{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ibrahim Salami",
  "title_listed": "EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas/",
  "publish_date": "2025-12-20",
  "corpus_stage": "",
  "notes": "",
  "id": "90c2f4ba853c",
  "collected_at": "2026-10-02T12:01:56",
  "title": "EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas\nLearn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales data.\n[Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/)\nDecember 20, 20259 min read\n![Exploratory Data Analysis with Pandas](../images/001.jpg)\nImage by Author. Created with Gemini\nHi everyone! Welcome back to the “EDA in Public” series! This is Part 2 of the series; if you haven’t seen Part 1 yet, read it [here.](https://towardsdatascience.com/eda-in-public-part-1-cleaning-exploring-sales-data-with-pandas/) Here’s a recap of what we conquered.\n[![](../images/001.jpg)EDA in Public (Part 1): Cleaning and Exploring Sales Data with PandasLearn how to clean messy CSVs, summarize data, and visualize basic trends in e-commerce sales.Ibrahim Salami · 10 min read](https://towardsdatascience.com/eda-in-public-part-1-cleaning-exploring-sales-data-with-pandas/)\nIn Part 1, we took a messy, half-million-row sales file and got it into shape. Specifically, we:\n  * Sampled the data for faster processing.\n  * Fixed missing values and standardised text entries (like ‘EIRE’ to ‘Ireland’).\n  * Filtered out all the noise — returns, cancellations, and transactions with zero price.\n  * **Engineered our most critical feature:** the `Revenue` column.\n  * Achieved our first business insight: the **Top 10 Revenue-Generating Countries**.\n\n\nWe now have a clean, revenue-ready Pandas DataFrame ready to be exploited for insights! By the end of this series, I want to have mastered exploratory data analysis using Pandas. Feel free to follow along if you’re a data enthusiast.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\nNow we shift our focus to delivering truly impactful business intelligence for NovaShop. Our overall goal for Part 2 is to answer the fundamental questions: _**What**_ **products are the best performers,** and _**when**_ **is the best time to sell them**?\nThis part is all about analysis using powerful Pandas aggregation techniques (`.groupby()`) and feature engineering using the datetime column (`.dt` accessor). We'll break down our analysis into two key areas:\n  * **Product Deep Dive:** Identifying the products that move the most units versus those that bring in the most cash.\n  * **Sales Timing (Time-Series):** Uncovering the seasonality of sales (monthly peaks) and the operational trends (busiest hour of the day).\n\n\nWithout further ado, let’s get to it.\n## Loading the clean data\nTo improve performance, let’s export our cleaned dataset and import it again for analysis\npython\n\n```\ndf.to_csv('online_retail_clean.csv', index=False)\n```\n\nNow, I can create a new project and start afresh.\nWhen loading CSVs that contain date columns, I try to use the `parse_dates` argument in `pd.read_csv()`. This saves me the step of manually converting the column type later, ensuring the date features are correct from the start.\npython\n\n```\nimport pandas as pd\ndf = pd.read_csv('online_retail_clean.csv', parse_dates=['InvoiceDate'])\nprint(f\"Successfully loaded clean data. Total rows: {len(df)}\")\n```\n\n**Output:**\nmarkup\n\n```\nSuccessfully loaded clean data. Total rows: 52933\n```\n\n## Product Deep Dive: Who Are the True Money Makers?\nWith the clean data loaded, let’s jump straight into product performance. Products can be considered \"best-sellers\" in two different ways: by volume (units sold) or by value (revenue generated).\n### Insight 2: Top 10 Bestsellers (by Quantity)\nThe quantity list tells us which products are popular and in high demand, even if their price is low. This is important for warehouse stocking and inventory management.\nWe’ll group by the product `Description`, sum the `Quantity`, and sort it descendingly.\npython\n\n```\n# Top 10 Bestsellers (by Quantity)\ndf.groupby(‘Description’)[‘Quantity’].sum().sort_values(ascending=False).head(10)\n```\n\n**Output:**\nmarkup\n\n```\nDescriptionMEDIUM CERAMIC TOP STORAGE JAR 74698WORLD WAR 2 GLIDERS ASSTD DESIGNS 9419GIN + TONIC DIET METAL SIGN 5016ASSORTED COLOURS SILK FAN 4684JUMBO BAG RED RETROSPOT 4671PACK OF 72 RETROSPOT CAKE CASES 3561MINI PAINT SET VINTAGE 3285ASSORTED COLOUR BIRD ORNAMENT 2783BROCADE RING PURSE 2525POPCORN HOLDER 2508Name: Quantity, dtype: int64\n```\n\n### Insight 3: Top 10 Revenue Drivers (The True Money-Makers)\nThis is what NovaShop really wants to know. These products drive profitability. They might not sell as many units as the items in the previous list, but their higher price or larger bulk orders make them far more valuable.\nWe follow the same process, but this time we aggregate on the `Revenue` column we engineered in Part 1.\npython\n\n```\n# Top 10 Revenue Drivers (The True Money-Makers)\ndf.groupby(‘Description’)[‘Revenue’].sum().sort_values(ascending=False).head(10)\n```\n\n**Output:**\nmarkup\n\n```\nDescriptionMEDIUM CERAMIC TOP STORAGE JAR 77785.74DOTCOM POSTAGE 18456.13REGENCY CAKESTAND 3 TIER 15093.38AMAZON FEE 13541.33PARTY BUNTING 9491.60GIN + TONIC DIET METAL SIGN 9480.51JUMBO BAG RED RETROSPOT 8996.65Manual 8966.96CHILLI LIGHTS 8395.20POSTAGE 7194.51Name: Revenue, dtype: float64\n```\n\n**Discussion: What These Lists Tell NovaShop**\nThis is where the power of EDA truly shines, because these two lists tell a very different story:\n  * **The All-Star Product:** The **“MEDIUM CERAMIC TOP STORAGE JAR”** is clearly the winner, dominating both lists by a massive margin. It’s high volume and high value. This item should be NovaShop’s primary focus for inventory, marketing, and expansion.\n  * **A surprising revenue source:** While exploring the data, I noticed items like _“DOTCOM POSTAGE,” “AMAZON FEE,” “Manual,”_ and _“POSTAGE.”_ These aren’t actual products — they’re service fees and manual adjustments. Yet together, they bring in almost **£48,000** in revenue. NovaShop should double-check whether these fees are being counted in their gross profit. It’s an easy thing to miss, but it could be a quiet and meaningful source of income hiding in plain sight.\n  * **The High-Value Physical Goods:** Items like the **“REGENCY CAKESTAND 3 TIER”** and **“CHILLI LIGHTS”** make the revenue list but not the quantity list. This tells us they are high-price items that contribute significantly to the total cash flow, and they are worth targeted marketing efforts.\n\n\nWe now have a complete picture of what NovaShop sells best. Time to figure out when!\n## Preparing for Time-Series Analysis: Feature Engineering\nWe know what sold well. Now, let’s figure out when sales peak. Our goal is to break down the `InvoiceDate` column into components that allow us to group and analyze sales by year, month, day, and even hour.\nThis is a classic feature engineering move. Since we already made sure `InvoiceDate` is a proper Pandas `datetime` object (thanks to `parse_dates` in our loading step!), we can use the extremely useful `.dt` accessor.\n### Extracting Time Components\nTo understand seasonality (monthly) and operational efficiency (hourly), we’ll create four new columns.\n  * Year — For long-term comparisons\n  * Month — For identifying seasonal peaks (e.g., Q4 rush).\n  * DayName — To find the busiest day of the week.\n  * Hour — To determine peak operational hours.\n\n\nLet’s generate these columns:\npython\n\n```\nprint(“\\n — — Time Series Feature Extraction — -”)\n\n# Extract Year, Month, DayName, and Hour from the InvoiceDate column\ndf[‘Year’] = df[‘InvoiceDate’].dt.year\ndf[‘Month’] = df[‘InvoiceDate’].dt.month\ndf[‘DayName’] = df[‘InvoiceDate’].dt.day_name()\ndf[‘Hour’] = df[‘InvoiceDate’].dt.hour\nprint(“New time features added: Year, Month, DayName, Hour.”)\nprint(df[[‘InvoiceDate’, ‘Year’, ‘Month’, ‘DayName’, ‘Hour’]].head())\n```\n\n**Output:**\nmarkup\n\n```\n--- Time Series Feature Extraction ---New time features added: Year, Month, DayName, Hour.InvoiceDate Year Month DayName Hour0 2011-06-01 12:05:00 2011 6 Wednesday 121 2011-05-27 17:14:00 2011 5 Friday 172 2011-04-21 17:05:00 2011 4 Thursday 173 2011-11-16 10:39:00 2011 11 Wednesday 104 2011-03-10 08:40:00 2011 3 Thursday 8\n```\n\nPerfect! We can move right into mapping the sales patterns in the next section.\n## Mapping Sales Patterns (Time-Series Insights)\nWith our time features ready, we can now ask and answer our questions about when sales occur. We’ll start big (monthly trends) and drill down small (hourly trends).\n### Insight 4: Sales Trends by Month (Seasonality)\nMonthly aggregation is crucial for spotting **seasonality** — the predictable pattern of sales repeating every year. This data is used for financial forecasting and planning inventory levels for the peak season.\nWe’ll group by the `Month` column and calculate the total revenue for each.\npython\n\n```\nprint(“\\n — — Insight 4: Sales Trends by Month (Seasonality) — -”)\n# Group by month and sum the revenue\nmonthly_revenue = df.groupby(‘Month’)[‘Revenue’].sum().sort_values(ascending=False)\n# Print the results, which should show the seasonal peaks\nprint(monthly_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Insight 4: Sales Trends by Month (Seasonality) ---Month11 143576.3601 142013.42012 133178.98010 119533.5409 103929.4203 72968.2708 71302.8905 70424.5106 68722.5207 68713.8314 51882.0102 50178.730Name: Revenue, dtype: float64\n```\n\n**Interpretation: The Massive Q4 and January Surprise**\nThe data clearly shows that NovaShop’s sales are heavily skewed toward the end of the year, confirming the typical retail **Q4 (October, November, December) rush**.\n  * **Peak Season:** November is the peak month by a slight margin, followed closely by December. This is the time to stock up on the **Medium Ceramic Top Storage Jars** and run targeted ads.\n  * **The January Surge:** The second-highest month is January! This is an excellent insight for NovaShop. It suggests customers may be using Christmas gift money or taking advantage of post-holiday sales. This period should not be treated as a slump but as a secondary high-volume sales opportunity.\n  * **Low Season:** February and April are the lowest performers, which helps NovaShop plan for lower staffing and marketing budgets during those periods.\n\n\n### Insight 5: Sales Trends by Day of Week and Hour (Operational Planning)\nWhile seasonality is about finance and inventory, daily and hourly trends are about **operations**. NovaShop can use this to schedule warehouse staff, optimize ad spend, and time email campaigns.  \nWe’ll run two separate aggregations here: one for the day of the week and one for the hour of the day.\n**Revenue by Day of Week**\npython\n\n```\n# 1. Sales by Day of Week\ndaily_revenue = df.groupby(‘DayName’)[‘Revenue’].sum()\nprint(“\\n — — Revenue by Day of Week — -”)\nprint(daily_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Revenue by Day of Week ---DayNameFriday 163861.320Monday 171026.230Sunday 83125.890Thursday 219342.980Tuesday 282796.741Wednesday 176271.320Name: Revenue, dtype: float64\n```\n\n**Interpretation: Tuesday is the Power Day**\nThe data reveals a clear and actionable pattern for the work week:\n  * **Peak Sales** : **Tuesday** absolutely dominates the week, generating almost 30% more revenue than the next busiest day, Thursday. This is a prime day for launching new products or running high-impact flash sales.\n  * **Slump Days:** Unsurprisingly, sales drop dramatically on **Sunday.** NovaShop might consider this a low-priority day for email marketing or customer service staffing.\n  * **Missing Day:** The dataset contains no transactions for **Saturday** in our sample (or perhaps the full set, which is common in B2B datasets).\n\n\n**Revenue by Hour of Day**\npython\n\n```\n# 2. Sales by Hour of Day\nhourly_revenue = df.groupby(‘Hour’)[‘Revenue’].sum().sort_index()\nprint(“\\n — — Revenue by Hour of Day — -”)\nprint(hourly_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Revenue by Hour of Day ---Hour7 2830.9108 26597.8009 76768.75010 209809.97111 115233.60012 142474.46013 130348.29014 119241.52015 145178.98016 70620.55017 36148.03018 14022.79019 4397.13020 2751.700\n```\n\n**Interpretation: The Late Morning Boom**\nThe hourly data points directly to operational needs:\n  * **Massive Peak:** Sales explode at 10 AM (Hour 10), generating almost double the revenue of the next few hours. This is the optimal time for NovaShop to launch email marketing and social media campaigns.\n  * **Sustained Activity:** Sales remain high from 11 AM through 3 PM, suggesting a strong midday and post-lunch shopping window.\n  * **End of Day:** Activity falls off sharply after 4 PM, with very little revenue generated in the evening (7 PM onward). This indicates NovaShop’s customer base is primarily desk-based, shopping during traditional working hours.\n\n\n## Conclusion: Setting the Stage for Customer Segmentation\nWe have officially finished the heavy-duty EDA! We know what sells best (the Ceramic Jar and fees), where sales are strongest (the UK), and when the sales peaks occur (Q4, Tuesdays, and 10 AM).\nHowever, we are still missing the most valuable piece of the puzzle: **The Who**. The best companies don’t just know what sells; they know who their most valuable customers are and how to keep them.\nIn [Part 3](https://towardsdatascience.com/eda-in-public-part-3-rfm-analysis-for-customer-segmentation-in-pandas/), we will dive into the most advanced analysis technique in this series: RFM (Recency, Frequency, Monetary) Customer Segmentation. This will allow us to classify customers into tiers like “Champions,” “Loyal Customers,” and “At-Risk,” giving NovaShop a true roadmap for personalised marketing.\nI hope you found this article helpful. Feel free to say hi to me on any of these platforms. I truly appreciate your feedback.\n[Medium](https://medium.com/@ibbysalam)\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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Created with Gemini\nHi everyone! Welcome back to the “EDA in Public” series! This is Part 2 of the series; if you haven’t seen Part 1 yet, read it [here.](https://towardsdatascience.com/eda-in-public-part-1-cleaning-exploring-sales-data-with-pandas/) Here’s a recap of what we conquered.\n[![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/12/EDA-with-Pandas-img.jpg)EDA in Public (Part 1): Cleaning and Exploring Sales Data with PandasLearn how to clean messy CSVs, summarize data, and visualize basic trends in e-commerce sales.Ibrahim Salami · 10 min read](https://towardsdatascience.com/eda-in-public-part-1-cleaning-exploring-sales-data-with-pandas/)\nIn Part 1, we took a messy, half-million-row sales file and got it into shape. Specifically, we:\n  * Sampled the data for faster processing.\n  * Fixed missing values and standardised text entries (like ‘EIRE’ to ‘Ireland’).\n  * Filtered out all the noise — returns, cancellations, and transactions with zero price.\n  * **Engineered our most critical feature:** the `Revenue` column.\n  * Achieved our first business insight: the **Top 10 Revenue-Generating Countries**.\n\n\nWe now have a clean, revenue-ready Pandas DataFrame ready to be exploited for insights! By the end of this series, I want to have mastered exploratory data analysis using Pandas. Feel free to follow along if you’re a data enthusiast.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\nNow we shift our focus to delivering truly impactful business intelligence for NovaShop. Our overall goal for Part 2 is to answer the fundamental questions: _**What**_ **products are the best performers,** and _**when**_ **is the best time to sell them**?\nThis part is all about analysis using powerful Pandas aggregation techniques (`.groupby()`) and feature engineering using the datetime column (`.dt` accessor). We'll break down our analysis into two key areas:\n  * **Product Deep Dive:** Identifying the products that move the most units versus those that bring in the most cash.\n  * **Sales Timing (Time-Series):** Uncovering the seasonality of sales (monthly peaks) and the operational trends (busiest hour of the day).\n\n\nWithout further ado, let’s get to it.\n## Loading the clean data\nTo improve performance, let’s export our cleaned dataset and import it again for analysis\npython\n\n```\ndf.to_csv('online_retail_clean.csv', index=False)\n```\n\nNow, I can create a new project and start afresh.\nWhen loading CSVs that contain date columns, I try to use the `parse_dates` argument in `pd.read_csv()`. This saves me the step of manually converting the column type later, ensuring the date features are correct from the start.\npython\n\n```\nimport pandas as pd\ndf = pd.read_csv('online_retail_clean.csv', parse_dates=['InvoiceDate'])\nprint(f\"Successfully loaded clean data. Total rows: {len(df)}\")\n```\n\n**Output:**\nmarkup\n\n```\nSuccessfully loaded clean data. Total rows: 52933\n```\n\n## Product Deep Dive: Who Are the True Money Makers?\nWith the clean data loaded, let’s jump straight into product performance. Products can be considered \"best-sellers\" in two different ways: by volume (units sold) or by value (revenue generated).\n### Insight 2: Top 10 Bestsellers (by Quantity)\nThe quantity list tells us which products are popular and in high demand, even if their price is low. This is important for warehouse stocking and inventory management.\nWe’ll group by the product `Description`, sum the `Quantity`, and sort it descendingly.\npython\n\n```\n# Top 10 Bestsellers (by Quantity)\ndf.groupby(‘Description’)[‘Quantity’].sum().sort_values(ascending=False).head(10)\n```\n\n**Output:**\nmarkup\n\n```\nDescriptionMEDIUM CERAMIC TOP STORAGE JAR 74698WORLD WAR 2 GLIDERS ASSTD DESIGNS 9419GIN + TONIC DIET METAL SIGN 5016ASSORTED COLOURS SILK FAN 4684JUMBO BAG RED RETROSPOT 4671PACK OF 72 RETROSPOT CAKE CASES 3561MINI PAINT SET VINTAGE 3285ASSORTED COLOUR BIRD ORNAMENT 2783BROCADE RING PURSE 2525POPCORN HOLDER 2508Name: Quantity, dtype: int64\n```\n\n### Insight 3: Top 10 Revenue Drivers (The True Money-Makers)\nThis is what NovaShop really wants to know. These products drive profitability. They might not sell as many units as the items in the previous list, but their higher price or larger bulk orders make them far more valuable.\nWe follow the same process, but this time we aggregate on the `Revenue` column we engineered in Part 1.\npython\n\n```\n# Top 10 Revenue Drivers (The True Money-Makers)\ndf.groupby(‘Description’)[‘Revenue’].sum().sort_values(ascending=False).head(10)\n```\n\n**Output:**\nmarkup\n\n```\nDescriptionMEDIUM CERAMIC TOP STORAGE JAR 77785.74DOTCOM POSTAGE 18456.13REGENCY CAKESTAND 3 TIER 15093.38AMAZON FEE 13541.33PARTY BUNTING 9491.60GIN + TONIC DIET METAL SIGN 9480.51JUMBO BAG RED RETROSPOT 8996.65Manual 8966.96CHILLI LIGHTS 8395.20POSTAGE 7194.51Name: Revenue, dtype: float64\n```\n\n**Discussion: What These Lists Tell NovaShop**\nThis is where the power of EDA truly shines, because these two lists tell a very different story:\n  * **The All-Star Product:** The **“MEDIUM CERAMIC TOP STORAGE JAR”** is clearly the winner, dominating both lists by a massive margin. It’s high volume and high value. This item should be NovaShop’s primary focus for inventory, marketing, and expansion.\n  * **A surprising revenue source:** While exploring the data, I noticed items like _“DOTCOM POSTAGE,” “AMAZON FEE,” “Manual,”_ and _“POSTAGE.”_ These aren’t actual products — they’re service fees and manual adjustments. Yet together, they bring in almost **£48,000** in revenue. NovaShop should double-check whether these fees are being counted in their gross profit. It’s an easy thing to miss, but it could be a quiet and meaningful source of income hiding in plain sight.\n  * **The High-Value Physical Goods:** Items like the **“REGENCY CAKESTAND 3 TIER”** and **“CHILLI LIGHTS”** make the revenue list but not the quantity list. This tells us they are high-price items that contribute significantly to the total cash flow, and they are worth targeted marketing efforts.\n\n\nWe now have a complete picture of what NovaShop sells best. Time to figure out when!\n## Preparing for Time-Series Analysis: Feature Engineering\nWe know what sold well. Now, let’s figure out when sales peak. Our goal is to break down the `InvoiceDate` column into components that allow us to group and analyze sales by year, month, day, and even hour.\nThis is a classic feature engineering move. Since we already made sure `InvoiceDate` is a proper Pandas `datetime` object (thanks to `parse_dates` in our loading step!), we can use the extremely useful `.dt` accessor.\n### Extracting Time Components\nTo understand seasonality (monthly) and operational efficiency (hourly), we’ll create four new columns.\n  * Year — For long-term comparisons\n  * Month — For identifying seasonal peaks (e.g., Q4 rush).\n  * DayName — To find the busiest day of the week.\n  * Hour — To determine peak operational hours.\n\n\nLet’s generate these columns:\npython\n\n```\nprint(“\\n — — Time Series Feature Extraction — -”)\n\n# Extract Year, Month, DayName, and Hour from the InvoiceDate column\ndf[‘Year’] = df[‘InvoiceDate’].dt.year\ndf[‘Month’] = df[‘InvoiceDate’].dt.month\ndf[‘DayName’] = df[‘InvoiceDate’].dt.day_name()\ndf[‘Hour’] = df[‘InvoiceDate’].dt.hour\nprint(“New time features added: Year, Month, DayName, Hour.”)\nprint(df[[‘InvoiceDate’, ‘Year’, ‘Month’, ‘DayName’, ‘Hour’]].head())\n```\n\n**Output:**\nmarkup\n\n```\n--- Time Series Feature Extraction ---New time features added: Year, Month, DayName, Hour.InvoiceDate Year Month DayName Hour0 2011-06-01 12:05:00 2011 6 Wednesday 121 2011-05-27 17:14:00 2011 5 Friday 172 2011-04-21 17:05:00 2011 4 Thursday 173 2011-11-16 10:39:00 2011 11 Wednesday 104 2011-03-10 08:40:00 2011 3 Thursday 8\n```\n\nPerfect! We can move right into mapping the sales patterns in the next section.\n## Mapping Sales Patterns (Time-Series Insights)\nWith our time features ready, we can now ask and answer our questions about when sales occur. We’ll start big (monthly trends) and drill down small (hourly trends).\n### Insight 4: Sales Trends by Month (Seasonality)\nMonthly aggregation is crucial for spotting **seasonality** — the predictable pattern of sales repeating every year. This data is used for financial forecasting and planning inventory levels for the peak season.\nWe’ll group by the `Month` column and calculate the total revenue for each.\npython\n\n```\nprint(“\\n — — Insight 4: Sales Trends by Month (Seasonality) — -”)\n# Group by month and sum the revenue\nmonthly_revenue = df.groupby(‘Month’)[‘Revenue’].sum().sort_values(ascending=False)\n# Print the results, which should show the seasonal peaks\nprint(monthly_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Insight 4: Sales Trends by Month (Seasonality) ---Month11 143576.3601 142013.42012 133178.98010 119533.5409 103929.4203 72968.2708 71302.8905 70424.5106 68722.5207 68713.8314 51882.0102 50178.730Name: Revenue, dtype: float64\n```\n\n**Interpretation: The Massive Q4 and January Surprise**\nThe data clearly shows that NovaShop’s sales are heavily skewed toward the end of the year, confirming the typical retail **Q4 (October, November, December) rush**.\n  * **Peak Season:** November is the peak month by a slight margin, followed closely by December. This is the time to stock up on the **Medium Ceramic Top Storage Jars** and run targeted ads.\n  * **The January Surge:** The second-highest month is January! This is an excellent insight for NovaShop. It suggests customers may be using Christmas gift money or taking advantage of post-holiday sales. This period should not be treated as a slump but as a secondary high-volume sales opportunity.\n  * **Low Season:** February and April are the lowest performers, which helps NovaShop plan for lower staffing and marketing budgets during those periods.\n\n\n### Insight 5: Sales Trends by Day of Week and Hour (Operational Planning)\nWhile seasonality is about finance and inventory, daily and hourly trends are about **operations**. NovaShop can use this to schedule warehouse staff, optimize ad spend, and time email campaigns.  \nWe’ll run two separate aggregations here: one for the day of the week and one for the hour of the day.\n**Revenue by Day of Week**\npython\n\n```\n# 1. Sales by Day of Week\ndaily_revenue = df.groupby(‘DayName’)[‘Revenue’].sum()\nprint(“\\n — — Revenue by Day of Week — -”)\nprint(daily_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Revenue by Day of Week ---DayNameFriday 163861.320Monday 171026.230Sunday 83125.890Thursday 219342.980Tuesday 282796.741Wednesday 176271.320Name: Revenue, dtype: float64\n```\n\n**Interpretation: Tuesday is the Power Day**\nThe data reveals a clear and actionable pattern for the work week:\n  * **Peak Sales** : **Tuesday** absolutely dominates the week, generating almost 30% more revenue than the next busiest day, Thursday. This is a prime day for launching new products or running high-impact flash sales.\n  * **Slump Days:** Unsurprisingly, sales drop dramatically on **Sunday.** NovaShop might consider this a low-priority day for email marketing or customer service staffing.\n  * **Missing Day:** The dataset contains no transactions for **Saturday** in our sample (or perhaps the full set, which is common in B2B datasets).\n\n\n**Revenue by Hour of Day**\npython\n\n```\n# 2. Sales by Hour of Day\nhourly_revenue = df.groupby(‘Hour’)[‘Revenue’].sum().sort_index()\nprint(“\\n — — Revenue by Hour of Day — -”)\nprint(hourly_revenue)\n```\n\n**Output:**\nmarkup\n\n```\n--- Revenue by Hour of Day ---Hour7 2830.9108 26597.8009 76768.75010 209809.97111 115233.60012 142474.46013 130348.29014 119241.52015 145178.98016 70620.55017 36148.03018 14022.79019 4397.13020 2751.700\n```\n\n**Interpretation: The Late Morning Boom**\nThe hourly data points directly to operational needs:\n  * **Massive Peak:** Sales explode at 10 AM (Hour 10), generating almost double the revenue of the next few hours. This is the optimal time for NovaShop to launch email marketing and social media campaigns.\n  * **Sustained Activity:** Sales remain high from 11 AM through 3 PM, suggesting a strong midday and post-lunch shopping window.\n  * **End of Day:** Activity falls off sharply after 4 PM, with very little revenue generated in the evening (7 PM onward). This indicates NovaShop’s customer base is primarily desk-based, shopping during traditional working hours.\n\n\n## Conclusion: Setting the Stage for Customer Segmentation\nWe have officially finished the heavy-duty EDA! We know what sells best (the Ceramic Jar and fees), where sales are strongest (the UK), and when the sales peaks occur (Q4, Tuesdays, and 10 AM).\nHowever, we are still missing the most valuable piece of the puzzle: **The Who**. The best companies don’t just know what sells; they know who their most valuable customers are and how to keep them.\nIn [Part 3](https://towardsdatascience.com/eda-in-public-part-3-rfm-analysis-for-customer-segmentation-in-pandas/), we will dive into the most advanced analysis technique in this series: RFM (Recency, Frequency, Monetary) Customer Segmentation. This will allow us to classify customers into tiers like “Champions,” “Loyal Customers,” and “At-Risk,” giving NovaShop a true roadmap for personalised marketing.\nI hope you found this article helpful. Feel free to say hi to me on any of these platforms. I truly appreciate your feedback.\n[Medium](https://medium.com/@ibbysalam)\n[LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/)\n[Twitter](https://x.com/IbbySalam)\n[YouTube](https://www.youtube.com/@ibbysalam)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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