Recall that although it introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, xarray is inspired by and borrows heavily from pandas. So, to answer the question you can proceed as follows.
from glob import glob
import numpy as np
import pandas as pd
# Get the list of all the csv files in data path
csv_flist = glob(data_path + "/*.csv")
df_list = []
for _file in csv_flist:
# get the file name from the data path
file_name = _file.split("/")[-1]
# extract the date from a file name, e.g. "data.2018-06-01.csv"
date = file_name.split(".")[1]
# read the read the data in _file
df = pd.read_csv(_file)
# add a column date knowing that all the data in df are recorded at the same date
df["date"] = np.repeat(date, df.shape[0])
df["date"] = df.date.astype("datetime64[ns]") # reset date column to a correct date format
# append df to df_list
df_list.append(df)
Let's check e.g. the first df in df_list
print(df_list[0])
status user_id weight date
0 healthy 1 72 2019-06-01
1 obese 2 103 2019-06-01
Concatenate all the dfs along axis=0
df_all = pd.concat(df_list, ignore_index=True).sort_index()
print(df_all)
status user_id weight date
0 healthy 1 72 2019-06-01
1 obese 2 103 2019-06-01
2 healthy 1 70 2018-06-01
3 healthy 2 90 2018-06-01
Set the index of df_all to a multiIndex of two levels with levels[0] = "date" and levels[1]="user_id".
data = df_all.set_index(["date", "user_id"]).sort_index()
print(data)
status weight
date user_id
2018-06-01 1 healthy 70
2 healthy 90
2019-06-01 1 healthy 72
2 obese 103
Subsequently, you can convert the resulting pandas.DataFrame into an xarray.Dataset using .to_xarray() as follows.
xds = data.to_xarray()
print(xds)
<xarray.Dataset>
Dimensions: (date: 2, user_id: 2)
Coordinates:
* date (date) datetime64[ns] 2018-06-01 2019-06-01
* user_id (user_id) int64 1 2
Data variables:
status (date, user_id) object 'healthy' 'healthy' 'healthy' 'obese'
weight (date, user_id) int64 70 90 72 103
Which will fully answer the question.
