CSV viewer
View, filter and sort CSV data in seconds
Trusted by over 10,000 every month
View and filter CSV files online
Our CSV viewer allows you to upload and view CSV files directly in your browser. It provides a clean and organized display for easy navigation and analysis of your data.
This tool supports large CSV files and offers fast loading times, making it suitable for quick data reviews without the need for additional software.
Simply upload your CSV file and start exploring your data immediately.
Easily upload and view CSV files directly in your browser.
Displays data in a clean and organized manner for easy navigation.
Supports large CSV files, ensuring smooth performance.
User-friendly interface with quick loading times.
No need for additional software—just upload and view.
Ideal for quick data reviews and analysis.
CSV format
CSV (Comma Separated Values) files are the most common format for storing tabular data. Values in a row are separated by commas and rows are separated by newlines.
CSV files often start with a header row that has column names, but this is not required.
Each row in a CSV file must have the same number of values as the header row.
CSV files do no 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 CSV files.
How to view and filter CSV files online
- Upload your CSV file
- Your file will be loaded and then you can view your CSV data
- Sort data by clicking a column name
- Filter a column by clicking the three dots
- Export your CSV file in CSV or Excel format by clicking the export button
How to view and filter CSV files in Python with Pandas
First, we need to install pandas
pip install pandas
Then we can load the CSV file into a dataframe.
df = pd.read_csv('path/to/file.csv')
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 CSV 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 CSV file.
duckdb.sql("""SELECT * from path/to/file.csv""")
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.csv limit 5""")
We can sort rows using the ORDER BY clause and a SQL comparison operator.
duckdb.sql("""SELECT * from path/to/file.csv 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.csv 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.csv
rows
32
Flights 1m
1 Million flights including arrival and departure delays.
filename
flights-1m.csv
rows
1000000
Iris
Iris plant species data set.
filename
iris.csv
rows
50
House price
Housing price dataset.
filename
house-price.csv
rows
545
Weather
Weather dataset with temperature, rainfall, sunshine and wind measurements.
filename
weather.csv
rows
366