前言
数据可视化的第三方库挺多的，这里我次要举荐三个，别离是 Pygal、Bokeh、Plotly，废话不多说，间接上~~
举荐
数据可视化的库有挺多的，这里举荐几个比拟罕用的：
- Matplotlib
- Pygal
- Bokeh
- Seaborn
- Ggplot
- Plotly
- Pyechart
Pygal
pygal官网地址（http://www.pygal.org/en/stable/）
装置pygal模块
pygal模块的装置非常简单，只需输出一行pip命令即可
1 pip install pygal装置实现：
pygal模块介绍
pygal是Python的第三方库，他的次要性能就是数据可视化，行将数字转化成图表的模式来出现，它提供的图表款式有柱状图、折线图、饼状图、雷达图......
柱状图
单列柱状图
import pygalview = pygal.Bar()#图表名view.title = '柱状图'#增加数据view.add('numbers', [0,2,4,6,8,10])#在浏览器中查看#view.render_in_browser()#保留为view.svg(也能够保留为jpg)view.render_to_file('view.svg')效果图：
留神：svg图片用零碎自带的图片查看器关上可能会显示全彩色，能够尝试应用Google浏览器关上
多列柱状图
#增加数据view.add('numbers', [0,2,4,6,8,10])view.add('numbers_2', [0,1,3,5,7,9])重叠柱状图
view = pygal.StackedBar()横向柱状图
view = pygal.HorizontalStackedBar()折线图
简略折线图
import pygalview = pygal.Line()#图表名view.title = '折线图'#增加数据view.add('numbers', [0,2,4,6,8,10])view.add('numbers_2', [0,1,3,5,7,9])#在浏览器中查看#view.render_in_browser()#保留为view.svg(也能够保留为jpg)view.render_to_file('view.svg')效果图：
纵向折线图
view = pygal.HorizontalLine()重叠折线图
view = pygal.StackedLine(fill=True)饼状图
简略饼状图
import pygalview = pygal.Pie()#图表名view.title = '饼状图'#增加数据view.add('A', 31)view.add('B', 55)view.add('C', 14)#保留为view.svg(也能够保留为jpg)view.render_to_file('view.svg')效果图：
多级饼状图
#增加数据view.add('A', [31,25])view.add('B', [55,38])view.add('C', [14,37])圆环图
#设置空心圆半径view = pygal.Pie(inner_radius=0.4)半圆图
view = pygal.Pie(half_pie=True)雷达图
根底雷达图
import pygalview = pygal.Radar()#图表名view.title = '雷达图'#增加数据(能够为任意个)view.add('A', [31,56,34,67,34])view.add('B', [23,18,57,45,35])view.add('C', [14,45,76,34,76])#保留为view.svg(也能够保留为jpg)view.render_to_file('view.svg')效果图：
plotly
plotly 文档地址（https://plot.ly/python/#finan...）
Plotly 是一款用来做数据分析和可视化的在线平台，性能十分弱小,能够在线绘制很多图形比方条形图、散点图、饼图、直方图等等。而且还是反对在线编辑，以及多种语言python、javascript、matlab、R等许多API。它在python中应用也很简略，间接用pip install plotly就能够了。举荐最好在jupyter notebook中应用，pycharm操作不是很不便。应用Plotly能够画出很多媲美Tableau的高质量图：
这里尝试做了折线图、散点图和直方图，代码如下：
首先导入库
from plotly.graph_objs import Scatter,Layoutimport plotlyimport plotly.offline as pyimport numpy as npimport plotly.graph_objs as go#setting offilneplotly.offline.init_notebook_mode(connected=True)下面几行代码次要是援用一些库，plotly有在线和离线两种模式，在线模式须要有账号能够云编辑。我选用的离线模式，plotly设置为offline模式就能够间接在notebook外面显示了。
1.制作折线图
N = 100random_x = np.linspace(0,1,N)random_y0 = np.random.randn(N)+5random_y1 = np.random.randn(N)random_y2 = np.random.randn(N)-5#Create tracestrace0 = go.Scatter( x = random_x, y = random_y0, mode = 'markers', name = 'markers')trace1 = go.Scatter( x = random_x, y = random_y1, mode = 'lines+markers', name = 'lines+markers')trace2 = go.Scatter( x = random_x, y = random_y2, mode = 'lines', name = 'lines')data = [trace0,trace1,trace2]py.iplot(data)随机设置4个参数，一个x轴的数字和三个y轴的随机数据，制作出三种不同类型的图。trace0是markers，trace1是lines和markers,trace3是lines。而后把三种图放在data这个列表外面，调用py.iplot(data)即可。
绘制的图片零碎默认配色也挺难看的~
2.制作散点图
trace1 = go.Scatter( y = np.random.randn(500), mode = 'markers', marker = dict( size = 16, color = np.random.randn(500), colorscale = 'Viridis', showscale = True ))data = [trace1]py.iplot(data)把mode设置为markers就是散点图，而后marker外面设置一组参数，比方色彩的随机范畴，散点的大小，还有图例等等。
3.直方图
trace0 = go.Bar( x = ['Jan','Feb','Mar','Apr', 'May','Jun', 'Jul','Aug','Sep','Oct','Nov','Dec'], y = [20,14,25,16,18,22,19,15,12,16,14,17], name = 'Primary Product', marker=dict( color = 'rgb(49,130,189)' ))trace1 = go.Bar( x = ['Jan','Feb','Mar','Apr', 'May','Jun', 'Jul','Aug','Sep','Oct','Nov','Dec'], y = [19,14,22,14,16,19,15,14,10,12,12,16], name = 'Secondary Product', marker=dict( color = 'rgb(204,204,204)' ))data = [trace0,trace1]py.iplot(data)Bokeh
条形图
这配色看着还挺舒服的，比 pyecharts 条形图的配色难看一点。
from bokeh.io import show, output_filefrom bokeh.models import ColumnDataSourcefrom bokeh.palettes import Spectral6from bokeh.plotting import figureoutput_file("colormapped_bars.html")# 配置输入文件名fruits = ['Apples', '魅族', 'OPPO', 'VIVO', '小米', '华为'] # 数据counts = [5, 3, 4, 2, 4, 6] # 数据source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))p = figure(x_range=fruits, y_range=(0,9), plot_height=250, title="Fruit Counts", toolbar_location=None, tools="")# 条形图配置项p.vbar(x='fruits', top='counts', width=0.9, color='color', legend="fruits", source=source)p.xgrid.grid_line_color = None # 配置网格线色彩p.legend.orientation = "horizontal" # 图表方向为程度方向p.legend.location = "top_center"show(p) # 展现图表年度条形图
能够比照不同工夫点的量。
from bokeh.io import show, output_filefrom bokeh.models import ColumnDataSource, FactorRangefrom bokeh.plotting import figureoutput_file("bars.html") # 输入文件名fruits = ['Apple', '魅族', 'OPPO', 'VIVO', '小米', '华为'] # 参数years = ['2015', '2016', '2017'] # 参数data = {'fruits': fruits, '2015': [2, 1, 4, 3, 2, 4], '2016': [5, 3, 3, 2, 4, 6], '2017': [3, 2, 4, 4, 5, 3]}x = [(fruit, year) for fruit in fruits for year in years]counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) source = ColumnDataSource(data=dict(x=x, counts=counts))p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year", toolbar_location=None, tools="")p.vbar(x='x', top='counts', width=0.9, source=source)p.y_range.start = 0p.x_range.range_padding = 0.1p.xaxis.major_label_orientation = 1p.xgrid.grid_line_color = Noneshow(p)饼图
from collections import Counterfrom math import piimport pandas as pdfrom bokeh.io import output_file, showfrom bokeh.palettes import Category20cfrom bokeh.plotting import figurefrom bokeh.transform import cumsumoutput_file("pie.html")x = Counter({ '中国': 157, '美国': 93, '日本': 89, '巴西': 63, '德国': 44, '印度': 42, '意大利': 40, '澳大利亚': 35, '法国': 31, '西班牙': 29})data = pd.DataFrame.from_dict(dict(x), orient='index').reset_index().rename(index=str, columns={0:'value', 'index':'country'})data['angle'] = data['value']/sum(x.values()) * 2*pidata['color'] = Category20c[len(x)]p = figure(plot_height=350, title="Pie Chart", toolbar_location=None, tools="hover", tooltips="@country: @value")p.wedge(x=0, y=1, radius=0.4, start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'), line_color="white", fill_color='color', legend='country', source=data)p.axis.axis_label=Nonep.axis.visible=Falsep.grid.grid_line_color = Noneshow(p)条形图
from bokeh.io import output_file, showfrom bokeh.models import ColumnDataSourcefrom bokeh.palettes import GnBu3, OrRd3from bokeh.plotting import figureoutput_file("stacked_split.html")fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']years = ["2015", "2016", "2017"]exports = {'fruits': fruits, '2015': [2, 1, 4, 3, 2, 4], '2016': [5, 3, 4, 2, 4, 6], '2017': [3, 2, 4, 4, 5, 3]}imports = {'fruits': fruits, '2015': [-1, 0, -1, -3, -2, -1], '2016': [-2, -1, -3, -1, -2, -2], '2017': [-1, -2, -1, 0, -2, -2]}p = figure(y_range=fruits, plot_height=250, x_range=(-16, 16), title="Fruit import/export, by year", toolbar_location=None)p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports), legend=["%s exports" % x for x in years])p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports), legend=["%s imports" % x for x in years])p.y_range.range_padding = 0.1p.ygrid.grid_line_color = Nonep.legend.location = "top_left"p.axis.minor_tick_line_color = Nonep.outline_line_color = Noneshow(p)散点图
from bokeh.plotting import figure, output_file, showoutput_file("line.html")p = figure(plot_width=400, plot_height=400)p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)show(p)六边形图
这两天，马蜂窝刚被发现数据造假，这不，与马蜂窝应应景。
import numpy as npfrom bokeh.io import output_file, showfrom bokeh.plotting import figurefrom bokeh.util.hex import axial_to_cartesianoutput_file("hex_coords.html")q = np.array([0, 0, 0, -1, -1, 1, 1])r = np.array([0, -1, 1, 0, 1, -1, 0])p = figure(plot_width=400, plot_height=400, toolbar_location=None) # p.grid.visible = False # 配置网格是否可见p.hex_tile(q, r, size=1, fill_color=["firebrick"] * 3 + ["navy"] * 4, line_color="white", alpha=0.5)x, y = axial_to_cartesian(q, r, 1, "pointytop")p.text(x, y, text=["(%d, %d)" % (q, r) for (q, r) in zip(q, r)], text_baseline="middle", text_align="center")show(p)环比条形图
这个实现挺厉害的，看了一眼就吸引了我。我在代码中都做了一些正文，心愿对你了解有帮忙。注：圆心为正地方，即直角坐标系中标签为（0，0）的中央。
from collections import OrderedDictfrom math import log, sqrtimport numpy as npimport pandas as pdfrom six.moves import cStringIO as StringIOfrom bokeh.plotting import figure, show, output_fileantibiotics = """bacteria, penicillin, streptomycin, neomycin, gram结核分枝杆菌, 800, 5, 2, negative沙门氏菌, 10, 0.8, 0.09, negative变形杆菌, 3, 0.1, 0.1, negative肺炎克雷伯氏菌, 850, 1.2, 1, negative布鲁氏菌, 1, 2, 0.02, negative铜绿假单胞菌, 850, 2, 0.4, negative大肠杆菌, 100, 0.4, 0.1, negative产气杆菌, 870, 1, 1.6, negative红色葡萄球菌, 0.007, 0.1, 0.001, positive溶血性链球菌, 0.001, 14, 10, positive草绿色链球菌, 0.005, 10, 40, positive肺炎双球菌, 0.005, 11, 10, positive"""drug_color = OrderedDict([# 配置两头标签名称与色彩 ("盘尼西林", "#0d3362"), ("链霉素", "#c64737"), ("新霉素", "black"),])gram_color = { "positive": "#aeaeb8", "negative": "#e69584",}# 读取数据df = pd.read_csv(StringIO(antibiotics), skiprows=1, skipinitialspace=True, engine='python')width = 800height = 800inner_radius = 90outer_radius = 300 - 10minr = sqrt(log(.001 * 1E4))maxr = sqrt(log(1000 * 1E4))a = (outer_radius - inner_radius) / (minr - maxr)b = inner_radius - a * maxrdef rad(mic): return a * np.sqrt(np.log(mic * 1E4)) + bbig_angle = 2.0 * np.pi / (len(df) + 1)small_angle = big_angle / 7# 整体配置p = figure(plot_width=width, plot_height=height, title="", x_axis_type=None, y_axis_type=None, x_range=(-420, 420), y_range=(-420, 420), min_border=0, outline_line_color="black", background_fill_color="#f0e1d2")p.xgrid.grid_line_color = Nonep.ygrid.grid_line_color = None# annular wedgesangles = np.pi / 2 - big_angle / 2 - df.index.to_series() * big_angle #计算角度colors = [gram_color[gram] for gram in df.gram] # 配置色彩p.annular_wedge( 0, 0, inner_radius, outer_radius, -big_angle + angles, angles, color=colors,)# small wedgesp.annular_wedge(0, 0, inner_radius, rad(df.penicillin), -big_angle + angles + 5 * small_angle, -big_angle + angles + 6 * small_angle, color=drug_color['盘尼西林'])p.annular_wedge(0, 0, inner_radius, rad(df.streptomycin), -big_angle + angles + 3 * small_angle, -big_angle + angles + 4 * small_angle, color=drug_color['链霉素'])p.annular_wedge(0, 0, inner_radius, rad(df.neomycin), -big_angle + angles + 1 * small_angle, -big_angle + angles + 2 * small_angle, color=drug_color['新霉素'])# 绘制大圆和标签labels = np.power(10.0, np.arange(-3, 4))radii = a * np.sqrt(np.log(labels * 1E4)) + bp.circle(0, 0, radius=radii, fill_color=None, line_color="white")p.text(0, radii[:-1], [str(r) for r in labels[:-1]], text_font_size="8pt", text_align="center", text_baseline="middle")# 半径p.annular_wedge(0, 0, inner_radius - 10, outer_radius + 10, -big_angle + angles, -big_angle + angles, color="black")# 细菌标签xr = radii[0] * np.cos(np.array(-big_angle / 2 + angles))yr = radii[0] * np.sin(np.array(-big_angle / 2 + angles))label_angle = np.array(-big_angle / 2 + angles)label_angle[label_angle < -np.pi / 2] += np.pi # easier to read labels on the left side# 绘制各个细菌的名字p.text(xr, yr, df.bacteria, angle=label_angle, text_font_size="9pt", text_align="center", text_baseline="middle")# 绘制圆形，其中数字别离为 x 轴与 y 轴标签p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)# 绘制文字p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()], text_font_size="7pt", text_align="left", text_baseline="middle")# 绘制矩形，两头标签局部。其中 -40，-40，-40 为三个矩形的 x 轴坐标。18，0，-18 为三个矩形的 y 轴坐标p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13, color=list(drug_color.values()))# 配置两头标签文字、文字大小、文字对齐形式p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color), text_font_size="9pt", text_align="left", text_baseline="middle")output_file("burtin.html", title="burtin.py example")show(p)元素周期表
元素周期表，这个实现好牛逼啊，间隔初三刚开始学化学曾经很边远了，想当年我还是化学课代表呢！因为根本用不到化学了，这里就不实现了。