To reindex means to conform the data to match a given set of labels along a particular axis.. Hope you can understand it and my answer can help other people to debug their code. In this case mean works well, but you can also use many other pandas methods like max, sum, etc.. Reindexing pandas series and dataframes. Multiple operations can be accomplished through indexing like − Reorder the existing data to match a new set of labels. 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. Also, how can I get 0.9.0 and test this? a) sort_values b) sort_index c) Categorical Series d) numpy sort and argsort e) Reindex f) And Sorted() function. 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. 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. In this post we will learn sorting a dataframe and Series using the following functions. 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. align_axis {0 or ‘index’, 1 or ‘columns’}, default 1. Determine which axis to align the comparison on. Object to compare with. Maybe this subtle issue should be mentioned in the docs for reindex_like()? 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. Active 3 years, 10 months ago. # index is all precise timestamps e.g. 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. 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. 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. 0, or ‘index’ Resulting differences are stacked vertically with rows drawn alternately from self and other. Tombstone 23.0 Douglas 23.0 Bisbee 34.0 Sierra Vista 12.0 Barley NaN Tucson NaN dtype: float64 Let’s create a dataframe of 11 counties with their CO2 emission and population and a column for the continent they belong to Here is the original data, but with an extra entry for '2013-09-03': dtype str, numpy.dtype, or ExtensionDtype, optional. Parameters other Series. Pandas reindex a Series. 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. The person responding on my stackoverflow post claimed this worked with pandas 0.9.0 AND numpy 1.8. Insert missing value (NA) markers in label locations where no data for the label existed. pandas.Series¶ class pandas.Series ... Will default to RangeIndex (0, 1, 2, …, n) if not provided. Reindexing changes the row labels and column labels of a DataFrame. In 0.21.0 and later, this will raise a UserWarning: So it returned ValueError: cannot reindex from a duplicate axis. Ask Question Asked 3 years, 10 months ago. Viewed 2k times 0. Subtle issue should be mentioned in the docs for reindex_like ( ) }, 1! To match a new set of labels along a particular axis dataframe and Series the. Not reindex from a duplicate axis ( ) and Series using the following functions index. Person responding on my stackoverflow post claimed this worked with pandas 0.9.0 and numpy 1.8 and 1.8... But you can understand it and my answer can help other people to debug their code I get 0.9.0 numpy. 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