Update README.md

README.md

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----
-license: gpl
----
+---
+license: gpl
+language:
+- en
+tags:
+- finance
+- banking
+size_categories:
+- 1K<n<10K
+---
+# Africa Automated Teller Machines (ATMs) (per 100,000 adults) Dataset
+
+## Overview
+This dataset contains automated teller machines (atms) (per 100,000 adults) data for African countries from the World Bank.
+
+## Data Details
+- **Indicator Code**: FB.ATM.TOTL.P5
+- **Description**: Automated Teller Machines (ATMs) (per 100,000 adults)
+- **Geographic Coverage**: 52 African countries
+- **Time Period**: 2004-2023
+- **Data Points**: 858 observations
+- **Coverage**: 24.44% of possible country-year combinations
+
+## File Formats
+
+### Main Dataset (`fb_atm_totl_p5_africa.csv`)
+- **Rows**: 54 countries
+- **Columns**: 65 years (1960-2024)
+- **Structure**: Countries as rows, years as columns
+- **Missing Value Treatment**: Interpolation → Forward Fill → Backward Fill
+- **Use Case**: Cross-sectional analysis, heatmaps, correlation analysis
+
+## Data Quality
+
+### Coverage Statistics
+- **Total Observations**: 858
+- **Coverage Rate**: 24.44%
+- **Earliest Data**: 2004
+- **Latest Data**: 2023
+
+### Countries with No Data
+2 countries have no observations:
+Eritrea, Somalia
+
+## Usage Examples
+
+### Python
+```python
+import pandas as pd
+
+# Load main dataset
+df = pd.read_csv('fb_atm_totl_p5_africa.csv', index_col=[0, 1])
+```
+
+### R
+```r
+# Load main dataset
+df <- read.csv('fb_atm_totl_p5_africa.csv', row.names=c(1,2))
+```
+
+## Source
+World Bank Open Data - Automated Teller Machines (ATMs) (per 100,000 adults)
+- **Original File**: API_FB.ATM.TOTL.P5_DS2_en_excel_v2_21147.xls
+- **Processed**: 2025-08-18
+
+## License
+This dataset is derived from World Bank Open Data and is available under the Creative Commons Attribution 4.0 International License.