目录

一、seaborn概述二、数据整理01折线图02柱形图03直方图三、绘图01设定调色盘02柱状图03技术图04点图05箱型图06小提琴图

一、seaborn概述

Seaborn是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图。详情请查阅官网：seaborn

二、数据整理

import seaborn as sns
import numpy as np 
import matplotlib as mpl
from matplotlib import pyplot as plt 
import pandas as pd 
from datetime import datetime,timedelta
%matplotlib inline
plt.rcParams["font.sans-serif"]=["SimHei"] # 用来正常显示中文标签
plt.rcParams["axes.unicode_minus"]=False # 用来正常显示负号
from datetime import datetime 

films=["穿过寒冬拥抱你","反贪风暴5：最终章","李茂扮太子","误杀2","以年为单位的恋爱","黑客帝国：矩阵重启","雄狮少年","魔法满屋","汪汪队立大功大电影","爱情神话"]
regions=["中国","英国","澳大利亚","美国","美国","中国","英国","澳大利亚","美国","美国"]
bos=["61,181","44,303","42,439","22,984","13,979","61,181","44,303","41,439","20,984","19,979"]
persons=["31","23","56","17","9","31","23","56","17","9"]
prices=["51","43","56","57","49","51","43","56","57","49"]
showdate=["2022-12-03","2022-12-05","2022-12-01","2022-12-02","2022-11-05","2022-12-03","2022-12-05","2022-12-01","2022-12-02","2022-11-05"]
ftypes=["剧情","动作","喜剧","剧情","剧情","爱情","动作","动画","动画","动画"]
points=["8.1","9.0","7.9","6.7","3.8","8.1","9.0","7.9","6.7","3.8"]
filmdescript={
    "ftypes":ftypes,
    "bos":bos,
    "prices":prices,
    "persons":persons,
    "regions":regions,
    "showdate":showdate,
    "points":points
}

import numpy as np
import pandas as pd
cnbo2021top5=pd.DataFrame(filmdescript,index=films)
cnbo2021top5[["prices","persons"]]=cnbo2021top5[["prices","persons"]].astype(int)
cnbo2021top5["bos"]=cnbo2021top5["bos"].str.replace(",","").astype(int)
cnbo2021top5["showdate"]=cnbo2021top5["showdate"].astype("datetime64")
cnbo2021top5["points"]=cnbo2021top5["points"].apply(lambda x:float(x) if x!="" else 0)

cnbo2021top5

# 常用调色盘
r_hex = "#dc2624"     # red,       RGB = 220,38,36	
dt_hex = "#2b4750"    # dark teal, RGB = 43,71,80	
tl_hex = "#45a0a2"    # teal,      RGB = 69,160,162	
r1_hex = "#e87a59"    # red,       RGB = 232,122,89	
tl1_hex = "#7dcaa9"   # teal,      RGB = 125,202,169	
g_hex = "#649E7D"     # green,     RGB = 100,158,125	
o_hex = "#dc8018"     # orange,    RGB = 220,128,24	
tn_hex = "#C89F91"    # tan,       RGB = 200,159,145	
g50_hex = "#6c6d6c"   # grey-50,   RGB = 108,109,108	
bg_hex = "#4f6268"    # blue grey, RGB = 79,98,104	
g25_hex = "#c7cccf"   # grey-25,   RGB = 199,204,207

color=["#dc2624" ,"#2b4750","#45a0a2","#e87a59","#7dcaa9","#649E7D","#dc8018","#C89F91","#6c6d6c","#4f6268","#c7cccf"]
sns.set_palette(color)

01 折线图

def sinplot(flip=1):
    x = np.linspace(0, 14, 100)
    for i in range(1, 7):
        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)
sinplot()
# 对两种画图进行比较
fig = plt.figure()
sns.set()
sinplot()

plt.rcParams["font.sans-serif"]=["SimHei"] # 用来正常显示中文标签
plt.rcParams["axes.unicode_minus"]=False # 用来正常显示负号
plt.figure(figsize=(14,8))
plt.title("中国电影票房2021top10")
plt.xlabel("电影名称")
plt.ylabel("电影票房")
sns.lineplot(data=cnbo2021top5[["bos"]])
plt.xticks(rotation=45)

02 柱形图

cnbo2021top5ftgb=cnbo2021top5.groupby(["ftypes"])["bos","persons","prices","points"].mean()
cnbo2021top5ftgb=cnbo2021top5ftgb.reset_index().replace()
cnbo2021top5ftgb

### 02 条形图
plt.figure(figsize=(14,8))
plt.title("中国电影票房2021top10")
sns.barplot(x=cnbo2021top5ftgb["ftypes"],y=cnbo2021top5ftgb["persons"])
plt.xlabel("电影类型")
plt.ylabel("场均人次")
plt.xticks(rotation=45)
plt.show()

03 直方图

### 03 直方图
plt.figure(figsize=(14,8))
plt.title("中国电影票房2021top10")
sns.histplot(x=cnbo2021top5["bos"],bins=15) # x=cnbo2021top5ftgb["ftypes"],y=cnbo2021top5ftgb["persons"]
plt.xlabel("电影类型")
plt.ylabel("场均人次")
plt.xticks(rotation=45)
plt.show()

三、绘图

上面的数据只有十部电影，而下面的数据是我整理出来的电影数据：

Excel：300部电影数据整理

import pandas as pd 
cnboo=pd.read_excel("cnboNPPD1.xlsx")
cnboo

01 设定调色盘

# 设定调色盘
sns.set_palette(color)
sns.palplot(sns.color_palette(color,11)) # 表示11种颜色

02 柱状图

sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(25,20))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.barplot(x="TYPE",
            y="PRICE",
            hue="TYPE",
            data=cnboo)

03 技术图

sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(15,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.countplot(x="TYPE",data=cnboo)

04 点图

sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(15,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.pointplot(x="TYPE",y="PRICE",data=cnboo)
plt.show()

sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(25,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.pointplot(x="TYPE",y="PRICE",hue="REGION",data=cnboo)
plt.show()

05 箱型图

### 05 箱型图
sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(35,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.boxplot(x="TYPE",y="PERSONS",hue="REGION",data=cnboo) # ,markers=["^","o"],linestyles=["-","--"]
plt.show()
# 图中的单个点代表在此数据当中的异常值

06 小提琴图

### 06 小提琴图
sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(35,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.violinplot(x="TYPE",y="PRICE",hue="REGION",data=cnboo) # ,markers=["^","o"],linestyles=["-","--"]
plt.show()

绘制横着的小提琴图：

sns.set_palette(color)
sns.palplot(sns.color_palette(color,11))
plt.figure(figsize=(35,10))
plt.title("电影票房")
plt.xticks(rotation=45)
sns.violinplot(x="PERSONS",y="PRICE",hue="REGION",data=cnboo,orient="h")
plt.show()

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关键词： 数据整理 中国电影 澳大利亚 希望大家 爱情神话