🔃 Pandas Sorting and Reindexing – Organize Your Data Efficiently

---

🧲 Introduction – Why Sort and Reindex?

Sorting and reindexing are key steps in data cleaning, presentation, and efficient lookup. Pandas provides powerful tools to sort DataFrames by index or values, and to reindex rows or columns, helping you manage missing values and ensure consistent structure.

🎯 In this guide, you’ll learn:

• How to sort by index and by column values
• How to reindex with new labels or reshuffle the structure
• Handle missing labels during reindexing
• Reset or set a custom index

---

📥 1. Create a Sample DataFrame

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Score': [88, 95, 70, 82]
}, index=['d', 'b', 'a', 'c'])

print(df)

👉 Output:

     Name  Score
d   Alice     88
b     Bob     95
a  Charlie     70
c   David     82

---

🔢 2. Sort by Index

df_sorted_index = df.sort_index()

✔️ Sorts the DataFrame by its row index (alphabetical by default).

👉 Output:

     Name  Score
a  Charlie     70
b     Bob     95
c   David     82
d   Alice     88

---

Descending Order

df.sort_index(ascending=False)

✔️ Reverse sort the index.

---

🔠 3. Sort by Column Values

df_sorted_score = df.sort_values(by='Score')

✔️ Sorts DataFrame by the Score column in ascending order.

👉 Output:

     Name  Score
a  Charlie     70
c   David     82
d   Alice     88
b     Bob     95

---

Multiple Columns Sorting

df.sort_values(by=['Score', 'Name'], ascending=[True, False])

✔️ Sorts by Score, then by Name (descending) for tied scores.

---

🔁 4. Reindex Rows with a New Order

df_reindexed = df.reindex(['a', 'b', 'c', 'd'])

✔️ Reorders rows by the specified index list.

👉 Output:

     Name  Score
a  Charlie     70
b     Bob     95
c   David     82
d   Alice     88

---

🧱 5. Reindex with Missing Labels

df.reindex(['a', 'b', 'x'])

✔️ Returns a DataFrame where label 'x' does not exist—fills with NaN.

👉 Output:

     Name  Score
a  Charlie   70.0
b     Bob    95.0
x     NaN     NaN

---

🧹 6. Fill Missing Data When Reindexing

df.reindex(['a', 'b', 'x'], fill_value='Unknown')

✔️ Fills missing labels with a default value.

---

🔧 7. Reset and Set Index

Reset Index

df_reset = df.reset_index()

✔️ Moves current index to a column and resets to default integer index.

Set a Column as Index

df_set = df.reset_index().set_index('Name')

✔️ Uses the 'Name' column as the new index.

---

🧮 8. Reindex Columns

df.reindex(columns=['Score', 'Name'])

✔️ Changes the column order of the DataFrame.

---

📌 Summary – Key Takeaways

Sorting and reindexing help structure your DataFrame for clean reports, comparisons, and computation. Pandas offers .sort_index(), .sort_values(), and .reindex() with great flexibility for reshaping and managing tabular data.

🔍 Key Takeaways:

• Sort by index with .sort_index(), or by column with .sort_values()
• Reindex with .reindex() to reorder or align datasets
• Handle missing index/column labels gracefully
• Reset or set index with .reset_index() and .set_index()

⚙️ Real-world relevance: Used in data transformation, report formatting, merging datasets, and time series alignment.

---

❓ FAQs – Sorting and Reindexing in Pandas

❓ What’s the difference between sorting and reindexing?

• Sorting changes the order based on existing data.
• Reindexing aligns or reshapes using explicit label lists.

---

❓ What happens if I reindex with labels that don’t exist?
Pandas fills those rows or columns with NaN unless fill_value is specified.

---

❓ How do I sort in descending order?
Use:

df.sort_values(by='Score', ascending=False)

---

❓ Can I reindex both rows and columns at once?
Yes:

df.reindex(index=['a', 'b'], columns=['Score', 'Name'])

---

❓ Does sorting or reindexing modify the DataFrame?
❌ No—unless you use inplace=True.

---

Share Now :