Python Working with CSV

CSV is one of the most common file formats for data sharing.

        You will use CSV when:
        - exporting reports
        - moving data between systems
        - working with spreadsheets
        - doing analytics
        
        ## Reading CSV using csv.reader
        
        ```python
        import csv
        
        with open("data.csv", "r", newline="") as file:
            reader = csv.reader(file)
            header = next(reader)  # skip header
            for row in reader:
                print(row)
        ```
        
        ## Writing CSV using csv.writer
        
        ```python
        import csv
        
        rows = [
            ["Name", "Age", "City"],
            ["Tom", 25, "Austin"],
            ["Sarah", 28, "Miami"]
        ]
        
        with open("output.csv", "w", newline="") as file:
            writer = csv.writer(file)
            writer.writerows(rows)
        ```
        
        ## DictReader and DictWriter (best for real apps)
        
        ```python
        import csv
        
        with open("data.csv", "r", newline="") as file:
            reader = csv.DictReader(file)
            for row in reader:
                print(row["Name"], row["Age"])
        ```
        
        ```python
        import csv
        
        data = [
            {"Name": "Tom", "Age": 25, "City": "Austin"},
            {"Name": "Sarah", "Age": 28, "City": "Miami"}
        ]
        
        with open("output.csv", "w", newline="") as file:
            writer = csv.DictWriter(file, fieldnames=["Name", "Age", "City"])
            writer.writeheader()
            writer.writerows(data)
        ```
        
        ## Custom delimiter (TSV or pipe-separated)
        
        ```python
        import csv
        
        with open("data.tsv", "r", newline="") as file:
            reader = csv.reader(file, delimiter="\t")
            for row in reader:
                print(row)
        ```
        
        ## Pandas (best for analysis + big data)
        
        ```python
        import pandas as pd
        
        df = pd.read_csv("data.csv")
        print(df.head())
        
        df.to_csv("cleaned.csv", index=False)
        ```
        
        ## Graph: CSV workflow
        
        ```mermaid
        flowchart LR
          A[CSV file] --> B[Read: csv/pandas]
          B --> C[Clean/transform]
          C --> D[Write new CSV]
          D --> E[Share/report/import]
        ```
        
        ## Remember
        
        - Use newline="" when writing CSV (important)
        - DictReader is easier in apps
        - Pandas is best for large datasets and analysis