How to save the file to the data asset of the analysis project using project_lib [Another article](https://qiita.com/ttsuzuku/items/eac3e4bedc020da93bc1#%E3%83%87%E3%83%BC%E3% 82% BF% E8% B3% 87% E7% 94% A3% E3% 81% B8% E3% 81% AE% E3% 83% 87% E3% 83% BC% E3% 82% BF% E3% 81% AE% E4% BF% 9D% E5% AD% 98-% E5% 88% 86% E6% 9E% 90% E3% 83% 97% E3% 83% AD% E3% 82% B8% E3% 82% A7 I wrote it in% E3% 82% AF% E3% 83% 88), but it took some tricks to save it in Excel format. After a lot of research, stackoverflow's this article It was valid.
Here is an example that I actually tried.
Pandas dataframe used
#Sample data Iris import pandas as pd from sklearn.datasets import load_iris iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df['iris_type'] = iris.target_names[iris.target] df.head()
Let's save this data in Excel format. The flow is once saved as an Excel file in the environment with pandas.to_excel,
#Output to the environment once as an Excel file filename = 'iris.xlsx' df.to_excel(filename, index=False) !pwd !ls -l # -output- # /home/wsuser/work # total 12 # -rw-r-----. 1 wsuser watsonstudio 8737 May 28 06:53 iris.xlsx
Read it as an io byte stream and save it in your analysis project with project_lib.
from project_lib import Project project = Project.access() import io with open(filename, 'rb') as z: data = io.BytesIO(z.read()) project.save_data(filename, data, set_project_asset=True, overwrite=True)
Make sure it was saved in your analysis project.
Just in case, I will download it and take a look at the contents. You have successfully saved 150 lines of Iris data in Excel format.