Sorting, Ranking, and Reindexing

Data rarely arrives in the ideal order for analysis — that’s where sorting, ranking, and reindexing become essential.

Whether you need to organize data by column values or rearrange rows manually, Pandas makes it easy and intuitive.

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

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import pandas as pd

# Create a DataFrame
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Score": [85, 90, 78]
}
df = pd.DataFrame(data)

# Sort by Score in descending order
df.sort_values(by="Score", ascending=False)

# Output:
#    Name  Score
# 1    Bob     90
# 0  Alice     85
# 2 Charlie     78

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

To rank values, use .rank(). It assigns a rank number to each value and handles ties automatically.

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df["Score"].rank(ascending=False)

# Output:
# 0    2.0
# 1    1.0
# 2    3.0
# Name: Score, dtype: float64

This is useful for leaderboards or percentile-based groupings.

Reindexing lets you reset or customize the order of rows with .reindex().

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df.reindex([2, 0, 1])

# Output:
#    Name  Score
# 2 Charlie     78
# 0  Alice     85
# 1    Bob     90

Use reindexing when aligning datasets, resetting indexes, or creating customized data views.