Based matplotlib seaborn and data mining, data visualization

Using the Data visualization tools matplotlib python and seaborn visualize
the required data using Part numpy libraries and library generation pandas array, matrix, dataframe.

• Import the required libraries:

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

• FIG drawing functions (plt.plot ())

Select the graphic theme	plt.sytle.use (): five kinds of themes: Dark Grid (darkgrid), white mesh (whitegrid), black (dark), all white (white), full scale (ticks) defaults to full scale
Plotted in FIG.	matplotlib.pyplot.plot (* args, scalex = True, scaley = True, data = None, ** kwargs): * args include the required data, color and style of curve
Display graphics	matplotlib.pyplot.show（args，* kw ）

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

plt.style.use("seaborn-darkgrid")
#定义x
x=np.arange(0,3*np.pi,0.1)
#生成正弦函数
y=np.sin(x)
#调用plot函数实现可视化
plt.plot(x,y)
plt.show()

• Draw subgraph:
  the painting into a Bucher a subgraph
  above, relating to generation function

Add subgraph	matplotlib.pyplot.subplot (* args, ** kwargs): adding the current submap in FIG. * Args is an integer of 3 or an integer of three separate, the location descriptor of FIG.
Drawing subgraph	plt.plot ()
Sub-graph display	plt.show()
Set Title	plt.title(" ")

plt.style.use("seaborn-darkgrid")
x=np.arange(0,4*np.pi,0.1)
y=np.sin(x)
y2=np.cos(x)
#将原图分成两行一列两个子图，并取第一个子图
plt.subplot(2,1,1)
plt.plot(x,y)
plt.subplot(2,1,2)
plt.plot(x,y2)
plt.show()

• Modify the parameter value

Set the image size	plt.figure (figsize = (8,6), dpi = 80) # Create a 8x6 sized image, dpi = 80 indicates 80 points per inch resolution
Curve set color, width, label style	plt.plot (X, C, color = "blue", linewidth = 1.0, label = "Blue", linestyle = "-") C denotes a function of blue, a line width of one pixel, sign legend table " Blue ", linestyle a graph style
Display settings legend	plt.legend ()
Range provided the x-axis	plt.xlim ()
Setting the x axis scale	plt.xticks ()
Set y-axis range	plt.ylim ()
Set y axis scale	plt.yticks ()
Save image	plt.savefig()

plt.style.use("seaborn-dark")
plt.figure(figsize=(4,3),dpi=80)
#创建一个1*!的子图 取第一个
plt.subplot(111)
x=np.linspace(-np.pi, np.pi, 256,endpoint=True)
C=np.cos(x)
S=np.sin(x)
plt.plot(x, C, color="blue",linewidth=1.0,label="Blue",linestyle="--")
# 绘制一个绿色的，线宽为1个像素的正弦曲线，设置图例标签Blue，linestyle表示曲线的样式
plt.plot(x, S, color="green", linewidth=1.0, label="Green", linestyle="-.")

plt.legend()
plt.xlim(-4.0,4.0)
plt.xticks(np.linspace(-4,4,9,endpoint=True))
plt.ylim(-1.0,1.0)
plt.yticks(np.linspace(-1,1,5,endpoint=True))
plt.show()

• Draw Scatter
  matplotlib achieve Scatter

Draw a scatter plot	matplotlib.pyplot.scatter (x, y, s = None, c = None, marker = None, cmap = None, norm = None, vmin = None, vmax = None, alpha = None, linewidths = None, verts = None, edgecolors = None, *, data = None, ** kwargs): draw scattergram. X, y-axis data representing xy, s represents a scalar, c denotes color, marker tag indicates style.

a = np.random.randint(0,20,15)	# 随机生成数据
b = np.random.randint(0,20,15)
print(a)
print(b)i
plt.scatter(a, b)					# 绘制散点图
plt.show()

seaborn achieve Scatter:
create dataframe

Draw a scatter plot

seaborn.jointplot (x, y, data = None, kind = 'scatter', stat_func = None, color = None, height = 6, ratio = 5, space = 0.2, dropna = True, xlim = None, ylim = None, joint_kws = None, marginal_kws = None, annot_kws = None, ** kwargs): with two variables are plotted. color: color; size: 6 default, the scale size of the map (squares); ratio: the ratio of the center of FIG. FIG side edge; space: FIG interval size and the center side map; s: the dot size; linewidth: line width; {x, y} lim: x, y-axis range.

data=pd.DataFrame(np.random.randn(5,2),columns=list('AB'))
print(data)
sns.jointplot(x='A',y='B',data=data)
plt.show()

• A histogram

matplotlib achieve Histogram	matplotlib.pyplot.bar (x, height, width = 0.8, bottom = None, *, align = 'center', data = None, ** kwargs): a histogram. X: abscissa; height: height of the bar; width: each strip width, color: the color of each bar.
Add a grid display	plt.grid ()
seaborn achieve Histogram	seaborn.countplot (x = None, y = None, hue = None, data = None, order = None, hue_order = None, orient = None, color = None, palette = None, saturation = 0.75, dodge = True, ax = none, ** kwargs): histogram. x, y: xy axis; data: data; hue: forming a histogram classified according to the classification in the column name value; order, hue_order: histogram for controlling the sequence; palette: palette, controls a different color.

from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']	# 解决中文不显示问题
level = ['tk', 'shtk', 'hztk']
x = range(len(level))		# 横坐标
y = [1,3,2]				# 纵坐标
plt.figure(figsize=(4,3),dpi=80)		# 创建画布
plt.bar(x, y, width=0.5, color=['b','r','g'])# 绘制柱状图
plt.xticks(x,level)
plt.grid(linestyle="--", alpha=0.5) # 添加网格显示
plt.show()

df = pd.DataFrame(['优秀',"不错","666","666","不错","不错"],columns=["level"])
sns.countplot(x="level",data=df)
plt.figure(figsize=(4,3),dpi=80)
plt.show()

• Draw a histogram

matplotlib histogram for

matplotlib.pyplot.hist (x, bins = None, range = None, density = None, weights = None, cumulative = False, bottom = None, histtype = 'bar', align = 'mid', orientation = 'vertical', rwidth = None, log = False, color = None, label = None, stacked = False, normed = None, *, data = None, ** kwargs): histogrammed. X: specify each bin (bin) of data distribution, corresponding to the x-axis; bins: Specifies the number of bin (box), i.e. a total of several bar chart; normed: specified density, that is, each bar graph the proportion ratio, the default is 1; color: specifies the color of the bar graph.

seaborn histogram for

seaborn.distplot (a, bins = None, hist = True, kde = True, rug = False, fit = None, hist_kws = None, kde_kws = None, rug_kws = None, fit_kws = None, color = None, vertical = False, norm_hist = False, axlabel = None, label = None, ax = None): histogrammed. A: Data; hist: whether to display the histogram; kde: whether kernel density estimation; bins: dividing the control histogram; fit: fitting parameter map control

x = np.random.randint(0,30,90)
print(x)
plt.figure(dpi=100)
distance = 2		# 设置组距
plt.hist(x,facecolor="blue", edgecolor="black", alpha=0.7) # 绘制直方图
plt.xticks(range(min(x), max(x))[::2]) # 修改x轴刻度显示
plt.grid(linestyle="--", alpha=0.5) # 添加网格显示
plt.show()#  显示图像

sns.distplot(x, kde=True)