2019년 2월 23일 토요일

[Python] Chart Example (matplotlib)


Category Plot


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import matplotlib.pyplot as plt

data = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}
names = list(data.keys())
values = list(data.values())

fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
axs[0].bar(names, values)
axs[1].scatter(names, values)
axs[2].plot(names, values)
fig.suptitle('Categorical Plotting')

plt.show()


Multiple Line Plot


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import matplotlib.pyplot as plt

cat = ["bored", "happy", "bored", "bored", "happy", "bored"]
dog = ["happy", "happy", "happy", "happy", "bored", "bored"]
activity = ["combing", "drinking", "feeding", "napping", "playing", "washing"]

fig, ax = plt.subplots()
ax.plot(activity, dog, label="dog")
ax.plot(activity, cat, label="cat")
ax.legend()

plt.show()


Bar Plot


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from matplotlib.ticker import FuncFormatter
import matplotlib.pyplot as plt
import numpy as np

x = np.arange(4)
money = [1.5e5, 2.5e6, 5.5e6, 2.0e7]

def millions(x, pos):
    return '$%1.1fM' % (x * 1e-6)

formatter = FuncFormatter(millions)

fig, ax = plt.subplots()
ax.yaxis.set_major_formatter(formatter)
plt.bar(x, money)
plt.xticks(x, ('Bill', 'Fred', 'Mary', 'Sue'))
plt.show()


Multiple Bar Plot


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import numpy as np
import matplotlib.pyplot as plt

men_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)
women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)

ind = np.arange(len(men_means))  # the x locations for the groups
width = 0.35  # the width of the bars

fig, ax = plt.subplots()
rects1 = ax.bar(ind - width/2, men_means, width,  color='SkyBlue', label='Men')
rects2 = ax.bar(ind + width/2, women_means, width,  color='IndianRed', label='Women')

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Scores')
ax.set_title('Scores by group and gender')
ax.set_xticks(ind)
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
ax.legend()

plt.show()


Stacked Bar Plot


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import numpy as np
import matplotlib.pyplot as plt

N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N)    # the x locations for the groups
width = 0.5       # the width of the bars: can also be len(x) sequence

p1 = plt.bar(ind, menMeans, width)
p2 = plt.bar(ind, womenMeans, width, bottom=menMeans)

plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))

plt.show()


Horizontal Bar Plot


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import matplotlib.pyplot as plt
import numpy as np

# Fixing random state for reproducibility
np.random.seed(19680801)


plt.rcdefaults()
fig, ax = plt.subplots()

# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))

ax.barh(y_pos, performance, align='center', color='green', ecolor='black')

ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('How fast do you want to go today?')

plt.show()

Stacked Area Plot


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import numpy as np
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y1 = [1, 1, 2, 3, 5]
y2 = [0, 4, 2, 6, 8]
y3 = [1, 3, 5, 7, 9]

y = np.vstack([y1, y2, y3])

labels = ["Fibonacci ", "Evens", "Odds"]

fig, ax = plt.subplots()
ax.stackplot(x, y1, y2, y3, labels=labels)
ax.legend(loc='upper left')
plt.show()

fig, ax = plt.subplots()
ax.stackplot(x, y)
plt.show()


Pie Plot


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import matplotlib.pyplot as plt

# Pie chart, where the slices will be ordered and plotted counter-clockwise:
labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
sizes = [15, 30, 45, 10]
explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')

fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)
ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.

plt.show()


Pie Plot II


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import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))

recipe = ["375 g flour",
          "75 g sugar",
          "250 g butter",
          "300 g berries"]

data = [float(x.split()[0]) for x in recipe]
ingredients = [x.split()[-1] for x in recipe]


def func(pct, allvals):
    absolute = int(pct/100.*np.sum(allvals))
    return "{:.1f}%\n({:d} g)".format(pct, absolute)


wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),
                                  textprops=dict(color="w"))

ax.legend(wedges, ingredients,
          title="Ingredients",
          loc="center left",
          bbox_to_anchor=(1, 0, 0.5, 1))

plt.setp(autotexts, size=8, weight="bold")

ax.set_title("Matplotlib bakery: A pie")

plt.show()


Pie Ring Plot



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import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.7
vals = np.array([60., 37., 29.])
labels = ['Frogs', 'Hogs', 'Dogs']

cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))

ax.pie(vals, radius=1, colors=outer_colors, labels=labels,
       autopct='%1.1f%%', wedgeprops=dict(width=size, edgecolor='w'))
'''
ax.pie(vals.flatten(), radius=1-size, colors=inner_colors,
       wedgeprops=dict(width=size, edgecolor='w'))
'''
ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()


Multiple RIng Plot



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import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots()

size = 0.3
vals = np.array([[60., 32.], [37., 40.], [29., 10.]])

cmap = plt.get_cmap("tab20c")
outer_colors = cmap(np.arange(3)*4)
inner_colors = cmap(np.array([1, 2, 5, 6, 9, 10]))

ax.pie(vals.sum(axis=1), radius=1, colors=outer_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.pie(vals.flatten(), radius=1-size, colors=inner_colors,
       wedgeprops=dict(width=size, edgecolor='w'))

ax.set(aspect="equal", title='Pie plot with `ax.pie`')
plt.show()


Multiple Plot


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import numpy as np
import matplotlib.pyplot as plt

np.random.seed(19680801)

n_bins = 10
x = np.random.randn(1000, 3)

fig, axes = plt.subplots(nrows=2, ncols=2)
ax0, ax1, ax2, ax3 = axes.flatten()

colors = ['red', 'tan', 'lime']
ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)
ax0.legend(prop={'size': 10})
ax0.set_title('bars with legend')

ax1.hist(x, n_bins, density=True, histtype='bar', stacked=True)
ax1.set_title('stacked bar')

ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
ax2.set_title('stack step (unfilled)')

# Make a multiple-histogram of data-sets with different length.
x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
ax3.hist(x_multi, n_bins, histtype='bar')
ax3.set_title('different sample sizes')

fig.tight_layout()
plt.show()






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