Create the MelaSale.csv using Python Pandas containing data as shown in Table 4.6.
**Table 4.6 -- Day-wise mela sales data (in Rs):**

| Week 1 | Week 2 | Week 3 |
|---|---|---|
| 5000 | 4000 | 4000 |
| 5900 | 3000 | 5800 |
| 6500 | 5000 | 3500 |
| 3500 | 5500 | 2500 |
| 4000 | 3

Question

Create the MelaSale.csv using Python Pandas containing data as shown in Table 4.6.
**Table 4.6 -- Day-wise mela sales data (in Rs):**

| Week 1 | Week 2 | Week 3 |
|---|---|---|
| 5000 | 4000 | 4000 |
| 5900 | 3000 | 5800 |
| 6500 | 5000 | 3500 |
| 3500 | 5500 | 2500 |
| 4000 | 3000 | 3000 |
| 5300 | 4300 | 5300 |
| 7900 | 5900 | 6000 |

Accepted Answer

> [!TLDR]
> This task asks you to create a CSV file (`MelaSale.csv`) from scratch using Python Pandas, with the exact day-wise mela sales data given in Table 4.6 — three weeks of sales figures across seven days.

The core idea here is **DataFrame construction from raw data**. You are not reading an existing file; you are *writing* one. The natural tool is `pandas.DataFrame` to hold the data in memory, then `to_csv()` to persist it. The shape of the question is a **code / query task** — you must produce a complete, runnable Python script that creates the CSV exactly as specified.

First, understand the structure. Table 4.6 shows three columns (Week 1, Week 2, Week 3) and seven rows — one for each day of the week. The sales figures are given in the order: row 1: 5000, 4000, 4000; row 2: 5900, 3000, 5800; and so on. There is no explicit day-name column in the table, but a well-formed CSV should include a day index or day names for clarity. The question says "day-wise" — I will add a `Day` column with values Monday through Sunday, as that is the natural interpretation and makes the data usable.

```python
import pandas as pd

# Data from Table 4.6: each inner list is one day's sales for Week 1, Week 2, Week 3
data = {
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],
    'Week 1': [5000, 5900, 6500, 3500, 4000, 5300, 7900],
    'Week 2': [4000, 3000, 5000, 5500, 3000, 4300, 5900],
    'Week 3': [4000, 5800, 3500, 2500, 3000, 5300, 6000]
}

df = pd.DataFrame(data)

# Write to CSV without the default index column
df.to_csv('MelaSale.csv', index=False)

print("MelaSale.csv created successfully.")
print(df)
```

**Key lines explained:**

- `pd.DataFrame(data)`: This constructs a DataFrame from a dictionary. Each key becomes a column name, and the list of values becomes that column's data. The order of keys determines column order in the DataFrame.
- `df.to_csv('MelaSale.csv', index=False)`: The `index=False` parameter is critical — w

Concept understanding — DataFrame Querying

DataFrame Querying: Finding What You Need in a Table

The Precise Meaning

Why It Matters

A Simple Example in Words

Week 1	Week 2	Week 3
5000	4000	4000
5900	3000	5800
6500	5000	3500
3500	5500	2500
4000	3000	3000
5300	4300	5300
7900	5900	6000

🎓Unlock everything free for 14 days

Unlock everything free for 14 days