![Revisit consent button](../images/body-df29beacd7496220cc94.svg) We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg) We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more NecessaryAlways Active Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data. * Cookie BCTempID * Duration 10 minutes * Description No description available. * Cookie __cf_bm * Duration 1 hour * Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. * Cookie AWSALBCORS * Duration 7 days * Description Amazon Web Services set this cookie for load balancing. * Cookie _cfuvid * Duration session * Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services * Cookie li_gc * Duration 6 months * Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes. * Cookie __hssrc * Duration session * Description This cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session. * Cookie __hssc * Duration 1 hour * Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie. * Cookie wpEmojiSettingsSupports * Duration session * Description WordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly. * Cookie BCSessionID * Duration 1 year 1 month 4 days * Description Blueconic sets this cookie as a unique identifier for the BlueConic profile. * Cookie _octo * Duration 1 year * Description No description available. * Cookie logged_in * Duration 1 year * Description No description available. * Cookie __Secure-YEC * Duration past * Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video * Cookie __eoi * Duration 6 months * Description Description is currently not available. * Cookie AWSALBTGCORS * Duration 7 days * Description No description available. * Cookie login-status-p * Duration past * Description Description is currently not available. * Cookie AWSALBTG * Duration 7 days * Description No description available. * Cookie csrf_token * Duration session * Description No description available. * Cookie token_v2 * Duration 1 day * Description Description is currently not available. * Cookie D * Duration 1 year * Description Description is currently not available. * Cookie PHPSESSID * Duration session * Description This cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed. * Cookie VISITOR_PRIVACY_METADATA * Duration 6 months * Description YouTube sets this cookie to store the user's cookie consent state for the current domain. * Cookie cookietest * Duration session * Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience. * Cookie __Host-airtable-session * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie __Host-airtable-session.sig * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie m * Duration 1 year 1 month 4 days * Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly. * Cookie BIGipServer* * Duration session * Description Marketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. * Cookie __cfruid * Duration session * Description Cloudflare sets this cookie to identify trusted web traffic. * Cookie _GRECAPTCHA * Duration 6 months * Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks. * Cookie __Secure-YNID * Duration 6 months * Description Google cookie used to protect user security and prevent fraud, especially during the login process. * Cookie cookieyes-consent * Duration 1 year * Description CookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors. Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features. * Cookie lidc * Duration 1 day * Description LinkedIn sets the lidc cookie to facilitate data center selection. * Cookie brw * Duration 1 year * Description No description available. * Cookie brwConsent * Duration 5 minutes * Description Description is currently not available. * Cookie WMF-Uniq * Duration 1 year * Description Description is currently not available. * Cookie loom_anon_comment * Duration 1 year * Description No description available. * Cookie loom_referral_video * Duration session * Description Description is currently not available. * Cookie VISITOR_INFO1_LIVE * Duration 6 months * Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. * Cookie yt-remote-connected-devices * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie ytidb::LAST_RESULT_ENTRY_KEY * Duration Never Expires * Description The cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future. * Cookie yt-remote-device-id * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie yt-remote-session-name * Duration session * Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video. * Cookie yt-remote-fast-check-period * Duration session * Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos. * Cookie yt-remote-session-app * Duration session * Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player. * Cookie yt-remote-cast-available * Duration session * Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player. * Cookie yt-remote-cast-installed * Duration session * Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video. * Cookie cp_session * Duration 3 months * Description Codepen sets this cookie for Help systems found in the website. * Cookie loid * Duration 1 year 1 month 4 days * Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform. Analytics Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc. * Cookie __hstc * Duration 6 months * Description Hubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session). * Cookie hubspotutk * Duration 6 months * Description HubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts. * Cookie _ga * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors. * Cookie _ga_* * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to store and count page views. * Cookie __Host-psifi.analyticsTrace * Duration 6 hours * Description Description is currently not available. * Cookie __Host-psifi.analyticsTraceV2 * Duration 6 hours * Description Description is currently not available. * Cookie _gh_sess * Duration session * Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form. * Cookie YSC * Duration session * Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. * Cookie ajs_anonymous_id * Duration 1 year * Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before. * Cookie vuid * Duration 1 year 1 month 4 days * Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. Performance Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors. * Cookie AWSALB * Duration 7 days * Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target. * Cookie acq * Duration past * Description Description is currently not available. * Cookie acq.sig * Duration past * Description Description is currently not available. * Cookie ptc * Duration 2 years * Description No description available. Advertisement Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns. * Cookie muc_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website. * Cookie guest_id_marketing * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie guest_id_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie personalization_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting. * Cookie guest_id * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences. * Cookie bcookie * Duration 1 year * Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs. * Cookie __Secure-ROLLOUT_TOKEN * Duration 6 months * Description YouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment. * Cookie yt.innertube::nextId * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie yt.innertube::requests * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie session_tracker * Duration session * Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website. * Cookie edgebucket * Duration session * Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content. * Cookie did * Duration 1 year * Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year. Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation [](https://towardsdatascience.com/) Publish AI, ML & data-science insights to a global community of data professionals. Sign in[Submit an Article](https://write.insightmediagroup.io) [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/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # EDA in Public (Part 2): Product Deep Dive & Time-Series Analysis in Pandas Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales data. [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) December 20, 20259 min read ![Exploratory Data Analysis with Pandas](../images/001.jpg) Image by Author. Created with Gemini Hi 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. [![](../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/) In Part 1, we took a messy, half-million-row sales file and got it into shape. Specifically, we: * Sampled the data for faster processing. * Fixed missing values and standardised text entries (like ‘EIRE’ to ‘Ireland’). * Filtered out all the noise — returns, cancellations, and transactions with zero price. * **Engineered our most critical feature:** the `Revenue` column. * Achieved our first business insight: the **Top 10 Revenue-Generating Countries**. We 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. Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap. Now 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**? This 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: * **Product Deep Dive:** Identifying the products that move the most units versus those that bring in the most cash. * **Sales Timing (Time-Series):** Uncovering the seasonality of sales (monthly peaks) and the operational trends (busiest hour of the day). Without further ado, let’s get to it. ## Loading the clean data To improve performance, let’s export our cleaned dataset and import it again for analysis python ``` df.to_csv('online_retail_clean.csv', index=False) ``` Now, I can create a new project and start afresh. When 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. python ``` import pandas as pd df = pd.read_csv('online_retail_clean.csv', parse_dates=['InvoiceDate']) print(f"Successfully loaded clean data. Total rows: {len(df)}") ``` **Output:** markup ``` Successfully loaded clean data. Total rows: 52933 ``` ## Product Deep Dive: Who Are the True Money Makers? With 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). ### Insight 2: Top 10 Bestsellers (by Quantity) The 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. We’ll group by the product `Description`, sum the `Quantity`, and sort it descendingly. python ``` # Top 10 Bestsellers (by Quantity) df.groupby(‘Description’)[‘Quantity’].sum().sort_values(ascending=False).head(10) ``` **Output:** markup ``` DescriptionMEDIUM 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 ``` ### Insight 3: Top 10 Revenue Drivers (The True Money-Makers) This 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. We follow the same process, but this time we aggregate on the `Revenue` column we engineered in Part 1. python ``` # Top 10 Revenue Drivers (The True Money-Makers) df.groupby(‘Description’)[‘Revenue’].sum().sort_values(ascending=False).head(10) ``` **Output:** markup ``` DescriptionMEDIUM 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 ``` **Discussion: What These Lists Tell NovaShop** This is where the power of EDA truly shines, because these two lists tell a very different story: * **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. * **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. * **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. We now have a complete picture of what NovaShop sells best. Time to figure out when! ## Preparing for Time-Series Analysis: Feature Engineering We 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. This 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. ### Extracting Time Components To understand seasonality (monthly) and operational efficiency (hourly), we’ll create four new columns. * Year — For long-term comparisons * Month — For identifying seasonal peaks (e.g., Q4 rush). * DayName — To find the busiest day of the week. * Hour — To determine peak operational hours. Let’s generate these columns: python ``` print(“\n — — Time Series Feature Extraction — -”) # Extract Year, Month, DayName, and Hour from the InvoiceDate column df[‘Year’] = df[‘InvoiceDate’].dt.year df[‘Month’] = df[‘InvoiceDate’].dt.month df[‘DayName’] = df[‘InvoiceDate’].dt.day_name() df[‘Hour’] = df[‘InvoiceDate’].dt.hour print(“New time features added: Year, Month, DayName, Hour.”) print(df[[‘InvoiceDate’, ‘Year’, ‘Month’, ‘DayName’, ‘Hour’]].head()) ``` **Output:** markup ``` --- 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 ``` Perfect! We can move right into mapping the sales patterns in the next section. ## Mapping Sales Patterns (Time-Series Insights) With 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). ### Insight 4: Sales Trends by Month (Seasonality) Monthly 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. We’ll group by the `Month` column and calculate the total revenue for each. python ``` print(“\n — — Insight 4: Sales Trends by Month (Seasonality) — -”) # Group by month and sum the revenue monthly_revenue = df.groupby(‘Month’)[‘Revenue’].sum().sort_values(ascending=False) # Print the results, which should show the seasonal peaks print(monthly_revenue) ``` **Output:** markup ``` --- 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 ``` **Interpretation: The Massive Q4 and January Surprise** The 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**. * **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. * **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. * **Low Season:** February and April are the lowest performers, which helps NovaShop plan for lower staffing and marketing budgets during those periods. ### Insight 5: Sales Trends by Day of Week and Hour (Operational Planning) While 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. We’ll run two separate aggregations here: one for the day of the week and one for the hour of the day. **Revenue by Day of Week** python ``` # 1. Sales by Day of Week daily_revenue = df.groupby(‘DayName’)[‘Revenue’].sum() print(“\n — — Revenue by Day of Week — -”) print(daily_revenue) ``` **Output:** markup ``` --- Revenue by Day of Week ---DayNameFriday 163861.320Monday 171026.230Sunday 83125.890Thursday 219342.980Tuesday 282796.741Wednesday 176271.320Name: Revenue, dtype: float64 ``` **Interpretation: Tuesday is the Power Day** The data reveals a clear and actionable pattern for the work week: * **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. * **Slump Days:** Unsurprisingly, sales drop dramatically on **Sunday.** NovaShop might consider this a low-priority day for email marketing or customer service staffing. * **Missing Day:** The dataset contains no transactions for **Saturday** in our sample (or perhaps the full set, which is common in B2B datasets). **Revenue by Hour of Day** python ``` # 2. Sales by Hour of Day hourly_revenue = df.groupby(‘Hour’)[‘Revenue’].sum().sort_index() print(“\n — — Revenue by Hour of Day — -”) print(hourly_revenue) ``` **Output:** markup ``` --- 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 ``` **Interpretation: The Late Morning Boom** The hourly data points directly to operational needs: * **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. * **Sustained Activity:** Sales remain high from 11 AM through 3 PM, suggesting a strong midday and post-lunch shopping window. * **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. ## Conclusion: Setting the Stage for Customer Segmentation We 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). However, 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. In [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. I hope you found this article helpful. Feel free to say hi to me on any of these platforms. I truly appreciate your feedback. [Medium](https://medium.com/@ibbysalam) [LinkedIn](https://www.linkedin.com/in/ibrahim-salami-059863228/) [Twitter](https://x.com/IbbySalam) [YouTube](https://www.youtube.com/@ibbysalam) ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-542334df68a32cd56c4a.jpeg) Written By ## [Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [See all from Ibrahim Salami](https://towardsdatascience.com/author/ibrahim.salami/) [Exploratory Data Analysis](https://towardsdatascience.com/tag/exploratory-data-analysis/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Pandas](https://towardsdatascience.com/tag/pandas/)[Programming](https://towardsdatascience.com/tag/programming/)[Python](https://towardsdatascience.com/tag/python/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Feda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas%2F&title=EDA%20in%20Public%20\(Part%202\)%3A%20Product%20Deep%20Dive%20%26%20Time-Series%20Analysis%20in%C2%A0Pandas) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Feda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas%2F&title=EDA%20in%20Public%20\(Part%202\)%3A%20Product%20Deep%20Dive%20%26%20Time-Series%20Analysis%20in%C2%A0Pandas) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Feda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas%2F&text=EDA%20in%20Public%20\(Part%202\)%3A%20Product%20Deep%20Dive%20%26%20Time-Series%20Analysis%20in%C2%A0Pandas) Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. [Write for TDS](https://towardsdatascience.com/submissions/) ## Related Articles * [![](../images/002.jpg)](https://towardsdatascience.com/machine-learning-that-pays-the-bills-choosing-models-in-business-contexts-e9003fd434a1/) ## [Machine learning that pays the bills: choosing models in business contexts](https://towardsdatascience.com/machine-learning-that-pays-the-bills-choosing-models-in-business-contexts-e9003fd434a1/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Sophisticated models look good on research papers but might not pay off in business use cases [Fabio Baccarin](https://towardsdatascience.com/author/fabiogbaccarin/) December 28, 202014 min read * [![](../images/003.jpeg)](https://towardsdatascience.com/what-stops-you-from-getting-your-first-data-science-job-ccf93e4187d4/) ## [What Stops You From Getting Your First Data Science Job?](https://towardsdatascience.com/what-stops-you-from-getting-your-first-data-science-job-ccf93e4187d4/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Walkthrough of the data science interview funnel and steps to get your first offer [Alex Vamvakaris](https://towardsdatascience.com/author/alex.vamvakaris.ds/) March 23, 202312 min read * [![](../images/004.jpeg)](https://towardsdatascience.com/the-lessons-i-learned-from-failing-the-job-interviews-at-shopee-singapore-dd24b9f2bf3d/) ## [Data Science Interview at Shopee Singapore](https://towardsdatascience.com/the-lessons-i-learned-from-failing-the-job-interviews-at-shopee-singapore-dd24b9f2bf3d/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) Here are some tips for preparing for your next data science interviews. [Lai Woen Yon](https://towardsdatascience.com/author/laiwoenyon/) August 15, 202118 min read * [![](../images/005.jpeg)](https://towardsdatascience.com/time-series-modeling-with-orbit-101-f6a262c1eaf6/) ## [Time Series Modeling with Orbit 101](https://towardsdatascience.com/time-series-modeling-with-orbit-101-f6a262c1eaf6/) [Programming](https://towardsdatascience.com/category/programming/) An introductory tutorial for Orbit (Object-Oriented Bayesian Time Series Forecasting) [Ariel Jiang](https://towardsdatascience.com/author/arieljiangaj/) April 28, 202212 min read * [![](../images/006.jpg)](https://towardsdatascience.com/3-key-career-decisions-for-junior-data-scientists-728b20991120/) ## [3 Key Career Decisions for Junior Data Scientists](https://towardsdatascience.com/3-key-career-decisions-for-junior-data-scientists-728b20991120/) [Data Science](https://towardsdatascience.com/category/data-science/) A roadmap to avoiding the "default path" and finding fulfilment in work [Matt Chapman](https://towardsdatascience.com/author/mattchapmanmsc/) November 2, 20237 min read * [![](../images/007.jpeg)](https://towardsdatascience.com/the-devils-roulette-a-failure-of-expected-value-e0a4bae97302/) ## [The Devil's Roulette: a Failure of Expected Value](https://towardsdatascience.com/the-devils-roulette-a-failure-of-expected-value-e0a4bae97302/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Notes on Risk, Reward and Ruin in Stochastic Games [Douglas Hamilton](https://towardsdatascience.com/author/dougmh/) May 24, 20218 min read * [![](../images/008.jpg)](https://towardsdatascience.com/exploratory-data-analysis-21c06b8f669/) ## [Exploratory Data Analysis](https://towardsdatascience.com/exploratory-data-analysis-21c06b8f669/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) There are a lot of engineers who have never been involved in the field of... [Kirill Bobrov](https://towardsdatascience.com/author/luminousmen/) May 12, 20219 min read * [![](../images/009.jpg)](https://towardsdatascience.com/log-breadcrumbs-only-show-logs-leading-up-to-an-error-82b9f4c15520/) ## [Log Breadcrumbs: only show Logs leading up to an Error](https://towardsdatascience.com/log-breadcrumbs-only-show-logs-leading-up-to-an-error-82b9f4c15520/) [Programming](https://towardsdatascience.com/category/programming/) Hot to log Breadcrumbs using Python's built-int logging package [Mike Huls](https://towardsdatascience.com/author/mikehuls/) November 5, 20245 min read * [![](../images/010.jpeg)](https://towardsdatascience.com/all-you-need-to-know-about-non-inferiority-test-c58a74ec4cc5/) ## [All you need to know about Non-Inferiority Hypothesis Test](https://towardsdatascience.com/all-you-need-to-know-about-non-inferiority-test-c58a74ec4cc5/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) A non-inferiority test proves that a new treatment is not worse than the standard by more than a clinically acceptable margin [Prateek Jain](https://towardsdatascience.com/author/prateekkrjain/) October 18, 20245 min read * [YouTube](https://www.youtube.com/c/TowardsDataScience) * [X](https://x.com/TDataScience) * [LinkedIn](https://www.linkedin.com/company/towards-data-science/) * [Threads](https://www.threads.net/@towardsdatascience) * [Bluesky](https://bsky.app/profile/towardsdatascience.com) [](https://towardsdatascience.com/) Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/) © Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network) #### Recommended Articles Close * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/4970/image?etag=1780565225463&width=500&height=500) Productivity Tips, Data Career Insights, and Other Recent Must-Reads ](https://towardsdatascience.com/productivity-tips-data-career-insights-and-other-recent-must-reads-5c47eb7a2fe0/ "Productivity Tips, Data Career Insights, and Other Recent Must-Reads") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ "Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ "The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs") * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ "Does Your Company Have a Data Strategy?")