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Version: Python

How to import CSV or other delimited files

This guide will show you how to import data from CSV (and other delimited) files into Deephaven tables by using the read_csv method. If you prefer a drag and drop approach, see uploading table data using the UI. The two most common ways to load a CSV file are using the file path or the file URL. The header of the CSV file determines the column names.

The basic syntax follows:

from deephaven import read_csv

read_csv(path: str,
header: Dict[str, DataType]=None,
headless: bool=False,
header_row: int = 0
delimiter: str=",",
quote: str="\"",
ignore_surrounding_spaces: bool = True,
trim: bool = False,
charset: str = "utf-8")

See our read_csv reference article for full details on every parameter. In this guide, we cover some standard examples and will walk you through each query.

You can follow along with our examples using CSV files taken from Deephaven's examples repository. We encourage you to use your own files by replacing the file paths in our queries.


If you're using our files, follow the directions in the README to mount the content from Deephaven's examples repository onto /data in the Deephaven Docker container.

Standard CSV files

The Deephaven Query Language makes importing and manipulating data easy and efficient. In this example, we will import a CSV file into a new, in-memory Deephaven table.

This example imports R.A. Fisher's classic iris flower dataset commonly used in machine learning applications.

from deephaven import read_csv

iris = read_csv("")

If using the file path and the CSV is in the root data/examples directory, change the command to read:

from deephaven import read_csv

iris = read_csv("/data/examples/Iris/csv/iris.csv")

/data mount point

By default, all Deephaven deployments mount ./data in the local deployment directory to the /data volume in the running Deephaven container. This means that if the Deephaven console is used to write data to /data/abc/file.csv, that file will be visible at ./data/abc/file.csv on the local file system of your computer. Similarly, if the local file abc.parquet is copied to ./data/abc.parquet, then the file can be accessed at /data/abc.parquet on the Deephaven server.

If the ./data directory does not exist when Deephaven is launched, it will be created.

See Docker data volumes to learn more about the relation between locations in the container and the local file system.

Headerless CSV files

CSV files don't always have headers. The example below uses the headerless DeNiro CSV and includes an additional headless argument.

from deephaven import read_csv

deniro = read_csv("", headless=True)

Because no column names are provided, the table will produce default column names (Column1, Column2, etc.). You can explicitly set the column names, as shown below.

from deephaven import read_csv
import deephaven.dtypes as dht

header = {"Year": dht.int_, "Score": dht.int_, "Title": dht.string}
deniro = read_csv("", header=header, headless=True)

Other formats

Tab-delimited data

Deephaven allows you to specify other delimiters as a second argument if your file is not comma-delimited. In the example below, we import a tab-delimited file, which requires a second argument.

from deephaven import read_csv
deniro_tsv = read_csv("", delimiter="\t")

Pipe-delimited data

Any character can be used as a delimiter. The pipe character (|) is common. In the example below, we supply the delimiter | as the second argument.

from deephaven import read_csv

deniro_psv = read_csv("", delimiter="|")


By default, quoted values that have leading and trailing white space include the white space when reading the CSV file. For example, if " Taxi Driver " is in the CSV file, it will be read as Taxi Driver.

By setting trim to true when reading the CSV file, these leading and trailing white space will be removed. So " Taxi Driver " will be read as Taxi Driver.