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Reshape Data: Melt
Reshape the data so that each row represents sales data for a product across different quarters.
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Easy · Reshape Data: Melt core interview pattern
Answer-first summary
Reshape the data so that each row represents sales data for a product across different quarters.
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This problem requires reshaping a given DataFrame from a wide format to a long format. The goal is to represent sales data of each product across different quarters, where each row shows a product's sales for a specific quarter. The challenge is in effectively transforming the structure using the pandas melt function.
Problem Statement
You are given a DataFrame with products and their sales across four quarters. The columns represent product names and the corresponding sales for each quarter. The task is to reshape this data so that each row represents sales data for a specific product in a given quarter.
The expected output format should include three columns: 'product', 'quarter', and 'sales'. The values for 'quarter' should correspond to the quarter names (quarter_1, quarter_2, etc.), and 'sales' should show the respective sales data for each product in the given quarter.
Examples
Example 1
Input: See original problem statement.
Output: See original problem statement.
DataFrame report +-------------+--------+ | Column Name | Type | +-------------+--------+ | product | object | | quarter_1 | int | | quarter_2 | int | | quarter_3 | int | | quarter_4 | int | +-------------+--------+
Example 2
Input: +-------------+-----------+-----------+-----------+-----------+ | product | quarter_1 | quarter_2 | quarter_3 | quarter_4 | +-------------+-----------+-----------+-----------+-----------+ | Umbrella | 417 | 224 | 379 | 611 | | SleepingBag | 800 | 936 | 93 | 875 | +-------------+-----------+-----------+-----------+-----------+
Output: +-------------+-----------+-------+ | product | quarter | sales | +-------------+-----------+-------+ | Umbrella | quarter_1 | 417 | | SleepingBag | quarter_1 | 800 | | Umbrella | quarter_2 | 224 | | SleepingBag | quarter_2 | 936 | | Umbrella | quarter_3 | 379 | | SleepingBag | quarter_3 | 93 | | Umbrella | quarter_4 | 611 | | SleepingBag | quarter_4 | 875 | +-------------+-----------+-------+
The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.
Constraints
Solution Approach
Reshaping Data with Pandas Melt
Use pandas' melt function to transform the DataFrame from wide format to long format. The 'product' column will remain unchanged, while the 'quarter' and 'sales' columns will be generated from the quarter columns.
Handling Column Renaming
After reshaping the data, it is necessary to rename the columns appropriately. Rename 'variable' to 'quarter' and 'value' to 'sales' to match the expected output format.
Efficiency Considerations
Consider optimizing the approach for large datasets by checking memory usage and execution time when using the melt function in pandas. The time and space complexity depend on the specific dataset and reshaping method.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
The time complexity of this solution is dependent on the number of rows and columns in the DataFrame, with the melt function generally operating in linear time. Space complexity will vary depending on the DataFrame's size, as new rows are created during the transformation.
What Interviewers Usually Probe
- Ability to recognize and use pandas functions like melt to solve data manipulation problems.
- Knowledge of handling data reshaping and formatting within DataFrames.
- Familiarity with memory and time efficiency concerns in data transformation tasks.
Common Pitfalls or Variants
Common pitfalls
- Failing to rename the columns correctly after using melt, leading to incorrect output format.
- Not understanding the difference between wide and long formats, causing confusion during transformation.
- Not considering the potential memory and time complexity when dealing with large datasets.
Follow-up variants
- Reshaping data with different column structures, such as additional product attributes.
- Working with larger datasets where efficiency becomes a significant concern.
- Handling more complex data structures with hierarchical index levels or multi-index DataFrames.
FAQ
What is the core interview pattern for the 'Reshape Data: Melt' problem?
The core pattern is transforming data from wide format to long format using functions like pandas melt, which is a common interview task in data-related roles.
How can I optimize the solution for large datasets?
To optimize for large datasets, consider evaluating memory usage and execution time. Using chunking or other pandas optimizations can help with large-scale data transformations.
What is the significance of the 'quarter' column in this problem?
The 'quarter' column is generated by the melt function from the original quarter columns. It ensures that each row represents a specific product's sales in a given quarter.
What do I need to remember after using pandas melt?
After using pandas melt, remember to rename the resulting columns appropriately to match the expected output format, i.e., 'quarter' and 'sales'.
What other pandas functions can help with data reshaping?
Other pandas functions, such as pivot, pivot_table, and stack/unstack, can help with reshaping data in various ways depending on the desired outcome.
Solution
Solution 1
#### Python3
import pandas as pd
def meltTable(report: pd.DataFrame) -> pd.DataFrame:
return pd.melt(report, id_vars=['product'], var_name='quarter', value_name='sales')