How To Read a File Line by Line Into a List in Python

Most of the time, we process data from a file so that we can manipulate it from memory. This data can be numeric, string, or a combination of both. In this article, I will discuss how to open a file for reading with the built-in function open() and the use of Pandas library to manipulate data in the file. This also includes reading the contents of a file line by line and saving the same to a list.

Things To Learn in This Article

1. Open the file for reading
2. Read the contents of a file line by line
3. Store the read lines into a list data type
   • For loop
   • List comprehension
   • Readlines
   • Readline
4. Read file using pandas

1. Open the File for Reading

Python's built-in function open() can be used to open a file for reading and writing. It is defined below based on the Python documentation.

open(file, mode='r', buffering=-1, encoding=None,
     errors=None, newline=None, closefd=True, opener=None)

These are the supported values for the mode.

Here is an example script called main.py that will open the file countries.txt for reading.

main.py

with open('countries.txt', mode='r') as f:
    # other stuff

An Alternative Method of Opening a File

An alternative way of opening a file for reading is the following.

f = open('countries.txt', mode='r')
# other stuff
f.close()

You have to close the file object explicitly with close(). Anyhow use the with statement whenever possible, as the context manager will handle the entry and exit execution of the code; hence closing the file object  close() is not needed.

2. Read the Contents of a File Line by Line

Let us go back to our code in main.py. I will add a block of code that will read the contents of the file line by line and print it.

main.py

with open('countries.txt', mode='r') as f:
    for lines in f:
        line = lines.rstrip()  # rstrip() will remove the newline character
        print(line)  # print to console

countries.txt

Australia
China
Philippines

Ensure that the main.py and countries.txt are on the same directory. That is because of the code above. In my case, they are in F:\Project\8thesource path.

Execute the main.py from the command line.

PS F:\Project\8thesource> python main.py

output

Australia
China
Philippines

There we have it. We read countries.txt line by line using the open() function and file object manipulation. The first line printed was Australia, followed by China, and finally the Philippines. It is consistent according to the sequence of how they were written in countries.txt file.

3. Store the Read Lines Into a List Data Type

Python has a popular data type called list that can store other object types or a combination of object types. A list of integers could be [1, 2, 3]. A list of strings could be ['one', 'two', 'three']. A list of integer and string could be [1, 'city', 45]. A list of lists could be [[1, 2], [4, 6]]. A list of tuples could be [(1, 2), ('a', 'b')]. A list of dictionaries could be [{'fruit': 'mango'}, {'count': 100}].

I will modify the main.py to store the read lines into a list.

a) For Loop

main.py

data_list = []  # a list as container for read lines

with open('countries.txt', mode='r') as f:
    for lines in f:
        line = lines.rstrip()  # remove the newline character
        data_list.append(line)  # add the line in the list
print(data_list)

The countries.txt is the file name. We open it for reading with symbol r. We use the for loop to read each line and save it to a list called data_list. After saving all the lines to a list via append method, the items in the list are then printed.

After executing the main.py, we got the following output.

output

['Australia', 'China', 'Philippines']

b) List Comprehension

Another option to save the read lines into the list is by the use of list comprehension. It uses a for loop behind the scene and is more compact but not beginner-friendly.

with open('countries.txt', mode='r') as f:
    data = [item.rstrip() for item in f]
print(data)

output

['Australia', 'China', 'Philippines']

c) Readlines

Yet another option to save the read lines in a list is the method readlines().

with open('countries.txt', mode='r') as f:
    data = f.readlines()
print(data)

The output still has the newline character \n.

output

['Australia\n', 'China\n', 'Philippines']

This newline character can be removed by reading each items on that list and strip it. The readlines method is not an ideal solution if the file is big.

d) Readline

Another option to save the read line is by the use of the readline method.

data_list = []
with open('countries.txt', mode='r') as f:
    while True:
        line = f.readline()
        line = line.rstrip()  # remove the newline character \n
        if line == '':
            break
        data_list.append(line)

print(data_list)

output

['Australia', 'China', 'Philippines']

4. Read File Using Pandas

For people aspiring to become data scientists, knowledge of processing files is a must. One of the tools that should be learned is the Pandas library. This can be used to manipulate data. It can read files, including popular csv or comma-separated values formatted file.

Here is a sample scenario, we are given a capitals.csv file that contains the name of the country in the first column and the corresponding capital in the second column. Our job is to get a list of country and capital names.

capitals.csv

Country,Capital
Australia,Canberra
China,Beijing
Philippines,Manila
Japan,Tokyo

For this particular job, it is better to use the Pandas library. The expected outputs are the country list [Australia, China, Philippines, Japan] and the capital list [Canberra, Beijing, Manila, Tokyo].

Let us create capitals.py to read the capitals.csv using Pandas.

capitals.py

"""
requirements:
    pandas

Install pandas with
    pip install pandas
"""

import pandas as pd

# Build a dataframe based from the csv file.
df = pd.read_csv('capitals.csv')
print(df)

command line

PS F:\Project\8thesource> python capitals.py

output

       Country   Capital
0    Australia  Canberra
1        China   Beijing
2  Philippines    Manila
3        Japan     Tokyo

Now we need to get the values in the Country and Capital columns and convert those to a list.

import pandas as pd

# Build a dataframe based from the csv file.
df = pd.read_csv('capitals.csv')
print(df)

# Get the lists of country and capital names.
country_names = df['Country'].to_list()
capital_names = df['Capital'].to_list()

Pandas is very smart about this. It easily gets the tasks that we are after.

Now let us print those lists.

import pandas as pd

# Build a dataframe based from the csv file.
df = pd.read_csv('capitals.csv')
print(df)

# Get the lists of country and capital names.
country_names = df['Country'].to_list()
capital_names = df['Capital'].to_list()

# Print names
print('Country names:')
print(country_names)

print('Capital names:')
print(capital_names)

output

       Country   Capital
0    Australia  Canberra
1        China   Beijing
2  Philippines    Manila
3        Japan     Tokyo
Country names:
['Australia', 'China', 'Philippines', 'Japan']
Capital names:
['Canberra', 'Beijing', 'Manila', 'Tokyo']

That is it. We got the country and capital names as lists.

5. Conclusion

We use the built-in function open() to open and read the contents of a file and utilize the for loop to read it line by line, then save it to a list — a Python data type. There are also options, such as list comprehension, readlines and readline to save data into the list. Depending on the tasks and file given, we can use the Pandas library to process a csv file.

For further reading, have a look at Python's built-in function open() and the very useful Pandas Python library.