Sorting, Ranking, and Reindexing

Data rarely comes in the perfect order for analysis. That's where sorting, ranking, and reindexing come in.

Whether you want to sort by column values or manually change the order of rows, pandas makes it simple.

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 this when aligning datasets or creating custom views.