Adventures in Machine Learning

Managing High-Dimensional Data Using 3D Pandas DataFrames in Python

Big data has become a ubiquitous term due to the vast and complex data that organizations now handle. While traditional data analysis tools can handle most data, certain data requires more than just the typical two-dimensional dataset handling.

What do you do when you have a 3D dataset? How do you handle a high dimensional dataset accurately?

In this article, we will showcase the methods of creating a 3D pandas DataFrame, and the vital tools needed to achieve this task.

Creating a 3D pandas DataFrame

Creating a 3D pandas DataFrame requires a unique and sophisticated module known as xarray. Xarray is a Python package developed by Open Data Science that is designed to handle complex and multi-dimensional data.

It can run on NumPy arrays and Cartesian products, giving it an excellent ability to manage 3D datasets. With the use of xarray, we can create a DataFrame with two or more dimensions.

Let’s take a look at a scenario where we want to create a DataFrame of a person’s height, weight, and age as per their gender. Suppose we have five males and six females who, over time, have had their height, weight, and age recorded.

We can represent it in a table-based structure in two dimensions, but how do we represent the gender of the people? This is where a 3D pandas DataFrame comes in handy.

To create a 3D DataFrame using xarray and NumPy, we can use the following steps:

First, we have to import the libraries; NumPy, Pandas, xarray, and Matplotlib. Matplotlib is an optional library, mostly used to visualize data.


import numpy as np

import pandas as pd

import xarray as xr

import matplotlib.pyplot as plt


Once the libraries are imported, the next step is to create a NumPy array with the data we want to store in the DataFrame. In our example, we create a 3D NumPy array with dimensions for gender, height, weight, and age.


data = np.random.rand(2, 3, 4, 5)

coords = {

‘gender’: [‘male’, ‘female’],

‘height’: np.arange(160, 190, 10),

‘weight’: np.arange(50, 80, 10),

‘age’: np.arange(20, 40, 5),


dims = [‘gender’, ‘height’, ‘weight’, ‘age’]


We then convert the NumPy array to a 3D pandas DataFrame using xarrays DataArray method, as shown below:


df = xr.DataArray(







This converts the NumPy array into a 3D DataFrame with four dimensions. The first dimension represents the gender, while the second, third, and fourth dimensions represent height, weight, and age.

Therefore, our data is grouped by gender, and for each gender, we have height, weight, and age for each observation.


Let’s take a look at a practical example to better understand creating a 3D pandas DataFrame using xarray and NumPy. In this example, we will create a DataFrame representing the number of visitors to a zoo, grouped by the type of animal, the month, and the year. Our dataset will have three dimensions – type of animal, month, and year.

We begin by importing the necessary libraries: xarray, NumPy, and Pandas. “`

import xarray as xr

import numpy as np

import pandas as pd


Next, we initiate our dataset with corresponding coordinates. “`

data = np.random.randn(3, 12, 5)

animal_types = [‘Lion’, ‘Giraffe’, ‘Elephant’]

months = pd.date_range(‘2021-01-01’, ‘2021-12-31′, freq=’M’)

years = np.arange(2015, 2018)


The coordinates we are using are; ‘animal_types’ which contains three animals, ‘months’ which contains the months of each year, and ‘years’ which contains the years of observation.

Lastly, we create a DataFrame from the NumPy array using xarray. “`

df = xr.DataArray(data, dims=(‘animal_types’, ‘month’, ‘year’), coords=[animal_types, months, years]



The newly created DataFrame, df contains 540 rows of data distributed on three levels of dimensions.

Each data point represents the number of visitors for a particular animal type per month for the respective year.


In conclusion, creating a 3D pandas DataFrame using xarray and NumPy offers a more sophisticated way of managing complex data. It handles data in four or more dimensions effortlessly, making it the best tool for exploring high-dimensional datasets.

In this article, we have outlined the basic steps for creating a 3D pandas DataFrame that can be used to store, analyze, and showcase multidimensional data accurately and efficiently. With this information at hand, you can now handle and analyze your 3D datasets with ease, having acquired the necessary knowledge and skills to use xarray and NumPy.In todays world, businesses and organizations handle an enormous amount of data, and some data requires more complex and sophisticated data structure handling.

While most platforms can handle two-dimensional datasets, three-dimensional datasets present a different challenge. In such cases, working with a 3D pandas DataFrame offers an efficient way to manage and analyze data.

In this article, well take a deeper look at converting a 3D dataset to a pandas DataFrame using the to_dataframe() function.

Converting a 3D Dataset to a Pandas DataFrame

To convert a 3D dataset to a pandas DataFrame, we use the to_dataframe() function, which is a very powerful and straightforward method specifically designed for creating pandas DataFrames. This function converts a pandas-compatible xarray DataArray object into a pandas DataFrame.

To start with, we need to import the necessary libraries, including xarray and pandas:


import xarray as xr

import pandas as pd


We then create a 3D dataset that we want to convert to a pandas DataFrame as shown below:


data = xr.DataArray(

[[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],

[[[9, 10], [11, 12]], [[13, 14], [15, 16]]]],

dims=(“w”, “x”, “y”, “z”),

coords={“w”: [0, 1], “x”: [0, 1], “y”: [0, 1], “z”: [0, 1]})


The dataset we have created can be thought of as a four-dimensional dataset with dimensions w, x, y, and z.

Using the to_dataframe() function, we can convert the dataset into a pandas DataFrame as shown below:


df = data.to_dataframe(name=’values’).reset_index()


This method converts the 3D dataset stored in a DataArray to a pandas DataFrame.

Note that we assign a name to our data with the ‘name’ parameter within the to_dataframe() function. We also use the reset_index() method to move our newly converted columns back to the DataFrame from its multi-index structure inherited from the DataArray.

We can then result with a pandas DataFrame with the dimensions w, x, y, and z at every row.


Here is an example that illustrates how to use to_dataframe() function to convert a 3D dataset to a pandas DataFrame. Lets imagine that we have been collecting data on different soccer players, and we want to create a pandas DataFrame with player name, goals scored, and game number.

We can create a 3D dataset using xarray as shown below:


import xarray as xr

# 3D dataset representing player name, goals scored, and game number

data = xr.DataArray(

[[[0, 1], [2, 3]], [[4, 5], [6, 7]]],

dims=(“player”, “game”, “goals”),


“player”: [“Messi”, “Ronaldo”],

“game”: [“Game 1”, “Game 2”],

“goals”: [“Scored Goals”, “Attempted Goals”]



The above code creates a 3D dataset representing player name, goals scored, and game number.

We can then convert this 3D dataset into a pandas DataFrame using the to_dataframe() function as shown below:


df = data.to_dataframe(name=’Goals Statistics’).reset_index()


The code above converts the 3D dataset into a pandas DataFrame and assigns the name ‘Goals Statistics’ to the data.

This creates a new column with the name Goals Statistics showing the goals scored. We can then further manipulate our DataFrame data as we would with any typical pandas DataFrame.

Conclusion and Additional Resources

In conclusion, converting a 3D dataset to a pandas DataFrame using the to_dataframe() function allows one to manipulate numerous complex datasets effectively. By converting data into a pandas DataFrame, it becomes easy to read, manipulate and analyze 3D and multi-dimensional data with relative ease.

Using the clear and straightforward xarray, to_dataframe() function eliminates the complexity of handling enormous amounts of data by creating a clear and accessible framework. If you would like to learn more about working with 3D data in Python, you can refer to the xarray documentation, which is a comprehensive guide on working with multidimensional arrays using pandas DataFrames and Numpy arrays.

Another great resource is the Think Python textbook by Allen B. Downey, which offers a solid introduction to Python for data analysis.

Additionally, there are numerous online tutorials, guides, and forums where you can learn more about creating and manipulating pandas DataFrames. With these resources at your disposal, converting 3D datasets to pandas DataFrames will be an incredibly easy task.

In conclusion, creating and converting a 3D dataset to a pandas DataFrame using xarray libraries and its to_dataframe() function is a useful way of exploring and analyzing complex and high-dimensional data. We have seen that xarray can generate a 3D pandas DataFrame with a clear data structure and organize it into multi-dimensional arrays.

The to_dataframe() function, on the other hand, converts a pandas-compatible DataArray into a pandas DataFrame. The takeaways from this article are that the use of xarray and pandas can simplify complex data processing, and the to_dataframe() function can help to create a pandas DataFrame with clarity and ease.

Overall, this article aims to serve as a guide for those who want to work with high-dimensional datasets in Python.

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