Reading Text or CSV File and Breaking into Groups Separated by Space

In this article, we will explore a common problem of reading data from a text file (or a CSV file) and breaking the data into groups separated by spaces. We will discuss several ways to solve this problem using Python programming language.

Introduction

The problem statement is as follows: given a text or CSV file containing data as a list of numbers, we need to read this file line by line, identify blank values in the list, and create groups of numbers whenever a blank value is found. For example, if we have the following data:

20
40
25

50
60
80

10
25
34
75
50

50
60

The expected output would be:

final_list = [
    [20, 40, 25],
    [50, 60, 80],
    [10, 25, 34, 75, 50],
    [50, 60]
]

In this article, we will explore several ways to solve this problem using Python programming language.

Using itertools.groupby

One way to solve this problem is by using the groupby function from the itertools module. The groupby function groups consecutive elements of an iterable (such as a list) into a group based on some common attribute. In our case, we can use the bool function as the key to group consecutive elements.

Here is how you can do it:

from itertools import groupby

with open(r"codeMaster.csv") as fp:
    line = fp.readlines()

line = [i.strip() for i in line]

print([list(g) for k, g in groupby(line, key=bool) if k])

In this code:

• We first read all the lines from the CSV file into a list using fp.readlines().
• We then strip each line of any leading or trailing whitespace using [i.strip() for i in line].
• Finally, we use groupby to group consecutive elements based on whether they are blank (using bool(i) as the key).
• The resulting groups are then printed.

The output of this code will be:

[['20', '40', '25'], ['50', '60', '80'], ['10', '25', '34', '75', '50'], ['50', '60']]

This solution works because bool(i) returns True if the string is not blank and False otherwise. The groupby function then groups all consecutive True values into one group and all consecutive False values into another group.

More Pythonic Way

Another way to solve this problem is by using a more pythonic approach that involves creating an empty list and appending elements to it as you read the CSV file. Here is how you can do it:

with open(r"CodeMaster.csv") as fp:
    line = fp.readlines()

line = [i.strip() for i in line]
result = [[]]
for i in line:
    if not i:
        result.append([])
    else:
        result[-1].append(i)
print(result)

In this code:

• We first read all the lines from the CSV file into a list using fp.readlines().
• We then strip each line of any leading or trailing whitespace using [i.strip() for i in line].
• We create an empty list result and append it to itself to start with one group.
• We then iterate over each element in the line. If the element is blank, we append a new empty group to the result. Otherwise, we append the element to the last group.
• Finally, we print the resulting groups.

The output of this code will be:

[['20', '40', '25'], ['50', '60', '80'], ['10', '25', '34', '75', '50'], ['50', '60']]

This solution works because we create an empty list and append elements to it as we read the CSV file. When we encounter a blank value, we append a new empty group to the result.

Conclusion

In this article, we explored several ways to solve the problem of reading data from a text file (or a CSV file) and breaking the data into groups separated by spaces. We discussed using itertools.groupby and a more pythonic approach that involves creating an empty list and appending elements to it as you read the CSV file.

Both solutions work well, but they have different advantages. The groupby solution is more concise and uses less memory because it doesn’t require creating an extra list for each group. On the other hand, the more pythonic approach is more intuitive and easier to understand because it involves a clear and simple algorithm that is easy to follow.

Regardless of which solution you choose, the end result will be the same: a list of groups separated by blank values.

Last modified on 2024-04-22