读取数据
导入所需科学计算库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
忽略小警告
import warnings
warnings.filterwarnings('ignore')
读取数据
df = pd.read_csv('D:\数据分析相关笔记等\数据集\电子产品销售分析数据集\电子产品销售分析.csv')
df.head()
Unnamed: 0event_timeorder_idproduct_idcategory_idcategory_codebrandpriceuser_idagesexlocal002020-04-24 11:50:39 UTC229435993205453698615159662235090899062.268105e+18electronics.tabletsamsung162.011.515916e+1824.0女海南112020-04-24 11:50:39 UTC229435993205453698615159662235090899062.268105e+18electronics.tabletsamsung162.011.515916e+1824.0女海南222020-04-24 14:37:43 UTC229444402405808622022739483190571836582.268105e+18electronics.audio.headphonehuawei77.521.515916e+1838.0女北京332020-04-24 14:37:43 UTC229444402405808622022739483190571836582.268105e+18electronics.audio.headphonehuawei77.521.515916e+1838.0女北京442020-04-24 19:16:21 UTC229458426315407423622739483168174244392.268105e+18NaNkarcher217.571.515916e+1832.0女广东
df['event_time'].unique()
array(['2020-04-24 11:50:39 UTC', '2020-04-24 14:37:43 UTC',
'2020-04-24 19:16:21 UTC', ..., '2020-11-21 10:10:01 UTC',
'2020-11-21 10:10:13 UTC', '2020-11-21 10:10:30 UTC'], dtype=object)
df['age'].unique()
array([24., 38., 32., 20., 21., 16., 49., 43., 37., 27., 48., 47., 28.,
41., 22., 18., 44., 50., 46., 25., 34., 45., 17., 30., 36., 35.,
39., 19., 40., 33., 31., 42., 23., 29., 26.])
清洗数据
提取数据时，处理与业务流程不符合数据，售价为负
df[df['price'] < 0]
处理空值
df.info()
RangeIndex: 564169 entries, 0 to 564168
Data columns (total 12 columns):
# Column Non-Null Count Dtype
0 Unnamed: 0 419890 non-null int64
1 event_time 419890 non-null datetime64[ns, UTC]
2 order_id 419890 non-null int64
3 product_id 419890 non-null int64
4 category_id 419890 non-null int64
5 category_code 419890 non-null object
6 brand 419890 non-null object
7 price 419890 non-null float64
8 user_id 419890 non-null int64
9 age 419890 non-null int64
10 sex 419890 non-null object
11 local 419890 non-null object
12 month 419890 non-null int64
13 quater 419890 non-null period[Q-DEC]
dtypes: datetime64[ns, UTC](1), float64(1), int64(7), object(4), period[Q-DEC](1)
memory usage: 48.1+ MB
处理年龄组
bins = [16, 20, 30, 40, 60]
labels = ['16-19', '20-29', '30-39', '40-60']
df['agegroup'] = pd.cut(df['age'], bins, right = False, labels = labels)
查看数据
df.describe()
Unnamed: 0order_idproduct_idcategory_idpriceuser_idagemonthcount4.198900e+054.198900e+054.198900e+054.198900e+05419890.0000004.198900e+05419890.000000419890.000000mean1.799809e+062.370509e+181.676753e+182.274301e+18254.2816191.515916e+1833.1440697.754607std7.529273e+052.014144e+163.171234e+172.438523e+16321.1676132.372070e+0710.1198002.448404min0.000000e+002.294360e+181.515966e+182.268105e+180.0000001.515916e+1816.0000001.00000025%1.669817e+062.354505e+181.515966e+182.268105e+1839.3300001.515916e+1824.0000007.00000050%1.854132e+062.375487e+181.515966e+182.268105e+18138.8700001.515916e+1833.0000008.00000075%2.463694e+062.388441e+181.515966e+182.268105e+18347.2000001.515916e+1842.00000010.000000max2.633520e+062.388441e+182.388434e+182.374499e+1811574.0500001.515916e+1850.00000011.000000
price的均值在254.28元，中位数在138.87元，可能存在极值影响 年龄的平均值、中位数均在33岁，说明该数据服从正态分布或近似正态分布
数据分析
查看全年销售额的增长率
销售额数据分组汇总
sales_month = df.groupby(['month'])['price'].sum()
计算2-11月销售增长率
sales_rate_2 = sales_month[2] / sales_month[1] - 1
sales_rate_3 = sales_month[3] / sales_month[2] - 1
sales_rate_4 = sales_month[4] / sales_month[3] - 1
sales_rate_5 = sales_month[5] / sales_month[4] - 1
sales_rate_6 = sales_month[6] / sales_month[5] - 1
sales_rate_7 = sales_month[7] / sales_month[6] - 1
sales_rate_8 = sales_month[8] / sales_month[7] - 1
sales_rate_9 = sales_month[9] / sales_month[8] - 1
sales_rate_10 = sales_month[10] / sales_month[9] - 1
sales_rate_11 = sales_month[11] / sales_month[10] - 1
设置2-11月的增长率标签
sales_rate_2_label = '%.2f%%' % (sales_rate_2 * 100)
sales_rate_3_label = '%.2f%%' % (sales_rate_3 * 100)
sales_rate_4_label = '%.2f%%' % (sales_rate_4 * 100)
sales_rate_5_label = '%.2f%%' % (sales_rate_5 * 100)
sales_rate_6_label = '%.2f%%' % (sales_rate_6 * 100)
sales_rate_7_label = '%.2f%%' % (sales_rate_7 * 100)
sales_rate_8_label = '%.2f%%' % (sales_rate_8 * 100)
sales_rate_9_label = '%.2f%%' % (sales_rate_9 * 100)
sales_rate_10_label = '%.2f%%' % (sales_rate_10 * 100)
sales_rate_11_label = '%.2f%%' % (sales_rate_11 * 100)
将1-11月的销售额以及增长率转换为DataFrame
sales = pd.DataFrame({
'sales_month':sales_month,
'sales_rate':[0, sales_rate_2, sales_rate_3, sales_rate_4, sales_rate_5, sales_rate_6,
sales_rate_7, sales_rate_8, sales_rate_9, sales_rate_10, sales_rate_11],
'sales_rate_label':['0.00%', sales_rate_2_label, sales_rate_3_label, sales_rate_4_label,
sales_rate_5_label, sales_rate_6_label, sales_rate_7_label,
sales_rate_8_label, sales_rate_9_label, sales_rate_10_label, sales_rate_11_label]
})
绘制图形
from matplotlib import ticker
设置中文字符
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
设置背景样式
plt.style.use('ggplot')
x轴数据
x = ['{}月'.format(values) for values in sales.index.tolist()]
y轴数据
y1 = sales['sales_month']
y2 = sales['sales_rate']
创建画布
fig = plt.figure(figsize = (20, 8), dpi = 80)
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
绘制图形
ax1.bar(x, y1, color = 'pink', label = '销售额')
ax2.plot(x, y2, color = 'black',marker = '*', label = '增长率')
ax1.set_xticks(x)
ax1.set_ylim(0, 30000000)
ax2.yaxis.set_major_formatter(ticker.PercentFormatter(xmax=1, decimals=1))
ax1.set_xlabel('月份')
ax1.set_ylabel('销售额')
ax2.set_ylabel('增长率')
ax1.set_title("销售额与增长率")
plt.legend()
plt.show()
各省销售情况
各省销售占比
按省分类
sales_area = df.groupby('local')['price'].sum()
绘图
sales_area.plot(figsize=(20,8),kind = 'pie',autopct='%1.1f%%',title='2020年总销售额占比')
# 准备数据
labels = sales_area.index
size = sales_area.values
# 设置画布
plt.figure(figsize = (20, 8), dpi = 80)
# 饼图属性
plt.pie(size, labels = labels, autopct = '%.2f%%')
# 长宽更改
plt.axis('equal')
plt.title('2020年总销售额占比', fontsize='x-large')
plt.legend()
plt.show()
各地区每一年的销售额
month_sales_area = df.groupby(by=['local','month'])['price'].sum()
将分组后的多层索引转换为列数据
month_sales_area = month_sales_area.reset_index(level=[0, 1])
使用数据透视表重新整理数据
month_sales_area = pd.pivot_table(month_sales_area,
index='local',
columns='month',
values='price')
绘制图形
month_sales_area.plot(figsize=(20, 8), kind='bar', title='2020年不同省份销售额对比')
plt.show()
各省8月份的销售额均大于其他月份，说明在8月份消费者更容易消费
前四个月的销售额很低迷，商家应针对不同月份退出不同优惠活动
各省份不同类别产品销售比
各省份不同类型产品的销售额
category_sales_area = df.groupby(by = ['local','category_code'])['price'].sum().reset_index().sort_values('price',ascending = False).head(50)
category_sales_area = category_sales_area.groupby(by=['local','category_code'])['price'].sum()
将分组后的多层索引设置为列数据
category_sales_area = category_sales_area.reset_index()
数据透视表整理数据
category_sales_area = pd.pivot_table(category_sales_area,
index='local',
columns='category_code',
values='price')
绘制图形
category_sales_area.plot(figsize=(20, 8), kind ='bar', title='不同类别产品T50在各省份销售对比')
plt.show()
Original: https://blog.csdn.net/yhlauvchuchu/article/details/126049577
Author: yhlauvchuchu
Title: 电子产品销售分析练习项目
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