[人工智能]2022.5.7_Mobile.csv 饼图柱状图散点图折线图

题目：

一、实验内容：
现有一个手机评论数据Mobile_new.csv文件，该文件的数据列包括手机品牌、价格和评分，请完成数据分析任务并将结果可视化，部分数据如下：

（1） 按手机品牌统计各评分个数。
（2） 完成绘制各品牌评分的*散点图、*折线图、'BlackBerry’评分柱状图、'ZTE’评分饼图的任务。

import pandas as pd

data=pd.read_csv("p:/data/Mobile.csv",header=None)

D:\anconda\lib\site-packages\IPython\core\interactiveshell.py:3165: DtypeWarning: Columns (4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

data

327592 rows × 21 columns

data_test=data.iloc[:,:3]

data_test

327592 rows × 3 columns

data_test.columns

Int64Index([0, 1, 2], dtype='int64')

data_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 327592 entries, 0 to 327591
Data columns (total 3 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   0       327592 non-null  object 
 1   1       327592 non-null  float64
 2   2       327592 non-null  int64  
dtypes: float64(1), int64(1), object(1)
memory usage: 7.5+ MB

data_test[(data_test[0]=='BlackBerry') & (data_test[1]==106.85) & (data_test[2]==4)]

92 rows × 3 columns

data_samsung=data_test.iloc[0:6,]
data_samsung

data_BlackBerry=data_test.iloc[66854]
print(data_BlackBerry)

0    BlackBerry
1        106.85
2             4
Name: 66854, dtype: object

data_all=data_samsung.append(data_BlackBerry)

data_all=data_all.append(data_test.iloc[66861])

data_all

-----------------START---------------------

import pandas as pd
import matplotlib.pyplot as plt

data_new=pd.read_csv("p:/data/Mobile_new.csv",header=None)

data_new=data_new.iloc[:,0:3]

data_new.columns=["品牌","价格","评分"]

data_new["品牌"]=pd.DataFrame(data_new["品牌"])  #转化为DataFrame类型

data_new["品牌"].value_counts()

BlackBerry    57
ZTE           55
Samsung       13
Huawei        10
Name: 品牌, dtype: int64

data_new.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 135 entries, 0 to 134
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   品牌      135 non-null    object 
 1   价格      135 non-null    float64
 2   评分      135 non-null    int64  
dtypes: float64(1), int64(1), object(1)
memory usage: 3.3+ KB

# data_new["评分"]=data_new["评分"].astype(str)

data_sam=data_new[(data_new["品牌"]=="Samsung")]
# data_sam=data_new[(data_new["品牌"]=="Samsung")&(data_new["评分"]=="5")]
data_sam=data_sam["评分"].value_counts(sort=False)
print("Samsung的各评分个数为：\n",data_sam)


data_BlackBerry=data_new[(data_new["品牌"]=="BlackBerry")]
data_BlackBerry=data_BlackBerry["评分"].value_counts(sort=False)
print("BlackBerry的各评分个数为：\n",data_BlackBerry)


data_ZTE=data_new[(data_new["品牌"]=="ZTE")]
data_ZTE=data_ZTE["评分"].value_counts(sort=False)
print("ZTE的各评分个数为：\n",data_ZTE)


data_Huawei=data_new[(data_new["品牌"]=="Huawei")]
data_Huawei=data_Huawei["评分"].value_counts(sort=False)
print("Huawei的各评分个数为：\n",data_Huawei)

Samsung的各评分个数为：
 1    2
2    2
3    1
4    2
5    6
Name: 评分, dtype: int64
BlackBerry的各评分个数为：
 1    14
2     3
3     2
4     8
5    30
Name: 评分, dtype: int64
ZTE的各评分个数为：
 1     5
2     5
3     6
4    13
5    26
Name: 评分, dtype: int64
Huawei的各评分个数为：
 1    1
3    2
5    7
Name: 评分, dtype: int64

绘制各品牌评分散点图 折线图

data_samy=data_sam.tolist()
data_samx=data_sam.index.tolist()

data_BlackBerryy=data_BlackBerry.tolist()
data_BlackBerryx=data_BlackBerry.index.tolist()

data_ZTEy=data_ZTE.tolist()
data_ZTEx=data_ZTE.index.tolist()

data_Huaweiy=data_Huawei.tolist()
data_Huaweix=data_Huawei.index.tolist()

plt.rcParams['font.sans-serif']=['SimHei']   #用黑体显示中文
plt.rcParams['axes.unicode_minus']=False     #正常显示负号
plt.figure()
plt.title("各品牌评分的散点图")
plt.xlabel('评分')
plt.ylabel('数量（个）')
plt.scatter(data_ZTEx,data_ZTEy,c='g',marker='x')
plt.scatter(data_samx,data_samy,c='r',marker='o')
plt.scatter(data_BlackBerryx,data_BlackBerryy,c='black',marker='.')
plt.scatter(data_Huaweix,data_Huaweiy,c='b',marker='v')
plt.legend(["ZTE","Samsung","BlackBerry","Huawei"])
plt.show()

plt.figure()
plt.title("各品牌评分的折线图")
plt.xlabel('评分')
plt.ylabel('数量（个）')
plt.plot(data_ZTEx,data_ZTEy,"g--")
plt.plot(data_samx,data_samy,"r--")
plt.plot(data_BlackBerryx,data_BlackBerryy,"y--")
plt.plot(data_Huaweix,data_Huaweiy,"b--")
plt.legend(["ZTE","Samsung","BlackBerry","Huawei"])
plt.show()

'BlackBerry’评分柱状图

plt.figure()
plt.title("BlackBerry评分柱状图")
plt.xlabel('评分')
plt.ylabel('数量（个）')
plt.bar(data_BlackBerryx,data_BlackBerryy,color="red",edgecolor="white")
plt.legend(["BlackBerry"])
plt.show()

'ZTE’评分饼图

plt.figure(figsize=(8,8))
plt.title("ZTE评分饼图")
label=["1","2","3","4","5"]
plt.pie(data_ZTEy,explode=[0.01,0.01,0.01,0.01,0.01],labels=label)
plt.legend(["评分为：1","评分为：2","评分为：3","评分为：4","评分为：5"])
plt.show()

?

?

创作打卡挑战赛

赢取流量/现金/CSDN周边激励大奖