How can I reshape long data into a wide format with multiple variables using Pandas?

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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