TabularData: add a new how arg to dropna(#6892)

Borrowed this idea from pandas api. Current dropna methods will drop a
column or row if any data is missing. But it is also quite useful to
drop those columns or rows only if all of its elements are missing. This
is mostly used for pruning a table.

dvc/compare.py

@@ -182,21 +182,42 @@ class TabularData(MutableSequence[Sequence["CellT"]]):
             {k: self._columns[k][i] for k in keys} for i in range(len(self))
         ]
 
-    def dropna(self, axis: str = "rows"):
+    def dropna(self, axis: str = "rows", how="any"):
         if axis not in ["rows", "cols"]:
             raise ValueError(
                 f"Invalid 'axis' value {axis}."
                 "Choose one of ['rows', 'cols']"
             )
+<<<<<<< HEAD
         to_drop: Set = set()
+=======
+        if how not in ["any", "all"]:
+            raise ValueError(
+                f"Invalid 'how' value {how}." "Choose one of ['any', 'all']"
+            )
+
+        match_line: Set = set()
+        match = True
+        if how == "all":
+            match = False
+
+>>>>>>> 8ad4b8aa (TabularData: add a new how `arg` to dropna(#6892))
         for n_row, row in enumerate(self):
             for n_col, col in enumerate(row):
-                if col == self._fill_value:
+                if (col == self._fill_value) is match:
                     if axis == "rows":
-                        to_drop.add(n_row)
+                        match_line.add(n_row)
                         break
                     else:
-                        to_drop.add(self.keys()[n_col])
+                        match_line.add(self.keys()[n_col])
+
+        to_drop = match_line
+        if how == "all":
+            if axis == "rows":
+                to_drop = set(range(len(self)))
+            else:
+                to_drop = set(self.keys())
+            to_drop -= match_line
 
         if axis == "rows":
             for name in self.keys():

tests/unit/test_tabular_data.py

@@ -180,28 +180,43 @@ def test_row_from_dict():
 
 
 @pytest.mark.parametrize(
-    "axis,expected",
+    "axis,how,data,expected",
     [
         (
             "rows",
+            "any",
+            [["foo"], ["foo", "bar"], ["foo", "bar", "foobar"]],
             [
                 ["foo", "bar", "foobar"],
             ],
         ),
-        ("cols", [["foo"], ["foo"], ["foo"]]),
+        (
+            "rows",
+            "all",
+            [["foo"], ["foo", "bar"], ["", "", ""]],
+            [
+                ["foo", "", ""],
+                ["foo", "bar", ""],
+            ],
+        ),
+        (
+            "cols",
+            "any",
+            [["foo"], ["foo", "bar"], ["foo", "bar", "foobar"]],
+            [["foo"], ["foo"], ["foo"]],
+        ),
+        (
+            "cols",
+            "all",
+            [["foo"], ["foo", "bar"], ["", "", ""]],
+            [["foo", ""], ["foo", "bar"], ["", ""]],
+        ),
     ],
 )
-def test_dropna(axis, expected):
+def test_dropna(axis, how, data, expected):
     td = TabularData(["col-1", "col-2", "col-3"])
-    td.extend([["foo"], ["foo", "bar"], ["foo", "bar", "foobar"]])
-    assert list(td) == [
-        ["foo", "", ""],
-        ["foo", "bar", ""],
-        ["foo", "bar", "foobar"],
-    ]
-
-    td.dropna(axis)
-
+    td.extend(data)
+    td.dropna(axis, how)
     assert list(td) == expected