bump version, Merge remote-tracking branch 'pandasMap'

README.rst

@@ -492,15 +492,14 @@ for ``DataFrame.progress_apply`` and ``DataFrameGroupBy.progress_apply``:
     import numpy as np
     from tqdm import tqdm
 
-    ...
-
     df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
 
-    # Register `pandas.progress_apply` with `tqdm`
+    # Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
     # (can use `tqdm_gui`, `tqdm_notebook`, optional kwargs, etc.)
     tqdm.pandas(desc="my bar!")
 
     # Now you can use `progress_apply` instead of `apply`
+    # and `progress_map` instead of `map`
     df.progress_apply(lambda x: x**2)
     # can also groupby:
     # df.groupby(0).progress_apply(lambda x: x**2)

examples/pandas_progress_apply.py

@@ -1,14 +1,15 @@
 import pandas as pd
 import numpy as np
-from tqdm import tqdm, tqdm_pandas
+from tqdm import tqdm
 
 df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
 
-# Create and register a new `tqdm` instance with `pandas`
-# (can use tqdm_gui, optional kwargs, etc.)
-tqdm_pandas(tqdm())
+# Register `pandas.progress_apply` and `pandas.Series.map_apply` with `tqdm`
+# (can use `tqdm_gui`, `tqdm_notebook`, optional kwargs, etc.)
+tqdm.pandas(desc="my bar!")
 
 # Now you can use `progress_apply` instead of `apply`
+# and `progress_map` instead of `map`
 df.progress_apply(lambda x: x**2)
 # can also groupby:
 # df.groupby(0).progress_apply(lambda x: x**2)

tqdm/_tqdm.py

@@ -379,55 +379,72 @@ def pandas(tclass, *targs, **tkwargs):
         """
         from pandas.core.frame import DataFrame
         from pandas.core.series import Series
-        from pandas.core.groupby import DataFrameGroupBy, SeriesGroupBy
+        from pandas.core.groupby import DataFrameGroupBy
+        from pandas.core.groupby import SeriesGroupBy
+        from pandas.core.groupby import GroupBy
+        from pandas.core.groupby import PanelGroupBy
+        from pandas import Panel
 
         deprecated_t = [tkwargs.pop('deprecated_t', None)]
 
-        def inner(df, func, *args, **kwargs):
-            """
-            Parameters
-            ----------
-            df  : (DataFrame|Series)[GroupBy]
-                Data (may be grouped).
-            func  : function
-                To be applied on the (grouped) data.
-            *args, *kwargs  : optional
-                Transmitted to `df.apply()`.
-            """
-            # Precompute total iterations
-            total = getattr(df, 'ngroups', None)
-            if total is None:  # not grouped
-                total = len(df) if isinstance(df, Series) \
-                    else df.size // len(df)
-            else:
-                total += 1  # pandas calls update once too many
+        def inner_generator(df_function='apply'):
+            def inner(df, func, *args, **kwargs):
+                """
+                Parameters
+                ----------
+                df  : (DataFrame|Series)[GroupBy]
+                    Data (may be grouped).
+                func  : function
+                    To be applied on the (grouped) data.
+                *args, *kwargs  : optional
+                    Transmitted to `df.apply()`.
+                """
+                # Precompute total iterations
+                total = getattr(df, 'ngroups', None)
+                if total is None:  # not grouped
+                    total = len(df) if isinstance(df, Series) \
+                        else df.size // len(df)
+                else:
+                    total += 1  # pandas calls update once too many
 
-            # Init bar
-            if deprecated_t[0] is not None:
-                t = deprecated_t[0]
-                deprecated_t[0] = None
-            else:
-                t = tclass(*targs, total=total, **tkwargs)
+                # Init bar
+                if deprecated_t[0] is not None:
+                    t = deprecated_t[0]
+                    deprecated_t[0] = None
+                else:
+                    t = tclass(*targs, total=total, **tkwargs)
 
-            # Define bar updating wrapper
-            def wrapper(*args, **kwargs):
-                t.update()
-                return func(*args, **kwargs)
+                # Define bar updating wrapper
+                def wrapper(*args, **kwargs):
+                    t.update()
+                    return func(*args, **kwargs)
 
-            # Apply the provided function (in *args and **kwargs)
-            # on the df using our wrapper (which provides bar updating)
-            result = df.apply(wrapper, *args, **kwargs)
+                # Apply the provided function (in *args and **kwargs)
+                # on the df using our wrapper (which provides bar updating)
+                result = getattr(df, df_function)(wrapper, *args, **kwargs)
 
-            # Close bar and return pandas calculation result
-            t.close()
-            return result
+                # Close bar and return pandas calculation result
+                t.close()
+                return result
+            return inner
 
         # Monkeypatch pandas to provide easy methods
         # Enable custom tqdm progress in pandas!
-        DataFrame.progress_apply = inner
-        DataFrameGroupBy.progress_apply = inner
-        Series.progress_apply = inner
-        SeriesGroupBy.progress_apply = inner
+        Series.progress_apply = inner_generator()
+        SeriesGroupBy.progress_apply = inner_generator()
+        Series.progress_map = inner_generator('map')
+        SeriesGroupBy.progress_map = inner_generator('map')
+
+        DataFrame.progress_apply = inner_generator()
+        DataFrameGroupBy.progress_apply = inner_generator()
+        DataFrame.progress_applymap = inner_generator('applymap')
+
+        Panel.progress_apply = inner_generator()
+        PanelGroupBy.progress_apply = inner_generator()
+
+        GroupBy.progress_apply = inner_generator()
+        GroupBy.progress_aggregate = inner_generator('aggregate')
+        GroupBy.progress_transform = inner_generator('transform')
 
     def __init__(self, iterable=None, desc=None, total=None, leave=True,
                  file=sys.stderr, ncols=None, mininterval=0.1,

tqdm/_version.py

@@ -8,7 +8,7 @@
 __all__ = ["__version__"]
 
 # major, minor, patch, -extra
-version_info = 4, 8, 1
+version_info = 4, 8, 2
 
 # Nice string for the version
 __version__ = '.'.join(map(str, version_info))

tqdm/tests/tests_pandas.py

@@ -60,6 +60,26 @@ def test_pandas_apply():
                 '100% at least twice', our_file.read()))
 
 
+@with_setup(pretest, posttest)
+def test_pandas_map():
+    """ Test pandas.Series.progress_map """
+    try:
+        from numpy.random import randint
+        import pandas as pd
+    except:
+        raise SkipTest
+
+    with closing(StringIO()) as our_file:
+        tqdm.pandas(file=our_file, leave=True, ascii=True)
+        dfs = pd.DataFrame(randint(0, 50, (500, 3)),
+                           columns=list('abc'))
+        dfs.a.progress_map(lambda x: None)
+
+        if our_file.getvalue().count('100%') < 1:
+            raise AssertionError("\nExpected:\n{0}\nIn:{1}\n".format(
+                '100% at least twice', our_file.getvalue()))
+
+
 @with_setup(pretest, posttest)
 def test_pandas_leave():
     """ Test pandas with `leave=True` """