LeetCode 2889 Reshape Data: Pivot in Python | Pandas Pivot Table Tutorial for Beginners

Solve LeetCode 2889 "Reshape Data: Pivot" in Python with this beginner-friendly Pandas tutorial! This problem asks you to create a pivot table from a DataFrame, using `month` as the index, `city` as the columns, and `temperature` as the values. We’ll use `pandas`’ `pivot()` method to reshape the data in one line, perfect for summarizing weather data. Great for Python learners, data science beginners, or anyone prepping for coding interviews with Pandas!

🔍 **What You'll Learn:**
- Understanding LeetCode 2889’s requirements
- Using `pandas`’ `pivot()` to create a pivot table
- Reshaping data with index, columns, and values
- Testing with example weather data

💻 **Code Used in This Video:**
import pandas as pd

def pivotTable(weather: pd.DataFrame) - pd.DataFrame:
return result

# Test case
data = [
["January", "Jacksonville", 13],
["January", "ElPaso", 5],
["February", "Jacksonville", 15],
["February", "ElPaso", 7]
]
df = pd.DataFrame(data, columns=["month", "city", "temperature"])
result = pivotTable(df)
print(result)
# Output:
# city ElPaso Jacksonville
# month
# February 7 15
# January 5 13

# Test with single month
data_single = [["March", "Chicago", 10], ["March", "Miami", 20]]
df_single = pd.DataFrame(data_single, columns=["month", "city", "temperature"])
result_single = pivotTable(df_single)
print(result_single)
# Output:
# city Chicago Miami
# month
# March 10 20

🌟 **Why Solve LeetCode 2889?**
This problem teaches you how to create pivot tables with `pandas`—a key skill for data science and coding interviews! We’ll show how `pivot(index='month', columns='city', values='temperature')` reshapes the DataFrame into a table with months as rows, cities as columns, and temperatures as values. It’s a great way to learn data reshaping, with a time complexity of O(n) where n is the number of rows. Master this, and you’ll be ready for more advanced Pandas challenges!

📚 **Who’s This For?**
- Python beginners learning Pandas
- Data science enthusiasts working with DataFrames
- Coders prepping for data-focused interviews

👍 Like, subscribe, and comment: What Pandas problem should we solve next? Next up: Pandas melting—stay tuned!

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