Read and write files

CSV stores text rows separated by delimiters. read_csv creates a DataFrame and attempts to infer column types. to_csv writes the selected data; index=False avoids saving the pandas row index as an extra column.

df = pd.read_csv(DATA_DIR / "students.csv", dtype={"Student_ID": "string"})
result = df.loc[df["Score"].ge(85), ["Student_ID", "Name", "Score"]]
result.to_csv(OUTPUT_DIR / "high_scores.csv", index=False)
print(result)
print("Saved outputs/high_scores.csv")

  Student_ID    Name  Score
0       S001   Amira     88
2       S003   Chloe     91
5       S006  Fatima     95
9       S010   Jamal     89
Saved outputs/high_scores.csv

Watch for this. CSV does not preserve every pandas dtype. Check types and missing-value rules when reloading.

df = pd.read_csv(DATA_DIR / "students.csv", usecols=["Student_ID", "Name", "Attendance"], dtype={"Student_ID": "string"})
result = df.loc[df["Attendance"].lt(85)]
result.to_csv(OUTPUT_DIR / "attendance_followup.csv", index=False)
print(result)

What you should be able to explain. Inspect imports and choose export columns deliberately.

JSON can store a list of records, each represented by a dictionary. orient="records" makes this structure explicit. Re-reading a file checks that the exported shape matches what you intended.

df = pd.read_json(DATA_DIR / "students.json", orient="records")
result = df[["Student_ID", "Campus", "Score"]].head(3)
result.to_json(OUTPUT_DIR / "student_preview.json", orient="records", indent=2)
print(result)
print((OUTPUT_DIR / "student_preview.json").read_text())

  Student_ID Campus  Score
0       S001    RAK     88
1       S002  Dubai     72
2       S003    RAK     91
[
  {
    "Student_ID":"S001",
    "Campus":"RAK",
    "Score":88
  },
  {
    "Student_ID":"S002",
    "Campus":"Dubai",
    "Score":72
  },
  {
    "Student_ID":"S003",
    "Campus":"RAK",
    "Score":91
  }
]

Watch for this. File formats can change date and nullable dtype representations; a round trip needs validation.

df = students().head(2)[["Student_ID", "Program"]]
path = OUTPUT_DIR / "program_preview.json"
df.to_json(path, orient="records", indent=2)
result = pd.read_json(path, orient="records")
print(result)

What you should be able to explain. Choose a file orientation that matches the receiving system.

An Excel workbook can contain several sheets. sheet_name selects one by name. The openpyxl engine handles the provided .xlsx workbook and is listed in the installation requirements.

df = pd.read_excel(DATA_DIR / "students.xlsx", sheet_name="Students", engine="openpyxl")
result = df.groupby("Campus", as_index=False)["Score"].mean()
result.to_excel(OUTPUT_DIR / "campus_report.xlsx", sheet_name="Summary", index=False, engine="openpyxl")
print(result.round(2))

  Campus  Score
0  Dubai  76.83
1    RAK  81.00

Watch for this. If openpyxl is missing, install the provided requirements before running this cell.

df = pd.read_excel(DATA_DIR / "students.xlsx", sheet_name="Students", engine="openpyxl")
result = df.groupby("Program").size().rename("Students").reset_index()
result.to_excel(OUTPUT_DIR / "program_counts.xlsx", sheet_name="Counts", index=False, engine="openpyxl")
print(result)

What you should be able to explain. Excel I/O needs a workbook path and the correct worksheet.

SQL retrieves data from tables using a query. This example creates a temporary in-memory database from the fictional data. The parameter placeholder passes a value separately from the query text.

with sqlite3.connect(":memory:") as connection:
    students().to_sql("students", connection, index=False, if_exists="replace")
    result = pd.read_sql_query(
        "SELECT Student_ID, Name, Score FROM students WHERE Score >= ? ORDER BY Score DESC",
        connection, params=(85,))
print(result)

  Student_ID    Name  Score
0       S006  Fatima     95
1       S003   Chloe     91
2       S010   Jamal     89
3       S001   Amira     88

Watch for this. Only use if_exists="replace" here on the temporary classroom table; on a real database it replaces the existing table.

with sqlite3.connect(":memory:") as connection:
    students().to_sql("students", connection, index=False, if_exists="replace")
    result = pd.read_sql_query(
        "SELECT Campus, AVG(Score) AS Mean_Score, COUNT(*) AS Students FROM students GROUP BY Campus ORDER BY Campus",
        connection)
print(result.round(2))

What you should be able to explain. Pandas can receive SQL results without loading every source row.