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tidy tests, docs
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@ -447,7 +447,7 @@ Pandas Integration
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~~~~~~~~~~~~~~~~~~
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Due to popular demand we've added support for ``pandas`` -- here's an example
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for ``DataFrameGroupBy.progress_apply``:
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for ``DataFrame.progress_apply`` and ``DataFrameGroupBy.progress_apply``:
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.. code:: python
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@ -455,6 +455,8 @@ for ``DataFrameGroupBy.progress_apply``:
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import numpy as np
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from tqdm import tqdm, tqdm_pandas
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...
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df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
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# Create and register a new `tqdm` instance with `pandas`
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@ -462,7 +464,9 @@ for ``DataFrameGroupBy.progress_apply``:
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tqdm_pandas(tqdm())
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# Now you can use `progress_apply` instead of `apply`
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df.groupby(0).progress_apply(lambda x: x**2)
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df.progress_apply(lambda x: x**2)
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# can also groupby:
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# df.groupby(0).progress_apply(lambda x: x**2)
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In case you're interested in how this works (and how to modify it for your
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own callbacks), see the
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@ -9,18 +9,20 @@ df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
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tqdm_pandas(tqdm())
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# Now you can use `progress_apply` instead of `apply`
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df.groupby(0).progress_apply(lambda x: x**2)
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df.progress_apply(lambda x: x**2)
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# can also groupby:
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# df.groupby(0).progress_apply(lambda x: x**2)
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""" Source code for `tqdm_pandas` (really simple!) """
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# def tqdm_pandas(t):
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# from pandas.core.groupby import DataFrameGroupBy
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# def inner(groups, func, *args, **kwargs):
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# t.total = len(groups) + 1
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# from pandas.core.frame import DataFrame
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# def inner(df, func, *args, **kwargs):
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# t.total = groups.size // len(groups)
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# def wrapper(*args, **kwargs):
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# t.update(1)
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# return func(*args, **kwargs)
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# result = groups.apply(wrapper, *args, **kwargs)
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# result = df.apply(wrapper, *args, **kwargs)
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# t.close()
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# return result
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# DataFrameGroupBy.progress_apply = inner
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# DataFrame.progress_apply = inner
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@ -32,20 +32,20 @@ def tqdm_pandas(t): # pragma: no cover
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from pandas.core.frame import DataFrame
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from pandas.core.groupby import DataFrameGroupBy
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def inner(groups, func, *args, **kwargs):
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def inner(df, func, *args, **kwargs):
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"""
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Parameters
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----------
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groups : DataFrame[GroupBy]
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(Grouped) data.
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df : DataFrame[GroupBy]
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Data (may be grouped).
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func : function
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To be applied on the (grouped) data.
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*args and *kwargs are transmitted to DataFrameGroupBy.apply()
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"""
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t.total = getattr(groups, 'ngroups', None)
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t.total = getattr(df, 'ngroups', None)
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if t.total is None: # not grouped
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t.total = groups.size // len(groups)
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t.total = df.size // len(df)
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else:
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t.total += 1 # pandas calls update once too many
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@ -53,7 +53,7 @@ def tqdm_pandas(t): # pragma: no cover
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t.update()
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return func(*args, **kwargs)
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result = groups.apply(wrapper, *args, **kwargs)
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result = df.apply(wrapper, *args, **kwargs)
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t.close()
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@ -5,7 +5,7 @@ from tests_tqdm import with_setup, pretest, posttest, StringIO, closing
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@with_setup(pretest, posttest)
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def test_pandas():
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def test_pandas_groupby_apply():
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""" Test pandas.DataFrame.groupby(0).progress_apply """
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try:
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from numpy.random import randint
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@ -31,7 +31,7 @@ def test_pandas():
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@with_setup(pretest, posttest)
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def test_pandas():
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def test_pandas_apply():
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""" Test pandas.DataFrame.progress_apply """
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try:
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from numpy.random import randint
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@ -39,7 +39,7 @@ def test_pandas():
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import pandas as pd
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except:
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raise SkipTest
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with closing(StringIO()) as our_file:
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df = pd.DataFrame(randint(0, 100, (1000, 6)))
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tqdm_pandas(tqdm(file=our_file, leave=True, ascii=True))
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