import numpy as np
import pandas as pd
res = []
for _ in range(2):
res1 = []
# Only occurs when dataframe is used with measure
data = np.zeros((30, 21))
idx = np.random.randint(0, 5, 30)
df = pd.DataFrame(data, index=idx).loc[3]
#df = pd.DataFrame(data[::5, :]) # Uncomment for example of correct behavior
res1.append(pd.DataFrame(sum(data.dot(df.T))))
tmp = pd.concat(res1, keys=[1], names=['level1'])
res.append(tmp)
final = pd.concat(res, keys=[i for i in range(2)], names=['level2'])
print(final) Problem description
In python, datatypes generally don't matter. A dataframe is a dataframe, but as shown in the example code concat'ing dataframes with an index does not have the same behavior as dataframes without an index. The label for a level of the index is dropped. This is a small bug. Run it several times (10-12 seems to do it) and you will see a much more worrisome issue: on occasion, the label is not dropped. Yes, the output of concat is random.
Expected Outputlevel2 level1
0 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
1 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
pd.show_versions()
level2
0 1 0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
5 0.0
1 1 0 0.0
1 0.0
2 0.0
3 0.0
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