reindex

In answering this stackoverflow question , I found some interesting behavior when using a fill method while reindexing a dataframe. This old bug report in pandas says that df.reindex(newIndex,method='ffill') should be equivalent to df.reindex(newIndex).ffill() , but that is NOT the behavior I'm witnessing Here's a code snippet that illustrates the behavior df = pd.DataFrame({'values': 2}, index=pd.DatetimeIndex(['2016-06-02', '2016-05-04', '2016-06-03'])) newIndex = pd.DatetimeIndex(['2016-05-04', '2016-06-01', '2016-06-02', '2016-06-03', '2016-06-05']) print(df.reindex(newIndex).ffill())

I am trying to re-index my Elastic search setup, currently looking at the Elastic search documentation and an example using the Python API I'm a little bit confused as to how this all works though. I was able to obtain the scroll ID from the Python API: es = Elasticsearch("myhost") index = "myindex" query = {"query":{"match_all":{}}} response = es.search(index= index, doc_type= "my-doc-type", body= query, search_type= "scan", scroll= "10m") scroll_id = response["_scroll_id"] Now my question is, what use is this to me? What does knowing the scrolling id even give me? The documentation says to

So I imported and merged 4 csv's into one dataframe called data. However, upon inspecting the dataframe's index with: index_series = pd.Series(data.index.values) index_series.value_counts() I see that multiple index entries have 4 counts. I want to completely reindex the data dataframe so each row now has a unique index value. I tried: data.reindex(np.arange(len(data))) which gave the error "ValueError: cannot reindex from a duplicate axis." A google search leads me to think this error is because the there are up to 4 rows that share a same index value. Any idea how I can do this reindexing

I have a dataframe with sporadic dates as the index, and columns = 'id' and 'num'. I would like to pd.groupby the 'id' column, and apply the reindex to each group in the dataframe. My sample dataset looks like this: id num 2015-08-01 1 3 2015-08-05 1 5 2015-08-06 1 4 2015-07-31 2 1 2015-08-03 2 2 2015-08-06 2 3 My expected output once pd.reindex with ffill is: id num 2015-08-01 1 3 2015-08-02 1 3 2015-08-03 1 3 2015-08-04 1 3 2015-08-05 1 5 2015-08-06 1 4 2015-07-31 2 1 2015-08-01 2 1 2015-08-02 2 1 2015-08-03 2 2 2015-08-04 2 2 2015-08-05 2 2 2015-08-06 2 3 I have tried this, among other

I just used array_filter to remove entries that had only the value '' from an array, and now I want to apply certain transformations on it depending on the placeholder starting from 0, but unfortunately it still retains the original index. I looked for a while and couldn't see anything, perhaps I just missed the obvious, but my question is... How can I easily reset the indexes of the array to begin at 0 and go in order in the NEW array, rather than have it retain old indexes? If you call array_values on your array, it will be reindexed from zero. If you are using Array filter do it as follows