Table of Contents
To create a 3D Pandas DataFrame, you must first create a 2D data frame, and then use the “stack” method to convert it into a 3D data frame. To illustrate this, let’s take a look at an example. We’ll create a 2D data frame with the numbers 1 to 6 in the columns A, B, and C and then use the ‘stack’ method to make it 3D. The result will be a 3D data frame with the numbers 1 to 6 in the columns A, B, C, A1, B1, and C1.
You can use the module to quickly create a 3D pandas DataFrame.
This tutorial explains how to create the following 3D pandas DataFrame using functions from the xarray module:
product_A product_B product_C
year quarter
2021 Q1 1.624345 0.319039 50
Q2 -0.611756 0.319039 50
Q3 -0.528172 0.319039 50
Q4 -1.072969 0.319039 50
2022 Q1 0.865408 -0.249370 50
Q2 -2.301539 -0.249370 50
Q3 1.744812 -0.249370 50
Q4 -0.761207 -0.249370 50
Example: Create 3D Pandas DataFrame
The following code shows how to create a 3D dataset using functions from xarray and NumPy:
import numpy as np
import xarray as xr
#make this example reproducible
np.random.seed(1)
#create 3D dataset
xarray_3d = xr.Dataset(
{"product_A": (("year", "quarter"), np.random.randn(2, 4))},
coords={
"year": [2021, 2022],
"quarter": ["Q1", "Q2", "Q3", "Q4"],
"product_B": ("year", np.random.randn(2)),
"product_C": 50,
},
)
#view 3D dataset
print(xarray_3d)
Dimensions: (year: 2, quarter: 4)
Coordinates:
* year (year) int32 2021 2022
* quarter (quarter) <U2 'Q1' 'Q2' 'Q3' 'Q4'
product_B (year) float64 0.319 -0.2494
product_C int32 50
Data variables:
product_A (year, quarter) float64 1.624 -0.6118 -0.5282 ... 1.745 -0.7612 Note: The NumPy function returns sample values from the .
We can then use the to_dataframe() function to convert this dataset to a pandas DataFrame:
#convert xarray to DataFrame
df_3d = xarray_3d.to_dataframe()
#view 3D DataFrame
print(df_3d)
product_A product_B product_C
year quarter
2021 Q1 1.624345 0.319039 50
Q2 -0.611756 0.319039 50
Q3 -0.528172 0.319039 50
Q4 -1.072969 0.319039 50
2022 Q1 0.865408 -0.249370 50
Q2 -2.301539 -0.249370 50
Q3 1.744812 -0.249370 50
Q4 -0.761207 -0.249370 50 The result is a 3D pandas DataFrame that contains information on the number of sales made of three different products during two different years and four different quarters per year.
We can use the type() function to confirm that this object is indeed a pandas DataFrame:
#display type of df_3d
type(df_3d)
pandas.core.frame.DataFrame
The object is indeed a pandas DataFrame.
The following tutorials explain how to perform other common functions in pandas: