python爬虫28 | 你爬下的数据不分析一波可就亏了啊，使用python进行数据可视化

matplotlib

python -m pip install -U pip setuptools
python -m pip install matplotlib

import numpy as np
import  .pyplot as plt

x = np.linspace(-np.pi, np.pi, 256)

cos = np.cos(x)
sin = np.sin(x)

plt.plot(x, cos, '--', linewidth=2)
plt.plot(x, sin)

plt.show()

# 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()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(0)

mu = 200
sigma = 25
x = np.random.normal(mu, sigma, size=100)

fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 4))

ax0.hist(x, 20, normed=1, histtype='stepfilled', facecolor='g', alpha=0.75)
ax0.set_title('stepfilled')

# Create a histogram by providing the bin edges (unequally spaced).
bins = [100, 150, 180, 195, 205, 220, 250, 300]
ax1.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
ax1.set_title('unequal bins')

fig.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")


tips = sns.load_dataset("tips")
sns.relplot(x="total_bill", y="tip", data=tips);
plt.show()

fmri = sns.load_dataset("fmri")
sns.relplot(x="timepoint", y="signal", hue="event", kind="line", data=fmri);
plt.show()

titanic = sns.load_dataset("titanic")
sns.catplot(x="sex", y="survived", hue="class", kind="bar", data=titanic);
plt.show()

from pyecharts.charts import Bar
from pyecharts import options as opts

bar = (
    Bar()
    .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
    .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
    .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
    .set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况"))
)
bar.render()

def pie_base() -> Pie:
    c = (
        Pie()
        .add("", [list(z) for z in zip(Faker.choose(), Faker.values())])
        .set_global_opts(title_opts=opts.TitleOpts(title="Pie-基本示例"))
        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
    )
    return c

# 需要安装 snapshot_selenium
make_snapshot(driver, pie_base().render(), "pie.png")

words = [
    ("Sam S Club", 10000),
    ("Macys", 6181),
    ("Amy Schumer", 4386),
    ("Jurassic World", 4055),
    ("Charter Communications", 2467),
    ("Chick Fil A", 2244),
    ("Planet Fitness", 1868),
    ("Pitch Perfect", 1484),
    ("Express", 1112),
    ("Home", 865),
    ("Johnny Depp", 847),
    ("Lena Dunham", 582),
    ("Lewis Hamilton", 555),
    ("KXAN", 550),
    ("Mary Ellen Mark", 462),
    ("Farrah Abraham", 366),
    ("Rita Ora", 360),
    ("Serena Williams", 282),
    ("NCAA baseball tournament", 273),
    ("Point Break", 265),
]


def wordcloud_base() -> WordCloud:
    c = (
        WordCloud()
        .add("", words, word_size_range=[20, 100])
        .set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-基本示例"))
    )
    return c

# 需要安装 snapshot_selenium
make_snapshot(driver, wordcloud_base().render(), "WordCloud.png")