python script sample

 # Create a new DataFrame that includes only the title_name and genres columns, and remove the square brackets and spaces from the genres column

df = dim_titles_roku_python[['title_name', 'genres']].copy()  # make a copy to avoid modifying the original DataFrame

df.loc[:, 'genres'] = df['genres'].str.replace('[', '', regex=True).str.replace(']', '', regex=True).str.replace(' ', '', regex=True)

# df['genres'] = df['genres'].str.replace('[', '').str.replace(']', '').str.replace(' ', '')

# Use the `apply` function to split the genres column into a list of individual genres

df = df.apply(

    lambda x: pd.Series(x['genres'].split(',')),

    axis=1).stack().reset_index(level=1, drop=True)

# Use the `reset_index` function to create a new DataFrame where each genre is a separate row

df = df.reset_index(name='genre')

# Use the `groupby` and `size` functions to count the number of occurrences of each genre

df = df.groupby('genre').size().reset_index(name='frequency')

# Sort the resulting DataFrame by the frequency column in descending order

df = df.sort_values(by='frequency', ascending=False).head(10)

print(df)

Top & Bottom Categories each Year for Men and Woman based on Product Reviews

 df = dim_category_nike_python.copy()

#print(df)

product = dim_product_nike_python.copy()

product = product.merge(df,on='category_id',how='inner')

product = product[['order_date','gender','category_name','product_reviews']]

product['order_date'] = pd.to_datetime(product['order_date']).dt.year

product=product.groupby(['order_date','gender','category_name'])['product_reviews'].mean().reset_index()

product['top_rank']=product.groupby(['order_date','gender'])['product_reviews'].rank(ascending=False)

product['bottom_rank']=product.groupby(['order_date','gender'])['product_reviews'].rank(ascending=True)

new_df = product[(product['top_rank']==1)|(product['bottom_rank']==1)]

new_df = new_df.copy()

new_df.loc[:,'Category_type'] = new_df.apply(lambda row: 'Top' if row['top_rank']== 1 else 'Bottom',axis=1 )

grouped  =new_df.groupby(['order_date','gender','Category_type'])['category_name'].first().reset_index()

pivot_df = grouped.pivot(index='order_date',columns=['gender','Category_type'],values='category_name')

pivot_df.columns = [  f"{gender}_{abc}_Category"  for gender,abc in pivot_df.columns ]

pivot_df = pivot_df.reset_index()

column = {'men_Bottom_Category':'Men_Bottom_Category',

    'woman_Top_Category':'Woman_Top_Category',

    'woman_Bottom_Category':'Woman_Bottom_Category',

    'men_Top_Category':'Men_Top_Category',

    'order_date':'order_year'

    

}

pivot_df=pivot_df.rename(columns = column)

#print(product[product['order_date']==2020])

print(pivot_df)

python script to aggregate , merge, sort etc

 df= fact_orders_grubhub_python.merge(dim_delivery_person_grubhub_python, on='Delivery_person_ID')

# Use the `groupby` and `agg` functions to compute the average age and rating for each city_type

df = df.groupby('city_type').agg({

    'Delivery_person_Age': 'mean',

    'Delivery_person_Ratings': 'mean'

}).reset_index()

# Use the `round` function to round the age and rating columns to 2 decimal places

df['Delivery_person_Age'] = df['Delivery_person_Age'].round(2)

df['Delivery_person_Ratings'] = df['Delivery_person_Ratings'].round(2)

# Rename the columns to match the names in the SQL query

df.rename(columns={

    'Delivery_person_Age': 'avg_age',

    'Delivery_person_Ratings': 'avg_rating'

}, inplace=True)

print(df.head(5))

Qlik connection string switch by environment

 

 Environment var.

LET v.SYS.ServerName = UPPER(ComputerName());

 

//storing environment name into a varibale

LET v.SYS.EnvironmentName = if(index('$(v.SYS.ServerName)','DV'),'dev connection string name', 

                            if(index('$(v.SYS.ServerName)','UA'),'UAT connection string name'                   ));

LIB CONNECT TO  $(v.SYS.EnvironmentName);

Customer rating using SQL

 WITH CustomerRatings AS (

    SELECT

        customer_id,

        AVG(rating) AS avg_rating

    FROM

        wlmt_reviews

    GROUP BY

        customer_id

)

SELECT

    CASE

        WHEN avg_rating >= 4 THEN 'Promoter'

        ELSE 'Neutral' END AS segment,

    COUNT(*) AS customer_count,

    ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM CustomerRatings),2) AS percentage

FROM

    CustomerRatings

GROUP BY

    segment

ORDER BY

    percentage DESC;

    

    

    

   

Find top 3 rated Netflix series using CTE

 WITH FiveRatedseries AS

    (

    SELECT

            c.series_id,

            series_name,

            COUNT(DISTINCT r.profile_id) AS TotalProfileCount,

            AVG(rating) AS AvgRating

    FROM            content c 

    INNER JOIN      series s ON s.series_id=c.series_id

    INNER JOIN      reviews_ntf r   ON r.content_id=c.content_id

    GROUP  BY c.series_id,

            series_name

   

    )

SELECT series_name,TotalProfileCount,ROUND(AvgRating,2)AS AvgRating

FROM FiveRatedseries

WHERE TotalProfileCount >=  5

ORDER BY AvgRating DESC

LIMIT 3

 

Find maximun difference in the order for the same customer SQL

 WITH CustomerData AS(

SELECT      customer_id,

            oz.order_id, 

            product_id,

            SUM(quantity)  AS purchase_quantity, 

            SUM(quantity) - LAG(SUM(quantity)) OVER(PARTITION BY customer_id,product_id ORDER BY order_id) quantity_diff

FROM        orders_amz oz

INNER JOIN  orderdetails o ON o.order_id=oz.order_id

GROUP BY customer_id,oz.order_id, product_id)

SELECT      c.customer_id, 

            p.product_id, 

            product_name

FROM        products_amz p 

INNER JOIN  CustomerData c on c.product_id=p.product_id

ORDER BY    quantity_diff DESC

LIMIT 1

 

CTE query to find top selling products

 WITH MonthlySales AS (

    SELECT

        p.product_name,

        s.store_id,

        SUM(s.units_sold) as units_sold,

        SUM(s.units_sold) OVER (PARTITION BY p.product_id) as total_units,

        RANK() OVER (PARTITION BY p.product_id ORDER BY SUM(s.units_sold) DESC) as store_rank

    FROM

        product p

    JOIN

        sales s ON p.product_id = s.product_id

    WHERE

        YEAR(p.launch_date)=2022

        AND s.sale_date BETWEEN p.launch_date AND DATE_ADD(p.launch_date, INTERVAL 1 MONTH)

    GROUP BY

        p.product_name, s.store_id

)

SELECT

    ms.product_name,

    st.location_city,

    st.location_country,

    ms.units_sold,

    ms.total_units,

    ROUND(100.0 * ms.units_sold / ms.total_units, 2) as store_influence_percentage

FROM

    MonthlySales ms

JOIN

    stores st ON ms.store_id = st.store_id

WHERE

    ms.store_rank = 1

ORDER BY

    store_influence_percentage DESC;

Year part extract in SQL

 SELECT 

p.product_name,

p.category,

SUM(s.units_sold) AS total_quantity_sold,

SUM(s.units_sold*s.unit_price) AS total_revenue

FROM product p

INNER JOIN sales s ON s.product_id=p.product_id

WHERE EXTRACT(YEAR FROM s.sale_date) = 2023

GROUP BY p.product_name

ORDER BY total_quantity_sold DESC 

LIMIT 5

Find the top-performing employee in terms of both revenue generated and projects completed in January 2023.

 SELECT e.employee_id, e.role

FROM employee_dimension e

JOIN employee_performance_fact ep 

ON e.employee_id = ep.employee_id

WHERE ep.month = 'January' AND ep.year = 2023

AND (ep.revenue_generated, ep.projects_completed) = (

    SELECT MAX(revenue_generated), MAX(projects_completed)

    FROM employee_performance_fact

    WHERE month = 'January' AND year = 2023)

Customer with the Highest Total Spending in Each City

 SELECT city, name, SUM(amount) AS TotalSpending

FROM customers_prc c

JOIN orders_prc o ON c.customerid = o.customer_id

GROUP BY city, name

HAVING TotalSpending = (SELECT MAX(TotalSpending) 

FROM (SELECT city, name, SUM(amount) AS TotalSpending 

        FROM customers_prc c JOIN orders_prc o ON c.customerid = o.customer_id GROUP BY city, name) AS Subquery 

WHERE Subquery.city = c.city);