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# Sorting, Ranking, and Reindexing

Data rarely comes in the perfect order for analysis — that’s where sorting and reindexing come in. Whether you want to sort by column values or change row order manually, Pandas makes it simple.

 

## Sorting Values

You can sort a DataFrame based on one or more columns using `.sort_values()`.

```python title="Sort by Score"
df.sort_values(by="Score", ascending=False)
```

This helps you find top performers, earliest dates, or lowest prices.

 

## Ranking Data

To rank values, use `.rank()`. It assigns a rank number to each value, handling ties gracefully.

```python title="Rank Scores"
df["Score"].rank(ascending=False)
```

This is great for leaderboards or percentile-based groupings.

 

## Reindexing Rows

Reindexing lets you reset or customize the order of your DataFrame’s rows with `.reindex()`.

```python title="Reorder Rows by Index"
df.reindex([2, 0, 1])
```

Use this when aligning datasets or creating custom views.

 

## Summary Table

| Feature | Method | Purpose |
| --------------- | --------------- | ---------------------------- |
| Sort Rows | `sort_values()` | Order rows by column values |
| Assign Rankings | `rank()` | Give rank numbers to values |
| Reorder Rows | `reindex()` | Set a custom row index order |

 

## What’s Next?

You’ll now work with missing and duplicate data to keep your datasets clean and reliable.

The `.reindex()` method is used to reorder rows based on a new index. This is particularly useful for aligning datasets or creating custom views by setting a desired order for the DataFrame rows.

Feature	Method	Purpose
Sort Rows	`sort_values()`	Order rows by column values
Assign Rankings	`rank()`	Give rank numbers to values
Reorder Rows	`reindex()`	Set a custom row index order

Sorting, Ranking, and Reindexing

Sorting Values

Ranking Data

Reindexing Rows

Summary Table

What’s Next?

Which method would you use to reorder rows of a DataFrame based on a custom index?