dfSorted = df.sort_values(['price'])
dfSorted['rank'] = np.arange(1, len(df) + 1)
# price top5
dfPriceTop5 = dfSorted.head(5)
# price reverse top 5
dfPriceReverseTop5 = df.sort_values(['price'], ascending=[0]).head(5)
print("av:", df['price'].mean())
print("std:", df['price'].std())
print("var:", df['price'].var())
d = {'col1': df['price'].mean(), 'col2': []}
dfResult = pd.DataFrame(data=d)
# 1개의 엑셀 파일에 저장
dfs = [{
"df": dfSorted,
"sheetName": "sorted"
}, {
"df": dfPriceTop5,
"sheetName": "priceTop5"
}, {
"df": dfPriceReverseTop5,
"sheetName": "priceReverseTop5"
}, {
"df": dfResult,
"sheetName": "dfResult"
}]
save(dfs, "./productAnalyze.xlsx")
import pandas as pd
from libs.excelSaver import save
pd.set_option('display.max_colwidth', -1)
df = pd.read_json("./195297.json")
print(df.count())
dfSorted = df.sort_values(['price'], ascending=[0])
dfTop10 = dfSorted.head(10)
save(dfTop10, 'priceTop10.xlsx')
import pandas as pd
from libs.excelSaver import save
df = pd.read_json("./result.json")
print(df.count())
# 가격이 가장 비싼 제품, 가격이 가장 싼 제품
df = df.sort_values(['price'], ascending=[0])
print(df.columns)
print(df['name'])
# 매일유업 - 경쟁자 분석, 우리들이 마케팅 잘 하고 있나
# name에 우유가 포함되어 있는 데이터만 뽑아보세요.
# how to filter include some word pandas
# 특정 단어가 포함되어 있는 데이터 필터링 하기
drinks = ["우유", "두유", "커피", "라떼" "콜라", "오렌지", "수"]
for drink in drinks:
resultDf = df[df['name'].str.contains(drink)]
save(resultDf, drink + ".xlsx")
print(resultDf.count())
import pandas as pd
from libs.excelSaver import save
pd.set_option('display.max_colwidth', -1)
df = pd.read_json("./195297.json")
brands = ["킨더", "페레로로쉐", "벨지안", "엠엔앰", "누텔라", "마켓오", "롯데"]
for brandName in brands[:1]:
dfFiltered = df[df['name'].str.contains(brandName)]
dfSortedDesc = dfFiltered.sort_values(['price'], ascending=[0])
dfSortedAsc = dfFiltered.sort_values(['price'], ascending=[1])
dfPriceUpper = dfSortedDesc[dfSortedDesc['price'] >= 10000]
dictList = [
{'df':dfSortedDesc.head(10), 'sheetName': 'top10'},
{'df':dfSortedAsc.head(10), 'sheetName': 'tail10'},
{'df':dfPriceUpper, 'sheetName': 'upper10000'},
]
save(dictList, brandName + ".xlsx")
# price top10
# price 뒤에서 top10
import pandas as pd
from libs.excelSaver import save
pd.set_option('display.max_colwidth', -1)
df = pd.read_json("./195297.json")
dicts = [
{
'df': df,
'sheetName': 'total'
},
]
save(dicts, "초콜렛.xlsx")
print(df.count())
for competitor in competitors:
result = df[df['name'].str.contains(competitor)]
# 합계 평균 표준편차
sum = result['price'].sum()
mean = result['price'].mean()
std = result['price'].std()
max = result['price'].max()
min = result['price'].min()
count = result['price'].min()
row = {
"competitor": competitor,
"sum": sum,
"mean": mean,
"max": max,
"min": min,
"count": count
}
reportTable.append(row)
return reportTable
reportTable = getReportTable(competitors)
dfReportTable = pd.DataFrame(reportTable)
save(dfReportTable, "week3Report.xlsx")
# json으로 만들고
# excel file로 저장 해보세요