa0', 'Indonesia']
    ['Amano', 'Madagascar', '129', '2007', '70%', 'U.S.A.', '3.5', 'Trinitario', 'Madagascar']
    ['Amano', 'Cuyagua', '147', '2007', '70%', 'U.S.A.', '3', 'Â\xa0', 'Venezuela']
    ['Amano', 'Ocumare', '175', '2007', '70%', 'U.S.A.', '3.75', 'Criollo', 'Venezuela']
    ['Amatller (Simon Coll)', 'Ghana', '322', '2009', '70%', 'Spain', '3', 'Forastero', 'Ghana']
    ['Amatller (Simon Coll)', 'Ecuador', '327', '2009', '70%', 'Spain', '2.75', 'Â\xa0', 'Ecuador']
    ['Amatller (Simon Coll)', 'Ecuador', '464', '2009', '85%', 'Spain', '2.75', 'Â\xa0', 'Ecuador']
    ['Amatller (Simon Coll)', 'Ghana', '464', '2009', '85%', 'Spain', '3', 'Forastero', 'Ghana']
    ['Amazona', 'LamasdelChanka, San Martin, Oro Verde coop', '1145', '2013', '72%', 'Peru', '3.25', 'Â\xa0', 'Peru']
    ['Ambrosia', 'Venezuela', '1498', '2015', '70%', 'Canada', '3.25', 'Â\xa0', 'Venezuela']
    ['Ambrosia', 'Peru', '1498', '2015', '68%', 'Canada', '3.5', 'Â\xa0', 'Peru']
    ['Amedei', 'Piura, Blanco de Criollo', '979', '2012', '70%', 'Italy', '3.75', 'Â\xa0', 'Peru']
    ['Amedei', 'Porcelana', '111', '2007', '70%', 'Italy', '4', 'Criollo (Porcelana)', 'Venezuela']
    ['Amedei', 'Nine', '111', '2007', '75%', 'Italy', '4', 'Blend', 'Â\xa0']
    ['Amedei', 'Chuao', '111', '2007', '70%', 'Italy', '5', 'Trinitario', 'Venezuela']
    ['Amedei', 'Ecuador', '123', '2007', '70%', 'Italy', '3', 'Trinitario', 'Ecuador']
    ['Amedei', 'Jamaica', '123', '2007', '70%', 'Italy', '3', 'Trinitario', 'Jamaica']
    ['Amedei', 'Grenada', '123', '2007', '70%', 'Italy', '3.5', 'Trinitario', 'Grenada']
    ['Amedei', 'Venezuela', '123', '2007', '70%', 'Italy', '3.75', 'Trinitario (85% Criollo)', 'Venezuela']
    ['Amedei', 'Madagascar', '123', '2007', '70%', 'Italy', '4', 'Trinitario (85% Criollo)', 'Madagascar']
    ['Amedei', 'Trinidad', '129', '2007', '70%', 'Italy', '3.5', 'Trinitario', 'Trinidad']
    ['Amedei', 'Toscano Black', '170', '2007', '63%', 'Italy', '3.5', 'Blend', 'Â\xa0']
    ['Amedei', 'Toscano Black', '40', '2006', '70%', 'Italy', '5', 'Blend', 'Â\xa0']
    ['Amedei', 'Toscano Black', '75', '2006', '66%', 'Italy', '4', 'Blend', 'Â\xa0']
    ['AMMA', 'Catongo', '1065', '2013', '75%', 'Brazil', '3.25', 'Forastero (Catongo)', 'Brazil']
    ['AMMA', 'Monte Alegre, 3 diff. plantations', '572', '2010', '85%', 'Brazil', '2.75', 'Forastero (Parazinho)', 'Brazil']
    ['AMMA', 'Monte Alegre, 3 diff. plantations', '572', '2010', '50%', 'Brazil', '3.75', 'Forastero (Parazinho)', 'Brazil']
    ['AMMA', 'Monte Alegre, 3 diff. plantations', '572', '2010', '75%', 'Brazil', '3.75', 'Forastero (Parazinho)', 'Brazil']
    ['AMMA', 'Monte Alegre, 3 diff. plantations', '572', '2010', '60%', 'Brazil', '4', 'Forastero (Parazinho)', 'Brazil']
    ['Anahata', 'Elvesia', '1259', '2014', '75%', 'U.S.A.', '3', 'Â\xa0', 'Dominican Republic']
    ['Animas', 'Alto Beni', '1852', '2016', '75%', 'U.S.A.', '3.5', 'Â\xa0', 'Bolivia']
    ['Ara', 'Madagascar', '1375', '2014', '75%', 'France', '3', 'Trinitario', 'Madagascar']
    ['Ara', 'Chiapas', '1379', '2014', '72%', 'France', '2.5', 'Â\xa0', 'Mexico']
    ['Arete', 'Camino Verde', '1602', '2015', '75%', 'U.S.A.', '3.5', 'Â\xa0', 'Ecuador']
    ['Artisan du Chocolat', 'Congo', '300', '2008', '72%', 'U.K.', '3.75', 'Forastero', 'Congo']
    ['Artisan du Chocolat (Casa Luker)', 'Orinoqua Region, Arauca', '1181', '2013', '72%', 'U.K.', '2.75', 'Trinitario', 'Colombia']
    ['Askinosie', 'Mababa', '1780', '2016', '68%', 'U.S.A.', '3.75', 'Trinitario', 'Tanzania']
    ['Askinosie', 'Tenende, Uwate', '647', '2011', '72%', 'U.S.A.', '3.75', 'Trinitario', 'Tanzania']
    ['Askinosie', 'Cortes', '661', '2011', '70%', 'U.S.A.', '3.75', 'Trinitario', 'Honduras']
    ['Askinosie', 'Davao', '331', '2009', '77%', 'U.S.A.', '3.75', 'Trinitario', 'Philippines']
    ['Askinosie', 'Xoconusco', '141', '2007', '75%', 'U.S.A.', '2.5', 'Trinitario', 'Mexico']
    ['Askinosie', 'San Jose del Tambo', '175', '2007', '70%', 'U.S.A.', '3', 'Forastero (Arriba)', 'Ecuador']
    ['Bahen & Co.', 'Houseblend', '999', '2012', '70%', 'Australia', '2.5', 'Blend', 'Â\xa0']
    ['Bakau', 'Bambamarca, 2015', '1454', '2015', '70%', 'Peru', '2.75', 'Â\xa0', 'Peru']
    ['Bakau', 'Huallabamba, 2015', '1454', '2015', '70%', 'Peru', '3.5', 'Â\xa0', 'Peru']
    ['Bar Au Chocolat', 'Bahia', '1554', '2015', '70%', 'U.S.A.', '3.5', 'Â\xa0', 'Brazil']
    ['Bar Au Chocolat', 'Maranon Canyon', '1295', '2014', '70%', 'U.S.A.', '4', 'Forastero (Nacional)', 'Peru']
    ['Bar Au Chocolat', 'Duarte Province', '983', '2012', '70%', 'U.S.A.', '3.25', 'Â\xa0', 'Dominican Republic']
    ['Bar Au Chocolat', 'Chiapas', '983', '2012', '70%', 'U.S.A.', '3.5', 'Â\xa0', 'Mexico']
    ['Bar Au Chocolat', 'Sambirano', '983', '2012', '70%', 'U.S.A.', '3.75', 'Trinitario', 'Madagascar']
    ["Baravelli's", 'single estate', '955', '2012', '80%', 'Wales', '2.75', 'Â\xa0', 'Costa Rica']
    ['Batch', 'Dominican Republic, Batch 3', '1840', '2016', '65%', 'U.S.A.', '3.5', 'Â\xa0', 'Domincan Republic']
    ['Batch', 'Brazil', '1868', '2016', '70%', 'U.S.A.', '3.75', 'Â\xa0', 'Brazil']
    ['Batch', 'Ecuador', '1880', '2016', '65%', 'U.S.A.', '3.25', 'Â\xa0', 'Ecuador']
    ['Beau Cacao', 'Asajaya E, NW Borneo, b. #132/4500', '1948', '2017', '73%', 'U.K.', '3', 'Â\xa0', 'Malaysia']
    ['Beau Cacao', 'Serian E., NW Borneo, b. #134/3800', '1948', '2017', '72%', 'U.K.', '3.25', 'Â\xa0', 'Malaysia']
    ['Beehive', 'Brazil, Batch 20316', '1784', '2016', '80%', 'U.S.A.', '2.75', 'Â\xa0', 'Brazil']
    ['Beehive', 'Dominican Republic, Batch 31616', '1784', '2016', '70%', 'U.S.A.', '2.75', 'Â\xa0', 'Domincan Republic']
    ['Beehive', 'Ecuador, Batch 31516', '1784', '2016', '70%', 'U.S.A.', '2.75', 'Â\xa0', 'Ecuador']
    ['Beehive', 'Ecuador', '1788', '2016', '90%', 'U.S.A.', '2.75', 'Â\xa0', 'Ecuador']
    ['Belcolade', 'Costa Rica', '586', '2010', '64%', 'Belgium', '2.75', 'Â\xa0', 'Costa Rica']
    ['Belcolade', 'Papua New Guinea', '586', '2010', '64%', 'Belgium', '2.75', 'Â\xa0', 'Papua New Guinea']
    ['Belcolade', 'Peru', '586', '2010', '64%', 'Belgium', '2.75', 'Â\xa0', 'Peru']
    ['Belcolade', 'Ecuador', '586', '2010', '71%', 'Belgium', '3.5', 'Â\xa0', 'Ecuador']
    ['Bellflower', 'Kakao Kamili, Kilombero Valley', '1800', '2016', '70%', 'U.S.A.', '3.5', 'Criollo, Trinitario', 'Tanzania']
    ['Bellflower', 'Alto Beni, Palos Blanco', '1804', '2016', '70%', 'U.S.A.', '3.25', 'Â\xa0', 'Bolivia']
    ['Vintage Plantations (Tulicorp)', 'Los Rios, Rancho Grande 2007', '153', '2007', '65%', 'U.S.A.', '3', 'Forastero (Arriba)', 'Ecuador']
    ['Violet Sky', 'Sambirano Valley', '1458', '2015', '77%', 'U.S.A.', '2.75', 'Trinitario', 'Madagascar']
    ['Wm', 'Wild Beniano, 2016, batch 128, Heirloom', '1912', '2016', '76%', 'U.S.A.', '3.5', 'Â\xa0', 'Bolivia']
    ['Wm', 'Ghana, 2013, batch 129', '1916', '2016', '75%', 'U.S.A.', '3.75', 'Â\xa0', 'Ghana']
    ['Woodblock', 'La Red', '769', '2011', '70%', 'U.S.A.', '3.5', 'Â\xa0', 'Dominican Republic']
    ['Xocolat', 'Hispaniola', '1057', '2013', '66%', 'Domincan Republic', '3', 'Â\xa0', 'Dominican Republic']
    ['Xocolla', 'Sambirano, batch 170102', '1948', '2017', '70%', 'U.S.A.', '2.75', 'Â\xa0', 'Madagascar']
    ['Xocolla', 'Hispaniola, batch 170104', '1948', '2017', '70%', 'U.S.A.', '2.5', 'Â\xa0', 'Dominican Republic']
    ['Zart Pralinen', 'UNOCACE', '1824', '2016', '70%', 'Austria', '2.75', 'Nacional (Arriba)', 'Ecuador']
    ['Zart Pralinen', 'San Juan Estate', '1824', '2016', '85%', 'Austria', '2.75', 'Trinitario', 'Trinidad']
    ['Zokoko', 'Guadalcanal', '1716', '2016', '78%', 'Australia', '3.75', 'Â\xa0', 'Solomon Islands']

Each row of the CSV file forms a list wherein every item can be easily accessed, as follows:

import csv
with open('chocolate.csv') as f:
    reader = csv.reader(f, delimiter=',')
    for row in reader:
        print("The {} company is located in {}.".format(row[0], row[5]))
    The Company company is located in Company Location.
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The Acalli company is located in U.S.A..
    The Acalli company is located in U.S.A..
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Aequare (Gianduja) company is located in Ecuador.
    The Aequare (Gianduja) company is located in Ecuador.
    The Ah Cacao company is located in Mexico.
    The Akesson's (Pralus) company is located in Switzerland.
    The Akesson's (Pralus) company is located in Switzerland.
    The Akesson's (Pralus) company is located in Switzerland.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Altus aka Cao Artisan company is located in U.S.A..
    The Altus aka Cao Artisan company is located in U.S.A..
    The Altus aka Cao Artisan company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amazona company is located in Peru.
    The Ambrosia company is located in Canada.
    The Ambrosia company is located in Canada.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The Anahata company is located in U.S.A..
    The Animas company is located in U.S.A..
    The Ara company is located in France.
    The Ara company is located in France.
    The Arete company is located in U.S.A..
    The Artisan du Chocolat company is located in U.K..
    The Artisan du Chocolat (Casa Luker) company is located in U.K..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Bahen & Co. company is located in Australia.
    The Bakau company is located in Peru.
    The Bakau company is located in Peru.
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Baravelli's company is located in Wales.
    The Batch company is located in U.S.A..
    The Batch company is located in U.S.A..
    The Batch company is located in U.S.A..
    The Beau Cacao company is located in U.K..
    The Beau Cacao company is located in U.K..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Bellflower company is located in U.S.A..
    The Bellflower company is located in U.S.A..
    The Vintage Plantations (Tulicorp) company is located in U.S.A..
    The Violet Sky company is located in U.S.A..
    The Wm company is located in U.S.A..
    The Wm company is located in U.S.A..
    The Woodblock company is located in U.S.A..
    The Xocolat company is located in Domincan Republic.
    The Xocolla company is located in U.S.A..
    The Xocolla company is located in U.S.A..
    The Zart Pralinen company is located in Austria.
    The Zart Pralinen company is located in Austria.
    The Zokoko company is located in Australia.

It’s possible to use the name of the columns instead of using their indices, which is usually more convenient for developers. In this case, instead of using the reader() method, we use the DictReader() method that returns a collection of dictionary objects. Let’s try it:

import csv
with open('chocolate.csv') as f:
    dict_reader = csv.DictReader(f, delimiter=',')
    for row in dict_reader:
        print("The {} company is located in {}.".format(row['Company'], row['Company Location']))
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The A. Morin company is located in France.
    The Acalli company is located in U.S.A..
    The Acalli company is located in U.S.A..
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Adi company is located in Fiji.
    The Aequare (Gianduja) company is located in Ecuador.
    The Aequare (Gianduja) company is located in Ecuador.
    The Ah Cacao company is located in Mexico.
    The Akesson's (Pralus) company is located in Switzerland.
    The Akesson's (Pralus) company is located in Switzerland.
    The Akesson's (Pralus) company is located in Switzerland.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alain Ducasse company is located in France.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Alexandre company is located in Netherlands.
    The Altus aka Cao Artisan company is located in U.S.A..
    The Altus aka Cao Artisan company is located in U.S.A..
    The Altus aka Cao Artisan company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amano company is located in U.S.A..
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amatller (Simon Coll) company is located in Spain.
    The Amazona company is located in Peru.
    The Ambrosia company is located in Canada.
    The Ambrosia company is located in Canada.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The Amedei company is located in Italy.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The AMMA company is located in Brazil.
    The Anahata company is located in U.S.A..
    The Animas company is located in U.S.A..
    The Ara company is located in France.
    The Ara company is located in France.
    The Arete company is located in U.S.A..
    The Artisan du Chocolat company is located in U.K..
    The Artisan du Chocolat (Casa Luker) company is located in U.K..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Askinosie company is located in U.S.A..
    The Bahen & Co. company is located in Australia.
    The Bakau company is located in Peru.
    The Bakau company is located in Peru.
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Bar Au Chocolat company is located in U.S.A..
    The Baravelli's company is located in Wales.
    The Batch company is located in U.S.A..
    The Batch company is located in U.S.A..
    The Batch company is located in U.S.A..
    The Beau Cacao company is located in U.K..
    The Beau Cacao company is located in U.K..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Beehive company is located in U.S.A..
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Belcolade company is located in Belgium.
    The Bellflower company is located in U.S.A..
    The Bellflower company is located in U.S.A..
    The Vintage Plantations (Tulicorp) company is located in U.S.A..
    The Violet Sky company is located in U.S.A..
    The Wm company is located in U.S.A..
    The Wm company is located in U.S.A..
    The Woodblock company is located in U.S.A..
    The Xocolat company is located in Domincan Republic.
    The Xocolla company is located in U.S.A..
    The Xocolla company is located in U.S.A..
    The Zart Pralinen company is located in Austria.
    The Zart Pralinen company is located in Austria.
    The Zokoko company is located in Australia.

Reading JSON Files

Another popular file format that we mainly use for storing and exchanging data is JSON. JSON stands for JavaScript Object Notation, and it allows us to store data with key-value pairs separated by commas.

In this section, we’re going to load a JSON file and work with it as a JSON object — not as a text file. To do that, we need to import the JSON module. Then, in the with context manager, we use the load() method that belongs to the json object. It loads the file’s content and stores it in the context variable as a dictionary. Let’s try it, but before running the code, download the movie.json file and put it in the current working directory.

import json
with open('movie.json') as f:
    content = json.load(f)
    print(content)
    {'Title': 'Bicentennial Man', 'Release Date': 'Dec 17 1999', 'MPAA Rating': 'PG', 'Running Time min': 132, 'Distributor': 'Walt Disney Pictures', 'Source': 'Based on Book/Short Story', 'Major Genre': 'Drama', 'Creative Type': 'Science Fiction', 'Director': 'Chris Columbus', 'Rotten Tomatoes Rating': 38, 'IMDB Rating': 6.4, 'IMDB Votes': 28827}

Let’s check the data type of the content variable:

print(type(content))
    <class 'dict'>

Its data type is a dictionary. So we can access each piece of information stored in the JSON file with its key. Let’s see how we can retrieve data from it:

print('{} directed by {}'.format(content['Title'], content['Director']))
    Bicentennial Man directed by Chris Columbus

Conclusion

This tutorial discussed file handling in Python, focusing on reading the content of files. You learned about the open() built-in function, the with context manager, and how to read the common file types such as text, CSV, and JSON.

Mehdi Lotfinejad

About the author

Mehdi Lotfinejad

Mehdi is a Senior Data Engineer and Team Lead at ADA. He is a professional trainer who loves writing data analytics tutorials.

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