数据分析与挖掘实战1

import pandas as pd
import matplotlib.pyplot as plt
import math
import numpy as np

#sales[sales.isna().values==True]

def pin_lv(arr):
    return arr.count()/sales.describe().loc[\'count\']


sale_path = \'E:\phil\workspace\selfwork\python\python practice of data analysis and mining\chapter3\catering_sale.xls\'
sales = pd.read_excel(sale_path, index_col=\'date\')
sales = sales.dropna()
sales_describe = sales.describe()
# 极差
sales_describe.loc[\'ji_cha\'] = sales_describe.loc[\'max\'] - sales_describe.loc[\'min\']
fen_zu_shu = math.ceil(sales_describe.loc[\'ji_cha\'] / 1000)
# 分组间隔
list_fen_zu = []
for index in range(fen_zu_shu + 1):
    list_fen_zu.append(index * 1000)

fen_zu_qu_jian = pd.cut(sales[\'sale\'], list_fen_zu)
group = sales.groupby(by=fen_zu_qu_jian)

group = group.agg([\'count\', \'sum\', pin_lv])
# 删除列的第一个分组sales
group.columns = group.columns.droplevel()
group[\'lei_ji\'] = group[\'pin_lv\'].cumsum(skipna=True)
# 填充nan的值,向前填充
group[\'lei_ji\'] = group[\'lei_ji\'].fillna(method=\'ffill\')

group.plot.bar(logy=True, figsize=[12, 6], rot=15)
plt.show()