我有這個 df： revenue pct_yoy pct_qoq2020-06-30 99.721 0.479013 0.0928332020-03-31 91.250 0.478283 0.0872162019-12-31 83.930 0.676253 0.1350942019-09-30 73.941 NaN 0.0966572019-06-30 67.424 NaN 0.0922932019-03-31 61.727 NaN 0.2328142018-09-30 50.070 NaN NaN但是，如果您使用 來查看最后一個索引值，則在將索引視為連續的季度時間序列時，2018我似乎會丟失。2018-12-31該指數直接跳至2018-9-30。如何確保插入任何缺失的季度日期及其nan各自列的值？我不太確定如何解決這個問題。
2 回答
至尊寶的傳說
TA貢獻1789條經驗 獲得超10個贊
您需要生成自己的季度日期列表，其中包括缺失的日期。然后您可以使用.reindex將數據框重新對齊到這個新的日期列表。
# Get the oldest and newest dates which will be the bounds
# for our new Index
first_date = df.index.min()
last_date = df.index.max()
# Generate dates for every 3 months (3M) from first_date up to last_date
quarterly = pd.date_range(first_date, last_date, freq="3M")
# realign our dataframe using our new quarterly date index
# this will fill NaN for dates that did not exist in the
# original index
out = df.reindex(quarterly)
# if you want to order this from most recent date to least recent date
# do: out.sort_index(ascending=False)
print(out)
revenue pct_yoy pct_qoq
2018-09-30 50.070 NaN NaN
2018-12-31 NaN NaN NaN
2019-03-31 61.727 NaN 0.232814
2019-06-30 67.424 NaN 0.092293
2019-09-30 73.941 NaN 0.096657
2019-12-31 83.930 0.676253 0.135094
2020-03-31 91.250 0.478283 0.087216
2020-06-30 99.721 0.479013 0.092833
守著一只汪
TA貢獻1872條經驗 獲得超4個贊
如果您的數據僅包含示例中的季度末日期，您可以使用resample和asfreq來填充缺失的quarter-ends
df_final = df.resample('Q').asfreq()[::-1]
Out[122]:
revenue pct_yoy pct_qoq
2020-06-30 99.721 0.479013 0.092833
2020-03-31 91.250 0.478283 0.087216
2019-12-31 83.930 0.676253 0.135094
2019-09-30 73.941 NaN 0.096657
2019-06-30 67.424 NaN 0.092293
2019-03-31 61.727 NaN 0.232814
2018-12-31 NaN NaN NaN
2018-09-30 50.070 NaN NaN
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