Sorting and Ranking

Two different questions: sort vs rank

• Sorting rearranges rows so they appear in order.
• Ranking assigns each value a position number (1st, 2nd, 3rd...) without rearranging anything — the rank becomes a new column or Series aligned to the original order.

Both are everyday analytics needs: sorting a leaderboard, ranking students by score.

Sorting by index: sort_index

import pandas as pd
import numpy as np
 
obj = pd.Series([4, 7, -3, 2, np.nan], index=["d", "a", "c", "b", "e"])
print(obj.sort_index())  # sorted lexicographically by label

Sorting by value: sort_values

print(obj.sort_values())

Missing values sort to the end by default. Use na_position="first" to flip that:

print(obj.sort_values(na_position="first"))

Sorting a DataFrame by one or more columns

frame = pd.DataFrame({"b": [4, 7, -3, 2], "a": [0, 1, 0, 1]})
 
print(frame.sort_values("b"))          # single column
print(frame.sort_values(["a", "b"]))   # tie-break: sort by "a" first, then "b"

Ranking: rank()

rank() assigns each value a position from 1 through the number of valid points, starting at the lowest value. Ties are handled by averaging their ranks by default:

s = pd.Series([7, -5, 7, 4, 2, 0, 4])
print(s.rank())

Both 7s tie for ranks 6 and 7, so they each get the average, 6.5. Other tie-break rules are available via method:

print(s.rank(method="first"))  # ties broken by order of appearance
print(s.rank(method="min"))    # ties get the lowest rank in the group
print(s.rank(ascending=False)) # rank from highest to lowest instead

Common pitfalls

• sort_values() on a Series with NaN puts them last — easy to miss if you don't scroll to the bottom of a big result.
• rank()'s default "average" method can produce fractional ranks (6.5) — if you need whole-number ranks for display, use method="first" or method="dense".
• Sorting returns a new object; it doesn't sort in place unless you pass inplace=True (and even then, prefer reassigning — it's clearer).

Visualize it

  flowchart LR
  A["Unsorted values"] -->|"sort_values()"| B["Rows physically reordered"]
  A -->|"rank()"| C["Same row order,
each value gets a rank number"]

🧪 Try It Yourself

Exercise 1 – Sort a Series by Value

Press Run to see what your code prints.

Exercise 2 – Put Missing Values First

Press Run to see what your code prints.

Exercise 3 – Rank Values With Averaged Ties

Press Run to see what your code prints.

Check your understanding

Next

Sorting rearranges rows you already have. Applying Functions (apply, map, applymap) covers transforming the values inside those rows.