Python datetime.datetime.month() Examples

The following are 8 code examples of datetime.datetime.month(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module datetime.datetime , or try the search function .
Example #1
Source File: statistics.py    From djacket with MIT License 5 votes vote down vote up 
def extract_month(utc_timestamp):
    """
        Extracts month from utc timestamp string.
    """

    datetime = parser.parse(utc_timestamp)
    return '{0}-{1}'.format(datetime.year, datetime.month)
Example #2
Source File: statistics.py    From djacket with MIT License 5 votes vote down vote up 
def extract_day(utc_timestamp):
    """
        Extracts day from utc timestamp string.
    """

    datetime = parser.parse(utc_timestamp)
    return '{0}-{1}-{2}'.format(datetime.year, datetime.month, datetime.day)
Example #3
Source File: statistics.py    From djacket with MIT License 5 votes vote down vote up 
def _for_commits_monthly(self, commits):
        """
            Returns number of commits per month for the given commits.
        """

        dates = [get_month(extract_month(commit.get_committer_date())) for commit in commits
                        if get_year(commit.get_committer_date()) == self.current_year]
        return {mn: dates.count(mn) if mn in dates else 0 for mn in range(1, 13)}
Example #4
Source File: test_categorical.py    From recruit with Apache License 2.0 4 votes vote down vote up 
def test_sort_datetimelike():
    # GH10505

    # use same data as test_groupby_sort_categorical, which category is
    # corresponding to datetime.month
    df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
                           datetime(2011, 2, 1), datetime(2011, 5, 1),
                           datetime(2011, 2, 1), datetime(2011, 1, 1),
                           datetime(2011, 5, 1)],
                    'foo': [10, 8, 5, 6, 4, 1, 7],
                    'bar': [10, 20, 30, 40, 50, 60, 70]},
                   columns=['dt', 'foo', 'bar'])

    # ordered=True
    df['dt'] = Categorical(df['dt'], ordered=True)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt', ordered=True)

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt', ordered=True)

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())

    # when categories is ordered, group is ordered by category's order
    assert_frame_equal(
        result_sort, df.groupby(col, sort=False, observed=False).first())

    # ordered = False
    df['dt'] = Categorical(df['dt'], ordered=False)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt')

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt')

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())
    assert_frame_equal(
        result_nosort, df.groupby(col, sort=False, observed=False).first())
Example #5
Source File: test_categorical.py    From vnpy_crypto with MIT License 4 votes vote down vote up 
def test_sort_datetimelike():
    # GH10505

    # use same data as test_groupby_sort_categorical, which category is
    # corresponding to datetime.month
    df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
                           datetime(2011, 2, 1), datetime(2011, 5, 1),
                           datetime(2011, 2, 1), datetime(2011, 1, 1),
                           datetime(2011, 5, 1)],
                    'foo': [10, 8, 5, 6, 4, 1, 7],
                    'bar': [10, 20, 30, 40, 50, 60, 70]},
                   columns=['dt', 'foo', 'bar'])

    # ordered=True
    df['dt'] = Categorical(df['dt'], ordered=True)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt', ordered=True)

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt', ordered=True)

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())

    # when categories is ordered, group is ordered by category's order
    assert_frame_equal(
        result_sort, df.groupby(col, sort=False, observed=False).first())

    # ordered = False
    df['dt'] = Categorical(df['dt'], ordered=False)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt')

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt')

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())
    assert_frame_equal(
        result_nosort, df.groupby(col, sort=False, observed=False).first())
Example #6
Source File: test_categorical.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 4 votes vote down vote up 
def test_sort_datetimelike():
    # GH10505

    # use same data as test_groupby_sort_categorical, which category is
    # corresponding to datetime.month
    df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
                           datetime(2011, 2, 1), datetime(2011, 5, 1),
                           datetime(2011, 2, 1), datetime(2011, 1, 1),
                           datetime(2011, 5, 1)],
                    'foo': [10, 8, 5, 6, 4, 1, 7],
                    'bar': [10, 20, 30, 40, 50, 60, 70]},
                   columns=['dt', 'foo', 'bar'])

    # ordered=True
    df['dt'] = Categorical(df['dt'], ordered=True)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt', ordered=True)

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt', ordered=True)

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())

    # when categories is ordered, group is ordered by category's order
    assert_frame_equal(
        result_sort, df.groupby(col, sort=False, observed=False).first())

    # ordered = False
    df['dt'] = Categorical(df['dt'], ordered=False)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt')

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt')

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())
    assert_frame_equal(
        result_nosort, df.groupby(col, sort=False, observed=False).first())
Example #7
Source File: test_categorical.py    From elasticintel with GNU General Public License v3.0 4 votes vote down vote up 
def test_groupby_sort_categorical_datetimelike(self):
        # GH10505

        # use same data as test_groupby_sort_categorical, which category is
        # corresponding to datetime.month
        df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
                               datetime(2011, 2, 1), datetime(2011, 5, 1),
                               datetime(2011, 2, 1), datetime(2011, 1, 1),
                               datetime(2011, 5, 1)],
                        'foo': [10, 8, 5, 6, 4, 1, 7],
                        'bar': [10, 20, 30, 40, 50, 60, 70]},
                       columns=['dt', 'foo', 'bar'])

        # ordered=True
        df['dt'] = Categorical(df['dt'], ordered=True)
        index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
                 datetime(2011, 5, 1), datetime(2011, 7, 1)]
        result_sort = DataFrame(
            [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
        result_sort.index = CategoricalIndex(index, name='dt', ordered=True)

        index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
                 datetime(2011, 5, 1), datetime(2011, 1, 1)]
        result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                                  columns=['foo', 'bar'])
        result_nosort.index = CategoricalIndex(index, categories=index,
                                               name='dt', ordered=True)

        col = 'dt'
        assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
        # when categories is ordered, group is ordered by category's order
        assert_frame_equal(result_sort, df.groupby(col, sort=False).first())

        # ordered = False
        df['dt'] = Categorical(df['dt'], ordered=False)
        index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
                 datetime(2011, 5, 1), datetime(2011, 7, 1)]
        result_sort = DataFrame(
            [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
        result_sort.index = CategoricalIndex(index, name='dt')

        index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
                 datetime(2011, 5, 1), datetime(2011, 1, 1)]
        result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                                  columns=['foo', 'bar'])
        result_nosort.index = CategoricalIndex(index, categories=index,
                                               name='dt')

        col = 'dt'
        assert_frame_equal(result_sort, df.groupby(col, sort=True).first())
        assert_frame_equal(result_nosort, df.groupby(col, sort=False).first())
Example #8
Source File: test_categorical.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
def test_sort_datetimelike():
    # GH10505

    # use same data as test_groupby_sort_categorical, which category is
    # corresponding to datetime.month
    df = DataFrame({'dt': [datetime(2011, 7, 1), datetime(2011, 7, 1),
                           datetime(2011, 2, 1), datetime(2011, 5, 1),
                           datetime(2011, 2, 1), datetime(2011, 1, 1),
                           datetime(2011, 5, 1)],
                    'foo': [10, 8, 5, 6, 4, 1, 7],
                    'bar': [10, 20, 30, 40, 50, 60, 70]},
                   columns=['dt', 'foo', 'bar'])

    # ordered=True
    df['dt'] = Categorical(df['dt'], ordered=True)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt', ordered=True)

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt', ordered=True)

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())

    # when categories is ordered, group is ordered by category's order
    assert_frame_equal(
        result_sort, df.groupby(col, sort=False, observed=False).first())

    # ordered = False
    df['dt'] = Categorical(df['dt'], ordered=False)
    index = [datetime(2011, 1, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 7, 1)]
    result_sort = DataFrame(
        [[1, 60], [5, 30], [6, 40], [10, 10]], columns=['foo', 'bar'])
    result_sort.index = CategoricalIndex(index, name='dt')

    index = [datetime(2011, 7, 1), datetime(2011, 2, 1),
             datetime(2011, 5, 1), datetime(2011, 1, 1)]
    result_nosort = DataFrame([[10, 10], [5, 30], [6, 40], [1, 60]],
                              columns=['foo', 'bar'])
    result_nosort.index = CategoricalIndex(index, categories=index,
                                           name='dt')

    col = 'dt'
    assert_frame_equal(
        result_sort, df.groupby(col, sort=True, observed=False).first())
    assert_frame_equal(
        result_nosort, df.groupby(col, sort=False, observed=False).first())