path/referrer snapshot time series: bad total time range after merge

This shows a range between Sep and Oct:

Whereas the expected range would be between Jan and Nov.

Initial investigation showed that in the above's special case the linkedin.com time series determined the overall range shown. That is, as part of merging individual time series the data ranges not within the linkedin.com time series range were lost.

Found the problem:

github-repo-stats/analyze.py

Line 519 in 47d5047

df_top_vu[ename] = edf["views_unique"]

Done 'right' million times before, here I somehow fell for pandas and expected it to do magic. Which it did not. Created this snippet to show the problem, and a solution:

dfa = pd.DataFrame(
    list(range(10)),
    columns=["A"],
    index=pd.date_range("2021-01-03", periods=10, freq="D"),
)


dfb = pd.DataFrame(
    list(range(10)),
    columns=["B"],
    index=pd.date_range("2021-01-10", periods=10, freq="D"),
)

print(dfa)
#             A
# 2021-01-03  0
# 2021-01-04  1
# 2021-01-05  2
# 2021-01-06  3
# 2021-01-07  4
# 2021-01-08  5
# 2021-01-09  6
# 2021-01-10  7
# 2021-01-11  8
# 2021-01-12  9


print(dfb)
#             B
# 2021-01-10  0
# 2021-01-11  1
# 2021-01-12  2
# 2021-01-13  3
# 2021-01-14  4
# 2021-01-15  5
# 2021-01-16  6
# 2021-01-17  7
# 2021-01-18  8
# 2021-01-19  9


# naive merge: just add a column from one dataframe to the other dataframe,
# and expect magic to happen
dfa['B'] = dfb['B']

# But what happens is that the index of the dataframe added to stays the same:

print(dfa)
#             A    B
# 2021-01-03  0  NaN
# 2021-01-04  1  NaN
# 2021-01-05  2  NaN
# 2021-01-06  3  NaN
# 2021-01-07  4  NaN
# 2021-01-08  5  NaN
# 2021-01-09  6  NaN
# 2021-01-10  7  0.0
# 2021-01-11  8  1.0
# 2021-01-12  9  2.0

A solution is to concat along a specific axis:

merged = pd.concat([dfa['A'], dfb['B']], axis=1)
print(merged)
#               A    B
# 2021-01-03  0.0  NaN
# 2021-01-04  1.0  NaN
# 2021-01-05  2.0  NaN
# 2021-01-06  3.0  NaN
# 2021-01-07  4.0  NaN
# 2021-01-08  5.0  NaN
# 2021-01-09  6.0  NaN
# 2021-01-10  7.0  0.0
# 2021-01-11  8.0  1.0
# 2021-01-12  9.0  2.0
# 2021-01-13  NaN  3.0
# 2021-01-14  NaN  4.0
# 2021-01-15  NaN  5.0
# 2021-01-16  NaN  6.0
# 2021-01-17  NaN  7.0
# 2021-01-18  NaN  8.0
# 2021-01-19  NaN  9.0