Python pandas.plotting方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def __getattribute__(self, item):
        """This method will override the parameters passed and convert any Modin
            DataFrames to pandas so that they can be plotted normally
        """
        if hasattr(pdplot, item):
            func = getattr(pdplot, item)
            if callable(func):

                def wrap_func(*args, **kwargs):
                    """Convert Modin DataFrames to pandas then call the function"""
                    args = tuple(
                        arg if not isinstance(arg, DataFrame) else to_pandas(arg)
                        for arg in args
                    )
                    kwargs = {
                        kwd: val if not isinstance(val, DataFrame) else to_pandas(val)
                        for kwd, val in kwargs.items()
                    }
                    return func(*args, **kwargs)

                return wrap_func
            else:
                return func
        else:
            return object.__getattribute__(self, item) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
        self.mpl_ge_2_1_0 = plotting._compat._mpl_ge_2_1_0()
        self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()
        self.mpl_ge_2_2_2 = plotting._compat._mpl_ge_2_2_2()
        self.mpl_ge_3_0_0 = plotting._compat._mpl_ge_3_0_0()

        self.bp_n_objects = 7
        self.polycollection_factor = 2
        self.default_figsize = (6.4, 4.8)
        self.default_tick_position = 'left'

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)}) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def _ok_for_gaussian_kde(kind):
    if kind in ['kde', 'density']:
        try:
            from scipy.stats import gaussian_kde  # noqa
        except ImportError:
            return False

    return plotting._compat._mpl_ge_1_5_0() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def outer(t=t):

        def wrapper(*args, **kwargs):
            warnings.warn("'pandas.tools.plotting.{t}' is deprecated, "
                          "import 'pandas.plotting.{t}' instead.".format(t=t),
                          FutureWarning, stacklevel=2)
            return getattr(_plotting, t)(*args, **kwargs)
        return wrapper 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def plot_multi(data, cols=None, spacing=.1, **kwargs):
    from pandas import plotting

    # Get default color style from pandas - can be changed to any other color list
    if cols is None: cols = data.columns
    if len(cols) == 0: return
    colors = getattr(getattr(plotting, '_style'), '_get_standard_colors')(num_colors=len(cols))

    # First axis
    ax = data.loc[:, cols[0]].plot(label=cols[0], color=colors[0], **kwargs)
    ax.set_ylabel(ylabel=cols[0])
    lines, labels = ax.get_legend_handles_labels()

    for n in range(1, len(cols)):
        # Multiple y-axes
        ax_new = ax.twinx()
        ax_new.spines['right'].set_position(('axes', 1 + spacing * (n - 1)))
        data.loc[:, cols[n]].plot(ax=ax_new, label=cols[n], color=colors[n % len(colors)])
        ax_new.set_ylabel(ylabel=cols[n])

        # Proper legend position
        line, label = ax_new.get_legend_handles_labels()
        lines += line
        labels += label

    ax.legend(lines, labels, loc=0)
    return ax 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def __dir__(self):
        """This allows tab completion of plotting library"""
        return dir(pdplot) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_le_1_2_1 = plotting._compat._mpl_le_1_2_1()
        self.mpl_ge_1_3_1 = plotting._compat._mpl_ge_1_3_1()
        self.mpl_ge_1_4_0 = plotting._compat._mpl_ge_1_4_0()
        self.mpl_ge_1_5_0 = plotting._compat._mpl_ge_1_5_0()
        self.mpl_ge_2_0_0 = plotting._compat._mpl_ge_2_0_0()
        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()
        self.mpl_ge_2_2_0 = plotting._compat._mpl_ge_2_2_0()

        if self.mpl_ge_1_4_0:
            self.bp_n_objects = 7
        else:
            self.bp_n_objects = 8
        if self.mpl_ge_1_5_0:
            # 1.5 added PolyCollections to legend handler
            # so we have twice as many items.
            self.polycollection_factor = 2
        else:
            self.polycollection_factor = 1

        if self.mpl_ge_2_0_0:
            self.default_figsize = (6.4, 4.8)
        else:
            self.default_figsize = (8.0, 6.0)
        self.default_tick_position = 'left' if self.mpl_ge_2_0_0 else 'default'

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)}) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import plotting [as 別名]
def setup_method(self, method):

        import matplotlib as mpl
        mpl.rcdefaults()

        self.mpl_le_1_2_1 = plotting._compat._mpl_le_1_2_1()
        self.mpl_ge_1_3_1 = plotting._compat._mpl_ge_1_3_1()
        self.mpl_ge_1_4_0 = plotting._compat._mpl_ge_1_4_0()
        self.mpl_ge_1_5_0 = plotting._compat._mpl_ge_1_5_0()
        self.mpl_ge_2_0_0 = plotting._compat._mpl_ge_2_0_0()
        self.mpl_ge_2_0_1 = plotting._compat._mpl_ge_2_0_1()

        if self.mpl_ge_1_4_0:
            self.bp_n_objects = 7
        else:
            self.bp_n_objects = 8
        if self.mpl_ge_1_5_0:
            # 1.5 added PolyCollections to legend handler
            # so we have twice as many items.
            self.polycollection_factor = 2
        else:
            self.polycollection_factor = 1

        if self.mpl_ge_2_0_0:
            self.default_figsize = (6.4, 4.8)
        else:
            self.default_figsize = (8.0, 6.0)
        self.default_tick_position = 'left' if self.mpl_ge_2_0_0 else 'default'
        # common test data
        from pandas import read_csv
        base = os.path.join(os.path.dirname(curpath()), os.pardir)
        path = os.path.join(base, 'tests', 'data', 'iris.csv')
        self.iris = read_csv(path)

        n = 100
        with tm.RNGContext(42):
            gender = np.random.choice(['Male', 'Female'], size=n)
            classroom = np.random.choice(['A', 'B', 'C'], size=n)

            self.hist_df = DataFrame({'gender': gender,
                                      'classroom': classroom,
                                      'height': random.normal(66, 4, size=n),
                                      'weight': random.normal(161, 32, size=n),
                                      'category': random.randint(4, size=n)})

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame({"A": np.random.uniform(size=20),
                                    "B": np.random.uniform(size=20),
                                    "C": np.arange(20) + np.random.uniform(
                                        size=20)})