Maplotlib and Seaborn (P1)

from matplotlib import pyplot as plt

x = range(2,26,2)
y = [15,13,14.5,17,20,25,26,26,27,22,18,15]

#设置图片大小
plt.figure(figsize=(20,8),dpi=80)



#绘图
plt.plot(x,y)

#设置x轴的刻度
_xtick_labels = [i/2 for i in range(4,49)]
plt.xticks(range(25,50))
plt.yticks(range(min(y),max(y)+1))

#保存
# plt.savefig("./t1.png")

#展示图形
plt.show()

import matplotlib.pyplot as plt


x = range(2,26,2)
y = [15,13,14.5,17,20,25,26,26,27,22,18,15]

plt.plot(x,y)
plt.savefig("./sig_size.png")
fig = plt.figure(figsize=(20, 8),dpi=80)
plt.show()

<Figure size 1600x640 with 0 Axes>

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(10,5))

x = range(2,26,2)
y = [15,13,14.5,17,20,25,26,26,27,22,18,15]

plt.plot(x,y)
plt.xticks(x[::2])
#当刻度密度太密集时候使用，列表的步长（间隔取值）来解决，matplotlib会自动帮我们对应
plt.show()

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

%matplotlib inline

pokemon = pd.read_csv('pokemon.csv')
print(pokemon.shape)
pokemon.head(15)

(807, 14)

sb.countplot(data = pokemon, x = 'generation_id', color = base_color, order = gen_order);

base_color = sb.color_palette()[1]

gen_order = pokemon['generation_id'].value_counts().index

base_color2 = sb.color_palette()[0]
sb.countplot(data = pokemon, y = 'type_1', color = base_color2, order = type_order)
plt.xticks(rotation = 90)
fig = plt.figure(figsize=(20,5))
type_order = pokemon['type_1'].value_counts().index

<Figure size 1440x360 with 0 Axes>

用plot（）函数绘制y=x^2函数的图形

import numpy as np
import matplotlib.pyplot as plt
plt.figure()
x=np.arange(-5,5,0.01)
y = x*x
plt.plot(x,y,'b-')#“b-解释：”b代表blue，-代表实线
plt.show()

用plot（）函数在坐标系中绘制多个图形

import matplotlib.pyplot as plt
import numpy as np
plt.figure()
plt.xlabel('x')
plt.ylabel('y')
plt.title('Simple Diagram')
x = np.arange(-5,5,0.01)
y,y2  = x*x,2*x+9
plt.plot(x,y,'b--',label ='x^2')
plt.plot(x,y2,'r-.',label ='2x+9')
#plt.plot(x,y,'b--,'x','y2','r-.')
plt.legend()#右下角的图例，需要设定label属性
plt.show()

import matplotlib.pyplot as plt
import numpy as np
plt.figure()
plt.xlabel('x')
plt.ylabel('y')
plt.title('Simple Diagram')
x = np.arange(-5,5,0.01)
y,y2  = x*x,2*x+9
plt.xlim(-8,8)#设置 x轴的范围
plt.ylim(0,10)
plt.plot(x,y,'b--',label ='x^2')
plt.plot(x,y2,'r-.',label ='2x+9')
#plt.plot(x,y,'b--,'x','y2','r-.')
plt.legend()#右下角的图例，需要设定label属性
plt.show()

使用subplot() 函数绘制多个子图

plt.figure()
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)
x=np.arange(-5,5,0.01)
y=x*x
plt.sca(ax2)
plt.plot(x,y,'b--',label ='x^2')

x2 = np.arange(-5,5,0.01)
y2=2*x2+9
plt.sca(ax1)
plt.plot(x2,y2,'r-.',label ='2x+9')
plt.legend()
plt.show()

保存图片和输出设置

plt.savefig("tang.svg",dpi=120)
plt.show()

<Figure size 432x288 with 0 Axes>

用plt.scatter()绘制散点图

plt.figure()
x=[2,1,2,3,4,5]
y=[1,2,2,5,4,3]
plt.scatter(x,y,s=60,c='r',marker='o')#s表示散点的大小，c表示颜色，marker表示散点的样式
plt.show()

用plt.hist()绘制直方图

mu,sigma = 100,20
x = mu+sigma*np.random.randn(20000)
plt.hist(x,bins=100,color='r')
plt.show()

用plt.pie()绘制饼图

labels = 'Frog','Hogs','Dogs','Logs'
sizes =[15,30,45,10]
explode = (0,0.1,0,0)
plt.pie(sizes,explode=explode,labels=labels,autopct='%1.1f%%',shadow=False,startangle=90)
plt.show()
"""该程序中，labels设置每个数据的标签，sizes设置每一块所占的比例，explode设置某一块或多块吐出来显示，突出多少
由值决定，0表示不突出，plt.pie()函数中的shadow设置阴影，这样的显示效果更逼真"""

 """ 该程序中，labels设置每个数据的标签，sizes设置每一块所占的比例，explode设置某一块或多块吐出来显示，突出多少\n由值决定，0表示不突出，plt.pie()函数中的shadow设置阴影，这样的显示效果更逼真 """

用plt.bar绘制条形图

y = [15,30,25,10,20,34,33,15]
x=np.arange(8)
plt.bar(x,y,color='r',width=0.5)
plt.plot(x,y,'b',marker='*')
for x1,yy in zip(x,y):
    plt.text(x1,yy+1,str(yy),ha='center',va='bottom')
plt.show()

用plt.viridis()绘制极坐标图

N = 20
#设置每个标记所在射线与极径的夹角
theta = np.linspace(0.0,2*np.pi,N,endpoint=False)
radii = 10*np.random.rand(N)
width = np.pi /4 * np.random.rand(N)
ax = plt.subplot(111,projection='polar')
bars = ax.bar(theta,radii,width=width,bottom = (0.0))
for r,bar in zip(radii,bars):
    bar.set_facecolor(plt.cm.viridis(r/10.))
    bar.set_alpha(0.5)
plt.show()

绝对频率与相对频率

pkmn_types = pokemon.melt(id_vars = ['id','species'],
                         value_vars = ['type_1','type_2'],
                         var_name = 'type_level',value_name = 'type').dropna()
pkmn_types[802:812]

type_counts = pkmn_types['type'].value_counts()
type_order = type_counts.index

base_color = sb.color_palette()[0]
sb.countplot(data = pkmn_types,y = 'type', color = base_color, order = type_order);
#plt.xticks(tick_props * n_pokemon,tick_names)
for i in range(type_counts.shape[0]):
    count = type_counts[i]
    pct_string = '{:0.1f}%' .format(100*count/n_pokemon)
    plt.text(count+1,i,pct_string,va = 'center')

n_pokemon = pokemon.shape[0]
     print(n_pokemon)
max_type_count = type_counts[0]
max_prop = max_type_count / n_pokemon
print(max_prop)

807
0.1623296158612144

tick_props = np.arange(0, max_prop,0.02)
tick_names = ['{:0.2f}'.format(v) for v in tick_props]

为离散数据选择图表

pokemon = pd.read_csv('pokemon.csv')
print(pokemon.shape)
pokemon.head(10)

 (807, 14)

bins = np.arange(0, pokemon['height'].max()+0.2,0.2)
plt.hist(data = pokemon,x = 'height',bins = bins)
plt.xlim((0,6))

(0.0, 6.0)

标尺和变换

bins = 10 ** np.arange(-1,3+0.1,0.1)
ticks = [0.1,0.3,1,3,10,30,100,300,1000]
labels = ['{}'.format(v) for v in ticks]
plt.hist(data = pokemon, x= 'weight', bins = bins)
plt.xscale('log')
plt.xticks(ticks,labels)

([<matplotlib.axis.XTick at 0x7faaa989c7c0>,
  <matplotlib.axis.XTick at 0x7faaae6432b0>,
  <matplotlib.axis.XTick at 0x7faaad9000a0>,
  <matplotlib.axis.XTick at 0x7faaade3fb80>,
  <matplotlib.axis.XTick at 0x7faaade33310>,
  <matplotlib.axis.XTick at 0x7faaade33a60>,
  <matplotlib.axis.XTick at 0x7faaade33d30>,
  <matplotlib.axis.XTick at 0x7faaade3fc40>,
  <matplotlib.axis.XTick at 0x7faaade31700>],
 [Text(0.1, 0, '0.1'),
  Text(0.3, 0, '0.3'),
  Text(1.0, 0, '1'),
  Text(3.0, 0, '3'),
  Text(10.0, 0, '10'),
  Text(30.0, 0, '30'),
  Text(100.0, 0, '100'),
  Text(300.0, 0, '300'),
  Text(1000.0, 0, '1000')])

np.log10(pokemon['weight'].describe())

count    2.906874
mean     1.790786
std      2.047350
min     -1.000000
25%      0.954243
50%      1.431364
75%      1.799341
max      2.999957
Name: weight, dtype: float64