Pandas reindex a Series. So, not sure what the difference in numpy is from 1.8 and 1.6.2 so might not be 'broken' in pandas 0.9.0. Also, how can I get 0.9.0 and test this? a b c 0 -1.0 0.1 -1.0 1 0.0 1.1 -1.0 2 1.0 2.1 -1.0 The column "identifiers" a , b and c are not known prior to processing. Viewed 2k times 0. Ask Question Asked 3 years, 10 months ago. The person responding on my stackoverflow post claimed this worked with pandas 0.9.0 AND numpy 1.8. # index is all precise timestamps e.g. align_axis {0 or ‘index’, 1 or ‘columns’}, default 1. Multiple operations can be accomplished through indexing like − Reorder the existing data to match a new set of labels. 2018-10-08 05:23:07 series = pandas.Series(data,index) # I want rounded date-times desired_index = pandas.date_range("2010-10-08",periods=10,freq="30min") Tutorials/API suggest the way to do this is to reindex then fill NaN values using interpolate. Here is the original data, but with an extra entry for '2013-09-03': Reindexing changes the row labels and column labels of a DataFrame. Object to compare with. pandas.Series.reindex¶ Series.reindex (index=None, **kwargs) [source] ¶ Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. Let’s create a dataframe of 11 counties with their CO2 emission and population and a column for the continent they belong to An alternative approach is resample, which can handle duplicate dates in addition to missing dates.For example: df.resample('D').mean() resample is a deferred operation like groupby so you need to follow it with another operation. df_temp['REMARK_TYPE'] = df_temp.REMARK.apply(lambda v: 1 if str(v)!='nan' else 0) Because df and df_temp have a different number of rows. In this case mean works well, but you can also use many other pandas methods like max, sum, etc.. There are a lot more columns in addition to a , b and c which are also not known before processing but the info about their existence known from their occurance in the dataframe only. Maybe this subtle issue should be mentioned in the docs for reindex_like()? You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it creates a new attribute rather than a new column. 0, or ‘index’ Resulting differences are stacked vertically with rows drawn alternately from self and other. pandas.Series¶ class pandas.Series ... Will default to RangeIndex (0, 1, 2, …, n) if not provided. To reindex means to conform the data to match a given set of labels along a particular axis.. In 0.21.0 and later, this will raise a UserWarning: dtype str, numpy.dtype, or ExtensionDtype, optional. Insert missing value (NA) markers in label locations where no data for the label existed. Tombstone 23.0 Douglas 23.0 Bisbee 34.0 Sierra Vista 12.0 Barley NaN Tucson NaN dtype: float64 a) sort_values b) sort_index c) Categorical Series d) numpy sort and argsort e) Reindex f) And Sorted() function. In this post we will learn sorting a dataframe and Series using the following functions. Parameters other Series. Reindexing pandas series and dataframes. So it returned ValueError: cannot reindex from a duplicate axis. Determine which axis to align the comparison on. If data is dict-like and index is None, then the values in the index are used to reindex the Series after it is created using the keys in the data. Hope you can understand it and my answer can help other people to debug their code. Active 3 years, 10 months ago. On my stackoverflow post claimed this worked with pandas 0.9.0 and numpy.... People to debug their code NaN Tucson NaN dtype: float64 Parameters other Series case mean works well but. 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