pandas+matplotlib——习题一

运行环境 jupyter notebook

import  matplotlib.pyplot as plt 
from pandas import DataFrame,Series
import pandas as pd
import numpy as np 

import scipy as sp
from pyecharts import Bar
from pyecharts import WordCloud

plt.rcParams["font.sans-serif"]=['SimHei']  # 用于正常显示中文标签
plt.rcParams['axes.unicode_minus']=False  # 用来正常显示负号

第一题

1.1 导入数据并查看【切片】

-># 读取datasets目录下chipo.csv并显示前十行数据（赋值给变量chipo)
chipo=pd.read_csv('datasets/chipo.csv')
chipo.iloc[0:10]   #切片显示 

$~$	Unna med: 0	order_id	quantity	item_name	choice_description	item_price
0	0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	1	Izze	[Clementine]	$3.39
2	2	1	1	Nantucket Nectar	[Apple]	$3.39
3	3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans…	$16.98
5	5	3	1	Chicken Bowl	[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou…	$10.98
6	6	3	1	Side of Chips	NaN	$1.69
7	7	4	1	Steak Burrito	[Tomatillo Red Chili Salsa, [Fajita Vegetables…	$11.75
8	8	4	1	Steak Soft Tacos	[Tomatillo Green Chili Salsa, [Pinto Beans, Ch…	$9.25
9	9	5	1	Steak Burrito	[Fresh Tomato Salsa, [Rice, Black Beans, Pinto…	$9.25

1.2 统计列[item_name]中每种商品出现的频率，绘制柱状图

item_name_01=chipo.groupby('item_name').count().sort_values(by='Unnamed: 0',ascending=False) 
item_name_01=item_name_01['Unnamed: 0']
item_name_01.head() 
-------------------输出：-----------------
item_name
Chicken Bowl           726
Chicken Burrito        553
Chips and Guacamole    479
Steak Burrito          368
Canned Soft Drink      301
Name: Unnamed: 0, dtype: int64 
---------------------------------------- 
item_name_02 = item_name_01.head()
y=item_name_02.values
x=item_name_02.index
plt.figure(figsize=(15,7))
plt.bar(x,y,label='商品名')
plt.title('购买次数最多的商品排名')
plt.xlabel('商品名称')
plt.ylabel('出现的订单次数')
plt.legend(loc='upper right')

1.3 根据列 [odrder_id] 分组，求出每个订单花费的总金额。然后根据每笔订单的总金额和其商品的总数量画出散点图(如上)。

function = lambda x: float(x[1:])
chipo['price'] = chipo['item_price'].agg(function)
data=DataFrame([chipo["order_id"],chipo["price"]]) 

data=data.T   #数据转置
price_X=data.groupby('order_id')['price'].sum()
order_id_Y=data.groupby('order_id').count()
x=price_X.values
y=order_id_Y.values

plt.scatter(x,y,label='商品名',color='g',s=50,marker='o')
plt.title('订单金额和订单关系')
plt.xlabel('金额')
plt.ylabel('商品数')

第二题

2.1 加载datasets下的tips.csv文件数据，并显示前五行记录

df_01 = pd.read_csv('datasets/tips.csv')
df_01.head(5)

$~$	Unnamed: 0	total_bill	tip	sex	smoker	day	time	size
0	0	16.99	1.01	Female	No	Sun	Dinner	2
1	1	10.34	1.66	Male	No	Sun	Dinner	3
2	2	21.01	3.50	Male	No	Sun	Dinner	3
3	3	23.68	3.31	Male	No	Sun	Dinner	2
4	4	24.59	3.61	Female	No	Sun	Dinner	4

2.2 在同一图中绘制:吸烟顾客与不吸烟顾客的消费金额与小费之间的散点图

smoker=df_01[df_01['smoker'] !='No']       #吸烟de人
no_smoker = df_01[df_01['smoker'] =='No']  # 不吸烟


plt.figure(figsize=(12,6))
plt.subplot(1,2,1)
plt.scatter(smoker['total_bill'],smoker['tip'])
plt.xlabel('消费总金额')
plt.ylabel('小费金额')
plt.title('吸烟顾客')

plt.subplot(1,2,2)
plt.scatter(no_smoker['total_bill'],no_smoker['tip'])
plt.xlabel('消费总金额')
plt.ylabel('小费金额')
plt.title('不吸烟顾客')

2.3 在同一图中绘制:女性与男性中吸烟与不吸烟顾客的消费金额与小费之间的散点图关系

#吸烟的人
smoker=df_01[df_01['smoker'] !='No']
male_smoker = smoker[smoker['sex']=='Male']
Female_smoker = smoker[smoker['sex']=='Female']

#不吸烟人
no_smoker = df_01[df_01['smoker'] =='No']
male_nosmoker= no_smoker[no_smoker['sex']=='Male']
Female_nosmoker = no_smoker[no_smoker['sex']=='Female']

plt.figure(figsize=(12,5))
plt.subplot(1,2,1)   #第一块画板
plt.scatter(male_smoker['total_bill'], 
            male_smoker['tip'],label='男性吸烟顾客')
plt.scatter(male_nosmoker['total_bill'], 
            male_nosmoker['tip'],label='男性不吸烟顾客')
plt.xlabel('消费总金额')
plt.ylabel('小费金额')
plt.title('sex=Male')
plt.legend()

plt.subplot(1,2,2)   #第一块画板
plt.scatter(Female_smoker['total_bill'], 
            Female_smoker['tip'],label='女性吸烟顾客')
plt.scatter(Female_nosmoker['total_bill'], 
            Female_nosmoker['tip'],label='女性不吸烟顾客') 

plt.xlabel('消费总金额')  
plt.ylabel('小费金额')
plt.title('sex=Female')
plt.legend()   #显示标签