diff --git a/python/damask/table.py b/python/damask/table.py
index 0c9f3ac40..2eacef58e 100644
--- a/python/damask/table.py
+++ b/python/damask/table.py
@@ -6,7 +6,7 @@ import numpy as np
 class Table():
     """Store spreadsheet-like data."""
   
-    def __init__(self,array,headings,comments=None):
+    def __init__(self,array,columns,comments=None):
         """
         New spreadsheet data.
         
@@ -14,8 +14,8 @@ class Table():
         ----------
         array : numpy.ndarray
             Data.
-        headings : dict
-            Column headings. Labels as keys and shape as tuple. Example 'F':(3,3) for a deformation gradient. 
+        columns : dict
+            Column labels and shape. Example 'F':(3,3) for a deformation gradient. 
         comments : iterable of str, optional
             Additional, human-readable information
         
@@ -24,8 +24,8 @@ class Table():
 
         d = {}
         i = 0
-        for label in headings:
-            for components in range(np.prod(headings[label])):
+        for label in columns:
+            for components in range(np.prod(columns[label])):
                 d[i] = label
                 i+=1
 
@@ -36,7 +36,7 @@ class Table():
         else:
             self.comments = [c for c in comments]
 
-        self.headings  = headings
+        self.columns = columns
 
     @staticmethod
     def from_ASCII(fname):
@@ -46,6 +46,12 @@ class Table():
         The first line needs to indicate the number of subsequent header lines as 'n header'.
         Vector data labels are indicated by '1_x, 2_x, ..., n_x'.
         Tensor data labels are indicated by '3x3:1_x, 3x3:2_x, ..., 3x3:9_x'.
+
+        Parameters
+        ----------
+        fname : file, str, or pathlib.Path
+            Filename or file for reading.
+
         """
         try:
             f = open(fname)
@@ -60,20 +66,20 @@ class Table():
         comments = [f.readline()[:-1] for i in range(header-1)]
         labels   = f.readline().split()
        
-        headings = {}
+        columns = {}
         for label in labels:
             tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label)
             if tensor_column:
                 my_shape = tensor_column.group().split(':',1)[0].split('x')
-                headings[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
+                columns[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
             else:
                 vector_column = re.match(r'[0-9]*?_',label)
                 if vector_column:
-                    headings[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
+                    columns[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
                 else:
-                    headings[label]=(1,)
+                    columns[label]=(1,)
         
-        return Table(np.loadtxt(f),headings,comments)
+        return Table(np.loadtxt(f),columns,comments)
 
     def get_array(self,label):
         """Return data as array."""
@@ -81,10 +87,22 @@ class Table():
             idx,key = label.split('_',1)
             return self.data[key].to_numpy()[:,int(idx)-1]
         else: 
-            return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
+            return self.data[label].to_numpy().reshape((-1,)+self.columns[label])
 
     def set_array(self,label,array,info):
-        """Set data."""
+        """
+        Modify data in the spreadsheet.
+
+        Parameters
+        ----------
+        label : str
+            Label for the new data
+        array : np.ndarray
+            New data
+        info : str
+            Human-readable information about the new data
+
+        """
         if np.prod(array.shape[1:],dtype=int) == 1: 
             self.comments.append('{}: {}'.format(label,info))
         else:
@@ -97,34 +115,56 @@ class Table():
         else: 
             self.data[label]       = array.reshape(self.data[label].shape)
 
-
     def get_labels(self):
         """Return the labels of all columns."""
-        return [label for label in self.headings]
+        return [label for label in self.columns]
 
     def add_array(self,label,array,info):
+        """
+        Add data to the spreadsheet.
+        
+        Parameters
+        ----------
+        label : str
+            Label for the new data
+        array : np.ndarray
+            New data
+        info : str
+            Human-readable information about the new data
+
+        """
         if np.prod(array.shape[1:],dtype=int) == 1: 
             self.comments.append('{}: {}'.format(label,info))
         else:
             self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
         
-        self.headings[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
+        self.columns[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
         size = np.prod(array.shape[1:],dtype=int) 
         new_data = pd.DataFrame(data=array.reshape(-1,size),
                                 columns=[label for l in range(size)])
         self.data = pd.concat([self.data,new_data],axis=1)
         
     def to_ASCII(self,fname):
+        """
+        Store as plain text file.
+        
+        Parameters
+        ----------
+        fname : file, str, or pathlib.Path
+            Filename or file for reading.
+
+        """
+
         labels = []
-        for l in self.headings:
-            if(self.headings[l] == (1,)):
+        for l in self.columns:
+            if(self.columns[l] == (1,)):
                 labels.append('{}'.format(l))
-            elif(len(self.headings[l]) == 1):
+            elif(len(self.columns[l]) == 1):
                 labels+=['{}_{}'.format(i+1,l)\
-                         for i in range(self.headings[l][0])]
+                         for i in range(self.columns[l][0])]
             else:
-                labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.headings[l]]),l)\
-                               for i in range(np.prod(self.headings[l],dtype=int))]
+                labels+=['{}:{}_{}'.format(i+1,'x'.join([str(d) for d in self.columns[l]]),l)\
+                               for i in range(np.prod(self.columns[l],dtype=int))]
 
         header = ['{} header'.format(len(self.comments)+1)]\
                + self.comments\