Uploading and working with local files

To work with data files (.csv, .txt, and so on) existing on your computer, upload the files directly to your project in the CML workspace. The presented code samples demonstrate how to access local data for CML workloads.

To upload the files:

Go to the project's Overview page.
Click Upload under the Files section.
Select the relevant data files to be uploaded.

These files will be uploaded to an NFS share available to each project.

The tips.csv dataset examples demonstrate how to work with local data stored in your project. Before you run these examples, create a folder called data in your project and upload the dataset file to it.

Python

import pandas as pd

tips = pd.read_csv('data/tips.csv')
  
tips \
  .query('sex == "Female"') \
  .groupby('day') \
  .agg({'tip' : 'mean'}) \
  .rename(columns={'tip': 'avg_tip_dinner'}) \
  .sort_values('avg_tip_dinner', ascending=False)

R

library(readr)
library(dplyr)

# load data from .csv file in project
tips <- read_csv("data/tips.csv")

# query using dplyr
tips %>%
  filter(sex == "Female") %>%
  group_by(day) %>%
  summarise(
    avg_tip = mean(tip, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_tip))