title: ValueError: Data must be positive (boxcox scipy)
body: <p>I'm trying to transform my dataset to a normal distribution. </p>
  
  <pre><code>0      8.298511e-03
  1      3.055319e-01
  2      6.938647e-02
  3      2.904091e-02
  4      7.422441e-02
  5      6.074046e-02
  6      9.265747e-04
  7      7.521846e-02
  8      5.960521e-02
  9      7.405019e-04
  10     3.086551e-02
  11     5.444835e-02
  12     2.259236e-02
  13     4.691038e-02
  14     6.463911e-02
  15     2.172805e-02
  16     8.210005e-02
  17     2.301189e-02
  18     4.073898e-07
  19     4.639910e-02
  20     1.662777e-02
  21     8.662539e-02
  22     4.436425e-02
  23     4.557591e-02
  24     3.499897e-02
  25     2.788340e-02
  26     1.707958e-02
  27     1.506404e-02
  28     3.207647e-02
  29     2.147011e-03
  30     2.972746e-02
  31     1.028140e-01
  32     2.183737e-02
  33     9.063370e-03
  34     3.070437e-02
  35     1.477440e-02
  36     1.036309e-02
  37     2.000609e-01
  38     3.366233e-02
  39     1.479767e-03
  40     1.137169e-02
  41     1.957088e-02
  42     4.921303e-03
  43     4.279257e-02
  44     4.363429e-02
  45     1.040123e-01
  46     2.930958e-02
  47     1.935434e-03
  48     1.954418e-02
  49     2.980253e-02
  50     3.643772e-02
  51     3.411437e-02
  52     4.976063e-02
  53     3.704608e-02
  54     7.044161e-02
  55     8.101365e-03
  56     9.310477e-03
  57     7.626637e-02
  58     8.149728e-03
  59     4.157399e-01
  60     8.200258e-02
  61     2.844295e-02
  62     1.046601e-01
  63     6.565680e-02
  64     9.825436e-04
  65     9.353639e-02
  66     6.535298e-02
  67     6.979044e-04
  68     2.772859e-02
  69     4.378422e-02
  70     2.020185e-02
  71     4.774493e-02
  72     6.346146e-02
  73     2.466264e-02
  74     6.636585e-02
  75     2.548934e-02
  76     1.113937e-06
  77     5.723409e-02
  78     1.533288e-02
  79     1.027341e-01
  80     4.294570e-02
  81     4.844853e-02
  82     5.579620e-02
  83     2.531824e-02
  84     1.661426e-02
  85     1.430836e-02
  86     3.157232e-02
  87     2.241722e-03
  88     2.946256e-02
  89     1.038383e-01
  90     1.868837e-02
  91     8.854596e-03
  92     2.391759e-02
  93     1.612714e-02
  94     1.007823e-02
  95     1.975513e-01
  96     3.581289e-02
  97     1.199747e-03
  98     1.263381e-02
  99     1.966746e-02
  100    4.040786e-03
  101    4.497264e-02
  102    4.030524e-02
  103    8.627087e-02
  104    3.248317e-02
  105    5.727582e-03
  106    1.781355e-02
  107    2.377991e-02
  108    4.299568e-02
  109    3.664353e-02
  110    5.167902e-02
  111    4.006848e-02
  112    7.072990e-02
  113    6.744938e-03
  114    1.064900e-02
  115    9.823497e-02
  116    8.992714e-03
  117    1.792453e-01
  118    6.817763e-02
  119    2.588843e-02
  120    1.048027e-01
  121    6.468491e-02
  122    1.035536e-03
  123    8.800684e-02
  124    5.975065e-02
  125    7.365861e-04
  126    4.209485e-02
  127    4.232421e-02
  128    2.371866e-02
  129    5.894714e-02
  130    7.177195e-02
  131    2.116566e-02
  132    7.579219e-02
  133    3.174744e-02
  134    0.000000e+00
  135    5.786439e-02
  136    1.458493e-02
  137    9.820156e-02
  138    4.373873e-02
  139    4.271649e-02
  140    5.532575e-02
  141    2.311324e-02
  142    1.644508e-02
  143    1.328273e-02
  144    3.908473e-02
  145    2.355468e-03
  146    2.519321e-02
  147    1.131868e-01
  148    1.708967e-02
  149    1.027661e-02
  150    2.439899e-02
  151    1.604058e-02
  152    1.134323e-02
  153    2.247722e-01
  154    3.408590e-02
  155    2.222239e-03
  156    1.659830e-02
  157    2.284733e-02
  158    4.618550e-03
  159    3.674162e-02
  160    4.131283e-02
  161    8.846273e-02
  162    2.504404e-02
  163    6.004396e-03
  164    1.986309e-02
  165    2.347111e-02
  166    3.865636e-02
  167    3.672307e-02
  168    6.658419e-02
  169    3.726879e-02
  170    7.600138e-02
  171    7.184871e-03
  172    1.142840e-02
  173    9.741311e-02
  174    8.165448e-03
  175    1.529210e-01
  176    6.648081e-02
  177    2.617601e-02
  178    9.547816e-02
  179    6.857775e-02
  180    8.129399e-04
  181    7.107914e-02
  182    5.884794e-02
  183    8.398721e-04
  184    6.972981e-02
  185    4.461767e-02
  186    2.264404e-02
  187    5.566633e-02
  188    6.595136e-02
  189    2.301914e-02
  190    7.488919e-02
  191    3.108619e-02
  192    4.989364e-07
  193    4.834949e-02
  194    1.422578e-02
  195    9.398186e-02
  196    4.870391e-02
  197    3.841369e-02
  198    6.406801e-02
  199    2.603315e-02
  200    1.692629e-02
  201    1.409982e-02
  202    4.099215e-02
  203    2.093724e-03
  204    2.640732e-02
  205    1.032129e-01
  206    1.581881e-02
  207    8.977325e-03
  208    1.941141e-02
  209    1.502126e-02
  210    9.923589e-03
  211    2.757357e-01
  212    3.096234e-02
  213    4.388900e-03
  214    1.784778e-02
  215    2.179550e-02
  216    3.944159e-03
  217    3.703552e-02
  218    4.033897e-02
  219    1.157076e-01
  220    2.400446e-02
  221    5.761179e-03
  222    1.899621e-02
  223    2.401468e-02
  224    4.458745e-02
  225    3.357898e-02
  226    5.331003e-02
  227    3.488753e-02
  228    7.466599e-02
  229    6.075236e-03
  230    9.815318e-03
  231    9.598735e-02
  232    7.103607e-03
  233    1.100602e-01
  234    5.677641e-02
  235    2.420500e-02
  236    9.213369e-02
  237    4.024043e-02
  238    6.987694e-04
  239    8.612055e-02
  240    5.663353e-02
  241    4.871693e-04
  242    4.533811e-02
  243    3.593244e-02
  244    1.982537e-02
  245    5.490786e-02
  246    5.603109e-02
  247    1.671653e-02
  248    6.522711e-02
  249    3.341356e-02
  250    2.378629e-06
  251    4.299939e-02
  252    1.223163e-02
  253    8.392798e-02
  254    4.272826e-02
  255    3.183946e-02
  256    4.431299e-02
  257    2.661024e-02
  258    1.686707e-02
  259    4.070924e-03
  260    3.325947e-02
  261    2.023611e-03
  262    2.402284e-02
  263    8.369778e-02
  264    1.375093e-02
  265    8.899898e-03
  266    2.148740e-02
  267    1.301483e-02
  268    8.355791e-03
  269    2.549934e-01
  270    2.792516e-02
  271    4.652563e-03
  272    1.556313e-02
  273    1.936942e-02
  274    3.547794e-03
  275    3.412516e-02
  276    3.932606e-02
  277    5.305868e-02
  278    2.354438e-02
  279    5.379380e-03
  280    1.904203e-02
  281    2.045495e-02
  282    3.275855e-02
  283    3.007389e-02
  284    8.227664e-02
  285    2.479949e-02
  286    6.573835e-02
  287    5.165842e-03
  288    7.599650e-03
  289    9.613557e-02
  290    6.690175e-03
  291    1.779880e-01
  292    5.076263e-02
  293    3.117607e-02
  294    7.495692e-02
  295    3.707768e-02
  296    7.086975e-04
  297    8.935981e-02
  298    5.624249e-02
  299    7.105331e-04
  300    3.339868e-02
  301    3.354603e-02
  302    2.041988e-02
  303    3.862522e-02
  304    5.977081e-02
  305    1.730081e-02
  306    6.909621e-02
  307    3.729478e-02
  308    3.940647e-07
  309    4.385336e-02
  310    1.391891e-02
  311    8.898305e-02
  312    3.840141e-02
  313    3.214408e-02
  314    4.284080e-02
  315    1.841022e-02
  316    1.528207e-02
  317    3.106559e-03
  318    3.945481e-02
  319    2.085094e-03
  320    2.464190e-02
  321    7.844914e-02
  322    1.526590e-02
  323    9.922147e-03
  324    1.649218e-02
  325    1.341602e-02
  326    8.124446e-03
  327    2.867380e-01
  328    2.663867e-02
  329    5.342012e-03
  330    1.752612e-02
  331    2.010863e-02
  332    3.581845e-03
  333    3.652284e-02
  334    4.484362e-02
  335    4.600939e-02
  336    2.213280e-02
  337    5.494917e-03
  338    2.016594e-02
  339    2.118010e-02
  340    2.964000e-02
  341    3.405549e-02
  342    1.014185e-01
  343    2.451624e-02
  344    7.966998e-02
  345    5.301538e-03
  346    8.198895e-03
  347    8.789368e-02
  348    7.222417e-03
  349    1.448276e-01
  350    5.676056e-02
  351    2.987054e-02
  352    6.851434e-02
  353    4.193034e-02
  354    7.025054e-03
  355    8.557358e-02
  356    5.812736e-02
  357    2.263676e-02
  358    2.922588e-02
  359    3.363161e-02
  360    1.495056e-02
  361    5.871619e-02
  362    6.235094e-02
  363    1.691340e-02
  364    5.361939e-02
  365    3.722318e-02
  366    9.828477e-03
  367    4.155345e-02
  368    1.327760e-02
  369    7.205372e-02
  370    4.151130e-02
  371    3.265365e-02
  372    2.879418e-02
  373    2.314340e-02
  374    1.653692e-02
  375    1.077611e-02
  376    3.481427e-02
  377    1.815487e-03
  378    2.232305e-02
  379    1.005192e-01
  380    1.491262e-02
  381    3.752658e-02
  382    1.271613e-02
  383    1.223707e-02
  384    8.088923e-03
  385    2.572550e-01
  386    2.300194e-02
  387    2.847960e-02
  388    1.782098e-02
  389    1.900759e-02
  390    3.647629e-03
  391    3.723368e-02
  392    4.079514e-02
  393    5.510332e-02
  394    3.072313e-02
  395    4.183566e-03
  396    1.891549e-02
  397    1.870293e-02
  398    3.182769e-02
  399    4.167840e-02
  400    1.343152e-01
  401    2.451973e-02
  402    7.567017e-02
  403    4.837843e-03
  404    6.477297e-03
  405    7.664675e-02
  Name: value, dtype: float64
  </code></pre>
  
  <p>This is the code I used for transforming dataset:</p>
  
  <pre><code>from scipy import stats
  x,_ = stats.boxcox(df)
  </code></pre>
  
  <p>I get this error:</p>
  
  <pre><code>            if any(x &lt;= 0):
  -&gt; 1031         raise ValueError("Data must be positive.")
     1032 
     1033     if lmbda is not None:  # single transformation
  
  ValueError: Data must be positive
  </code></pre>
  
  <p>Is it because my values are too small that it's producing an error? Not sure what I'm doing wrong. New to using boxcox, could be using it incorrectly in this example. Open to suggestions and alternatives. Thanks! </p>