探索資料分析

本檔案提供一些使用Data Distiller來探索和分析資料的基本範例和最佳實務。 Python 筆記本。

快速入門

在繼續本指南之前,請確定您已建立與Data Distiller的連線, Python 筆記本。 請參閱檔案以瞭解如何 連線 Python Notebook to Data Distiller.

取得基本統計資料 basic-statistics

使用以下程式碼來擷取資料集中的列數和相異設定檔數。

table_name = 'ecommerce_events'

basic_statistics_query = f"""
SELECT
    COUNT(_id) as "totalRows",
    COUNT(DISTINCT _id) as "distinctUsers"
FROM {table_name}"""

df = qs_cursor.query(basic_statistics_query, output="dataframe")
df

範例輸出

totalRows
distinctUsers
0
1276563
1276563

建立大型資料集的取樣版本 create-dataset-sample

如果您要查詢的資料集非常大,或不需要探索查詢的精確結果,請使用 抽樣功能 可用於資料Distiller查詢。 此程式分為兩個步驟:

  • 首先, 分析 要以指定取樣比率建立取樣版本的資料集
  • 接下來,查詢資料集的抽樣版本。 根據您套用至樣本資料集的函式,您可能想要將輸出縮放為數字以取得完整資料集

建立5%樣本 create-sample

以下範例會分析資料集並建立5%範例:

# A sampling rate of 10 is 100% in Query Service, so for 5% use a sampling rate 0.5
sampling_rate = 0.5

analyze_table_query=f"""
SET aqp=true;
ANALYZE TABLE {table_name} TABLESAMPLE SAMPLERATE {sampling_rate}"""

qs_cursor.query(analyze_table_query, output="raw")

檢視您的範例 view-sample

您可以使用 sample_meta 函式,可檢視從指定資料集建立的任何範例。 下列程式碼片段會示範如何使用 sample_meta 函式。

sampled_version_of_table_query = f'''SELECT sample_meta('{table_name}')'''

df_samples = qs_cursor.query(sampled_version_of_table_query, output="dataframe")
df_samples

範例輸出

sample_table_name
sample_dataset_id
parent_dataset_id
sample_type
sampling_rate
filter_condition_on_source_dataset
sample_num_rows
已建立
0
cmle_synthetic_data_experience_event_dataset_c…
650f7a09ed6c3e28d34d7fc2
64fb4d7a7d748828d304a2f4
色彩一致
0.5
6427
23/09/2023
11:51:37

查詢您的範例 query-sample-data

您可以從傳回的中繼資料中參照範例表格名稱,直接查詢範例。 然後,您可將結果乘以取樣比例,以取得預估值。

sample_table_name = df_samples[df_samples["sampling_rate"] == sampling_rate]["sample_table_name"].iloc[0]

count_query=f'''SELECT count(*) as cnt from {sample_table_name}'''

df = qs_cursor.query(count_query, output="dataframe")
# Divide by the sampling rate to extrapolate to the full dataset
approx_count = df["cnt"].iloc[0] / (sampling_rate / 100)

print(f"Approximate count: {approx_count} using {sampling_rate *10}% sample")

範例輸出

Approximate count: 1284600.0 using 5.0% sample

電子郵件漏斗分析 email-funnel-analysis

漏斗分析是瞭解達成目標結果所需步驟,以及有多少使用者完成每個步驟的方法。 以下範例說明通往使用者訂閱電子報的步驟的簡單漏斗分析。 訂閱結果以事件型別表示,該事件型別為 web.formFilledOut.

首先,執行查詢以取得每個步驟的使用者人數。

simple_funnel_analysis_query = f'''SELECT eventType, COUNT(DISTINCT _id) as "distinctUsers",COUNT(_id) as "distinctEvents" FROM {table_name} GROUP BY eventType ORDER BY distinctUsers DESC'''

funnel_df = qs_cursor.query(simple_funnel_analysis_query, output="dataframe")
funnel_df

範例輸出

eventType
distinctUsers
distinctEvents
0
directMarketing.emailSent
598840
598840
1
directMarketing.emailOpened
239028
239028
2
web.webpagedetails.pageViews
120118
120118
3
advertising.impressions
119669
119669
4
directMarketing.emailClicked
51581
51581
5
commerce.productViews
37915
37915
6
decisioning.propositionDisplay
37650
37650
7
web.webinteraction.linkClicks
37581
37581
8
web.formFilledOut
17860
17860
9
advertising.clicks
7610
7610
10
decisioning.propositionInteract
2964
2964
11
decisioning.propositionDismiss
2889
2889
12
commerce.purchases
2858
2858

繪製查詢結果 plot-results

接下來,使用下列工具繪製查詢結果: Python plotly 資料庫:

import plotly.express as px

email_funnel_events = ["directMarketing.emailSent", "directMarketing.emailOpened", "directMarketing.emailClicked", "web.formFilledOut"]
email_funnel_df = funnel_df[funnel_df["eventType"].isin(email_funnel_events)]

fig = px.funnel(email_funnel_df, y='eventType', x='distinctUsers')
fig.show()

範例輸出

eventType電子郵件漏斗的資訊圖。

事件關聯 event-correlations

另一種常見分析是計算事件型別與目標轉換事件型別之間的關聯。 在此範例中,訂閱事件由表示 web.formFilledOut. 此範例使用 Spark 資料Distiller查詢中可用的函式,用以實現下列步驟:

  1. 依設定檔計算每種事件型別的事件數。
  2. 彙總各設定檔中每種事件型別的計數,並計算每種事件型別的關聯,其中 web,formFilledOut.
  3. 將計數和相關性的資料流轉換為每個功能的Pearson相關係數(事件型別計數)表格與目標事件。
  4. 將結果以視覺化方式顯示在繪圖中。

此 Spark 函式會彙總資料以傳回小型結果表格,因此您可以在完整資料集上執行這類查詢。

large_correlation_query=f'''
SELECT SUM(webFormsFilled) as webFormsFilled_totalUsers,
       SUM(advertisingClicks) as advertisingClicks_totalUsers,
       SUM(productViews) as productViews_totalUsers,
       SUM(productPurchases) as productPurchases_totalUsers,
       SUM(propositionDismisses) as propositionDismisses_totaUsers,
       SUM(propositionDisplays) as propositionDisplays_totaUsers,
       SUM(propositionInteracts) as propositionInteracts_totalUsers,
       SUM(emailClicks) as emailClicks_totalUsers,
       SUM(emailOpens) as emailOpens_totalUsers,
       SUM(webLinkClicks) as webLinksClicks_totalUsers,
       SUM(webPageViews) as webPageViews_totalusers,
       corr(webFormsFilled, emailOpens) as webForms_EmailOpens,
       corr(webFormsFilled, advertisingClicks) as webForms_advertisingClicks,
       corr(webFormsFilled, productViews) as webForms_productViews,
       corr(webFormsFilled, productPurchases) as webForms_productPurchases,
       corr(webFormsFilled, propositionDismisses) as webForms_propositionDismisses,
       corr(webFormsFilled, propositionInteracts) as webForms_propositionInteracts,
       corr(webFormsFilled, emailClicks) as webForms_emailClicks,
       corr(webFormsFilled, emailOpens) as webForms_emailOpens,
       corr(webFormsFilled, emailSends) as webForms_emailSends,
       corr(webFormsFilled, webLinkClicks) as webForms_webLinkClicks,
       corr(webFormsFilled, webPageViews) as webForms_webPageViews
FROM(
    SELECT _{tenant_id}.cmle_id as userID,
            SUM(CASE WHEN eventType='web.formFilledOut' THEN 1 ELSE 0 END) as webFormsFilled,
            SUM(CASE WHEN eventType='advertising.clicks' THEN 1 ELSE 0 END) as advertisingClicks,
            SUM(CASE WHEN eventType='commerce.productViews' THEN 1 ELSE 0 END) as productViews,
            SUM(CASE WHEN eventType='commerce.productPurchases' THEN 1 ELSE 0 END) as productPurchases,
            SUM(CASE WHEN eventType='decisioning.propositionDismiss' THEN 1 ELSE 0 END) as propositionDismisses,
            SUM(CASE WHEN eventType='decisioning.propositionDisplay' THEN 1 ELSE 0 END) as propositionDisplays,
            SUM(CASE WHEN eventType='decisioning.propositionInteract' THEN 1 ELSE 0 END) as propositionInteracts,
            SUM(CASE WHEN eventType='directMarketing.emailClicked' THEN 1 ELSE 0 END) as emailClicks,
            SUM(CASE WHEN eventType='directMarketing.emailOpened' THEN 1 ELSE 0 END) as emailOpens,
            SUM(CASE WHEN eventType='directMarketing.emailSent' THEN 1 ELSE 0 END) as emailSends,
            SUM(CASE WHEN eventType='web.webinteraction.linkClicks' THEN 1 ELSE 0 END) as webLinkClicks,
            SUM(CASE WHEN eventType='web.webinteraction.pageViews' THEN 1 ELSE 0 END) as webPageViews
    FROM {table_name}
    GROUP BY userId
)
'''
large_correlation_df = qs_cursor.query(large_correlation_query, output="dataframe")
large_correlation_df

範例輸出

webFormsFilled_totalUsers
advertisingClicks_totalUsers
productViews_totalUsers
productPurchases_totalUsers
propositionDismisses_totaUsers
propositionDisplays_totaUsers
propositionInteracts_totalUsers
emailClicks_totalUsers
emailOpens_totalUsers
webLinksClicks_totalUsers
webForms_advertisingClicks
webForms_productViews
webForms_productPurchases
webForms_propositionDismisses
webForms_propositionInteracts
webForms_emailClicks
webForms_emailOpens
webForms_emailSends
webForms_webLinkClicks
webForms_webPageViews
0
17860
7610
37915
0
2889
37650
2964
51581
239028
37581
0.026805
0.2779
None
0.06014
0.143656
0.305657
0.218874
0.192836
0.259353
None

將列轉換為事件型別關聯 event-type-correlation

接下來,將以上查詢輸出中的單一資料列轉換為表格,顯示每個事件型別與目標訂閱事件的關聯:

cols = large_correlation_df.columns
corrdf = large_correlation_df[[col for col in cols if ("webForms_"  in col)]].melt()
corrdf["feature"] = corrdf["variable"].apply(lambda x: x.replace("webForms_", ""))
corrdf["pearsonCorrelation"] = corrdf["value"]

corrdf.fillna(0)

範例輸出

變數
功能
pearsonCorrelation
0
webForms_EmailOpens
0.218874
EmailOpens
0.218874
1
webForms_advertisingClicks
0.026805
廣告點選次數
0.026805
2
webForms_productViews
0.277900
產品檢視
0.277900
3
webForms_productPurchases
0.000000
productPurchases
0.000000
4
webForms_propositionDismisses
0.060140
propositionDismiss
0.060140
5
webForms_propositionInteracts
0.143656
propositionInteracts
0.143656
6
webForms_emailClicks
0.305657
電子郵件點按次數
0.305657
7
webForms_emailOpens
0.218874
emailOpens
0.218874
8
webForms_emailSends
0.192836
電子郵件傳送
0.192836
9
webForms_webLinkClicks
0.259353
webLinkClicks
0.259353
10
webForms_webPageViews
0.000000
webPageViews
0.000000

最後,您可以將下列各項的相關性視覺化: matplotlib Python 資料庫:

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(5,10))
sns.barplot(data=corrdf.fillna(0), y="feature", x="pearsonCorrelation")
ax.set_title("Pearson Correlation of Events with the outcome event")

事件結果的事件皮爾遜關聯長條圖

後續步驟

閱讀本檔案後,您已瞭解如何使用Data Distiller來探索和分析資料 Python 筆記本。 在機器學習環境中,從Experience Platform建立功能管道以饋送自訂模型的 機器學習的工程師功能.

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