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POReindexing and filling NaN values in Pandas
 

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  1. POReindexing and filling NaN values in Pandas
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    <p>Consider this dataset: </p> <pre><code>data_dict = {'ind' : [1, 2, 3, 4], 'location' : [301, 301, 302, 303], 'ind_var' : [4, 8, 10, 15], 'loc_var' : [1, 1, 7, 3]} df = pd.DataFrame(data_dict) df_indexed = df.set_index(['ind', 'location']) df_indexed </code></pre> <p>which looks like</p> <pre><code> ind_var loc_var ind location 1 301 4 1 2 301 8 1 3 302 10 7 4 303 15 3 </code></pre> <p>ind_var is a variable that varies by ind ( = individual) and loc_var varies by location. (I also have an extra variable that varies by both ind and location, but I'm omitting it to simplify the presentation)</p> <p>I need to transform the data to have each individual index contain all the possible locations. I can reindex in this way (just showing individuals 1 to 3):</p> <pre><code>new_shape = [(1, 301), (1, 302), (1, 303), (2, 301), (2, 302), (2, 303), (3, 301), (3, 302), (3, 303)] idx = pd.Index(new_shape) df2 = df_indexed.reindex(idx, method = None) df2.index.names = ['id', 'location'] </code></pre> <p>which gives</p> <pre><code> ind_var loc_var id location 1 301 4 1 302 NaN NaN 303 NaN NaN 2 301 8 1 302 NaN NaN 303 NaN NaN 3 301 NaN NaN 302 10 7 303 NaN NaN </code></pre> <p>but I need a way to fill the missing values, so that I get:</p> <pre><code> ind_var loc_var id location 1 301 4 1 302 4 7 303 4 3 2 301 8 1 302 8 7 303 8 3 3 301 10 1 302 10 7 303 10 3 </code></pre> <p>I tried two different things with no success:</p> <p>1) Using a loc_dict = {301 : 1, 302 : 7, 303 : 3} to replace loc_var and a ind_dict = {1 : 4, 2: 8, 3: 10, 4 : 15} to replace ind_var</p> <p>2) Using a groupby method.</p> <pre><code># First reset index df_non_indexed = df2.reset_index() df_non_indexed['loc_var'] = df_non_indexed.groupby(['location'])['loc_var'].transform(lambda x: x.fillna(method='ffill')) </code></pre> <p>This almost works, but only does the fill forward (or backwards)</p> <p>There must be a very simple way of doing this, but I haven't been able to figure it out! Thanks for your time.</p> <p><em>Note</em>: this is related to my question <a href="https://stackoverflow.com/questions/17688155/complicated-for-me-reshaping-from-wide-to-long-in-pandas" title="reshaping from wide to long">reshaping from wide to long</a>. I've taken a different approach and simplified in hope that this one is easier to understand.</p>
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    1. COI think you're second table has a typo. The values for (2, 201) should be moved to (2, 202)
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    2. COWhat is the desired output (not a fill forward?)
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      1. USAndy Hayden
    3. COMy first instinct would be to have a list of dictionaries `[{'301':(4,1), '302':(nan, nan), '303':(nan,nan)}, {'301':...}` and so on. If you worked with this dictionary, filling it in, might it then be easier to extract the data_dict that you want? Sorry unfamiliar with pandas
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