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Reshape Long Data into Wide Format with Pandas
When working with data in a long format, it can be necessary to reshape it into a wide format for better analysis and visualization. One common challenge is to reshape data based on multiple variables.
Consider the following dataframe:
salesman height product price Knut 6 bat 5 Knut 6 ball 1 Knut 6 wand 3 Steve 5 pen 2
The goal is to reshape this data into a wide format:
salesman height product_1 price_1 product_2 price_2 product_3 price_3 Knut 6 bat 5 ball 1 wand 3 Steve 5 pen 2 NA NA NA NA
While melt/stack/unstack are commonly used for reshaping data, they may not be suitable for this specific scenario.
A solution to this problem can be found using the following code:
<code class="python">import pandas as pd # Create sample data raw_data = { 'salesman': ['Knut', 'Knut', 'Knut', 'Steve'], 'height': [6, 6, 6, 5], 'product': ['bat', 'ball', 'wand', 'pen'], 'price': [5, 1, 3, 2] } df = pd.DataFrame(raw_data) # Reshape data df_wide = df.pivot_table(index=['salesman', 'height'], columns='product', values='price') # Reset index to get it in the desired format df_wide = df_wide.reset_index(level=[0, 1]) # Rename columns new_columns = ['salesman', 'height'] + [f'product_{i}' for i in range(1, df_wide.shape[1] - 1)] + [f'price_{i}' for i in range(1, df_wide.shape[1] - 1)] df_wide.columns = new_columns # Handle missing values df_wide.fillna("NA", inplace=True)</code>
The resulting dataframe df_wide will be in the desired wide format.
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