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

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())

print(obj.sort_values())

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

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

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"

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()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())

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

Both 77s tie for ranks 6 and 7, so they each get the average, 6.56.5. Other tie-break rules are available via methodmethod:

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

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()sort_values() on a Series with NaNNaN puts them last — easy to miss if you don’t scroll to the bottom of a big result.
• rank()rank()’s default "average""average" method can produce fractional ranks (6.56.5) — if you need whole-number ranks for display, use method="first"method="first" or method="dense"method="dense".
• Sorting returns a new object; it doesn’t sort in place unless you pass inplace=Trueinplace=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

Exercise 2 – Put Missing Values First

Exercise 3 – Rank Values With Averaged Ties

Next

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