python大数据分析-星际争霸2单位信息和用户类型数据分析

 1 #导入工具包
 2 import pandas as pd
 3 import pickle
 4 import numpy as np
 5 import re
 6 import sys
 7 from sklearn import linear_model
 8 
 9 
10 import seaborn as sns
11 import matplotlib.pyplot as plt
12 %matplotlib inline
13 
14 from sklearn.ensemble import RandomForestRegressor
15 from sklearn.svm import SVR
16 from sklearn import linear_model
17 from sklearn import ensemble 
18 from sklearn.metrics import mean_squared_error
19 from sklearn.ensemble import BaggingRegressor
20 
21 
22 from sklearn.ensemble import RandomForestRegressor
23 from sklearn.model_SELEction import KFold
24 import xgboost as xgb
25 from sklearn.model_SELEction import Train_test_split
26 
27 
28 from sklearn.model_SELEction import GridSearchCV
29 from sklearn.model_SELEction import cross_val_score, KFold
30 
31 from sklearn.linear_model import Lasso
32 from sklearn.linear_model import Ridge
33 from sklearn.kernel_ridge import KernelRidge
34 from sklearn.svm import SVR
35 from sklearn.ensemble import RandomForestRegressor
36 from sklearn import preprocessing
37 from mpl_toolkits.mplot3d import Axes3D
38 # 
39 
40 import os

plt.rcParams['font.sans-serif']=['SimHei']
data = pd.read_csv(r'C:UsersASUSDesktopStarCoop_UniTinfo.csv')
data

plt.rcParams['font.sans-serif']=['SimHei']
Userdata = pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv')
for index,rows in Userdata.iterrows():
    if(int(rows['value5']>1)):
        print(f"在{rows['value5']}难度的AI性格中不登场。")
Userdata

Userdata.plot(x='value5',y='Count',kind='bar',title='合作任务生产')
plt.show()

1 titanic=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv'))
2 titanic.head()

1 #数据维度
2 print('===数据维度：{:}行{:}列===n'.format(titanic.shape[0],titanic.shape[1]))

1 #查看各列数据类型
2 titanic.info()

1 #数据描述
2 titanic.describe()

1 #查看随机百分比和基础百分比以及对应的数据索引
2 titanic[['Fixed','Index7']][2155:4514]

1 df=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UniTinfo.csv'))
2 df.head()

#数据维度
print('===数据维度：{:}行{:}列===n'.format(df.shape[0],df.shape[1]))

1 #查看各列数据类型
2 df.info()

#数据描述
df.describe()

1 #查看单位Scout的造价
2 df[['index15','value64']][1303:1305]

1 #计算Scout的总价值
2 scout_price = df['value64'][1303]+df['value64'][1304]
3 print("侦察机的总价值是",scout_pricE)

1 #根据军费创建攻击波次-计算军费-休闲
2 titanic=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv'))
3 difficultyAsInt=input(int)
4 resourceAlLOCATIOn=9000
5 waveresource@R_978_1@R_616_11226@6@l=resourceAlLOCATIOn*titanic['Fixed'][4563]
6 print("当前要支付的军费",waveresource@R_978_1@R_616_11226@6@l)

 1 # 绘制垂直柱状图
 2 #计算相关单位的造价
 3 NovaACLaserTurret_price = df['value64'][1946]
 4 print("黑色行动炮台的总价值是",NovaACLaserTurret_pricE)
 5 NovaBoombotBurrowed_price = df['value64'][1794]+df['value64'][1795]
 6 print("黑色行动自爆者的总价值是",NovaBoombotBurrowed_pricE)
 7 KelMorianGrenadeTurret_price = df['value64'][6868]
 8 print("自动机炮的总价值是",KelMorianGrenadeTurret_pricE)
 9 SIDrone_price = df['value64'][7627]
10 print("被感染的SCV的总价值是",SIDrone_pricE)
11 SunkenColony_price = df['value64'][7947]
12 print("穿刺集群的总价值是",SunkenColony_pricE)
13 plt.rcParams['font.family'] = ['sans-serif']
14 plt.rcParams['font.sans-serif'] = ['SimHei']
15 plt.rcParams['axeS.Unicode_minus']=false #用来正常显示负号
16 plt.bar(['NovaACLaserTurret','NovaBoombotBurrowed','KelMorianGrenadeTurret','SIDrone','SunkenColony'], 
17         [NovaACLaserTurret_price, NovaBoombotBurrowed_price, KelMorianGrenadeTurret_price, SIDrone_price,SunkenColony_price], 
18         label="部分单位造价")
19 plt.legend()
20 plt.show()

1 x=['NovaACLaserTurret','NovaBoombotBurrowed','KelMorianGrenadeTurret','SIDrone','SunkenColony']
2 y=[NovaACLaserTurret_price, NovaBoombotBurrowed_price, KelMorianGrenadeTurret_price, SIDrone_price,SunkenColony_price]
3 plt.plot(x,y)
4 plt.show()
5 #了解每一个单位的价值

1 x=['NovaACLaserTurret','NovaBoombotBurrowed','KelMorianGrenadeTurret','SIDrone','SunkenColony']
2 y=[NovaACLaserTurret_price, NovaBoombotBurrowed_price, KelMorianGrenadeTurret_price, SIDrone_price,SunkenColony_price]
3 plt.pie(y,labels=X)
4 plt.show()

1 #绘制小提琴图
2 sns.violinplot(x="id",y="value16",data=df) 
3 #在sns.violinplot中，x是类别变量，y是数值型变量，data用于指定数据集

 1 fig = plt.figure()
 2 ax = Axes3D(fig)
 3 
 4 x = np.arange(NovaACLaserTurret_price/8*-1, NovaACLaserTurret_price/8, 0.1)
 5 y = np.arange(NovaACLaserTurret_price/8*-1, NovaACLaserTurret_price/8, 0.1)
 6 X, Y = np.meshgrid(x, y)
 7 Z = np.add(-np.power(X, 4), np.power(Y, 4))
 8 # 绘制三维曲面图
 9 surf = ax.plot_surface(X, Y, Z, cmap=cm.gist_rainbow)
10 # 设置颜色条
11 fig.colorbar(surf, shrink=0.5, aspect=5)
12 plt.show()

 1 fig = plt.figure()
 2 
 3 # 创建3d图形的两种方式
 4 # ax = Axes3D(fig)
 5 ax = fig.add_subplot(111, projection='3d')
 6 # X, Y value
 7 X = np.arange(NovaBoombotBurrowed_price/25*-1, NovaBoombotBurrowed_price/25, 0.25)
 8 Y = np.arange(NovaBoombotBurrowed_price/25*-1, NovaBoombotBurrowed_price/25, 0.25)
 9 X, Y = np.meshgrid(X, Y)    # x-y 平面的网格
10 R = np.sqrt(X ** 2 + Y ** 2)
11 # height value
12 Z = np.sin(R)
13 
14 # rStride:行之间的跨度  cStride:列之间的跨度
15 # rcount:设置间隔个数，默认50个，ccount:列的间隔个数  不能与上面两个参数同时出现
16 #vmax和vmin  颜色的最大值和最小值
17 ax.plot_surface(X, Y, Z, rStride=1, cStride=1, cmap=plt.get_cmap('rainbow'))
18 # zdir : 'z' | 'x' | 'y' 表示把等高线图投射到哪个面
19 # offset : 表示等高线图投射到指定页面的某个刻度
20 ax.contourf(X,Y,Z,zdir='z',offset=-2)
21 # 设置图像z轴的显示范围，x、y轴设置方式相同
22 ax.set_zlim(-2,2)

1 #计算InduStrialShip的总价值
2 InduStrialShip_price = df['value64'][1@R_616_11226@]+df['value64'][1059]
3 print("工业船的总价值是",InduStrialShip_pricE)

1 #计算GhostFemale_BlackOps的总价值
2 GhostFemale_BlackOps_price = df['value64'][970]+df['value64'][971]
3 print("黑色行动女幽灵的总价值是",GhostFemale_BlackOps_pricE)

1 #计算VoidCoopWarbot的总价值
2 VoidCoopWarbot_price = df['value64'][5826]+df['value64'][5827]
3 print("武装机器人的总价值是",VoidCoopWarbot_pricE)

#计算OdinMKII的总价值
OdinMKII_price = df['value64'][6103]+df['value64'][6104]
print("奥丁MKII的总价值是",OdinMKII_pricE)

1 #计算stukovInfestedDiamondBACk的总价值
2 stukovInfestedDiamondBACk_price = df['value64'][6409]+df['value64'][6410]
3 print("被感染的响尾蛇战车的总价值是",stukovInfestedDiamondBACk_pricE)

1 #计算Scourgenest的总价值
2 Scourgenest_price = df['value64'][6687]+df['value64'][6688]
3 print("爆蚊巢穴的总价值是",Scourgenest_pricE)

1 import matplotlib.pyplot as plt
2 plt.rcParams['font.family'] = ['sans-serif']
3 plt.rcParams['font.sans-serif'] = ['SimHei']
4 plt.rcParams['axeS.Unicode_minus']=false #用来正常显示负号
5 plt.bar(['Scourgenest','OdinMKII','VoidCoopWarbot','GhostFemale_BlackOps','InduStrialShip'], 
6         [Scourgenest_price, OdinMKII_price, VoidCoopWarbot_price, GhostFemale_BlackOps_price,InduStrialShip_price], 
7         label="部分单位造价")
8 plt.legend()
9 plt.show()

1 x=['Scourgenest','OdinMKII','VoidCoopWarbot','GhostFemale_BlackOps','InduStrialShip']
2 y=[Scourgenest_price, OdinMKII_price, VoidCoopWarbot_price, GhostFemale_BlackOps_price,InduStrialShip_price]
3 plt.pie(y,labels=X)
4 plt.show()

 1 import numpy as np
 2 import matplotlib.pyplot as plt
 3 
 4 x=['Scourgenest','OdinMKII','VoidCoopWarbot','GhostFemale_BlackOps','InduStrialShip']
 5 y=[Scourgenest_price, OdinMKII_price, VoidCoopWarbot_price, GhostFemale_BlackOps_price,InduStrialShip_price]
 6 
 7 plt.xlabel("x")
 8 plt.ylabel("v")
 9 plt.boxplot(Scourgenest_price,sym="o",whis=3)
10 plt.show()

 1 fig = plt.figure()
 2 #画散点图，计算星际争霸单位价格分布图
 3 ax = Axes3D(fig)
 4 x = np.random.randint(0,10,int(GhostFemale_BlackOps_price/15))
 5 y = np. random.randint(0,10,int(VoidCoopWarbot_price/16))
 6 z = np.random.randint(0,10,int(stukovInfestedDiamondBACk_price/6))
 7 ax. scatter(x, y, z)
 8 ax.set_xlabel('x')
 9 ax.set_ylabel( 'y' )
10 ax.set_zlabel('z' )
11 plt.show()
12 
13 #显示图案了解星际争霸单位价值情况

 1 import pandas as pd
 2 import pickle
 3 import numpy as np
 4 df=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UniTinfo.csv'))
 5 #计算GhostAcademyNova的总价值
 6 GhostAcademyNova_BlackOps_price = df['value64'][3991]+df['value64'][3992]
 7 print("幽灵军校的总价值是",GhostAcademyNova_BlackOps_pricE)
 8 SolarForge_price = df['value64'][5537]+df['value64'][5538]
 9 print("太阳锻炉的总价值是",SolarForge_pricE)
10 #计算TechReactor的建造价值
11 TechReactor_price = df['value64'][5505]+df['value64'][5506]
12 print("科技反应堆的总价值是",TechReactor_pricE)
13 #VoidRayTaldarim的价值
14 VoidRayTaldarim_price = df['value64'][5714]+df['value64'][5715]
15 print("毁灭者的总价值是",VoidRayTaldarim_pricE)
16 #NovaBoombot的价值
17 NovaBoombot_price = df['value64'][5966]+df['value64'][5967]
18 print("破坏无人机的总价值是",NovaBoombot_pricE)
19 #计算NovaGriffinTransportUnit的价值
20 NovaGriffinTransportUnit_price = df['value64'][2922]+df['value64'][2923]
21 print("狮鹫号的总价值是",NovaGriffinTransportUnit_pricE)
22 #计算ImpalerAbathur的价值
23 ImpalerAbathur_price = df['value64'][3059]+df['value64'][3060]
24 print("阿巴瑟穿刺者的总价值是",ImpalerAbathur_pricE)
25 #计算SIQueen
26 SIQueen_price = df['value64'][3371]+df['value64'][3372]
27 print("斯托科夫的虫巢女王的总价值是",SIQueen_pricE)
28 #计算Raven_BlackOps
29 Raven_BlackOps_price = df['value64'][9665]+df['value64'][9666]
30 print("诺娃的铁鸦的总价值是",Raven_BlackOps_pricE)
31 #计算MutatorWidowMine
32 MutatorWidowMine_price = df['value64'][9862]+df['value64'][9861]
33 print("埃蒙的寡妇雷的总价值是",MutatorWidowMine_pricE)
34 #计算SILiberator
35 SILiberator_price = df['value64'][10474]+df['value64'][10475]
36 print("被感染的解放者的总价值是",SILiberator_pricE)
37 #计算stukovInfestedSiegeTankUprooted
38 stukovInfestedSiegeTankUprooted_price = df['value64'][10337]+df['value64'][10338]
39 print("斯托科夫的攻城坦克的总价值是",stukovInfestedSiegeTankUprooted_pricE)
40 plt.rcParams['font.family'] = ['sans-serif']
41 plt.rcParams['font.sans-serif'] = ['SimHei']
42 plt.rcParams['axeS.Unicode_minus']=false #用来正常显示负号
43 plt.bar(['埃蒙寡妇雷','被感染的解放者','诺娃的铁鸦','阿巴瑟的穿刺者','斯托科夫的坦克'], 
44         [MutatorWidowMine_price, SILiberator_price, Raven_BlackOps_price, ImpalerAbathur_price,stukovInfestedSiegeTankUprooted_price], 
45         label="部分单位造价")
46 plt.legend()
47 plt.show()

 1 import pandas as pd
 2 import pickle
 3 import numpy as np
 4 import matplotlib.pyplot as plt
 5 df=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UniTinfo.csv'))
 6 #计算HybridBehemothCoopBoss的总价值
 7 HybridBehemothCoopBoss_price = df['value64'][10950]
 8 print("混合体不朽者的总价值是",HybridBehemothCoopBoss_pricE)
 9 #计算HybridDominatorCoopBoss的总价值
10 HybridDominatorCoopBoss_price = df['value64'][10921]
11 print("混合体支配者的总价值是",HybridDominatorCoopBoss_pricE)
12 #计算DehakaHatchery
13 DehakaHatchery_price = df['value64'][11034]
14 print("德哈卡孵化场的总价值是",DehakaHatchery_pricE)
15 #计算DehakaNydusDestroyer
16 DehakaNydusDestroyer_price = df['value64'][11368]
17 print("德哈卡虫道毁灭者的总价值是",DehakaNydusDestroyer_pricE)
18 #计算DehakaUltraliskLevel3
19 DehakaUltraliskLevel3_price = df['value64'][12199]+df['value64'][12200]
20 print("德哈卡暴龙兽的总价值是",DehakaUltraliskLevel3_pricE)
21 # 绘制水平柱状图
22 
23 plt.rcParams['font.family'] = ['sans-serif']
24 plt.rcParams['font.sans-serif'] = ['SimHei']
25 
26 plt.barh([u'混合体不朽者','混合体支配者','德哈卡孵化场','虫道毁灭者','暴龙兽'],
27          [HybridBehemothCoopBoss_price, HybridDominatorCoopBoss_price, DehakaHatchery_price, DehakaNydusDestroyer_price,DehakaUltraliskLevel3_price], 
28          label="德哈卡和混合体单位造价")
29 plt.legend()
30 plt.show()

1 import matplotlib.pyplot as plt
2 #显示突变因子列表
3 
4 plt.rcParams['font.sans-serif']=['SimHei']
5 ud=pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv')
6 ud[['Id3','Int','Id5']][7763:8078]

1 ud=pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv')
2 ud[['Id3','Int','Id5']][7763:8078]
3 for index,rows in ud.iterrows():
4     if(int(rows['Int']<1.0)):
5         print(f"{rows['Id3']}突变因子不会随机或在突变挑战中登场。")

1 titanic=pd.DataFrame(pd.read_csv(r'C:UsersASUSDesktopStarCoop_UserData.csv'))
2 new_titanic = titanic[2155:4514]
3 for index,rows in new_titanic.iterrows():
4     if(str(rows['Unit'])=="NaN"):
5         break
6 indexUnitType = 1
7 for index,rows in new_titanic.iterrows():
8     unitTypeTest = titanic['value'][2156]
9     print(unitTypeTest)

1 import scipy.ndimage as nd
2 
3 import matplotlib.pyplot as plt
4 
5 image=nd.imread("Eva Elfie.jpg")
6 
7 plt.imshow(imagE)
8 
9 plt.show()

 1 import scipy.ndimage as nd
 2 
 3 import matplotlib.pyplot as plt
 4 
 5 image=nd.imread("Eva Elfie.jpg")
 6 
 7 t=nd.rotate(image,45)
 8 
 9 plt.imshow(t)
10 
11 plt.show()

 1 import scipy.ndimage as nd
 2 
 3 import matplotlib.pyplot as plt
 4 
 5 image=nd.imread("Eva Elfie.jpg")
 6 
 7 t=nd.gaussian_filter(image,3)
 8 
 9 plt.imshow(t)
10 
11 plt.show()

 1 import scipy.ndimage as nd
 2 
 3 import matplotlib.pyplot as plt
 4 
 5 import numpy as np
 6 
 7 image=nd.imread("Eva Elfie.jpg",mode='L')
 8 
 9 lx,ly=image.shape
10 
11 im=image[int(lx/4):int(-lx/4),int(ly/4):int(-ly/4)]
12 
13 plt.imshow(im,cmap=plt.cm.gray)
14 
15 plt.show()