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TabularHow to Filter Rows in a CSV File by Column Value
3 methods — browser tool, Excel, and Python
Filtering a CSV means keeping only the rows that match a condition — for example, all customers in a specific state, orders above a certain amount, or records with a particular status. Here's how to do it three different ways.
Method 1: Using Tabular (browser, no software)
- 1Go to the Filter Rows tool on Tabular.
- 2Upload your CSV or XLSX file.
- 3Select the column to filter on.
- 4Choose a condition: equals, contains, starts with, is empty, greater than, etc.
- 5Enter the value to match.
- 6Click Run — only matching rows are kept. Download the result.
Tabular keeps rows that match your condition. To exclude rows (keep non-matching rows), use the 'does not equal' or 'does not contain' conditions.
Method 2: Using Excel AutoFilter
- 1Open your CSV in Excel.
- 2Click any cell in your data, then go to Data > Filter to enable AutoFilter.
- 3Click the dropdown arrow on the column header you want to filter.
- 4Select specific values, or use 'Text Filters' / 'Number Filters' for conditions.
- 5To export the filtered result: select the visible rows, copy, paste into a new sheet, then save as CSV.
Excel AutoFilter hides rows — it doesn't delete them. To get a true filtered CSV, you must copy the visible rows to a new sheet before saving.
Method 3: Using Python (pandas)
- 1Install pandas: pip install pandas
- 2Run the script below, adjusting the column name and condition.
python
import pandas as pd
df = pd.read_csv("input.csv")
# Keep rows where a column equals a value
df_filtered = df[df["status"] == "active"]
# Keep rows where a column contains a substring
# df_filtered = df[df["email"].str.contains("@gmail.com", na=False)]
# Keep rows where a number column exceeds a threshold
# df_filtered = df[df["amount"] > 100]
# Combine conditions
# df_filtered = df[(df["status"] == "active") & (df["amount"] > 100)]
df_filtered.to_csv("output.csv", index=False)When combining conditions in pandas, use & for AND and | for OR, and wrap each condition in parentheses. Python's 'and'/'or' keywords don't work with pandas Series.
Frequently asked questions
Can I filter on multiple columns at once?
In Tabular, each filter operation targets one column. For multi-column filtering, run the tool twice — filter on the first column, download, then upload the result and filter on the second column. In pandas, combine conditions with & (AND) or | (OR).
How do I filter rows that contain a partial match?
Use the 'contains' condition in Tabular. In Excel, use Text Filters > Contains. In pandas, use df[df['column'].str.contains('partial', na=False)].
How do I filter out rows instead of keeping them?
Use the inverse condition: 'does not equal', 'does not contain', or 'is not empty' in Tabular. In pandas, use the tilde operator: df[~df['column'].str.contains('value')].
How do I filter rows where a cell is blank?
In Tabular, select 'is empty' as the condition. In pandas, use df[df['column'].isna()] to keep only blank rows, or df[df['column'].notna()] to remove blank rows.
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