How to create pandas dataframes with more than 2 dimensions?
<p><strong>I want to be able to create <code>n-dimensional</code> dataframes.</strong>  I've heard of a method for 3D dataframes using <code>panels</code> in <code>pandas</code> but, if possible, I would like to extend the dimensions past 3 dims by combining different datasets into a <em>super dataframe</em></p>

<p>I tried this but I cannot figure out how to use these methods with my test dataset -> 
<a href="https://stackoverflow.com/questions/24290495/constructing-3d-pandas-dataframe">Constructing 3D Pandas DataFrame</a></p>

<p>Also, this did not help for my case -> <a href="https://stackoverflow.com/questions/23478297/pandas-dataframe-or-panel-to-3d-numpy-array">Pandas Dataframe or Panel to 3d numpy array</a></p>

<p>I made a random test dataset with arbitrary axis data trying to mimic a real situation; there are 3 axis (i.e. patients, years, and samples).  I tried adding a bunch of dataframes to a list and then making a dataframe with that but it didn't work :(  I even tried a <code>panel</code> as in the 2nd link above but I couldn't get it to work either.</p>

<p><strong>Does anybody know how to create a N-dimensional pandas dataframe w/ labels?</strong></p>

<p>The first way I tried: </p>

<pre><code>#Reproducibility
np.random.seed(1618033)

#Set 3 axis labels/dims
axis_1 = np.arange(2000,2010) #Years
axis_2 = np.arange(0,20) #Samples
axis_3 = np.array(["patient_%d" % i for i in range(0,3)]) #Patients

#Create random 3D array to simulate data from dims above
A_3D = np.random.random((years.size, samples.size, len(patients))) #(10, 20, 3)

#Create empty list to store 2D dataframes (axis_2=rows, axis_3=columns) along axis_1
list_of_dataframes=[]

#Iterate through all of the year indices
for i in range(axis_1.size):
    #Create dataframe of (samples, patients)
    DF_slice = pd.DataFrame(A_3D[i,:,:],index=axis_2,columns=axis_3)
    list_of_dataframes.append(DF_slice)
#     print(DF_slice) #preview of the 2D dataframes "slice" of the 3D array
#           patient_0  patient_1  patient_2
#      0    0.727753   0.154701   0.205916
#      1    0.796355   0.597207   0.897153
#      2    0.603955   0.469707   0.580368
#      3    0.365432   0.852758   0.293725
#      4    0.906906   0.355509   0.994513
#      5    0.576911   0.336848   0.265967
#     ...
#     19   0.583495   0.400417   0.020099

# DF_3D = pd.DataFrame(list_of_dataframes,index=axis_2, columns=axis_1)
# Error
# Shape of passed values is (1, 10), indices imply (10, 20)
</code></pre>

<p>2nd way I tried:</p>

<pre><code>DF = pd.DataFrame(axis_3,columns=axis_2) 
#Error:
#Shape of passed values is (1, 3), indices imply (20, 3)

# p={}
# for i in axis_1:
#     p[i]=DF
# panel= pd.Panel(p)
</code></pre>

<p>I could do something like this I guess, but I really like <code>pandas</code> and would rather use one of their methods if one exists:</p>

<pre><code>#Set data for query
query_year = 2007
query_sample = 15
query_patient = "patient_1"

#Index based on query
A_3D[
     (axis_1 == query_year).argmax(),
     (axis_2 == query_sample).argmax(),
     (axis_3 == query_patient).argmax()
]
#0.1231212416981845
</code></pre>

<p>It would be awesome to access the data in this way: </p>

<pre><code>DF_3D[query_year][query_sample][query_patient]
#Where DF_3D[query_year] would give a list of 2D arrays (row=sample, col=patient)
# DF_3D[query_year][query_sample] would give a 1D vector/list of patient data for a particular year, of a particular sample.
# and DF_3D[query_year][query_sample][query_patient] would be a particular sample of a particular patient of a particular year
</code></pre>