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# Introduction
CSV files are everywhere in data workflows, from database exports to API responses to spreadsheet downloads. While pandas works great, sometimes you need quick solutions that you can code using Python without having to install pandas.
Python’s built-in csv module combined with list comprehensions and generator expressions can handle most common CSV tasks in a single line of code. These one-liners are perfect for quick data exploration, ETL debugging, or when you’re working in constrained environments where external libraries aren’t available.
Let’s use a sample business dataset with 50 records: data.csv and get started!
🔗 Link to the code on GitHub
# 1. Find Column Sum
Calculate the total of any numeric column across all rows.
print(f"Total: ${sum(float(r[3]) for r in __import__('csv').reader(open(path)) if r[0] != 'transaction_id'):,.2f}")
Here, path is the variable holding the path to the sample CSV file. For this example, in Google Colab, it is path = "/content/data.csv".
Output:
Here, __import__('csv') imports the built-in CSV module inline. The generator expression skips the header row, converts column values to floats, sums them, and formats with currency notation. Adjust the column index (3) and header check as needed.
# 2. Group By Maximum
Find which group has the highest aggregate value across your dataset.
print(max({r[5]: sum(float(row[3]) for row in __import__('csv').reader(open(path)) if row[5] == r[5] and row[0] != 'transaction_id') for r in __import__('csv').reader(open(path)) if r[0] != 'transaction_id'}.items(), key=lambda x: x[1]))
Output:
('Mike Rodriguez', 502252.0)
The dictionary comprehension groups by column 5, summing column 3 values for each group. One pass collects group keys and a second does the aggregation. max() with a lambda finds the highest total. Adjust column indices for different group-by operations.
# 3. Filter and Display a Subset of Rows
Show only rows that match a specific condition with formatted output.
print("\n".join(f"{r[1]}: ${float(r[3]):,.2f}" for r in __import__('csv').reader(open(path)) if r[7] == 'Enterprise' and r[0] != 'transaction_id'))
Output:
Acme Corp: $45,000.00
Gamma Solutions: $78,900.00
Zeta Systems: $156,000.00
Iota Industries: $67,500.25
Kappa LLC: $91,200.75
Nu Technologies: $76,800.25
Omicron LLC: $128,900.00
Sigma Corp: $89,700.75
Phi Corp: $176,500.25
Omega Technologies: $134,600.50
Alpha Solutions: $71,200.25
Matrix Systems: $105,600.25
The generator expression filters rows where column 7 equals Enterprise, then formats columns 1 and 3. Using "\n".join(...) avoids printing a list of None values.
# 4. Group By Sum Distribution
Get totals for each unique value in a grouping column.
print({g: f"${sum(float(row[3]) for row in __import__('csv').reader(open(path)) if row[6] == g and row[0] != 'transaction_id'):,.2f}" for g in set(row[6] for row in __import__('csv').reader(open(path)) if row[0] != 'transaction_id')})
Output:
{'Asia Pacific': '$326,551.75', 'Europe': '$502,252.00', 'North America': '$985,556.00'}
The dictionary comprehension first extracts unique values from column 6 using a set comprehension, then calculates the sum of column 3 for each group. This is memory efficient due to generator expressions. Change column indices to group by different fields.
# 5. Threshold Filter with Sort
Find and rank all records above a certain numeric threshold.
print([(n, f"${v:,.2f}") for n, v in sorted([(r[1], float(r[3])) for r in list(__import__('csv').reader(open(path)))[1:] if float(r[3]) > 100000], key=lambda x: x[1], reverse=True)])
Output:
[('Phi Corp', '$176,500.25'), ('Zeta Systems', '$156,000.00'), ('Omega Technologies', '$134,600.50'), ('Omicron LLC', '$128,900.00'), ('Matrix Systems', '$105,600.25')]
This filters rows where column 3 exceeds 100000, creates tuples of name and numeric value, sorts by the numeric value, and then formats the values as currency for display. Adjust the threshold and columns as needed.
# 6. Count Unique Values
Quickly determine how many distinct values exist in any column.
print(len(set(r[2] for r in __import__('csv').reader(open(path)) if r[0] != 'transaction_id')))
Output:
Here, the set comprehension extracts unique values from column 2; len() counts them. This is useful for checking data diversity or finding distinct categories.
# 7. Conditional Aggregation
Calculate averages or other statistics for specific subsets of your data.
print(f"Average: ${sum(float(r[3]) for r in __import__('csv').reader(open(path)) if r[6] == 'North America' and r[0] != 'transaction_id') / sum(1 for r in __import__('csv').reader(open(path)) if r[6] == 'North America' and r[0] != 'transaction_id'):,.2f}")
Output:
This one-liner calculates the average of column 3 for rows matching the condition in column 6. It uses a sum divided by a count (via a generator expression). It reads the file twice but keeps memory usage low.
# 8. Multi-Column Filter
Apply multiple filter conditions simultaneously across different columns.
print("\n".join(f"{r[1]} | {r[2]} | ${float(r[3]):,.2f}" for r in __import__('csv').reader(open(path)) if r[2] == 'Software' and float(r[3]) > 50000 and r[0] != 'transaction_id'))
Output:
Zeta Systems | Software | $156,000.00
Iota Industries | Software | $67,500.25
Omicron LLC | Software | $128,900.00
Sigma Corp | Software | $89,700.75
Phi Corp | Software | $176,500.25
Omega Technologies | Software | $134,600.50
Nexus Corp | Software | $92,300.75
Apex Industries | Software | $57,800.00
It combines multiple filter conditions with and operators, checks string equality and numeric comparisons, and formats output with pipe separators for clean display.
# 9. Compute Column Statistics
Generate min, max, and average statistics for numeric columns in one shot.
vals = [float(r[3]) for r in __import__('csv').reader(open(path)) if r[0] != 'transaction_id']; print(f"Min: ${min(vals):,.2f} | Max: ${max(vals):,.2f} | Avg: ${sum(vals)/len(vals):,.2f}"); print(vals)
Output:
Min: $8,750.25 | Max: $176,500.25 | Avg: $62,564.13
[45000.0, 12500.5, 78900.0, 23400.75, 8750.25, 156000.0, 34500.5, 19800.0, 67500.25, 91200.75, 28750.0, 43200.5, 76800.25, 15600.75, 128900.0, 52300.5, 31200.25, 89700.75, 64800.0, 22450.5, 176500.25, 38900.75, 27300.0, 134600.5, 71200.25, 92300.75, 18900.5, 105600.25, 57800.0]
This creates a list of numeric values from column 3, then calculates min, max, and average in one line. The semicolon separates statements. It is more memory intensive than streaming but faster than multiple file reads for these statistics.
# 10. Export Filtered Data
Create a new CSV file containing only rows that meet your criteria.
__import__('csv').writer(open('filtered.csv','w',newline="")).writerows([r for r in list(__import__('csv').reader(open(path)))[1:] if float(r[3]) > 75000])
This reads the CSV, filters rows based on a condition, and writes them to a new file. The newline="" parameter prevents extra line breaks. Note that this example skips the header (it uses [1:]), so include it explicitly if you need a header in the output.
Wrapping Up
I hope you find these one-liners for CSV processing helpful.
Such one-liners are handy for:
- Quick data exploration and validation
- Simple data transformations
- Prototyping before writing full scripts
But you should avoid them for:
- Production data processing
- Files requiring complex error handling
- Multi-step transformations
These techniques work with Python’s built-in CSV module when you need quick solutions without setup overhead. Happy analyzing!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.