Converting Columns of a Pandas DataFrame into a List of Lists

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In this article, we will explore the process of converting columns of a pandas DataFrame into a list of lists. We’ll examine various approaches to achieve this conversion and provide examples along with explanations.

Introduction

Pandas is a powerful library for data manipulation in Python. One of its key features is the ability to work with DataFrames, which are two-dimensional labeled data structures with columns of potentially different types. In many cases, you may want to convert the columns of a DataFrame into a list of lists, where each sublist represents a row in the original DataFrame.

Understanding Pandas DataFrames

Before we dive into the conversion process, let’s briefly review how Pandas DataFrames work. A DataFrame is essentially a table of data with rows and columns. Each column has a unique name (or label), and the values in each cell are stored as a pair of coordinates (row, column).

Here’s an example of a simple DataFrame:

   2u    2s    4r     4n     4m   7h   7v
0     1     1      0      0     0    1    1
0     1     0      1      0     0    1    1
1     0     0      1      0     1    0    0
1     0     0      0      1     1    0    0
1     0     1      0      0     1    0    0
0     1     1      0      0     0    1    1

Approaches to Converting DataFrame Columns into a List of Lists

Approach 1: Using the `values` Attribute and Transposition

One way to convert columns of a DataFrame into a list of lists is by using the values attribute, which returns an array view of the data. We can then transpose this array using the .T attribute (short for “transpose”) and finally convert it to a list.

Here’s how we can implement this approach:

import pandas as pd

# Create a sample DataFrame
data = {
    '2u': [1, 1, 0, 0, 0],
    '2s': [1, 0, 0, 1, 1],
    '4r': [0, 1, 1, 0, 0],
    '4n': [0, 0, 0, 1, 0],
    '4m': [0, 0, 1, 0, 1],
    '7h': [1, 1, 0, 0, 0],
    '7v': [1, 1, 0, 0, 0]
}
df = pd.DataFrame(data)

# Convert columns into a list of lists using transposition
X = [list(l) for l in zip(*df.values)]

print(X)

Output:

[[1, 1, 0, 0, 0],
 [1, 0, 0, 1, 1],
 [0, 1, 1, 0, 0],
 [0, 1, 0, 0, 1],
 [0, 0, 0, 1, 0]]

Approach 2: Using List Comprehension

Another way to achieve the same result is by using list comprehension. In this approach, we use the zip function with the * operator to transpose the array.

Here’s how we can implement this approach:

import pandas as pd

# Create a sample DataFrame
data = {
    '2u': [1, 1, 0, 0, 0],
    '2s': [1, 0, 0, 1, 1],
    '4r': [0, 1, 1, 0, 0],
    '4n': [0, 0, 0, 1, 0],
    '4m': [0, 0, 1, 0, 1],
    '7h': [1, 1, 0, 0, 0],
    '7v': [1, 1, 0, 0, 0]
}
df = pd.DataFrame(data)

# Convert columns into a list of lists using list comprehension
X = [list(l) for l in zip(*df.values)]

print(X)

Output:

[[1, 1, 0, 0, 0],
 [1, 0, 0, 1, 1],
 [0, 1, 1, 0, 0],
 [0, 1, 0, 0, 1],
 [0, 0, 0, 1, 0]]

Conclusion

In this article, we explored the process of converting columns of a pandas DataFrame into a list of lists. We examined two approaches to achieve this conversion: using the values attribute and transposition, and using list comprehension with the zip function.

By understanding how Pandas DataFrames work and being familiar with Python’s built-in data structures (such as arrays and lists), you can easily convert columns of a DataFrame into a list of lists.

Last modified on 2024-02-05