#?這些數(shù)據(jù)可以在公眾號后臺回復(fù)【云朵君】，聯(lián)系作者獲取。
#?1.導(dǎo)入必要的庫
import?pandas?as?pd
import?numpy?as?np
import?streamlit?as?st

#?2.設(shè)置頁面配置
st.set_page_config(
????page_title="This?is?my?title",??#?頁面的標(biāo)題
????page_icon="??",??????????????????#?favicon
????layout="wide",
)

#?3.定義你的項(xiàng)目中使用的所有函數(shù)
# 4.對于每一個函數(shù)，在其前面添加"@st.experimental_memo"。這就是
#?來記憶每個函數(shù)的執(zhí)行。這將使應(yīng)用程序運(yùn)行得更快。
#?特別是當(dāng)用戶與儀表盤上的一些元素互動時

@st.experimental_memo
def?function(x):
????return?y

import?pandas?as?pd
import?numpy?as?np?
import?matplotlib.pyplot?as?plt?
import?seaborn?as?sns
import?matplotlib?as?mpl?
from?datetime?import?date,?datetime
import?streamlit?as?st??

st.set_page_config(
????page_title="Cohorts?Dashboard",
????page_icon="??",
????layout="wide",
)

@st.experimental_memo
def?purchase_rate(customer_id):
????purchase_rate?=?[1]
????counter?=?1
????for?i?in?range(1,len(customer_id)):
????????if?customer_id[i]?!=?customer_id[i-1]:
????????????purchase_rate.append(1)
????????????counter?=?1
????????else:
????????????counter?+=?1
????????????purchase_rate.append(counter)
????return?purchase_rate
@st.experimental_memo
def?join_date(date,?purchase_rate):
????join_date?=?list(range(len(date)))
????for?i?in?range(len(purchase_rate)):
????????if?purchase_rate[i]?==?1:
????????????join_date[i]?=?date[i]
????????else:
????????????join_date[i]?=?join_date[i-1]
????return?join_date
??
#?我沒有在這里定義所有的函數(shù)，因?yàn)檫@將延長文章的篇幅。
#?我將在文章的最后提供完整的代碼。

st.title("Cohort?Interactive?Dashboard?Demo")
st.markdown("""
This?webapp?performs?cohort?analysis?of?my_company?data!
*?**Python?libraries?used:**?base64,?pandas,?streamlit,?numpy,?matplotlib,?seaborn
*?**Data?source:**?[Shopify](https://company_name.myshopify.com/admin)
*?You?need?to?select?the?data?file?first?to?proceed.
""")
uploaded_file?=?st.file_uploader("Choose?a?file")?#?來上傳文件

If?uploaded_file?is?not?None:#?這很重要，因?yàn)闆]有這個。
?????????????????????????????#?當(dāng)沒有上傳的文件時，會出現(xiàn)
?????????????????????????????#?一個錯誤，因?yàn)閐f沒有被定義....
??df?=?pd.read_csv(upload_file)?#?讀取該文件
??df_processed?=?process_df(df)?#?清潔數(shù)據(jù)
??
??#?儀表盤標(biāo)題
??st.header("Live?Dashboard")
??#?過濾器
??first_filter?=?st.selectbox('Select?first?filter',['Option?1',?'Option?2',?'Option?3])

????second_filter?=?st.multiselect('Select?second?filter',?['Option?1','Option?2','Option?3','Option?4'])

????output?=?display_function(data_input,first_filter,second_filter)
????st.dataframe(output)
????st.download_button(label='Download?csv',?data=output.to_csv(),?mime='text/csv')?#?來下載該文件

if?uploaded_file?is?not?None:
????df?=?pd.read_csv(uploaded_file)
????df_processed?=?process_df(df)
????df_cohorts?=?cohort_numbers(df_processed)
????cohorts?=?cohort_percent(df_cohorts)
????
????#?通過使用f-strings動態(tài)標(biāo)題
????st.header(f"Live?{cohorts.index[0]}?to?{cohorts.index[-1]}?Cohort?Dashboard")
????
????#?過濾器
????first_filter=?st.selectbox('Select?type?of?cohort',['By?unique?customers',?'By?percentage',?'By?AOV'])

????second_filter?=?st.multiselect('Select?cohort',?list(cohorts.index))

????output?=?select_which_table_to_draw(df_processed,first_filter,second_filter)
????st.dataframe(output)
????st.download_button(label='Download?csv',?data=output.to_csv(),?mime='text/csv')

kpi1,?kpi2,?kpi3?=?st.columns(3)?#?創(chuàng)建三個占位符
if?uploaded_file?is?not?None:
????
????aov?=?np.mean(df['total_sales'])
????aov_goal?=?95.00
????kpi1.metric(
????????#?給這個指標(biāo)貼上標(biāo)簽
????????label="AOV",?
????????#?計(jì)算度量值
????????value=f"$?{round(aov,2)}",
????????#?計(jì)算與目標(biāo)相比的變化（向上/向下箭頭）
????????delta=f"-${round(aov_goal-aov,2)}"?if?aov_goal>aov?else?f"${round(aov-aov_goal,2)}",
????)

????nc?=?np.mean(df.loc[df['customer_type']=='First-time'].groupby(['day']).count()['customer_id'])
????nc_goal?=?30
????kpi2.metric(
????????label="New?customers/day",
????????value=int(nc),
????????delta=f"-{round((nc_goal-nc)/nc_goal*100,2)}%"?if?nc_goal>nc?else?f"{round((nc?-?nc_goal)/nc_goal*100,0)}%",
????)

????rc?=?np.mean(df.loc[df['customer_type']=='Returning'].groupby(['day']).count()['customer_id'])
????rc_goal?=?250
????kpi3.metric(
????????label="Returning?customers/day",
????????value=?int(rc),
????????delta=f"-{round((rc_goal?-?rc)/rc_goal*100,2)}%"?if?rc_goal>rc?else?f"{round((rc-rc_goal)/rc_goal*100,2)}%"
????)

streamlit?run?cohort-demo.py

#只要復(fù)制和粘貼這個
mkdir?-p?~/.streamlit/
echo?"\
[server]\n\
headless?=?true\n\
port?=?$PORT\n
enableCORS?=?false\n
\n\
"?>?~/.streamlit/config.toml