How to Create a 3D Pandas DataFrame

To create a 3D Pandas DataFrame, you can use a combination of the pd.Panel and pd.DataFrame objects. Here’s an example of how you can create a 3D DataFrame:

import pandas as pd
import numpy as np

# Create a 3D numpy array
data = np.random.randn(2, 3, 4)

# Create a panel with the 3D numpy array
panel = pd.Panel(data)

# Convert the panel to a 3D DataFrame
df = panel.to_frame()

# Print the resulting DataFrame
print(df)

In this example, we first create a 3D numpy array using the np.random.randn function. We then create a Pandas panel object using this numpy array, and convert it to a 3D DataFrame using the to_frame() method. Finally, we print the resulting DataFrame.

import pandas as pd
import numpy as np

# Create a 3D numpy array
data = np.random.randn(2, 3, 4)

# Create a MultiIndex
idx = pd.MultiIndex.from_product([range(s) for s in data.shape], names=['x', 'y', 'z'])

# Create a DataFrame with the 3D numpy array and the MultiIndex
df = pd.DataFrame({'value': data.flatten()}, index=idx)

# Print the resulting DataFrame
print(df)

In this example, we first create a 3D numpy array as before. We then create a MultiIndex using the pd.MultiIndex.from_product method, specifying the range of values for each dimension and giving them appropriate names. Finally, we create a DataFrame with the flattened numpy array and the MultiIndex, which gives us a 3D DataFrame.