Reading and writing CSV data in Python - Time & Space Complexity

Time Complexity: Reading and writing CSV data

O(n)

Understanding Time Complexity

When working with CSV files, it's important to know how the time to read or write data changes as the file grows.

We want to understand how the program's speed changes when the number of rows in the CSV changes.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import csv

def read_csv(filename):
    with open(filename, newline='') as file:
        reader = csv.reader(file)
        data = []
        for row in reader:
            data.append(row)
    return data

# This function reads all rows from a CSV file into a list

This code reads each row from a CSV file and stores it in a list.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through each row in the CSV file.
• How many times: Once for every row in the file (n times).

How Execution Grows With Input

As the number of rows increases, the time to read grows in a straight line.

Input Size (n)	Approx. Operations
10	About 10 row reads
100	About 100 row reads
1000	About 1000 row reads

Pattern observation: Doubling the rows roughly doubles the work done.

Final Time Complexity

Time Complexity: O(n)

This means the time to read the CSV grows directly with the number of rows.

Common Mistake

[X] Wrong: "Reading a CSV file always takes the same time no matter how big it is."

[OK] Correct: The program reads each row one by one, so more rows mean more time.

Interview Connect

Understanding how file reading time grows helps you write efficient data processing code and explain your reasoning clearly.

Self-Check

"What if we read the CSV file but only processed every other row? How would the time complexity change?"

Practice

1. What does the Python csv.reader function do when reading a CSV file?

easy

A. It deletes rows from the CSV file.

B. It reads the file and returns each row as a list of strings.

C. It converts CSV data into a dictionary automatically.

D. It writes data to a CSV file.

Solution

1. Step 1: Understand csv.reader purpose

   The csv.reader reads CSV files and returns each row as a list of strings representing columns.

2. Step 2: Differentiate from other functions

   Functions like csv.DictReader return dictionaries, and writing functions save data, not read it.

3. Final Answer:

   It reads the file and returns each row as a list of strings. -> Option B

4. Quick Check:

   csv.reader returns lists [OK]

Hint: csv.reader reads rows as lists, not dictionaries [OK]

Common Mistakes:

• Confusing csv.reader with csv.DictReader
• Thinking csv.reader writes data
• Assuming it deletes or modifies files

2. Which of the following is the correct way to open a CSV file for writing in Python?

easy

A. open('file.csv', 'w', newline='')

B. open('file.csv', 'r')

C. open('file.csv', 'a', encoding='utf-8')

D. open('file.csv', 'rb')

Solution

1. Step 1: Identify mode for writing CSV

   To write CSV files, open the file in write mode 'w' and use newline='' to prevent extra blank lines on Windows.

2. Step 2: Check other options

   'r' is read mode, 'a' is append (valid but not asked), 'rb' is binary read mode (not for writing text CSV).

3. Final Answer:

   open('file.csv', 'w', newline='') -> Option A

4. Quick Check:

   Write mode with newline='' [OK]

Hint: Use 'w' mode with newline='' to write CSV files correctly [OK]

Common Mistakes:

• Forgetting newline='' causes blank lines
• Using 'r' mode when writing
• Using binary mode for text CSV

3. What will be the output of this code snippet?

import csv
with open('data.csv', 'w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['Name', 'Age'])
    writer.writerow(['Alice', 30])

with open('data.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
        print(row)

medium

A. ['Name', 'Age']\n['Alice', 30]

B. Name,Age\nAlice,30

C. ['Name', 'Age']\n['Alice', '30']

D. Error because of missing encoding

Solution

1. Step 1: Writing rows with csv.writer

   The code writes two rows: header ['Name', 'Age'] and data ['Alice', 30]. Numbers are converted to strings when written.

2. Step 2: Reading rows with csv.reader and printing

   Reading returns each row as a list of strings. print(row) shows repr with quotes: ['Name', 'Age'] and ['Alice', '30'].

3. Final Answer:

   ['Name', 'Age']\n['Alice', '30'] -> Option C

4. Quick Check:

   csv.writer writes lists, csv.reader reads lists [OK]

Hint: csv.reader returns lists of strings, print shows quotes around all elements [OK]

Common Mistakes:

• Expecting printed rows as comma strings
• Confusing string and integer types in output
• Assuming encoding error without cause

4. Identify the error in this code that reads a CSV file:

import csv
with open('file.csv', 'r') as f:
    reader = csv.reader(f)
    for row in reader:
    print(row)

medium

A. Indentation error in the for loop

B. Missing import statement for csv module

C. File opened in wrong mode

D. csv.reader requires newline argument

Solution

1. Step 1: Check code indentation

   The print statement inside the for loop must be indented to be part of the loop body.

2. Step 2: Verify other parts

   Import is present, file mode 'r' is correct for reading, and newline argument is not needed for reading.

3. Final Answer:

   Indentation error in the for loop -> Option A

4. Quick Check:

   Python requires correct indentation [OK]

Hint: Indent inside loops to avoid syntax errors [OK]

Common Mistakes:

• Forgetting to indent inside loops
• Thinking newline is needed for reading
• Confusing file modes

5. You want to read a CSV file where the first row contains column names, and then write a new CSV file with only rows where the 'Age' column is greater than 25. Which approach is correct?

hard

A. Use csv.reader to read, skip first row manually, filter rows, then write with csv.writer.

B. Use csv.writer to read and write files directly without filtering.

C. Use csv.reader to read all rows, convert 'Age' to int, then write with csv.DictWriter without header.

D. Use csv.DictReader to read rows as dictionaries, filter by 'Age' key, then write with csv.DictWriter including header.

Solution

1. Step 1: Choose reading method with headers

   csv.DictReader reads CSV rows as dictionaries using the first row as keys, making it easy to filter by column names like 'Age'.

2. Step 2: Filter and write with headers

   Filter rows where 'Age' > 25, then write using csv.DictWriter with fieldnames to include headers properly.

3. Final Answer:

   Use csv.DictReader to read rows as dictionaries, filter by 'Age' key, then write with csv.DictWriter including header. -> Option D

4. Quick Check:

   DictReader + DictWriter for header and filtering [OK]

Hint: Use DictReader/DictWriter for header-based filtering [OK]

Common Mistakes:

• Skipping header manually instead of using DictReader
• Writing without headers causing missing columns
• Using csv.writer without filtering logic