In [2]:
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
import matplotlib.ticker as tkr
%matplotlib inline
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# plt의 내부 메소드
dir(plt)
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In [9]:
# 라인플롯
plt.plot([1,4,5,10])
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plt.plot([1, 4, 9, 16], c="b", lw=5, ls="--", marker="o", ms=15, mec="g", mew=5, mfc="r")
plt.xlim(-0.2, 3.2)
plt.ylim(-1, 18)
plt.show()
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# 여러개의 그래프 표현
t = np.arange(0., 5., 0.2)
plt.plot(t, t, 'r--', t, 0.5*t**2, 'bs:', t, 0.2*t**3, 'g^-')
plt.show()
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# matplotlib 1.5까진 hold(True)옵션을 줬어야했으나, matplotlib 2.0부터는 모든 플롯 명령에 hold(True)가 자동 적용
plt.plot([1, 4, 9, 16], c="b", lw=5, ls="--", marker="o", ms=15, mec="g", mew=5, mfc="r")
# plt.hold(True) # <- 1,5 버전에서는 이 코드가 필요하다.
plt.plot([9, 16, 4, 1], c="k", lw=3, ls=":", marker="s", ms=10, mec="m", mew=5, mfc="c")
# plt.hold(False) # <- 1,5 버전에서는 이 코드가 필요하다.
plt.show()
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X = np.linspace(-np.pi, np.pi, 256)
C, S = np.cos(X), np.sin(X)
plt.plot(X, C, label="cosine")
plt.plot(X, S, label="sine")
plt.legend(loc=2)
plt.show()
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Signature: plt.annotate(*args, **kwargs)
Docstring:
Annotate the point ``xy`` with text ``s``.
Additional kwargs are passed to `~matplotlib.text.Text`.
Parameters
----------
s : str
The text of the annotation
xy : iterable
Length 2 sequence specifying the *(x,y)* point to annotate
xytext : iterable, optional
Length 2 sequence specifying the *(x,y)* to place the text
at. If None, defaults to ``xy``.
xycoords : str, Artist, Transform, callable or tuple, optional
The coordinate system that ``xy`` is given in.
For a `str` the allowed values are:
================= ===============================================
Property Description
================= ===============================================
'figure points' points from the lower left of the figure
'figure pixels' pixels from the lower left of the figure
'figure fraction' fraction of figure from lower left
'axes points' points from lower left corner of axes
'axes pixels' pixels from lower left corner of axes
'axes fraction' fraction of axes from lower left
'data' use the coordinate system of the object being
annotated (default)
'polar' *(theta,r)* if not native 'data' coordinates
================= ===============================================
If a `~matplotlib.artist.Artist` object is passed in the units are
fraction if it's bounding box.
If a `~matplotlib.transforms.Transform` object is passed
in use that to transform ``xy`` to screen coordinates
If a callable it must take a
`~matplotlib.backend_bases.RendererBase` object as input
and return a `~matplotlib.transforms.Transform` or
`~matplotlib.transforms.Bbox` object
If a `tuple` must be length 2 tuple of str, `Artist`,
`Transform` or callable objects. The first transform is
used for the *x* coordinate and the second for *y*.
See :ref:`plotting-guide-annotation` for more details.
Defaults to ``'data'``
textcoords : str, `Artist`, `Transform`, callable or tuple, optional
The coordinate system that ``xytext`` is given, which
may be different than the coordinate system used for
``xy``.
All ``xycoords`` values are valid as well as the following
strings:
================= =========================================
Property Description
================= =========================================
'offset points' offset (in points) from the *xy* value
'offset pixels' offset (in pixels) from the *xy* value
================= =========================================
defaults to the input of ``xycoords``
arrowprops : dict, optional
If not None, properties used to draw a
`~matplotlib.patches.FancyArrowPatch` arrow between ``xy`` and
``xytext``.
If `arrowprops` does not contain the key ``'arrowstyle'`` the
allowed keys are:
========== ======================================================
Key Description
========== ======================================================
width the width of the arrow in points
headwidth the width of the base of the arrow head in points
headlength the length of the arrow head in points
shrink fraction of total length to 'shrink' from both ends
? any key to :class:`matplotlib.patches.FancyArrowPatch`
========== ======================================================
If the `arrowprops` contains the key ``'arrowstyle'`` the
above keys are forbidden. The allowed values of
``'arrowstyle'`` are:
============ =============================================
Name Attrs
============ =============================================
``'-'`` None
``'->'`` head_length=0.4,head_width=0.2
``'-['`` widthB=1.0,lengthB=0.2,angleB=None
``'|-|'`` widthA=1.0,widthB=1.0
``'-|>'`` head_length=0.4,head_width=0.2
``'<-'`` head_length=0.4,head_width=0.2
``'<->'`` head_length=0.4,head_width=0.2
``'<|-'`` head_length=0.4,head_width=0.2
``'<|-|>'`` head_length=0.4,head_width=0.2
``'fancy'`` head_length=0.4,head_width=0.4,tail_width=0.4
``'simple'`` head_length=0.5,head_width=0.5,tail_width=0.2
``'wedge'`` tail_width=0.3,shrink_factor=0.5
============ =============================================
Valid keys for `~matplotlib.patches.FancyArrowPatch` are:
=============== ==================================================
Key Description
=============== ==================================================
arrowstyle the arrow style
connectionstyle the connection style
relpos default is (0.5, 0.5)
patchA default is bounding box of the text
patchB default is None
shrinkA default is 2 points
shrinkB default is 2 points
mutation_scale default is text size (in points)
mutation_aspect default is 1.
? any key for :class:`matplotlib.patches.PathPatch`
=============== ==================================================
Defaults to None
annotation_clip : bool, optional
Controls the visibility of the annotation when it goes
outside the axes area.
If `True`, the annotation will only be drawn when the
``xy`` is inside the axes. If `False`, the annotation will
always be drawn regardless of its position.
The default is `None`, which behave as `True` only if
*xycoords* is "data".
Returns
-------
Annotation
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plt.plot(X, S, label="sine")
plt.scatter([0], [0], color="r", linewidth=10)
plt.annotate(r'$(0,0)$', xy=(0, 0), xycoords='data', xytext=(-50, 50),
textcoords='offset points', fontsize=16,
arrowprops=dict(arrowstyle="->", linewidth=3, color="g"))
plt.show()
figure : 도화지라고 생각하면 편함
plt.figure(figsize=(10,2)) 또는 plt.gcf()을 사용해 생성
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plt.figure(figsize=(10,5))
plt.plot(np.random.randn(100))
plt.show()
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# gcf로 figure를 2개 생성해서 그림
f1=plt.gcf()
plt.plot(np.random.randn(100))
f2=plt.gcf()
plt.plot(np.random.randn(100))
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x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
ax1 = plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'yo-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
print(ax1)
ax2 = plt.subplot(2, 1, 2)
plt.plot(x2, y2, 'r.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')
print(ax2)
plt.show()
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dates = pd.date_range('20160620', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))
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df
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fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
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# subplot을 True로 주고 layout 설정-! (이 경우 column name이 기준이 됩니다 )
df.plot(subplots=True, layout=(2,2), figsize=[10,5])
plt.show()
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df.plot(subplots=False, figsize=[10,5])
plt.show()
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data = pd.DataFrame(np.random.randint(1500, size=(100, 2)), columns=['meas', 'modeled'])
# data = pd.read_csv('data.csv')
x = data['meas']
y = data['modeled']
xmin = 0
xmax = 1500
ymin = 0
ymax = 1500
fig, ax = plt.subplots()
plt.hexbin(x, y, cmap=plt.cm.gnuplot2_r)
plt.axis([xmin, xmax, ymin, ymax])
plt.xlabel("Measured baseflow, in cfs")
plt.ylabel("Simulated baseflow, in cfs")
cb = plt.colorbar()
cb.set_label('count')
p2, = plt.plot([0,1500],[0,1500], c='g')
l2 = plt.legend([p2], ["1:1 Line"], loc=2)
ax.yaxis.set_major_formatter(
tkr.FuncFormatter(lambda y, p: format(int(y), ',')))
plt.show()
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