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8th day of python challenges 111-117

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2019-08-04 15:26:35 +03:00
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venv/lib/python3.6/site-packages/pandas/plotting/__init__.py Normal file
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"""
Plotting public API.
Authors of third-party plotting backends should implement a module with a
public ``plot(data, kind, **kwargs)``. The parameter `data` will contain
the data structure and can be a `Series` or a `DataFrame`. For example,
for ``df.plot()`` the parameter `data` will contain the DataFrame `df`.
In some cases, the data structure is transformed before being sent to
the backend (see PlotAccessor.__call__ in pandas/plotting/_core.py for
the exact transformations).
The parameter `kind` will be one of:
- line
- bar
- barh
- box
- hist
- kde
- area
- pie
- scatter
- hexbin
See the pandas API reference for documentation on each kind of plot.
Any other keyword argument is currently assumed to be backend specific,
but some parameters may be unified and added to the signature in the
future (e.g. `title` which should be useful for any backend).
Currently, all the Matplotlib functions in pandas are accessed through
the selected backend. For example, `pandas.plotting.boxplot` (equivalent
to `DataFrame.boxplot`) is also accessed in the selected backend. This
is expected to change, and the exact API is under discussion. But with
the current version, backends are expected to implement the next functions:
- plot (describe above, used for `Series.plot` and `DataFrame.plot`)
- hist_series and hist_frame (for `Series.hist` and `DataFrame.hist`)
- boxplot (`pandas.plotting.boxplot(df)` equivalent to `DataFrame.boxplot`)
- boxplot_frame and boxplot_frame_groupby
- tsplot (deprecated)
- register and deregister (register converters for the tick formats)
- Plots not called as `Series` and `DataFrame` methods:
- table
- andrews_curves
- autocorrelation_plot
- bootstrap_plot
- lag_plot
- parallel_coordinates
- radviz
- scatter_matrix
Use the code in pandas/plotting/_matplotib.py and
https://github.com/pyviz/hvplot as a reference on how to write a backend.
For the discussion about the API see
https://github.com/pandas-dev/pandas/issues/26747.
"""
from pandas.plotting._core import (
PlotAccessor,
boxplot,
boxplot_frame,
boxplot_frame_groupby,
hist_frame,
hist_series,
)
from pandas.plotting._misc import (
andrews_curves,
autocorrelation_plot,
bootstrap_plot,
deregister as deregister_matplotlib_converters,
lag_plot,
parallel_coordinates,
plot_params,
radviz,
register as register_matplotlib_converters,
scatter_matrix,
table,
)
__all__ = [
"PlotAccessor",
"boxplot",
"boxplot_frame",
"boxplot_frame_groupby",
"hist_frame",
"hist_series",
"scatter_matrix",
"radviz",
"andrews_curves",
"bootstrap_plot",
"parallel_coordinates",
"lag_plot",
"autocorrelation_plot",
"table",
"plot_params",
"register_matplotlib_converters",
"deregister_matplotlib_converters",
]

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venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/__init__.py Normal file
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from pandas._config import get_option
from pandas.plotting._matplotlib.boxplot import (
BoxPlot,
boxplot,
boxplot_frame,
boxplot_frame_groupby,
)
from pandas.plotting._matplotlib.converter import deregister, register
from pandas.plotting._matplotlib.core import (
AreaPlot,
BarhPlot,
BarPlot,
HexBinPlot,
LinePlot,
PiePlot,
ScatterPlot,
)
from pandas.plotting._matplotlib.hist import HistPlot, KdePlot, hist_frame, hist_series
from pandas.plotting._matplotlib.misc import (
andrews_curves,
autocorrelation_plot,
bootstrap_plot,
lag_plot,
parallel_coordinates,
radviz,
scatter_matrix,
)
from pandas.plotting._matplotlib.timeseries import tsplot
from pandas.plotting._matplotlib.tools import table
PLOT_CLASSES = {
"line": LinePlot,
"bar": BarPlot,
"barh": BarhPlot,
"box": BoxPlot,
"hist": HistPlot,
"kde": KdePlot,
"area": AreaPlot,
"pie": PiePlot,
"scatter": ScatterPlot,
"hexbin": HexBinPlot,
}
if get_option("plotting.matplotlib.register_converters"):
register(explicit=False)
def plot(data, kind, **kwargs):
# Importing pyplot at the top of the file (before the converters are
# registered) causes problems in matplotlib 2 (converters seem to not
# work)
import matplotlib.pyplot as plt
if kwargs.pop("reuse_plot", False):
ax = kwargs.get("ax")
if ax is None and len(plt.get_fignums()) > 0:
with plt.rc_context():
ax = plt.gca()
kwargs["ax"] = getattr(ax, "left_ax", ax)
plot_obj = PLOT_CLASSES[kind](data, **kwargs)
plot_obj.generate()
plot_obj.draw()
return plot_obj.result
__all__ = [
"plot",
"hist_series",
"hist_frame",
"boxplot",
"boxplot_frame",
"boxplot_frame_groupby",
"tsplot",
"table",
"andrews_curves",
"autocorrelation_plot",
"bootstrap_plot",
"lag_plot",
"parallel_coordinates",
"radviz",
"scatter_matrix",
"register",
"deregister",
]

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from collections import namedtuple
import warnings
from matplotlib.artist import setp
import numpy as np
from pandas.core.dtypes.generic import ABCSeries
from pandas.core.dtypes.missing import remove_na_arraylike
import pandas as pd
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib import converter
from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
from pandas.plotting._matplotlib.style import _get_standard_colors
from pandas.plotting._matplotlib.tools import _flatten, _subplots
class BoxPlot(LinePlot):
_kind = "box"
_layout_type = "horizontal"
_valid_return_types = (None, "axes", "dict", "both")
# namedtuple to hold results
BP = namedtuple("Boxplot", ["ax", "lines"])
def __init__(self, data, return_type="axes", **kwargs):
# Do not call LinePlot.__init__ which may fill nan
if return_type not in self._valid_return_types:
raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")
self.return_type = return_type
MPLPlot.__init__(self, data, **kwargs)
def _args_adjust(self):
if self.subplots:
# Disable label ax sharing. Otherwise, all subplots shows last
# column label
if self.orientation == "vertical":
self.sharex = False
else:
self.sharey = False
@classmethod
def _plot(cls, ax, y, column_num=None, return_type="axes", **kwds):
if y.ndim == 2:
y = [remove_na_arraylike(v) for v in y]
# Boxplot fails with empty arrays, so need to add a NaN
# if any cols are empty
# GH 8181
y = [v if v.size > 0 else np.array([np.nan]) for v in y]
else:
y = remove_na_arraylike(y)
bp = ax.boxplot(y, **kwds)
if return_type == "dict":
return bp, bp
elif return_type == "both":
return cls.BP(ax=ax, lines=bp), bp
else:
return ax, bp
def _validate_color_args(self):
if "color" in self.kwds:
if self.colormap is not None:
warnings.warn(
"'color' and 'colormap' cannot be used "
"simultaneously. Using 'color'"
)
self.color = self.kwds.pop("color")
if isinstance(self.color, dict):
valid_keys = ["boxes", "whiskers", "medians", "caps"]
for key, values in self.color.items():
if key not in valid_keys:
raise ValueError(
"color dict contains invalid "
"key '{0}' "
"The key must be either {1}".format(key, valid_keys)
)
else:
self.color = None
# get standard colors for default
colors = _get_standard_colors(num_colors=3, colormap=self.colormap, color=None)
# use 2 colors by default, for box/whisker and median
# flier colors isn't needed here
# because it can be specified by ``sym`` kw
self._boxes_c = colors[0]
self._whiskers_c = colors[0]
self._medians_c = colors[2]
self._caps_c = "k" # mpl default
def _get_colors(self, num_colors=None, color_kwds="color"):
pass
def maybe_color_bp(self, bp):
if isinstance(self.color, dict):
boxes = self.color.get("boxes", self._boxes_c)
whiskers = self.color.get("whiskers", self._whiskers_c)
medians = self.color.get("medians", self._medians_c)
caps = self.color.get("caps", self._caps_c)
else:
# Other types are forwarded to matplotlib
# If None, use default colors
boxes = self.color or self._boxes_c
whiskers = self.color or self._whiskers_c
medians = self.color or self._medians_c
caps = self.color or self._caps_c
setp(bp["boxes"], color=boxes, alpha=1)
setp(bp["whiskers"], color=whiskers, alpha=1)
setp(bp["medians"], color=medians, alpha=1)
setp(bp["caps"], color=caps, alpha=1)
def _make_plot(self):
if self.subplots:
self._return_obj = pd.Series()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
kwds = self.kwds.copy()
ret, bp = self._plot(
ax, y, column_num=i, return_type=self.return_type, **kwds
)
self.maybe_color_bp(bp)
self._return_obj[label] = ret
label = [pprint_thing(label)]
self._set_ticklabels(ax, label)
else:
y = self.data.values.T
ax = self._get_ax(0)
kwds = self.kwds.copy()
ret, bp = self._plot(
ax, y, column_num=0, return_type=self.return_type, **kwds
)
self.maybe_color_bp(bp)
self._return_obj = ret
labels = [l for l, _ in self._iter_data()]
labels = [pprint_thing(l) for l in labels]
if not self.use_index:
labels = [pprint_thing(key) for key in range(len(labels))]
self._set_ticklabels(ax, labels)
def _set_ticklabels(self, ax, labels):
if self.orientation == "vertical":
ax.set_xticklabels(labels)
else:
ax.set_yticklabels(labels)
def _make_legend(self):
pass
def _post_plot_logic(self, ax, data):
pass
@property
def orientation(self):
if self.kwds.get("vert", True):
return "vertical"
else:
return "horizontal"
@property
def result(self):
if self.return_type is None:
return super().result
else:
return self._return_obj
def _grouped_plot_by_column(
plotf,
data,
columns=None,
by=None,
numeric_only=True,
grid=False,
figsize=None,
ax=None,
layout=None,
return_type=None,
**kwargs
):
grouped = data.groupby(by)
if columns is None:
if not isinstance(by, (list, tuple)):
by = [by]
columns = data._get_numeric_data().columns.difference(by)
naxes = len(columns)
fig, axes = _subplots(
naxes=naxes, sharex=True, sharey=True, figsize=figsize, ax=ax, layout=layout
)
_axes = _flatten(axes)
ax_values = []
for i, col in enumerate(columns):
ax = _axes[i]
gp_col = grouped[col]
keys, values = zip(*gp_col)
re_plotf = plotf(keys, values, ax, **kwargs)
ax.set_title(col)
ax.set_xlabel(pprint_thing(by))
ax_values.append(re_plotf)
ax.grid(grid)
result = pd.Series(ax_values, index=columns)
# Return axes in multiplot case, maybe revisit later # 985
if return_type is None:
result = axes
byline = by[0] if len(by) == 1 else by
fig.suptitle("Boxplot grouped by {byline}".format(byline=byline))
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return result
def boxplot(
data,
column=None,
by=None,
ax=None,
fontsize=None,
rot=0,
grid=True,
figsize=None,
layout=None,
return_type=None,
**kwds
):
import matplotlib.pyplot as plt
# validate return_type:
if return_type not in BoxPlot._valid_return_types:
raise ValueError("return_type must be {'axes', 'dict', 'both'}")
if isinstance(data, ABCSeries):
data = data.to_frame("x")
column = "x"
def _get_colors():
# num_colors=3 is required as method maybe_color_bp takes the colors
# in positions 0 and 2.
return _get_standard_colors(color=kwds.get("color"), num_colors=3)
def maybe_color_bp(bp):
if "color" not in kwds:
setp(bp["boxes"], color=colors[0], alpha=1)
setp(bp["whiskers"], color=colors[0], alpha=1)
setp(bp["medians"], color=colors[2], alpha=1)
def plot_group(keys, values, ax):
keys = [pprint_thing(x) for x in keys]
values = [np.asarray(remove_na_arraylike(v)) for v in values]
bp = ax.boxplot(values, **kwds)
if fontsize is not None:
ax.tick_params(axis="both", labelsize=fontsize)
if kwds.get("vert", 1):
ax.set_xticklabels(keys, rotation=rot)
else:
ax.set_yticklabels(keys, rotation=rot)
maybe_color_bp(bp)
# Return axes in multiplot case, maybe revisit later # 985
if return_type == "dict":
return bp
elif return_type == "both":
return BoxPlot.BP(ax=ax, lines=bp)
else:
return ax
colors = _get_colors()
if column is None:
columns = None
else:
if isinstance(column, (list, tuple)):
columns = column
else:
columns = [column]
if by is not None:
# Prefer array return type for 2-D plots to match the subplot layout
# https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
result = _grouped_plot_by_column(
plot_group,
data,
columns=columns,
by=by,
grid=grid,
figsize=figsize,
ax=ax,
layout=layout,
return_type=return_type,
)
else:
if return_type is None:
return_type = "axes"
if layout is not None:
raise ValueError(
"The 'layout' keyword is not supported when " "'by' is None"
)
if ax is None:
rc = {"figure.figsize": figsize} if figsize is not None else {}
with plt.rc_context(rc):
ax = plt.gca()
data = data._get_numeric_data()
if columns is None:
columns = data.columns
else:
data = data[columns]
result = plot_group(columns, data.values.T, ax)
ax.grid(grid)
return result
def boxplot_frame(
self,
column=None,
by=None,
ax=None,
fontsize=None,
rot=0,
grid=True,
figsize=None,
layout=None,
return_type=None,
**kwds
):
import matplotlib.pyplot as plt
converter._WARN = False # no warning for pandas plots
ax = boxplot(
self,
column=column,
by=by,
ax=ax,
fontsize=fontsize,
grid=grid,
rot=rot,
figsize=figsize,
layout=layout,
return_type=return_type,
**kwds
)
plt.draw_if_interactive()
return ax
def boxplot_frame_groupby(
grouped,
subplots=True,
column=None,
fontsize=None,
rot=0,
grid=True,
ax=None,
figsize=None,
layout=None,
sharex=False,
sharey=True,
**kwds
):
converter._WARN = False # no warning for pandas plots
if subplots is True:
naxes = len(grouped)
fig, axes = _subplots(
naxes=naxes,
squeeze=False,
ax=ax,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
axes = _flatten(axes)
ret = pd.Series()
for (key, group), ax in zip(grouped, axes):
d = group.boxplot(
ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
)
ax.set_title(pprint_thing(key))
ret.loc[key] = d
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
else:
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = pd.concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
ret = df.boxplot(
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwds
)
return ret

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# being a bit too dynamic
from distutils.version import LooseVersion
import operator
def _mpl_version(version, op):
def inner():
try:
import matplotlib as mpl
except ImportError:
return False
return (
op(LooseVersion(mpl.__version__), LooseVersion(version))
and str(mpl.__version__)[0] != "0"
)
return inner
_mpl_ge_2_2_3 = _mpl_version("2.2.3", operator.ge)
_mpl_ge_3_0_0 = _mpl_version("3.0.0", operator.ge)
_mpl_ge_3_1_0 = _mpl_version("3.1.0", operator.ge)

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venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/hist.py Normal file
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import warnings
import numpy as np
from pandas.core.dtypes.common import is_integer, is_list_like
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass
from pandas.core.dtypes.missing import isna, remove_na_arraylike
import pandas.core.common as com
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib import converter
from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
from pandas.plotting._matplotlib.tools import _flatten, _set_ticks_props, _subplots
class HistPlot(LinePlot):
_kind = "hist"
def __init__(self, data, bins=10, bottom=0, **kwargs):
self.bins = bins # use mpl default
self.bottom = bottom
# Do not call LinePlot.__init__ which may fill nan
MPLPlot.__init__(self, data, **kwargs)
def _args_adjust(self):
if is_integer(self.bins):
# create common bin edge
values = self.data._convert(datetime=True)._get_numeric_data()
values = np.ravel(values)
values = values[~isna(values)]
hist, self.bins = np.histogram(
values,
bins=self.bins,
range=self.kwds.get("range", None),
weights=self.kwds.get("weights", None),
)
if is_list_like(self.bottom):
self.bottom = np.array(self.bottom)
@classmethod
def _plot(
cls,
ax,
y,
style=None,
bins=None,
bottom=0,
column_num=0,
stacking_id=None,
**kwds
):
if column_num == 0:
cls._initialize_stacker(ax, stacking_id, len(bins) - 1)
y = y[~isna(y)]
base = np.zeros(len(bins) - 1)
bottom = bottom + cls._get_stacked_values(ax, stacking_id, base, kwds["label"])
# ignore style
n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
cls._update_stacker(ax, stacking_id, n)
return patches
def _make_plot(self):
colors = self._get_colors()
stacking_id = self._get_stacking_id()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
kwds = self.kwds.copy()
label = pprint_thing(label)
kwds["label"] = label
style, kwds = self._apply_style_colors(colors, kwds, i, label)
if style is not None:
kwds["style"] = style
kwds = self._make_plot_keywords(kwds, y)
artists = self._plot(ax, y, column_num=i, stacking_id=stacking_id, **kwds)
self._add_legend_handle(artists[0], label, index=i)
def _make_plot_keywords(self, kwds, y):
"""merge BoxPlot/KdePlot properties to passed kwds"""
# y is required for KdePlot
kwds["bottom"] = self.bottom
kwds["bins"] = self.bins
return kwds
def _post_plot_logic(self, ax, data):
if self.orientation == "horizontal":
ax.set_xlabel("Frequency")
else:
ax.set_ylabel("Frequency")
@property
def orientation(self):
if self.kwds.get("orientation", None) == "horizontal":
return "horizontal"
else:
return "vertical"
class KdePlot(HistPlot):
_kind = "kde"
orientation = "vertical"
def __init__(self, data, bw_method=None, ind=None, **kwargs):
MPLPlot.__init__(self, data, **kwargs)
self.bw_method = bw_method
self.ind = ind
def _args_adjust(self):
pass
def _get_ind(self, y):
if self.ind is None:
# np.nanmax() and np.nanmin() ignores the missing values
sample_range = np.nanmax(y) - np.nanmin(y)
ind = np.linspace(
np.nanmin(y) - 0.5 * sample_range,
np.nanmax(y) + 0.5 * sample_range,
1000,
)
elif is_integer(self.ind):
sample_range = np.nanmax(y) - np.nanmin(y)
ind = np.linspace(
np.nanmin(y) - 0.5 * sample_range,
np.nanmax(y) + 0.5 * sample_range,
self.ind,
)
else:
ind = self.ind
return ind
@classmethod
def _plot(
cls,
ax,
y,
style=None,
bw_method=None,
ind=None,
column_num=None,
stacking_id=None,
**kwds
):
from scipy.stats import gaussian_kde
y = remove_na_arraylike(y)
gkde = gaussian_kde(y, bw_method=bw_method)
y = gkde.evaluate(ind)
lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
return lines
def _make_plot_keywords(self, kwds, y):
kwds["bw_method"] = self.bw_method
kwds["ind"] = self._get_ind(y)
return kwds
def _post_plot_logic(self, ax, data):
ax.set_ylabel("Density")
def _grouped_plot(
plotf,
data,
column=None,
by=None,
numeric_only=True,
figsize=None,
sharex=True,
sharey=True,
layout=None,
rot=0,
ax=None,
**kwargs
):
if figsize == "default":
# allowed to specify mpl default with 'default'
warnings.warn(
"figsize='default' is deprecated. Specify figure " "size by tuple instead",
FutureWarning,
stacklevel=5,
)
figsize = None
grouped = data.groupby(by)
if column is not None:
grouped = grouped[column]
naxes = len(grouped)
fig, axes = _subplots(
naxes=naxes, figsize=figsize, sharex=sharex, sharey=sharey, ax=ax, layout=layout
)
_axes = _flatten(axes)
for i, (key, group) in enumerate(grouped):
ax = _axes[i]
if numeric_only and isinstance(group, ABCDataFrame):
group = group._get_numeric_data()
plotf(group, ax, **kwargs)
ax.set_title(pprint_thing(key))
return fig, axes
def _grouped_hist(
data,
column=None,
by=None,
ax=None,
bins=50,
figsize=None,
layout=None,
sharex=False,
sharey=False,
rot=90,
grid=True,
xlabelsize=None,
xrot=None,
ylabelsize=None,
yrot=None,
**kwargs
):
"""
Grouped histogram
Parameters
----------
data : Series/DataFrame
column : object, optional
by : object, optional
ax : axes, optional
bins : int, default 50
figsize : tuple, optional
layout : optional
sharex : bool, default False
sharey : bool, default False
rot : int, default 90
grid : bool, default True
kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
Returns
-------
collection of Matplotlib Axes
"""
def plot_group(group, ax):
ax.hist(group.dropna().values, bins=bins, **kwargs)
converter._WARN = False # no warning for pandas plots
xrot = xrot or rot
fig, axes = _grouped_plot(
plot_group,
data,
column=column,
by=by,
sharex=sharex,
sharey=sharey,
ax=ax,
figsize=figsize,
layout=layout,
rot=rot,
)
_set_ticks_props(
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
)
fig.subplots_adjust(
bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3
)
return axes
def hist_series(
self,
by=None,
ax=None,
grid=True,
xlabelsize=None,
xrot=None,
ylabelsize=None,
yrot=None,
figsize=None,
bins=10,
**kwds
):
import matplotlib.pyplot as plt
if by is None:
if kwds.get("layout", None) is not None:
raise ValueError(
"The 'layout' keyword is not supported when " "'by' is None"
)
# hack until the plotting interface is a bit more unified
fig = kwds.pop(
"figure", plt.gcf() if plt.get_fignums() else plt.figure(figsize=figsize)
)
if figsize is not None and tuple(figsize) != tuple(fig.get_size_inches()):
fig.set_size_inches(*figsize, forward=True)
if ax is None:
ax = fig.gca()
elif ax.get_figure() != fig:
raise AssertionError("passed axis not bound to passed figure")
values = self.dropna().values
ax.hist(values, bins=bins, **kwds)
ax.grid(grid)
axes = np.array([ax])
_set_ticks_props(
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
)
else:
if "figure" in kwds:
raise ValueError(
"Cannot pass 'figure' when using the "
"'by' argument, since a new 'Figure' instance "
"will be created"
)
axes = _grouped_hist(
self,
by=by,
ax=ax,
grid=grid,
figsize=figsize,
bins=bins,
xlabelsize=xlabelsize,
xrot=xrot,
ylabelsize=ylabelsize,
yrot=yrot,
**kwds
)
if hasattr(axes, "ndim"):
if axes.ndim == 1 and len(axes) == 1:
return axes[0]
return axes
def hist_frame(
data,
column=None,
by=None,
grid=True,
xlabelsize=None,
xrot=None,
ylabelsize=None,
yrot=None,
ax=None,
sharex=False,
sharey=False,
figsize=None,
layout=None,
bins=10,
**kwds
):
converter._WARN = False # no warning for pandas plots
if by is not None:
axes = _grouped_hist(
data,
column=column,
by=by,
ax=ax,
grid=grid,
figsize=figsize,
sharex=sharex,
sharey=sharey,
layout=layout,
bins=bins,
xlabelsize=xlabelsize,
xrot=xrot,
ylabelsize=ylabelsize,
yrot=yrot,
**kwds
)
return axes
if column is not None:
if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
column = [column]
data = data[column]
data = data._get_numeric_data()
naxes = len(data.columns)
if naxes == 0:
raise ValueError("hist method requires numerical columns, " "nothing to plot.")
fig, axes = _subplots(
naxes=naxes,
ax=ax,
squeeze=False,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
_axes = _flatten(axes)
for i, col in enumerate(com.try_sort(data.columns)):
ax = _axes[i]
ax.hist(data[col].dropna().values, bins=bins, **kwds)
ax.set_title(col)
ax.grid(grid)
_set_ticks_props(
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
)
fig.subplots_adjust(wspace=0.3, hspace=0.3)
return axes

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import random
import matplotlib.lines as mlines
import matplotlib.patches as patches
import numpy as np
from pandas.core.dtypes.missing import notna
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib.style import _get_standard_colors
from pandas.plotting._matplotlib.tools import _set_ticks_props, _subplots
def scatter_matrix(
frame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
density_kwds=None,
hist_kwds=None,
range_padding=0.05,
**kwds
):
df = frame._get_numeric_data()
n = df.columns.size
naxes = n * n
fig, axes = _subplots(naxes=naxes, figsize=figsize, ax=ax, squeeze=False)
# no gaps between subplots
fig.subplots_adjust(wspace=0, hspace=0)
mask = notna(df)
marker = _get_marker_compat(marker)
hist_kwds = hist_kwds or {}
density_kwds = density_kwds or {}
# GH 14855
kwds.setdefault("edgecolors", "none")
boundaries_list = []
for a in df.columns:
values = df[a].values[mask[a].values]
rmin_, rmax_ = np.min(values), np.max(values)
rdelta_ext = (rmax_ - rmin_) * range_padding / 2.0
boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
for i, a in enumerate(df.columns):
for j, b in enumerate(df.columns):
ax = axes[i, j]
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == "hist":
ax.hist(values, **hist_kwds)
elif diagonal in ("kde", "density"):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
ax.plot(ind, gkde.evaluate(ind), **density_kwds)
ax.set_xlim(boundaries_list[i])
else:
common = (mask[a] & mask[b]).values
ax.scatter(
df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
)
ax.set_xlim(boundaries_list[j])
ax.set_ylim(boundaries_list[i])
ax.set_xlabel(b)
ax.set_ylabel(a)
if j != 0:
ax.yaxis.set_visible(False)
if i != n - 1:
ax.xaxis.set_visible(False)
if len(df.columns) > 1:
lim1 = boundaries_list[0]
locs = axes[0][1].yaxis.get_majorticklocs()
locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])]
adj = (locs - lim1[0]) / (lim1[1] - lim1[0])
lim0 = axes[0][0].get_ylim()
adj = adj * (lim0[1] - lim0[0]) + lim0[0]
axes[0][0].yaxis.set_ticks(adj)
if np.all(locs == locs.astype(int)):
# if all ticks are int
locs = locs.astype(int)
axes[0][0].yaxis.set_ticklabels(locs)
_set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
return axes
def _get_marker_compat(marker):
if marker not in mlines.lineMarkers:
return "o"
return marker
def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
import matplotlib.pyplot as plt
def normalize(series):
a = min(series)
b = max(series)
return (series - a) / (b - a)
n = len(frame)
classes = frame[class_column].drop_duplicates()
class_col = frame[class_column]
df = frame.drop(class_column, axis=1).apply(normalize)
if ax is None:
ax = plt.gca(xlim=[-1, 1], ylim=[-1, 1])
to_plot = {}
colors = _get_standard_colors(
num_colors=len(classes), colormap=colormap, color_type="random", color=color
)
for kls in classes:
to_plot[kls] = [[], []]
m = len(frame.columns) - 1
s = np.array(
[
(np.cos(t), np.sin(t))
for t in [2.0 * np.pi * (i / float(m)) for i in range(m)]
]
)
for i in range(n):
row = df.iloc[i].values
row_ = np.repeat(np.expand_dims(row, axis=1), 2, axis=1)
y = (s * row_).sum(axis=0) / row.sum()
kls = class_col.iat[i]
to_plot[kls][0].append(y[0])
to_plot[kls][1].append(y[1])
for i, kls in enumerate(classes):
ax.scatter(
to_plot[kls][0],
to_plot[kls][1],
color=colors[i],
label=pprint_thing(kls),
**kwds
)
ax.legend()
ax.add_patch(patches.Circle((0.0, 0.0), radius=1.0, facecolor="none"))
for xy, name in zip(s, df.columns):
ax.add_patch(patches.Circle(xy, radius=0.025, facecolor="gray"))
if xy[0] < 0.0 and xy[1] < 0.0:
ax.text(
xy[0] - 0.025, xy[1] - 0.025, name, ha="right", va="top", size="small"
)
elif xy[0] < 0.0 and xy[1] >= 0.0:
ax.text(
xy[0] - 0.025,
xy[1] + 0.025,
name,
ha="right",
va="bottom",
size="small",
)
elif xy[0] >= 0.0 and xy[1] < 0.0:
ax.text(
xy[0] + 0.025, xy[1] - 0.025, name, ha="left", va="top", size="small"
)
elif xy[0] >= 0.0 and xy[1] >= 0.0:
ax.text(
xy[0] + 0.025, xy[1] + 0.025, name, ha="left", va="bottom", size="small"
)
ax.axis("equal")
return ax
def andrews_curves(
frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds
):
import matplotlib.pyplot as plt
def function(amplitudes):
def f(t):
x1 = amplitudes[0]
result = x1 / np.sqrt(2.0)
# Take the rest of the coefficients and resize them
# appropriately. Take a copy of amplitudes as otherwise numpy
# deletes the element from amplitudes itself.
coeffs = np.delete(np.copy(amplitudes), 0)
coeffs.resize(int((coeffs.size + 1) / 2), 2)
# Generate the harmonics and arguments for the sin and cos
# functions.
harmonics = np.arange(0, coeffs.shape[0]) + 1
trig_args = np.outer(harmonics, t)
result += np.sum(
coeffs[:, 0, np.newaxis] * np.sin(trig_args)
+ coeffs[:, 1, np.newaxis] * np.cos(trig_args),
axis=0,
)
return result
return f
n = len(frame)
class_col = frame[class_column]
classes = frame[class_column].drop_duplicates()
df = frame.drop(class_column, axis=1)
t = np.linspace(-np.pi, np.pi, samples)
used_legends = set()
color_values = _get_standard_colors(
num_colors=len(classes), colormap=colormap, color_type="random", color=color
)
colors = dict(zip(classes, color_values))
if ax is None:
ax = plt.gca(xlim=(-np.pi, np.pi))
for i in range(n):
row = df.iloc[i].values
f = function(row)
y = f(t)
kls = class_col.iat[i]
label = pprint_thing(kls)
if label not in used_legends:
used_legends.add(label)
ax.plot(t, y, color=colors[kls], label=label, **kwds)
else:
ax.plot(t, y, color=colors[kls], **kwds)
ax.legend(loc="upper right")
ax.grid()
return ax
def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
import matplotlib.pyplot as plt
# random.sample(ndarray, int) fails on python 3.3, sigh
data = list(series.values)
samplings = [random.sample(data, size) for _ in range(samples)]
means = np.array([np.mean(sampling) for sampling in samplings])
medians = np.array([np.median(sampling) for sampling in samplings])
midranges = np.array(
[(min(sampling) + max(sampling)) * 0.5 for sampling in samplings]
)
if fig is None:
fig = plt.figure()
x = list(range(samples))
axes = []
ax1 = fig.add_subplot(2, 3, 1)
ax1.set_xlabel("Sample")
axes.append(ax1)
ax1.plot(x, means, **kwds)
ax2 = fig.add_subplot(2, 3, 2)
ax2.set_xlabel("Sample")
axes.append(ax2)
ax2.plot(x, medians, **kwds)
ax3 = fig.add_subplot(2, 3, 3)
ax3.set_xlabel("Sample")
axes.append(ax3)
ax3.plot(x, midranges, **kwds)
ax4 = fig.add_subplot(2, 3, 4)
ax4.set_xlabel("Mean")
axes.append(ax4)
ax4.hist(means, **kwds)
ax5 = fig.add_subplot(2, 3, 5)
ax5.set_xlabel("Median")
axes.append(ax5)
ax5.hist(medians, **kwds)
ax6 = fig.add_subplot(2, 3, 6)
ax6.set_xlabel("Midrange")
axes.append(ax6)
ax6.hist(midranges, **kwds)
for axis in axes:
plt.setp(axis.get_xticklabels(), fontsize=8)
plt.setp(axis.get_yticklabels(), fontsize=8)
return fig
def parallel_coordinates(
frame,
class_column,
cols=None,
ax=None,
color=None,
use_columns=False,
xticks=None,
colormap=None,
axvlines=True,
axvlines_kwds=None,
sort_labels=False,
**kwds
):
import matplotlib.pyplot as plt
if axvlines_kwds is None:
axvlines_kwds = {"linewidth": 1, "color": "black"}
n = len(frame)
classes = frame[class_column].drop_duplicates()
class_col = frame[class_column]
if cols is None:
df = frame.drop(class_column, axis=1)
else:
df = frame[cols]
used_legends = set()
ncols = len(df.columns)
# determine values to use for xticks
if use_columns is True:
if not np.all(np.isreal(list(df.columns))):
raise ValueError("Columns must be numeric to be used as xticks")
x = df.columns
elif xticks is not None:
if not np.all(np.isreal(xticks)):
raise ValueError("xticks specified must be numeric")
elif len(xticks) != ncols:
raise ValueError("Length of xticks must match number of columns")
x = xticks
else:
x = list(range(ncols))
if ax is None:
ax = plt.gca()
color_values = _get_standard_colors(
num_colors=len(classes), colormap=colormap, color_type="random", color=color
)
if sort_labels:
classes = sorted(classes)
color_values = sorted(color_values)
colors = dict(zip(classes, color_values))
for i in range(n):
y = df.iloc[i].values
kls = class_col.iat[i]
label = pprint_thing(kls)
if label not in used_legends:
used_legends.add(label)
ax.plot(x, y, color=colors[kls], label=label, **kwds)
else:
ax.plot(x, y, color=colors[kls], **kwds)
if axvlines:
for i in x:
ax.axvline(i, **axvlines_kwds)
ax.set_xticks(x)
ax.set_xticklabels(df.columns)
ax.set_xlim(x[0], x[-1])
ax.legend(loc="upper right")
ax.grid()
return ax
def lag_plot(series, lag=1, ax=None, **kwds):
# workaround because `c='b'` is hardcoded in matplotlibs scatter method
import matplotlib.pyplot as plt
kwds.setdefault("c", plt.rcParams["patch.facecolor"])
data = series.values
y1 = data[:-lag]
y2 = data[lag:]
if ax is None:
ax = plt.gca()
ax.set_xlabel("y(t)")
ax.set_ylabel("y(t + {lag})".format(lag=lag))
ax.scatter(y1, y2, **kwds)
return ax
def autocorrelation_plot(series, ax=None, **kwds):
import matplotlib.pyplot as plt
n = len(series)
data = np.asarray(series)
if ax is None:
ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0))
mean = np.mean(data)
c0 = np.sum((data - mean) ** 2) / float(n)
def r(h):
return ((data[: n - h] - mean) * (data[h:] - mean)).sum() / float(n) / c0
x = np.arange(n) + 1
y = [r(loc) for loc in x]
z95 = 1.959963984540054
z99 = 2.5758293035489004
ax.axhline(y=z99 / np.sqrt(n), linestyle="--", color="grey")
ax.axhline(y=z95 / np.sqrt(n), color="grey")
ax.axhline(y=0.0, color="black")
ax.axhline(y=-z95 / np.sqrt(n), color="grey")
ax.axhline(y=-z99 / np.sqrt(n), linestyle="--", color="grey")
ax.set_xlabel("Lag")
ax.set_ylabel("Autocorrelation")
ax.plot(x, y, **kwds)
if "label" in kwds:
ax.legend()
ax.grid()
return ax

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# being a bit too dynamic
import warnings
import matplotlib.cm as cm
import matplotlib.colors
import numpy as np
from pandas.core.dtypes.common import is_list_like
import pandas.core.common as com
def _get_standard_colors(
num_colors=None, colormap=None, color_type="default", color=None
):
import matplotlib.pyplot as plt
if color is None and colormap is not None:
if isinstance(colormap, str):
cmap = colormap
colormap = cm.get_cmap(colormap)
if colormap is None:
raise ValueError("Colormap {0} is not recognized".format(cmap))
colors = [colormap(num) for num in np.linspace(0, 1, num=num_colors)]
elif color is not None:
if colormap is not None:
warnings.warn(
"'color' and 'colormap' cannot be used " "simultaneously. Using 'color'"
)
colors = list(color) if is_list_like(color) else color
else:
if color_type == "default":
# need to call list() on the result to copy so we don't
# modify the global rcParams below
try:
colors = [c["color"] for c in list(plt.rcParams["axes.prop_cycle"])]
except KeyError:
colors = list(plt.rcParams.get("axes.color_cycle", list("bgrcmyk")))
if isinstance(colors, str):
colors = list(colors)
colors = colors[0:num_colors]
elif color_type == "random":
def random_color(column):
""" Returns a random color represented as a list of length 3"""
# GH17525 use common._random_state to avoid resetting the seed
rs = com.random_state(column)
return rs.rand(3).tolist()
colors = [random_color(num) for num in range(num_colors)]
else:
raise ValueError("color_type must be either 'default' or 'random'")
if isinstance(colors, str):
conv = matplotlib.colors.ColorConverter()
def _maybe_valid_colors(colors):
try:
[conv.to_rgba(c) for c in colors]
return True
except ValueError:
return False
# check whether the string can be convertible to single color
maybe_single_color = _maybe_valid_colors([colors])
# check whether each character can be convertible to colors
maybe_color_cycle = _maybe_valid_colors(list(colors))
if maybe_single_color and maybe_color_cycle and len(colors) > 1:
hex_color = [c["color"] for c in list(plt.rcParams["axes.prop_cycle"])]
colors = [hex_color[int(colors[1])]]
elif maybe_single_color:
colors = [colors]
else:
# ``colors`` is regarded as color cycle.
# mpl will raise error any of them is invalid
pass
# Append more colors by cycling if there is not enough color.
# Extra colors will be ignored by matplotlib if there are more colors
# than needed and nothing needs to be done here.
if len(colors) < num_colors:
try:
multiple = num_colors // len(colors) - 1
except ZeroDivisionError:
raise ValueError("Invalid color argument: ''")
mod = num_colors % len(colors)
colors += multiple * colors
colors += colors[:mod]
return colors

370
venv/lib/python3.6/site-packages/pandas/plotting/_matplotlib/timeseries.py Normal file
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# TODO: Use the fact that axis can have units to simplify the process
import functools
import warnings
import numpy as np
from pandas._libs.tslibs.frequencies import (
FreqGroup,
get_base_alias,
get_freq,
is_subperiod,
is_superperiod,
)
from pandas._libs.tslibs.period import Period
from pandas.core.dtypes.generic import (
ABCDatetimeIndex,
ABCPeriodIndex,
ABCTimedeltaIndex,
)
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib.converter import (
TimeSeries_DateFormatter,
TimeSeries_DateLocator,
TimeSeries_TimedeltaFormatter,
)
import pandas.tseries.frequencies as frequencies
from pandas.tseries.offsets import DateOffset
# ---------------------------------------------------------------------
# Plotting functions and monkey patches
def tsplot(series, plotf, ax=None, **kwargs):
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot() instead
"""
import matplotlib.pyplot as plt
warnings.warn(
"'tsplot' is deprecated and will be removed in a "
"future version. Please use Series.plot() instead.",
FutureWarning,
stacklevel=2,
)
# Used inferred freq is possible, need a test case for inferred
if ax is None:
ax = plt.gca()
freq, series = _maybe_resample(series, ax, kwargs)
# Set ax with freq info
_decorate_axes(ax, freq, kwargs)
ax._plot_data.append((series, plotf, kwargs))
lines = plotf(ax, series.index._mpl_repr(), series.values, **kwargs)
# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq, series.index)
return lines
def _maybe_resample(series, ax, kwargs):
# resample against axes freq if necessary
freq, ax_freq = _get_freq(ax, series)
if freq is None: # pragma: no cover
raise ValueError("Cannot use dynamic axis without frequency info")
# Convert DatetimeIndex to PeriodIndex
if isinstance(series.index, ABCDatetimeIndex):
series = series.to_period(freq=freq)
if ax_freq is not None and freq != ax_freq:
if is_superperiod(freq, ax_freq): # upsample input
series = series.copy()
series.index = series.index.asfreq(ax_freq, how="s")
freq = ax_freq
elif _is_sup(freq, ax_freq): # one is weekly
how = kwargs.pop("how", "last")
series = getattr(series.resample("D"), how)().dropna()
series = getattr(series.resample(ax_freq), how)().dropna()
freq = ax_freq
elif is_subperiod(freq, ax_freq) or _is_sub(freq, ax_freq):
_upsample_others(ax, freq, kwargs)
else: # pragma: no cover
raise ValueError("Incompatible frequency conversion")
return freq, series
def _is_sub(f1, f2):
return (f1.startswith("W") and is_subperiod("D", f2)) or (
f2.startswith("W") and is_subperiod(f1, "D")
)
def _is_sup(f1, f2):
return (f1.startswith("W") and is_superperiod("D", f2)) or (
f2.startswith("W") and is_superperiod(f1, "D")
)
def _upsample_others(ax, freq, kwargs):
legend = ax.get_legend()
lines, labels = _replot_ax(ax, freq, kwargs)
_replot_ax(ax, freq, kwargs)
other_ax = None
if hasattr(ax, "left_ax"):
other_ax = ax.left_ax
if hasattr(ax, "right_ax"):
other_ax = ax.right_ax
if other_ax is not None:
rlines, rlabels = _replot_ax(other_ax, freq, kwargs)
lines.extend(rlines)
labels.extend(rlabels)
if legend is not None and kwargs.get("legend", True) and len(lines) > 0:
title = legend.get_title().get_text()
if title == "None":
title = None
ax.legend(lines, labels, loc="best", title=title)
def _replot_ax(ax, freq, kwargs):
data = getattr(ax, "_plot_data", None)
# clear current axes and data
ax._plot_data = []
ax.clear()
_decorate_axes(ax, freq, kwargs)
lines = []
labels = []
if data is not None:
for series, plotf, kwds in data:
series = series.copy()
idx = series.index.asfreq(freq, how="S")
series.index = idx
ax._plot_data.append((series, plotf, kwds))
# for tsplot
if isinstance(plotf, str):
from pandas.plotting._matplotlib import PLOT_CLASSES
plotf = PLOT_CLASSES[plotf]._plot
lines.append(plotf(ax, series.index._mpl_repr(), series.values, **kwds)[0])
labels.append(pprint_thing(series.name))
return lines, labels
def _decorate_axes(ax, freq, kwargs):
"""Initialize axes for time-series plotting"""
if not hasattr(ax, "_plot_data"):
ax._plot_data = []
ax.freq = freq
xaxis = ax.get_xaxis()
xaxis.freq = freq
if not hasattr(ax, "legendlabels"):
ax.legendlabels = [kwargs.get("label", None)]
else:
ax.legendlabels.append(kwargs.get("label", None))
ax.view_interval = None
ax.date_axis_info = None
def _get_ax_freq(ax):
"""
Get the freq attribute of the ax object if set.
Also checks shared axes (eg when using secondary yaxis, sharex=True
or twinx)
"""
ax_freq = getattr(ax, "freq", None)
if ax_freq is None:
# check for left/right ax in case of secondary yaxis
if hasattr(ax, "left_ax"):
ax_freq = getattr(ax.left_ax, "freq", None)
elif hasattr(ax, "right_ax"):
ax_freq = getattr(ax.right_ax, "freq", None)
if ax_freq is None:
# check if a shared ax (sharex/twinx) has already freq set
shared_axes = ax.get_shared_x_axes().get_siblings(ax)
if len(shared_axes) > 1:
for shared_ax in shared_axes:
ax_freq = getattr(shared_ax, "freq", None)
if ax_freq is not None:
break
return ax_freq
def _get_freq(ax, series):
# get frequency from data
freq = getattr(series.index, "freq", None)
if freq is None:
freq = getattr(series.index, "inferred_freq", None)
ax_freq = _get_ax_freq(ax)
# use axes freq if no data freq
if freq is None:
freq = ax_freq
# get the period frequency
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
return freq, ax_freq
def _use_dynamic_x(ax, data):
freq = _get_index_freq(data)
ax_freq = _get_ax_freq(ax)
if freq is None: # convert irregular if axes has freq info
freq = ax_freq
else: # do not use tsplot if irregular was plotted first
if (ax_freq is None) and (len(ax.get_lines()) > 0):
return False
if freq is None:
return False
if isinstance(freq, DateOffset):
freq = freq.rule_code
else:
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if freq is None:
return False
# hack this for 0.10.1, creating more technical debt...sigh
if isinstance(data.index, ABCDatetimeIndex):
base = get_freq(freq)
x = data.index
if base <= FreqGroup.FR_DAY:
return x[:1].is_normalized
return Period(x[0], freq).to_timestamp(tz=x.tz) == x[0]
return True
def _get_index_freq(data):
freq = getattr(data.index, "freq", None)
if freq is None:
freq = getattr(data.index, "inferred_freq", None)
if freq == "B":
weekdays = np.unique(data.index.dayofweek)
if (5 in weekdays) or (6 in weekdays):
freq = None
return freq
def _maybe_convert_index(ax, data):
# tsplot converts automatically, but don't want to convert index
# over and over for DataFrames
if isinstance(data.index, (ABCDatetimeIndex, ABCPeriodIndex)):
freq = getattr(data.index, "freq", None)
if freq is None:
freq = getattr(data.index, "inferred_freq", None)
if isinstance(freq, DateOffset):
freq = freq.rule_code
if freq is None:
freq = _get_ax_freq(ax)
if freq is None:
raise ValueError("Could not get frequency alias for plotting")
freq = get_base_alias(freq)
freq = frequencies.get_period_alias(freq)
if isinstance(data.index, ABCDatetimeIndex):
data = data.to_period(freq=freq)
elif isinstance(data.index, ABCPeriodIndex):
data.index = data.index.asfreq(freq=freq)
return data
# Patch methods for subplot. Only format_dateaxis is currently used.
# Do we need the rest for convenience?
def format_timedelta_ticks(x, pos, n_decimals):
"""
Convert seconds to 'D days HH:MM:SS.F'
"""
s, ns = divmod(x, 1e9)
m, s = divmod(s, 60)
h, m = divmod(m, 60)
d, h = divmod(h, 24)
decimals = int(ns * 10 ** (n_decimals - 9))
s = r"{:02d}:{:02d}:{:02d}".format(int(h), int(m), int(s))
if n_decimals > 0:
s += ".{{:0{:0d}d}}".format(n_decimals).format(decimals)
if d != 0:
s = "{:d} days ".format(int(d)) + s
return s
def _format_coord(freq, t, y):
return "t = {0} y = {1:8f}".format(Period(ordinal=int(t), freq=freq), y)
def format_dateaxis(subplot, freq, index):
"""
Pretty-formats the date axis (x-axis).
Major and minor ticks are automatically set for the frequency of the
current underlying series. As the dynamic mode is activated by
default, changing the limits of the x axis will intelligently change
the positions of the ticks.
"""
from matplotlib import pylab
# handle index specific formatting
# Note: DatetimeIndex does not use this
# interface. DatetimeIndex uses matplotlib.date directly
if isinstance(index, ABCPeriodIndex):
majlocator = TimeSeries_DateLocator(
freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
)
minlocator = TimeSeries_DateLocator(
freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
)
subplot.xaxis.set_major_locator(majlocator)
subplot.xaxis.set_minor_locator(minlocator)
majformatter = TimeSeries_DateFormatter(
freq, dynamic_mode=True, minor_locator=False, plot_obj=subplot
)
minformatter = TimeSeries_DateFormatter(
freq, dynamic_mode=True, minor_locator=True, plot_obj=subplot
)
subplot.xaxis.set_major_formatter(majformatter)
subplot.xaxis.set_minor_formatter(minformatter)
# x and y coord info
subplot.format_coord = functools.partial(_format_coord, freq)
elif isinstance(index, ABCTimedeltaIndex):
subplot.xaxis.set_major_formatter(TimeSeries_TimedeltaFormatter())
else:
raise TypeError("index type not supported")
pylab.draw_if_interactive()

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# being a bit too dynamic
from math import ceil
import warnings
import matplotlib.table
import matplotlib.ticker as ticker
import numpy as np
from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
def format_date_labels(ax, rot):
# mini version of autofmt_xdate
try:
for label in ax.get_xticklabels():
label.set_ha("right")
label.set_rotation(rot)
fig = ax.get_figure()
fig.subplots_adjust(bottom=0.2)
except Exception: # pragma: no cover
pass
def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
if isinstance(data, ABCSeries):
data = data.to_frame()
elif isinstance(data, ABCDataFrame):
pass
else:
raise ValueError("Input data must be DataFrame or Series")
if rowLabels is None:
rowLabels = data.index
if colLabels is None:
colLabels = data.columns
cellText = data.values
table = matplotlib.table.table(
ax, cellText=cellText, rowLabels=rowLabels, colLabels=colLabels, **kwargs
)
return table
def _get_layout(nplots, layout=None, layout_type="box"):
if layout is not None:
if not isinstance(layout, (tuple, list)) or len(layout) != 2:
raise ValueError("Layout must be a tuple of (rows, columns)")
nrows, ncols = layout
# Python 2 compat
ceil_ = lambda x: int(ceil(x))
if nrows == -1 and ncols > 0:
layout = nrows, ncols = (ceil_(float(nplots) / ncols), ncols)
elif ncols == -1 and nrows > 0:
layout = nrows, ncols = (nrows, ceil_(float(nplots) / nrows))
elif ncols <= 0 and nrows <= 0:
msg = "At least one dimension of layout must be positive"
raise ValueError(msg)
if nrows * ncols < nplots:
raise ValueError(
"Layout of {nrows}x{ncols} must be larger "
"than required size {nplots}".format(
nrows=nrows, ncols=ncols, nplots=nplots
)
)
return layout
if layout_type == "single":
return (1, 1)
elif layout_type == "horizontal":
return (1, nplots)
elif layout_type == "vertical":
return (nplots, 1)
layouts = {1: (1, 1), 2: (1, 2), 3: (2, 2), 4: (2, 2)}
try:
return layouts[nplots]
except KeyError:
k = 1
while k ** 2 < nplots:
k += 1
if (k - 1) * k >= nplots:
return k, (k - 1)
else:
return k, k
# copied from matplotlib/pyplot.py and modified for pandas.plotting
def _subplots(
naxes=None,
sharex=False,
sharey=False,
squeeze=True,
subplot_kw=None,
ax=None,
layout=None,
layout_type="box",
**fig_kw
):
"""Create a figure with a set of subplots already made.
This utility wrapper makes it convenient to create common layouts of
subplots, including the enclosing figure object, in a single call.
Keyword arguments:
naxes : int
Number of required axes. Exceeded axes are set invisible. Default is
nrows * ncols.
sharex : bool
If True, the X axis will be shared amongst all subplots.
sharey : bool
If True, the Y axis will be shared amongst all subplots.
squeeze : bool
If True, extra dimensions are squeezed out from the returned axis object:
- if only one subplot is constructed (nrows=ncols=1), the resulting
single Axis object is returned as a scalar.
- for Nx1 or 1xN subplots, the returned object is a 1-d numpy object
array of Axis objects are returned as numpy 1-d arrays.
- for NxM subplots with N>1 and M>1 are returned as a 2d array.
If False, no squeezing is done: the returned axis object is always
a 2-d array containing Axis instances, even if it ends up being 1x1.
subplot_kw : dict
Dict with keywords passed to the add_subplot() call used to create each
subplots.
ax : Matplotlib axis object, optional
layout : tuple
Number of rows and columns of the subplot grid.
If not specified, calculated from naxes and layout_type
layout_type : {'box', 'horizontal', 'vertical'}, default 'box'
Specify how to layout the subplot grid.
fig_kw : Other keyword arguments to be passed to the figure() call.
Note that all keywords not recognized above will be
automatically included here.
Returns:
fig, ax : tuple
- fig is the Matplotlib Figure object
- ax can be either a single axis object or an array of axis objects if
more than one subplot was created. The dimensions of the resulting array
can be controlled with the squeeze keyword, see above.
**Examples:**
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True))
"""
import matplotlib.pyplot as plt
if subplot_kw is None:
subplot_kw = {}
if ax is None:
fig = plt.figure(**fig_kw)
else:
if is_list_like(ax):
ax = _flatten(ax)
if layout is not None:
warnings.warn(
"When passing multiple axes, layout keyword is " "ignored",
UserWarning,
)
if sharex or sharey:
warnings.warn(
"When passing multiple axes, sharex and sharey "
"are ignored. These settings must be specified "
"when creating axes",
UserWarning,
stacklevel=4,
)
if len(ax) == naxes:
fig = ax[0].get_figure()
return fig, ax
else:
raise ValueError(
"The number of passed axes must be {0}, the "
"same as the output plot".format(naxes)
)
fig = ax.get_figure()
# if ax is passed and a number of subplots is 1, return ax as it is
if naxes == 1:
if squeeze:
return fig, ax
else:
return fig, _flatten(ax)
else:
warnings.warn(
"To output multiple subplots, the figure containing "
"the passed axes is being cleared",
UserWarning,
stacklevel=4,
)
fig.clear()
nrows, ncols = _get_layout(naxes, layout=layout, layout_type=layout_type)
nplots = nrows * ncols
# Create empty object array to hold all axes. It's easiest to make it 1-d
# so we can just append subplots upon creation, and then
axarr = np.empty(nplots, dtype=object)
# Create first subplot separately, so we can share it if requested
ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
if sharex:
subplot_kw["sharex"] = ax0
if sharey:
subplot_kw["sharey"] = ax0
axarr[0] = ax0
# Note off-by-one counting because add_subplot uses the MATLAB 1-based
# convention.
for i in range(1, nplots):
kwds = subplot_kw.copy()
# Set sharex and sharey to None for blank/dummy axes, these can
# interfere with proper axis limits on the visible axes if
# they share axes e.g. issue #7528
if i >= naxes:
kwds["sharex"] = None
kwds["sharey"] = None
ax = fig.add_subplot(nrows, ncols, i + 1, **kwds)
axarr[i] = ax
if naxes != nplots:
for ax in axarr[naxes:]:
ax.set_visible(False)
_handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey)
if squeeze:
# Reshape the array to have the final desired dimension (nrow,ncol),
# though discarding unneeded dimensions that equal 1. If we only have
# one subplot, just return it instead of a 1-element array.
if nplots == 1:
axes = axarr[0]
else:
axes = axarr.reshape(nrows, ncols).squeeze()
else:
# returned axis array will be always 2-d, even if nrows=ncols=1
axes = axarr.reshape(nrows, ncols)
return fig, axes
def _remove_labels_from_axis(axis):
for t in axis.get_majorticklabels():
t.set_visible(False)
try:
# set_visible will not be effective if
# minor axis has NullLocator and NullFormattor (default)
if isinstance(axis.get_minor_locator(), ticker.NullLocator):
axis.set_minor_locator(ticker.AutoLocator())
if isinstance(axis.get_minor_formatter(), ticker.NullFormatter):
axis.set_minor_formatter(ticker.FormatStrFormatter(""))
for t in axis.get_minorticklabels():
t.set_visible(False)
except Exception: # pragma no cover
raise
axis.get_label().set_visible(False)
def _handle_shared_axes(axarr, nplots, naxes, nrows, ncols, sharex, sharey):
if nplots > 1:
if nrows > 1:
try:
# first find out the ax layout,
# so that we can correctly handle 'gaps"
layout = np.zeros((nrows + 1, ncols + 1), dtype=np.bool)
for ax in axarr:
layout[ax.rowNum, ax.colNum] = ax.get_visible()
for ax in axarr:
# only the last row of subplots should get x labels -> all
# other off layout handles the case that the subplot is
# the last in the column, because below is no subplot/gap.
if not layout[ax.rowNum + 1, ax.colNum]:
continue
if sharex or len(ax.get_shared_x_axes().get_siblings(ax)) > 1:
_remove_labels_from_axis(ax.xaxis)
except IndexError:
# if gridspec is used, ax.rowNum and ax.colNum may different
# from layout shape. in this case, use last_row logic
for ax in axarr:
if ax.is_last_row():
continue
if sharex or len(ax.get_shared_x_axes().get_siblings(ax)) > 1:
_remove_labels_from_axis(ax.xaxis)
if ncols > 1:
for ax in axarr:
# only the first column should get y labels -> set all other to
# off as we only have labels in the first column and we always
# have a subplot there, we can skip the layout test
if ax.is_first_col():
continue
if sharey or len(ax.get_shared_y_axes().get_siblings(ax)) > 1:
_remove_labels_from_axis(ax.yaxis)
def _flatten(axes):
if not is_list_like(axes):
return np.array([axes])
elif isinstance(axes, (np.ndarray, ABCIndexClass)):
return axes.ravel()
return np.array(axes)
def _get_all_lines(ax):
lines = ax.get_lines()
if hasattr(ax, "right_ax"):
lines += ax.right_ax.get_lines()
if hasattr(ax, "left_ax"):
lines += ax.left_ax.get_lines()
return lines
def _get_xlim(lines):
left, right = np.inf, -np.inf
for l in lines:
x = l.get_xdata(orig=False)
left = min(np.nanmin(x), left)
right = max(np.nanmax(x), right)
return left, right
def _set_ticks_props(axes, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None):
import matplotlib.pyplot as plt
for ax in _flatten(axes):
if xlabelsize is not None:
plt.setp(ax.get_xticklabels(), fontsize=xlabelsize)
if xrot is not None:
plt.setp(ax.get_xticklabels(), rotation=xrot)
if ylabelsize is not None:
plt.setp(ax.get_yticklabels(), fontsize=ylabelsize)
if yrot is not None:
plt.setp(ax.get_yticklabels(), rotation=yrot)
return axes

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from contextlib import contextmanager
import warnings
from pandas.util._decorators import deprecate_kwarg
from pandas.plotting._core import _get_plot_backend
def table(ax, data, rowLabels=None, colLabels=None, **kwargs):
"""
Helper function to convert DataFrame and Series to matplotlib.table
Parameters
----------
ax : Matplotlib axes object
data : DataFrame or Series
data for table contents
kwargs : keywords, optional
keyword arguments which passed to matplotlib.table.table.
If `rowLabels` or `colLabels` is not specified, data index or column
name will be used.
Returns
-------
matplotlib table object
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.table(
ax=ax, data=data, rowLabels=None, colLabels=None, **kwargs
)
def register(explicit=True):
"""
Register Pandas Formatters and Converters with matplotlib
This function modifies the global ``matplotlib.units.registry``
dictionary. Pandas adds custom converters for
* pd.Timestamp
* pd.Period
* np.datetime64
* datetime.datetime
* datetime.date
* datetime.time
See Also
--------
deregister_matplotlib_converter
"""
plot_backend = _get_plot_backend("matplotlib")
plot_backend.register(explicit=explicit)
def deregister():
"""
Remove pandas' formatters and converters
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed completely. Converters for types
pandas overwrites, like ``datetime.datetime``, are restored to their
original value.
See Also
--------
deregister_matplotlib_converters
"""
plot_backend = _get_plot_backend("matplotlib")
plot_backend.deregister()
def scatter_matrix(
frame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
density_kwds=None,
hist_kwds=None,
range_padding=0.05,
**kwds
):
"""
Draw a matrix of scatter plots.
Parameters
----------
frame : DataFrame
alpha : float, optional
amount of transparency applied
figsize : (float,float), optional
a tuple (width, height) in inches
ax : Matplotlib axis object, optional
grid : bool, optional
setting this to True will show the grid
diagonal : {'hist', 'kde'}
pick between 'kde' and 'hist' for
either Kernel Density Estimation or Histogram
plot in the diagonal
marker : str, optional
Matplotlib marker type, default '.'
hist_kwds : other plotting keyword arguments
To be passed to hist function
density_kwds : other plotting keyword arguments
To be passed to kernel density estimate plot
range_padding : float, optional
relative extension of axis range in x and y
with respect to (x_max - x_min) or (y_max - y_min),
default 0.05
kwds : other plotting keyword arguments
To be passed to scatter function
Returns
-------
numpy.ndarray
A matrix of scatter plots.
Examples
--------
>>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
>>> scatter_matrix(df, alpha=0.2)
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.scatter_matrix(
frame=frame,
alpha=alpha,
figsize=figsize,
ax=ax,
grid=grid,
diagonal=diagonal,
marker=marker,
density_kwds=density_kwds,
hist_kwds=hist_kwds,
range_padding=range_padding,
**kwds
)
def radviz(frame, class_column, ax=None, color=None, colormap=None, **kwds):
"""
Plot a multidimensional dataset in 2D.
Each Series in the DataFrame is represented as a evenly distributed
slice on a circle. Each data point is rendered in the circle according to
the value on each Series. Highly correlated `Series` in the `DataFrame`
are placed closer on the unit circle.
RadViz allow to project a N-dimensional data set into a 2D space where the
influence of each dimension can be interpreted as a balance between the
influence of all dimensions.
More info available at the `original article
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.889>`_
describing RadViz.
Parameters
----------
frame : `DataFrame`
Pandas object holding the data.
class_column : str
Column name containing the name of the data point category.
ax : :class:`matplotlib.axes.Axes`, optional
A plot instance to which to add the information.
color : list[str] or tuple[str], optional
Assign a color to each category. Example: ['blue', 'green'].
colormap : str or :class:`matplotlib.colors.Colormap`, default None
Colormap to select colors from. If string, load colormap with that
name from matplotlib.
kwds : optional
Options to pass to matplotlib scatter plotting method.
Returns
-------
class:`matplotlib.axes.Axes`
See Also
--------
plotting.andrews_curves : Plot clustering visualization.
Examples
--------
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6,
... 6.7, 4.6],
... 'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2,
... 3.3, 3.6],
... 'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4,
... 5.7, 1.0],
... 'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2,
... 2.1, 0.2],
... 'Category': ['virginica', 'virginica', 'setosa',
... 'virginica', 'virginica', 'versicolor',
... 'versicolor', 'setosa', 'virginica',
... 'setosa']
... })
>>> rad_viz = pd.plotting.radviz(df, 'Category') # doctest: +SKIP
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.radviz(
frame=frame,
class_column=class_column,
ax=ax,
color=color,
colormap=colormap,
**kwds
)
@deprecate_kwarg(old_arg_name="data", new_arg_name="frame")
def andrews_curves(
frame, class_column, ax=None, samples=200, color=None, colormap=None, **kwds
):
"""
Generate a matplotlib plot of Andrews curves, for visualising clusters of
multivariate data.
Andrews curves have the functional form:
f(t) = x_1/sqrt(2) + x_2 sin(t) + x_3 cos(t) +
x_4 sin(2t) + x_5 cos(2t) + ...
Where x coefficients correspond to the values of each dimension and t is
linearly spaced between -pi and +pi. Each row of frame then corresponds to
a single curve.
Parameters
----------
frame : DataFrame
Data to be plotted, preferably normalized to (0.0, 1.0)
class_column : Name of the column containing class names
ax : matplotlib axes object, default None
samples : Number of points to plot in each curve
color : list or tuple, optional
Colors to use for the different classes
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that name
from matplotlib.
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlip.axis.Axes`
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.andrews_curves(
frame=frame,
class_column=class_column,
ax=ax,
samples=samples,
color=color,
colormap=colormap,
**kwds
)
def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
"""
Bootstrap plot on mean, median and mid-range statistics.
The bootstrap plot is used to estimate the uncertainty of a statistic
by relaying on random sampling with replacement [1]_. This function will
generate bootstrapping plots for mean, median and mid-range statistics
for the given number of samples of the given size.
.. [1] "Bootstrapping (statistics)" in \
https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
Parameters
----------
series : pandas.Series
Pandas Series from where to get the samplings for the bootstrapping.
fig : matplotlib.figure.Figure, default None
If given, it will use the `fig` reference for plotting instead of
creating a new one with default parameters.
size : int, default 50
Number of data points to consider during each sampling. It must be
greater or equal than the length of the `series`.
samples : int, default 500
Number of times the bootstrap procedure is performed.
**kwds :
Options to pass to matplotlib plotting method.
Returns
-------
matplotlib.figure.Figure
Matplotlib figure.
See Also
--------
DataFrame.plot : Basic plotting for DataFrame objects.
Series.plot : Basic plotting for Series objects.
Examples
--------
.. plot::
:context: close-figs
>>> s = pd.Series(np.random.uniform(size=100))
>>> fig = pd.plotting.bootstrap_plot(s) # doctest: +SKIP
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.bootstrap_plot(
series=series, fig=fig, size=size, samples=samples, **kwds
)
@deprecate_kwarg(old_arg_name="colors", new_arg_name="color")
@deprecate_kwarg(old_arg_name="data", new_arg_name="frame", stacklevel=3)
def parallel_coordinates(
frame,
class_column,
cols=None,
ax=None,
color=None,
use_columns=False,
xticks=None,
colormap=None,
axvlines=True,
axvlines_kwds=None,
sort_labels=False,
**kwds
):
"""Parallel coordinates plotting.
Parameters
----------
frame : DataFrame
class_column : str
Column name containing class names
cols : list, optional
A list of column names to use
ax : matplotlib.axis, optional
matplotlib axis object
color : list or tuple, optional
Colors to use for the different classes
use_columns : bool, optional
If true, columns will be used as xticks
xticks : list or tuple, optional
A list of values to use for xticks
colormap : str or matplotlib colormap, default None
Colormap to use for line colors.
axvlines : bool, optional
If true, vertical lines will be added at each xtick
axvlines_kwds : keywords, optional
Options to be passed to axvline method for vertical lines
sort_labels : bool, False
Sort class_column labels, useful when assigning colors
.. versionadded:: 0.20.0
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlib.axis.Axes`
Examples
--------
>>> from matplotlib import pyplot as plt
>>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
'/pandas/tests/data/iris.csv')
>>> pd.plotting.parallel_coordinates(
df, 'Name',
color=('#556270', '#4ECDC4', '#C7F464'))
>>> plt.show()
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.parallel_coordinates(
frame=frame,
class_column=class_column,
cols=cols,
ax=ax,
color=color,
use_columns=use_columns,
xticks=xticks,
colormap=colormap,
axvlines=axvlines,
axvlines_kwds=axvlines_kwds,
sort_labels=sort_labels,
**kwds
)
def lag_plot(series, lag=1, ax=None, **kwds):
"""Lag plot for time series.
Parameters
----------
series : Time series
lag : lag of the scatter plot, default 1
ax : Matplotlib axis object, optional
kwds : Matplotlib scatter method keyword arguments, optional
Returns
-------
class:`matplotlib.axis.Axes`
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.lag_plot(series=series, lag=lag, ax=ax, **kwds)
def autocorrelation_plot(series, ax=None, **kwds):
"""
Autocorrelation plot for time series.
Parameters
----------
series: Time series
ax: Matplotlib axis object, optional
kwds : keywords
Options to pass to matplotlib plotting method
Returns
-------
class:`matplotlib.axis.Axes`
"""
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwds)
def tsplot(series, plotf, ax=None, **kwargs):
"""
Plots a Series on the given Matplotlib axes or the current axes
Parameters
----------
axes : Axes
series : Series
Notes
_____
Supports same kwargs as Axes.plot
.. deprecated:: 0.23.0
Use Series.plot() instead
"""
warnings.warn(
"'tsplot' is deprecated and will be removed in a "
"future version. Please use Series.plot() instead.",
FutureWarning,
stacklevel=2,
)
plot_backend = _get_plot_backend("matplotlib")
return plot_backend.tsplot(series=series, plotf=plotf, ax=ax, **kwargs)
class _Options(dict):
"""
Stores pandas plotting options.
Allows for parameter aliasing so you can just use parameter names that are
the same as the plot function parameters, but is stored in a canonical
format that makes it easy to breakdown into groups later
"""
# alias so the names are same as plotting method parameter names
_ALIASES = {"x_compat": "xaxis.compat"}
_DEFAULT_KEYS = ["xaxis.compat"]
def __init__(self, deprecated=False):
self._deprecated = deprecated
# self['xaxis.compat'] = False
super().__setitem__("xaxis.compat", False)
def __getitem__(self, key):
key = self._get_canonical_key(key)
if key not in self:
raise ValueError(
"{key} is not a valid pandas plotting option".format(key=key)
)
return super().__getitem__(key)
def __setitem__(self, key, value):
key = self._get_canonical_key(key)
return super().__setitem__(key, value)
def __delitem__(self, key):
key = self._get_canonical_key(key)
if key in self._DEFAULT_KEYS:
raise ValueError("Cannot remove default parameter {key}".format(key=key))
return super().__delitem__(key)
def __contains__(self, key):
key = self._get_canonical_key(key)
return super().__contains__(key)
def reset(self):
"""
Reset the option store to its initial state
Returns
-------
None
"""
self.__init__()
def _get_canonical_key(self, key):
return self._ALIASES.get(key, key)
@contextmanager
def use(self, key, value):
"""
Temporarily set a parameter value using the with statement.
Aliasing allowed.
"""
old_value = self[key]
try:
self[key] = value
yield self
finally:
self[key] = old_value
plot_params = _Options()