Upload Files

This section describes how to upload data from your own filesystem, download files to a container, and programmatically delete those files when you are done.

Uploading Files
If you have data files on your local machine that you want to analyze with Zepl, you can upload the file by clicking the right menu bar in your notebook and choosing the Upload file button
. Uploaded files are only accessible through the notebook in which they were uploaded. To access the same file in a different notebook, the file will need to be uploaded to each notebook separately. 
Common file formats uploaded include:
.CSV: Used to load small sample data files
.PARQUET: Used to upload sample data files
.PKL: Bring pre-trained models to your Zepl notebooks
Zepl supports files up to 25MB in size. Each notebook may not exceed 100MB in total
Accessing Data
Once the file is uploaded to the notebook, you can access the file through the following URL (where <file-name> is the name of the file):
http://zdata/<file-name>
Direct Access
Use the examples below to load you data directly from the file system endpoint (URL) where the data is located. This method will directly load data into into a data object using the language of your choice.
Python
Scala
PySpark
R
SparkR
%python
​
import pandas as pd
pandas_df = pd.read_csv('http://zdata/titanic3.csv', sep=',', header='infer')
%spark
​
import org.apache.spark.SparkFiles
​
sc.addFile("http://zdata/titanic3.csv")
val sparkDF = spark.read.format("csv")
  .option("delimiter", ",")
  .option("header", "true")
  .option("inferSchema", "true")
  .load(SparkFiles.get("titanic3.csv"))
%spark.pyspark
​
from pyspark import SparkFiles
​
sc.addFile('http://zdata/titanic3.csv')
sparkDF = spark.read.format('csv').options(delimiter=',', header='true', inferSchema='true').load(SparkFiles.get('titanic3.csv'))
%r
table <- read.table("http://zdata/titanic3.csv", header = TRUE, sep = ",", dec = ".")
%spark.r
​
spark.addFile("http://zdata/titanic3.csv")
sparkDF <- read.df(path = spark.getSparkFiles("bank.csv"), source = "csv", delimiter = ",", header = "true", inferSchema = "true")
If your file name has a spaces or other special characters you will need to either URL encode the name or remove the special characters from your file name when accessing the data. 
For example, uploading a file named titanic number three.csv would be accessed by this urlhttp://zdata/titanic%20number%20three.csv .
Load file to container filesystem
Zepl allows users to download data files directly to container's file system. Often this method is used when the original file needs to be modified after your notebook has executed.
1. Load file
%python
!wget http://zdata/<file-name>
Files cannot be downloaded during your trial. To enable this functionality, activate your account here: activate-your-organization​
2.  (optional) List loaded data
%python
!ls <file-name>
3. Load data into a data object with the language of your choice
Python
Scala
PySpark
R
SparkR
%python
​
import pandas as pd
pandas_df = pd.read_csv('titanic3.csv', sep=';', header='infer')
%spark
​
import org.apache.spark.SparkFiles
​
val sparkDF = spark.read.format("csv")
  .option("delimiter", ";")
  .option("header", "true")
  .option("inferSchema", "true")
  .load("titanic3.csv")
%spark.pyspark
​
from pyspark import SparkFiles
​
sparkDF = spark.read.format('csv').options(delimiter=';', header='true', inferSchema='true').load('titanic3.csv')
%r
​
table <- read.table("titanic3.csv", header = TRUE, sep = ",", dec = ".")
%spark.r
​
sparkDF <- read.df(path = "titanic3.csv", source = "csv", delimiter = ";", header = "true", inferSchema = "true")
Editing Data
You cannot edit data directly within Zepl, but you can overwrite the data file by uploading a file with the same name.
Overwritten data cannot be recovered.
Deleting Data
To delete data, click the red "x" button 
 next to the data file in the Files tab in your notebook.
Deleted data cannot be recovered.

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On this page

Uploading Files

Accessing Data

Direct Access

Load file to container filesystem

Editing Data

Deleting Data