TSV viewer
View, filter and sort TSV data in seconds
Trusted by over 10,000 every month
View and filter TSV files online
The TSV viewer enables you to upload and view tab-separated value files online. It presents your data in a clear and structured format, facilitating easy browsing and analysis.
Designed to handle both small and large TSV files efficiently, this tool ensures a smooth user experience without requiring any software installations.
Upload your TSV file and access your data instantly.
Effortlessly view tab-separated TSV files online.
Simple and clear layout for easy data exploration.
Handles both small and large TSV datasets efficiently.
No software installations—just drag, drop, and view.
Fast loading for a smooth user experience.
Ideal for reviewing and analyzing TSV data on the go.
TSV format
TSV (Tab Separated Values) files are the same as CSV files, except values in a row are separated by a tab.
Values within a row are separated by tabs. Rows are separated by newlines.
TSV files often start with a header row that has column names, but this is not required.
Each row in a TSV file mush have the same number of values as the header row.
TSV files do not enforce types or a schema. This means that each column can have multiple types, which can make analysis difficult and compression inefficient.
Parquet files can be easier to analyze and compress better than TSV files.
How to view and filter TSV files online
- Upload your TSV file
- Your file will be loaded and then you can view your TSV data
- Sort data by clicking a column name
- Filter a column by clicking the three dots
- Export your TSV file in CSV or Excel format by clicking the export button
How to view and filter TSV files in Python with Pandas
First, we need to install pandas
pip install pandas
Then we can load the TSV file into a dataframe.
df = pd.read_csv('path/to/file.tsv', sep='\t')
We can view the first few rows of the dataframe using the head method.
print(df.head(n=5))
The n parameter controls how many rows are returned. Increase it to show more rows.
We can view the last few rows of the dataframe using the tail method.
print(df.tail(n=5))
We can sort the dataframe using the sort_values method.
df = df.sort_values('column_name', ascending=true)
Just replace 'column_name' with the name of the column you want to sort by. The 'ascending' parameter controls whether the values will be sorted in 'ascending' or 'descending' order.
We can filter the dataframe using comparison operators. The following statement will filter a dataframe to rows where the value of the 'column_name' column is greater than 5.
df = df[df['column_name'] > 5]
How to view and filter TSV files in Python with DuckDB
First, we need to install duckdb for Python
pip install duckdb
The following duckdb query will create a view from the input TSV file.
duckdb.sql("""SELECT * from path/to/file.tsv""")
Sometimes we have large file and it's impractical to read the whole file. We can read the first 5 rows using the following.
duckdb.sql("""SELECT * from path/to/file.tsv limit 5""")
We can sort rows using the ORDER BY clause and a SQL comparison operator.
duckdb.sql("""SELECT * from path/to/file.tsv order by 'column_name' ASC limit 5""")
Just change 'column_name' for the column you want to sort by. Use ASC to sort ascending or DESC to sort descending.
We can also filter using SQL comparison operators and the WHERE clause.
duckdb.sql("""SELECT * from path/to/file.tsv where 'column_name' > 5 ASC limit 5""")
You can change the 'column_name' to change the column you want to filter by. The operator (>) and value (5) control how the filtering is applied to 'column_name'.
MT cars
Motor Trends Car Road Tests dataset.
filename
mtcars.tsv
rows
32
Flights 1m
1 Million flights including arrival and departure delays.
filename
flights-1m.tsv
rows
1000000
Iris
Iris plant species data set.
filename
iris.tsv
rows
50
House price
Housing price dataset.
filename
house-price.tsv
rows
545
Weather
Weather dataset with temperature, rainfall, sunshine and wind measurements.
filename
weather.tsv
rows
366