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    <p>Concatenating a list of <code>Series</code> returns a <code>DataFrame</code>. Thus, <code>serie_4</code> is a <code>DataFrame</code>. <code>serie_3</code> is a <code>Series</code>. Concatenating a <code>DataFrame</code> with a <code>Series</code> raises the exception.</p> <p>You could use</p> <pre><code>import pandas as pd serie_5 = pd.concat([serie_1, serie_2, serie_3], join='outer', axis=1) </code></pre> <p>instead.</p> <hr> <p>For example, </p> <pre><code>import functools import numpy as np import pandas as pd s1 = pd.Series([0,1], index=list('AB')) s2 = pd.Series([2,3], index=list('AC')) result = pd.concat([s1, s2], join='outer', axis=1, sort=False) print(result) </code></pre> <p>yields</p> <pre><code> 0 1 A 0.0 2.0 B 1.0 NaN C NaN 3.0 </code></pre> <p>Note that you'll get a ValueError if you try to concatenate a series with a non-unique index. For example,</p> <pre><code>s3 = pd.Series([0,1], index=list('AB'), name='s3') s4 = pd.Series([2,3], index=list('AA'), name='s4') # &lt;-- non-unique index result = pd.concat([s3, s4], join='outer', axis=1, sort=False) </code></pre> <p>raises </p> <pre><code>ValueError: cannot reindex from a duplicate axis </code></pre> <p>To work around this, reset the index and <a href="https://stackoverflow.com/a/30512931/190597">merge DataFrames</a> instead:</p> <pre><code>import functools s3 = pd.Series([0,1], index=list('AB'), name='s3') s4 = pd.Series([2,3], index=list('AA'), name='s4') # &lt;-- non-unique index result = functools.reduce( lambda left,right: pd.merge(left,right,on='index',how='outer'), [s.reset_index() for s in [s3,s4]]) print(result) </code></pre> <p>yields</p> <pre><code> index s3 s4 0 A 0 2.0 1 A 0 3.0 2 B 1 NaN </code></pre>
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    1. POSeveral time series to DataFrame
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    1. POSeveral time series to DataFrame
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    1. COOkay, then I undestand why I get this error. I also tested to concatenate a DataFrame with another DataFrame by change the code to this: serie_5 = pandas.concat([serie_4, pandas.DataFrame(serie_3)], join='outer', axis = 1). This means that I can concatinate two Series to a Dataframe and then this DataFrame with another DataFrame. I need to find a generic solution where I can add a number of Series in a loop and I do not the number on beforehand.
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    2. COjust create a Python list and append your Series into it and then provide it to pandas.concat as @unutbu was writing above.
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      1. USK.-Michael Aye
    3. COThe use of join looks generic enough! I changed it to "serie_5 = serie_4.join(serie_3, how = 'outer')" in order to ínclude 2012-01-12 in the example above. The reason I want to get a generic solution is that I want to combine several of different time series where there will be missing data and use Pandas functionality to handle the missing data. Thanks!
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