https://github.com/ndomah1/learning-power-bi
This repo offers an in-depth tutorial on Power BI, guiding users through installation, data import and transformation with Power Query, visualization creation, DAX fundamentals, drill-down techniques, conditional formatting, and more to build effective analytical dashboards.
https://github.com/ndomah1/learning-power-bi
bins conditional-formatting data-cleaning data-transformation dax drill-down lists power-bi
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This repo offers an in-depth tutorial on Power BI, guiding users through installation, data import and transformation with Power Query, visualization creation, DAX fundamentals, drill-down techniques, conditional formatting, and more to build effective analytical dashboards.
- Host: GitHub
- URL: https://github.com/ndomah1/learning-power-bi
- Owner: ndomah1
- Created: 2025-03-20T23:00:12.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-03-20T23:12:05.000Z (about 1 month ago)
- Last Synced: 2025-03-21T00:18:45.008Z (about 1 month ago)
- Topics: bins, conditional-formatting, data-cleaning, data-transformation, dax, drill-down, lists, power-bi
- Homepage:
- Size: 6.26 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Learning Power BI
## Table of Contents
- [Installing Power BI and Building the First Visualization](#installing-power-bi-and-building-the-first-visualization)
- [Downloading and Installing Power BI Desktop](#downloading-and-installing-power-bi-desktop)
- [Importing Data into Power BI](#importing-data-into-power-bi)
- [Transforming Data Using Power Query](#transforming-data-using-power-query)
- [Building the First Visualization](#building-the-first-visualization)
- [Objective: Identify the Best Store for Purchasing Items](#objective-identify-the-best-store-for-purchasing-items)
- [Customizing the Dashboard](#customizing-the-dashboard)
- [Using Power Query in Power BI](#using-power-query-in-power-bi)
- [Cleaning and Transforming Data](#cleaning-and-transforming-data)
- [Creating and Managing Relationships in Power BI](#creating-and-managing-relationships-in-power-bi)
- [Introduction to DAX (Data Analysis Expressions)](#introduction-to-dax-data-analysis-expressions)
- [DAX Essentials: Measures vs. Calculated Columns](#dax-essentials-measures-vs-calculated-columns)
- [Using Drill Down in Power BI](#using-drill-down-in-power-bi)
- [Conditional Formatting in Power BI](#conditional-formatting-in-power-bi)
- [Bins and Lists in Power BI](#bins-and-lists-in-power-bi)
- [Popular Visualizations in Power BI](#popular-visualizations-in-power-bi)
## **Installing Power BI and Building the First Visualization**
### **Downloading and Installing Power BI Desktop**
- Power BI Desktop is free to download from the [Microsoft Store](https://www.microsoft.com/en-us/download/details.aspx?id=58494).
- Once installed, open Power BI by searching for it in the Start menu.
### **Importing Data into Power BI**
1. Click on **"Get Data"** in the Power BI interface.
2. A window opens showing multiple data source options:
- Databases (SQL, PostgreSQL)
- Cloud services (Google Analytics, Blob Storage)
- Local files (Excel, CSV, JSON)
3. For this tutorial, an **Excel Workbook** is used:
- Click **Excel Workbook** → **Connect**.
- Select the file (e.g., "Apocalypse Food Prep.xlsx").
- Power BI will display available sheets. Select the desired sheet.
- Click **Load** to import the data.
### **Transforming Data Using Power Query**
- After loading the data, you can either:
- **Load it directly** into Power BI.
- **Transform it first** using Power Query.
- Power Query allows for:
- Renaming columns.
- Removing unnecessary data.
- Filtering rows (e.g., removing milk from an apocalypse food prep list).
#### **Example Transformation Steps:**
1. Renaming the "Date" column to **"Date_Purchased"**.
2. Filtering out unwanted data (e.g., removing "Milk" as it's not useful for long-term storage).
### **Understanding the Power BI Interface**
Power BI consists of three main tabs:
1. **Report Tab** - Where visualizations and dashboards are created.
2. **Data Tab** - Allows you to inspect and modify imported data.
3. **Model Tab** - Helps create relationships between multiple tables (covered in later tutorials).
## **Building the First Visualization**
### **Objective: Identify the Best Store for Purchasing Items**
- The dataset includes **store names, product types, and prices**.
- The goal is to find:
1. Where the least amount of money is spent for the same product.
2. Whether all products should be purchased at one store or different stores.
### **Steps to Create Visualizations:**
1. Select **"Store"** and **"Price"** columns.
2. Use a **Stacked Column Chart** to visualize total spending per store.
- Costco appears to be the cheapest overall.
- Target is the cheapest for rice.
3. Create a **Clustered Column Chart** to compare price differences per product across stores.
- Most products are cheapest at Costco.
- Rice is significantly cheaper at Target.
- The Stacked Column Chart showing spending at each store.
- The Clustered Column Chart comparing product prices across stores.
### **Customizing the Dashboard**
- Resize visualizations for a clearer layout.
- Change titles:
- "Best Store for Each Product"
- "Total Spending by Store"
- Add **Data Labels** to display exact numbers.
- Adjust display settings to show full numeric values instead of rounded figures.
### **Using Power Query in Power BI**
#### **What is Power Query?**
Power Query is a **data transformation tool** within Power BI that allows you to:
- Clean and reshape data before loading it into Power BI.
- Remove, add, or modify columns.
- Change data types.
- Apply transformations such as filtering, splitting, and unpivoting columns.
**Why use Power Query?** It makes raw data more structured and usable for creating accurate visualizations.
#### **Importing Data into Power Query**
1. Click **"Get Data"** → Select **Excel Workbook** → **Connect**.
2. Choose the **Apocalypse Food Prep.xlsx** file.
3. Select both:
- **Pivot Table**
- **Purchase Overview**
4. Click **Transform Data** instead of Load.
#### **Understanding the Power Query Interface**
- **Left Sidebar**: Shows queries (imported tables).
- **Top Ribbon**:
- **Home Tab**: Remove columns, keep/remove rows, split columns.
- **Transform Tab**: Change data types, pivot/unpivot columns, use first row as headers.
- **Add Column Tab**: Add calculated, index, or conditional columns.
- **Right Sidebar (Query Settings)**:
- Applied Steps: Tracks every modification for easy rollback.
## **Cleaning and Transforming Data**
### **Step 1: Remove Unnecessary Rows**
- The first two rows in the **Purchase Overview** table contain null values.
- Go to **Home Tab** → **Remove Rows** → **Remove Top Rows** → Enter **2** → **O**
### **Step 2: Use First Row as Headers**
- The actual column names are in the first row.
- Go to **Transform Tab** → Click **Use First Row as Headers**.
### **Step 3: Changing Data Types**
- Power Query automatically assigns data types.
- Ensure numbers representing money use a **Fixed Decimal** format.
- Click on the **data type icon** (e.g., "1.2") → Select **Fixed Decimal Number**.
- Apply this to all price-related columns.
### **Step 4: Removing Totals and Subtotals**
- The dataset contains **Costco Total, Target Total, Walmart Total, and Grand Total**, which are unnecessary.
- Filter them out:
- Click **Dropdown on Product Column** → **Text Filters** → **Does Not Contain "Total"** → **OK**.
- Remove the **Grand Total column**:
- Click on **Grand Total Column** → **Remove Columns**.
### **Step 5: Unpivoting Data for Better Analysis**
- Dates (January 1st, February 1st, etc.) are column headers but should be in rows.
- Select **all date columns**.
- Go to **Transform Tab** → Click **Unpivot Columns**.
- Rename the new columns:
- **Attribute → Date**
- **Value → Product Cost**
### **Finalizing and Loading the Data**
- Click **Close & Apply** to load the cleaned data into Power BI.
- Verify the data in the **Data View**.
- If more adjustments are needed, click **Transform Data** to return to Power Query.
## **Creating and Managing Relationships in Power BI**
### **What are Relationships in Power BI?**
- When working with multiple tables from **different sources or the same source**, relationships allow data to be **connected** for analysis.
- Power BI automatically **detects relationships**, but they can also be **manually created** or **edited**.
#### **Why relationships?**
- They help connect datasets **logically** for meaningful analysis.
- Avoids unnecessary duplication of data.
- Enables cross-table calculations in reports and dashboards.
**Key Tables Used in This Example:**
1. **Apocalypse Store** (Products being sold)
- Product ID
- Product Name
- Price
- Production Cost
2. **Apocalypse Sales** (Sales transactions)
- Customer ID
- Product ID
- Order ID
- Units Sold
- Date Purchased
3. **Customer Information** (Customer details)
- Customer ID
- Address, City, State, Zip Code
### **Importing Data and Navigating to the Model View**
1. Click **"Get Data"** → Select **Excel Workbook** → **Connect**.
2. Choose all three tables (**Apocalypse Store, Apocalypse Sales, Customer Information**).
3. Click **Load** (No need to transform the data in this case).
4. Navigate to **Model View (third tab on the left sidebar)** to see automatically created relationships.
### **Understanding Relationships in Power BI**
#### **Existing Automatically Created Relationships**
- Power BI detects relationships **based on matching column names** and **similar data types**.
- Relationships appear as **lines** connecting tables.
- **Double-clicking a relationship line** brings up the **Edit Relationship** window.
#### **Key Relationship Properties**
1. **Cardinality**:
- **One-to-Many (1:*M*)** – One table has unique values, and another has multiple corresponding values (e.g., Customers → Sales).
- **Many-to-One (*M*:1)** – Same as above, but flipped direction.
- *Many-to-Many (M:M)* – Both tables have non-unique values.
- **One-to-One (1:1)** – Both tables have unique values (rare case).
2. **Cross Filter Direction**:
- **Single** – One table filters another, but not vice versa.
- **Both** – Tables filter each other, treating them as a single table.
3. **Active vs. Inactive Relationships**:
- **Active** – The default relationship used for calculations.
- **Inactive** – Not used unless explicitly referenced in formulas.
#### **Editing an Existing Relationship**
1. **Open Model View** → **Double-click a relationship line**.
2. **Change the Join Column** (e.g., use **Customer ID** instead of **Customer Name** for better consistency).
3. **Set Cardinality** (e.g., Many-to-One for Sales and Customer Information).
4. **Change Cross Filter Direction** to **Both** if necessary.
5. **Click OK** to save changes.
#### **Manually Creating Relationships**
If Power BI **does not detect** a relationship, it can be manually created:
1. Open **Model View**.
2. Drag **Customer ID** from **Customer Information** → Drop onto **Customer ID** in **Apocalypse Sales**.
3. Drag **Product ID** from **Apocalypse Store** → Drop onto **Product ID** in **Apocalypse Sales**.
4. Double-click each new relationship to **set properties**.
- Change **Cross Filter Direction** to **Both** if needed.
- Set **Cardinality** correctly (**One-to-Many** for Customers → Sales).
#### **Verifying Relationships with a Visualization**
1. **Create a Table Visualization**.
2. Add **State** (from Customer Information).
3. Add **Product ID Count** (from Apocalypse Store).
4. Initially, the count might be incorrect (showing all products in all states).
5. **Switch Cross Filter Direction to Both** → The visualization updates to show the correct counts per state.
## **Introduction to DAX (Data Analysis Expressions)**
### **What is DAX?**
DAX (Data Analysis Expressions) is a **formula language** used in Power BI to create **calculated columns, measures, and tables**. It is similar to Excel formulas but optimized for Power BI's data model.
#### **Why use DAX?**
- Performs **advanced calculations** that cannot be done with basic visualizations.
- Enables **dynamic aggregations** (e.g., total sales, average revenue).
- Helps in creating **custom metrics** for reports and dashboards.
### **DAX Essentials: Measures vs. Calculated Columns**
| Feature | Measures | Calculated Columns |
| --- | --- | --- |
| **Stored in** | Data model (optimized) | Dataset (increases size) |
| **Calculation Timing** | Calculated **on the fly** | Calculated **when data is refreshed** |
| **Usage** | Summarized results | Row-level calculations |
| **Best For** | Aggregations (SUM, COUNT, AVERAGE) | Derived fields (profit, categories) |
📌 **Example:**
- **Measure**: `Total Sales = SUM(Sales[Revenue])`
- **Calculated Column**: `Profit = Sales[Revenue] - Sales[Cost]`
### **Creating a DAX Measure**
#### **Example: Counting Total Sales**
1. Navigate to the **Report Tab**.
2. Right-click **Apocalypse Sales** → **New Measure**.
3. Enter the following formula:
```
Count of Sales = COUNT(Apocalypse Sales[Order ID])
```
4. Press **Enter**. The measure now appears in the Fields pane.
5. Drag it into a table visualization to display the count.
### **Using DAX Aggregations**
#### **Example: Summing Total Products Sold**
1. Create a new measure:
```
Sum of Products Sold = SUM(Apocalypse Sales[Units Sold])
```
2. Drag it into a table visualization to see total products sold.
**Key Takeaway**: `SUM()` is an **aggregator function**, summing all values in a column.
### **Iterator vs. Aggregator Functions**
#### **Understanding SUM vs. SUMX**
- `SUM()` adds all values in a column (aggregates).
- `SUMX()` calculates **row by row** before summing the results.
#### **Example: Calculating Profit**
1. Create a new measure:
2. This calculates **total** profit across all products.
**Problem**: If we want **profit per product**, `SUM()` won’t work correctly. We need `SUMX()`.
#### **Solution: Using SUMX for Row-Level Calculation**
1. Create a new column:
```
Profit_Column = SUMX(Apocalypse Sales, Apocalypse Store[Price] - Apocalypse Store[Production Cost])
```
2. This ensures profit is calculated for each **row** before summing.
📌 **Screenshot Suggestion**: Show the difference between `SUM()` and `SUMX()` in a table.
### **Working with Date Functions in DAX**
DAX provides several **date-based functions** to analyze trends.
#### **Example: Extracting the Day of the Week**
1. Create a new column:
```
Day of Week = WEEKDAY(Apocalypse Sales[Date Purchased], 2)
```
- `2` makes Monday = `1`, Sunday = `7`.
2. Use this to analyze **sales trends by day**.
### **Using IF Statements in DAX**
The `IF()` function helps classify data.
#### **Example: Categorizing Order Sizes**
1. Create a new column:
```
Order_Size = IF(Apocalypse Sales[Units Sold] > 25, "Big Order", "Small Order")
```
2. This classifies **large vs. small orders**.
## **Using Drill Down in Power BI**
### **What is Drill Down?**
Drill Down allows users to **explore data hierarchically**, from a **high-level summary to detailed insights** within a visualization. It helps in:
- **Adding depth** to visualizations without cluttering the initial view.
- **Providing interactive insights** during presentations.
- **Allowing users to navigate up and down different data levels.**
#### **Drill Down Actions:**
1. **Drill Down** - Click a category to see its detailed breakdown.
2. **Drill Up** - Move back to a higher-level summary.
3. **Next Level** - Move down the hierarchy in a linear manner.
4. **Expand All** - Show all levels at once instead of drilling down one at a time.
### **Creating a Drill Down Hierarchy**
#### **Example: Exploring Store and Product Purchases**
1. Create a **Clustered Column Chart** visualization.
2. Add **Store Name** to the **X-axis**.
3. Add **Total Purchase Cost** to the **Y-axis**.
4. Add **Product Name** beneath **Store Name** in the X-axis.
5. Power BI will automatically create a **hierarchy**.
**What happens?**
- The visualization starts at the **store level**.
- Users can **drill down into a store** to see **product-level spending**.
### **Enabling Drill Down**
Once a hierarchy is created, **Drill Down options** appear:
1. **Drill Down Mode (Click to Expand)**:
- Turn on the **drill mode**.
- Click on a **store** (e.g., Target) to see a breakdown by **products purchased** there.
2. **Next Level in Hierarchy**:
- Click **“Go to Next Level”**.
- The chart shifts to show **only the product-level breakdown**, ignoring the store grouping.
3. **Expand All Levels**:
- Click **“Expand All”**.
- The chart displays **both store and product details together**, instead of separating them.
### **Example Use Cases for Drill Down**
#### **1. Sales Analysis**
- Start with **Total Sales by Region**.
- Drill down to **Sales by State**.
- Further drill down to **Sales by City**.
#### **2. Customer Order Tracking**
- Show **Total Orders by Customer**.
- Drill down to **Order IDs for each Customer**.
- Further drill down to **Products in each Order**.
#### **3. Financial Reporting**
- Begin with **Total Company Expenses**.
- Drill down to **Department-Level Expenses**.
- Further drill down to **Expense Categories (e.g., Salaries, Rent, Software)**.
### **Real-World Example: Order Tracking**
1. Create a **Bar Chart** with:
- **Customer Name** on the X-axis.
- **Units Sold** on the Y-axis.
2. Add **Order ID** beneath **Customer Name** in the X-axis.
3. Enable **Drill Down Mode**.
4. Click on a **customer** to see **their specific order IDs**.
**Use Case**: This is useful when a **stakeholder** wants to analyze specific orders behind high-volume customers.
## **Conditional Formatting in Power BI**
### **What is Conditional Formatting?**
Conditional Formatting allows users to **visually highlight** important data within tables and matrices by applying:
- **Color scales** (e.g., green for low values, red for high values).
- **Icons** to indicate specific thresholds.
- **Data bars** to show relative magnitude within cells.
#### **Why use Conditional Formatting?**
- Helps in **identifying trends and outliers quickly**.
- Provides **context to raw numbers**.
- Enhances **data storytelling in reports**.
### **Applying Conditional Formatting**
1. Create a **Table or Matrix visualization**.
2. Add relevant fields (e.g., **Product Name, Price, Units Sold**).
3. Right-click on a numeric column (e.g., **Price**).
4. Select **Conditional Formatting** → Choose one of the following:
- **Background Color**
- **Font Color**
- **Icons**
- **Data Bars**
### **Types of Conditional Formatting**
#### **1. Background Color Formatting**
- Colors the cell background based on value.
- Useful for identifying **low and high values at a glance**.
**Example: Coloring Products by Price**
1. Select **Price** column.
2. Apply **Gradient Scale**:
- **Low values** = Green (cheaper items).
- **High values** = Red (expensive items).
3. Click **OK**.
#### **2. Data Bars for Visual Emphasis**
- **Adds horizontal bars** inside table cells to compare values visually.
- Only available for **aggregated fields** (e.g., total sales, revenue).
📌 **Example: Comparing Units Sold**
1. Select **Units Sold**.
2. Apply **Data Bars**:
- **Short bars** for low sales.
- **Long bars** for high sales.
3. Click **OK**.
#### **3. Rule-Based Formatting**
- Custom **if-then rules** to color-code cells.
- Example: Flagging **low-selling products**.
**Example: Marking Low Sales in Red**
1. Select **Units Sold**.
2. Apply **Rule-Based Formatting**:
- **If Units Sold < 200 → Color: Red** (Low Sales).
- **If Units Sold ≥ 200 and ≤ 500 → Color: Yellow** (Average Sales).
- **If Units Sold > 500 → Color: Green** (High Sales).
3. Click **OK**.
#### **4. Icon-Based Formatting**
- Adds **icons** (e.g., arrows, checkmarks, flags) to visually categorize data.
- Works similarly to **Excel icon sets**.
**Example: Performance Indicator for Revenue**
1. Select **Revenue**.
2. Apply **Icon Formatting**:
- **Red Down Arrow** = Low Revenue.
- **Yellow Dash** = Average Revenue.
- **Green Up Arrow** = High Revenue.
3. Click **OK**.
### **Best Practices for Conditional Formatting**
- **Use sparingly** – Too many colors/icons can clutter reports.
- **Stick to meaningful colors** (e.g., Red = warning, Green = positive).
- **Use a combination** – **Data bars + color formatting** work well together.
- **Keep it accessible** – Ensure colors/icons are readable by all users.
## **Bins and Lists in Power BI**
### **What are Bins and Lists?**
Bins and Lists allow users to **group data into categories** for easier analysis.
- **Lists** group **specific values** together (e.g., grouping customers into "Top Buyers" and "Low Buyers").
- **Bins** categorize **numerical data into ranges** (e.g., age groups: 18-29, 30-39, etc.).
#### **Why use Bins and Lists?**
- **Simplifies large datasets** by grouping values into meaningful segments.
- **Improves visualization clarity** by reducing unique data points.
- **Helps in trend analysis** by focusing on grouped data instead of individual values.
### **Creating a List in Power BI**
A **List** groups categorical data manually.
#### **Example: Grouping Customers by Performance**
1. Navigate to **Apocalypse Sales** → Select **Customer**.
2. Right-click on **Customer** → Click **New Group**.
3. Rename the group to **Customer List**.
4. Select **Top-performing customers** → Click **Group**.
5. Rename this group to **Best Prepping Stores**.
6. Select **Low-performing customers** → Click **Group**.
7. Rename this group to **Worst Prepping Stores**.
8. Click **OK**.
**What happens?**
- A new column is created that classifies customers as **Best or Worst Prepping Stores**.
- This can now be used in visualizations.
### **Using Lists on Numerical Fields**
Lists can also be created for **numeric values**, such as **Order ID**.
#### **Example: Categorizing Order IDs**
1. Navigate to **Order ID**.
2. Right-click → Click **New Group**.
3. Rename the group to **Order Categories**.
4. Select **First 50 Order IDs** → Click **Group** → Rename to **First Orders**.
5. Select **Last 50 Order IDs** → Click **Group** → Rename to **Latest Orders**.
6. Click **OK**.
**Use Case**: This allows tracking of **early vs. recent orders** in a dataset.
### **Creating Bins in Power BI**
A **Bin** divides **numerical data into predefined ranges**.
#### **Example: Categorizing Customers by Age**
1. Navigate to **Customer Information** → Select **Age**.
2. Right-click **Age** → Click **New Group**.
3. Under **Bin Type**, select **Bin**.
4. Set **Bin Size** to **10** (to group ages by decade).
5. Click **OK**.
**What happens?**
- A new column is created grouping customers into age ranges:
- 10-19
- 20-29
- 30-39
- 40-49, etc.
### **Using Bins on Date Fields**
Bins can also be applied to **date-based data**.
#### **Example: Grouping Sales by Month**
1. Navigate to **Apocalypse Sales** → Select **Date Purchased**.
2. Right-click **Date Purchased** → Click **New Group**.
3. Under **Bin Type**, select **Bin**.
4. Set **Bin Size** to **1 Month**.
5. Click **OK**.
**What happens?**
- A new column is created that groups sales into **monthly periods**.
- This simplifies trend analysis.
### **Visualizing Bins and Lists**
#### **1. Comparing Age Groups (Bins)**
1. Create a **Bar Chart**.
2. Add **Age Bins** to the X-axis.
3. Add **Number of Buyers** to the Y-axis.
4. Analyze which age group has the most buyers.
#### **2. Analyzing Customer Performance (Lists)**
1. Create a **Clustered Column Chart**.
2. Add **Customer List (Best/Worst Prepping Stores)** to the X-axis.
3. Add **Units Sold** to the Y-axis.
4. Compare sales between **Best and Worst Prepping Stores**.
## **Popular Visualizations in Power BI**
### **Choosing the Right Visualization**
Power BI offers **various visualization types**, each suited for different kinds of analysis. This section covers **the most commonly used** visualizations.
#### **Why visualizations matter?**
- Helps in **interpreting complex data** easily.
- Enhances **data storytelling** in reports and dashboards.
- Different visualizations **highlight different insights**.
### **1. Stacked Bar Chart**
- **Best for** comparing **categories and subcategories**.
- Displays a total sum while breaking it into parts.
**Example: Units Sold by Product**
1. Select **Stacked Bar Chart**.
2. Add **Product Name** to the Y-axis.
3. Add **Units Sold** to the X-axis.
4. Drag **Product Name** to the **Legend** (color-coded by product).
### **2. 100% Stacked Column Chart**
- **Best for** understanding category proportions.
- Each column represents 100%, with sections showing **relative contributions**.
**Example: Customer Purchase Breakdown**
1. Select **100% Stacked Column Chart**.
2. Add **Customer** to the X-axis.
3. Add **Units Sold** to the Y-axis.
4. Add **Product Name** to the **Legend** (breaks sales into product categories).
### **3. Line Chart**
- **Best for** tracking trends over time.
- Ideal for **date-based analysis**.
**Example: Sales Over Time**
1. Select **Line Chart**.
2. Add **Date Purchased** to the X-axis.
3. Add **Units Sold** to the Y-axis.
4. Drag **Product Name** to the **Legend** (separate lines per product).
### **4. Line & Clustered Column Chart**
- **Best for** showing **two different types of measures** together.
- Combines **bar and line visualizations**.
**Example: Price vs. Production Cost**
1. Select **Line & Clustered Column Chart**.
2. Add **Product Name** to the X-axis.
3. Add **Price** to **Column Y-axis**.
4. Add **Production Cost** to **Line Y-axis**.
### **5. Scatter Plot**
- **Best for** identifying **outliers and relationships between variables**.
- Helps in **correlation analysis**.
**Example: Price vs. Production Cost**
1. Select **Scatter Chart**.
2. Add **Price** to the X-axis.
3. Add **Production Cost** to the Y-axis.
4. Add **Product Name** to **Values** (each product becomes a point).
5. Add **Product Name** to **Legend** (color-coded points).
### **6. Donut & Pie Charts (Use with Caution)**
- **Best for** **showing parts of a whole**, but **not always the most readable**.
- **Difficult to compare segment sizes** accurately.
**Example: Purchases by State**
1. Select **Donut Chart**.
2. Add **State** to **Category**.
3. Add **Total Purchased** to **Values**.
**Why analysts avoid pie charts?**
- **Hard to distinguish slight differences in values**.
- **Bar charts and column charts are often better alternatives**.
### **7. Card Visualization**
- **Best for** displaying **single summary values**.
- **Commonly used at the top of dashboards**.
**Example: Total Revenue**
1. Select **Card Visualization**.
2. Add **Total Purchased**.
**Use Case**: Shows **total sales, average revenue, or order count** in a **concise, prominent format**.
### **8. Table Visualization**
- **Best for** displaying **detailed data in a structured format**.
- **Similar to Excel tables**, allowing for row-level analysis.
**Example: Customer Orders**
1. Select **Table Visualization**.
2. Add **Customer Name** and **Units Sold**.
**Use Case**: Ideal for **detailed reports** where users need **raw numbers**.
### **Less Commonly Used Visualizations**
- **Area Charts** – Similar to line charts but shaded.
- **Maps** – Used when location-based data is involved.
- **Waterfall Charts** – Shows changes in a **measure over time** (e.g., profit/loss).
- **Tree Maps** – Similar to pie charts but rectangular.
**Tip**: Always **choose visualizations that improve readability and insight**.