如何对给定的数据矩阵中的数据进行压缩处理？

First, let's clean up your data for better readability:

class1 class2 class3 class4 class5 class6
1      <NA>   PATH   PATH   PATH   PATH   <NA>
2      PATH   PATH   VUS    <NA>   <NA>   <NA>
3      VUS    VUS    VUS    <NA>   <NA>   <NA>
4      PATH   PATH   VUS    <NA>   <NA>   VUS
5      <NA>   PATH   PATH   <NA>   <NA>   <NA>
6      PATH   VUS    VUS    <NA>   VUS    <NA>
7      MPATH  VUS    VUS    PATH   <NA>   <NA>
8      PATH   VUS    VUS    <NA>   <NA>   <NA>
9      VUS    VUS    VUS    <NA>   <NA>   <NA>
10     PATH   <NA>   VUS    ...    ...    ...

Given this is categorical data with repeated patterns and lots of missing values (<NA>), here are 3 practical, easy-to-implement compression strategies tailored to your dataset:

1. Aggregate by Unique Row Patterns #

Since many rows share identical value combinations, you can compress the data by counting how many times each unique row occurs. This cuts down on redundant rows while preserving all critical information.

Example Compressed Output: #

If you're using Python, pandas makes this trivial:

import pandas as pd

# Load your data into a DataFrame
df = pd.read_csv("your_data.csv", sep="\s+")

# Group all columns and count unique row occurrences
compressed_df = df.groupby(df.columns.tolist()).size().reset_index(name='Count')
print(compressed_df)

2. Integer Encoding for Categorical Values #

Replace each unique category with a smaller integer representation. Strings like "PATH" take more memory than integers (e.g., 0,1,2), so this reduces the dataset's footprint while keeping it structured for further analysis.

Example Mapping: #

• <NA> → 0
• VUS → 1
• PATH → 2
• MPATH → 3

Encoded Data Snippet: #

class1 class2 class3 class4 class5 class6
1      0      2      2      2      2      0
2      2      2      1      0      0      0
3      1      1      1      0      0      0
...

Implementation with pandas:

import pandas as pd

df = pd.read_csv("your_data.csv", sep="\s+")

# Encode each column, keeping NA as a distinct value (-1)
for col in df.columns:
    df[col], _ = pd.factorize(df[col], na_sentinel=-1)

3. Sparse Matrix Representation #

Since your dataset is full of <NA> values, a sparse matrix will only store the positions and values of non-NA entries. This is perfect if storage space is a top priority and missing data dominates your matrix.

Example Sparse Entry for Row 1: #

Row 1: (class2=PATH, class3=PATH, class4=PATH, class5=PATH)

Python implementation using scipy:

from scipy.sparse import csr_matrix
import pandas as pd
import joblib

df = pd.read_csv("your_data.csv", sep="\s+")

# First encode categorical values (method 2 above)
for col in df.columns:
    df[col], _ = pd.factorize(df[col], na_sentinel=0)

# Convert to sparse matrix
sparse_matrix = csr_matrix(df.values)

# Save efficiently to disk
joblib.dump(sparse_matrix, "compressed_sparse_data.joblib")

Quick Guide to Choosing a Method: #

• Use pattern aggregation if you just need to summarize the data without losing row-level counts.
• Use integer encoding if you want a compact, analysis-ready format.
• Use sparse matrices if storage space is critical and missing values are the majority.

内容的提问来源于stack exchange，提问作者puppetshow