matplotlib II

Matplotlib II¶

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matplotlib graph¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

scatter¶

• scatter함수 사용
• marker: scatter 모양 지정
• https://matplotlib.org/3.1.1/api/markers_api.html

In [2]:

data_1 = np.random.rand(512, 2)
data_2 = np.random.rand(512, 2)

plt.scatter(data_1[:, 0], data_1[:, 1], c="b", marker="x")
plt.scatter(data_2[:, 0], data_2[:, 1], c="r", marker="o")

plt.show()

In [3]:

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N)) ** 2
len(x), len(y), len(area)

Out[3]:

(50, 50, 50)

• area도 같은크기로 1:1 mapping이 되어야 한다.

In [4]:

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N)) ** 2
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

• s: 데이터의 크기를 지정, 데이터의 크기 비교가능하다.
• alpha: 투명도를 나타냄

bar chart¶

• bar함수 사용

In [5]:

data = [[5.0, 25.0, 50.0, 20.0], [4.0, 23.0, 51.0, 17], [6.0, 22.0, 52.0, 19]]

X = np.arange(0, 8, 2)
plt.bar(X + 0.00, data[0], color="b", width=0.50)
plt.bar(X + 0.50, data[1], color="g", width=0.50)
plt.bar(X + 1.0, data[2], color="r", width=0.50)
plt.xticks(X + 0.50, ("A", "B", "C", "D"))
plt.show()

In [6]:

data = np.array([[5.0, 25.0, 50.0, 20.0], [4.0, 23.0, 51.0, 17], [6.0, 22.0, 52.0, 19]])

color_list = ["b", "g", "r"]
data_label = ["A", "B", "C"]
X = np.arange(data.shape[1])

for i in range(3):
    plt.bar(
        X,
        data[i],
        bottom=np.sum(data[:i], axis=0),
        color=color_list[i],
        label=data_label[i],
    )
plt.legend()
plt.show()

In [7]:

A = [5.0, 30.0, 45.0, 22.0]
B = [5, 25, 50, 20]

X = range(4)

plt.bar(X, A, color="b")
plt.bar(X, B, color="r", bottom=60)
plt.show()

barh¶

In [8]:

women_pop = np.array([5, 30, 45, 22])
men_pop = np.array([5, 25, 50, 20])
X = np.arange(4)

plt.barh(X, women_pop, color="r")
plt.barh(X, -men_pop, color="b")
plt.show()

histogram¶

In [9]:

X = np.random.randn(1000)
plt.hist(X, bins=100)
plt.show()

boxplot¶

• https://towardsdatascience.com/understanding-boxplots-5e2df7bcbd51

In [10]:

data = np.random.randn(100, 5)
plt.boxplot(data)
plt.show()