Pandas分类对比：示例DataFrame创建与分位数分段实操

Here's a straightforward walkthrough of generating a sample DataFrame and using quantile-based binning to categorize values in a column:

1. Generate the Initial DataFrame #

First, we'll create a 10-row, 2-column DataFrame with random values using NumPy and Pandas:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10, 2), columns=list('AB'))

When run, this produces a DataFrame like this (your values will vary since we're using random data):

A         B
0  0.459759  0.152645
1  0.183613  0.756527
2 -1.836027  0.032433
3  0.264336  0.170171
4 -0.276347  0.208389
5  0.677709  0.725274
6 -0.547858  0.376683
7 -0.994759 -0.750373
8  0.556593  1.282167
9 -1.444533  0.589768

2. Apply Quantile Binning #

Next, we'll use pd.qcut() to split the values in column A into 4 quantile-based bins. The duplicates="drop" parameter ensures any duplicate bin edges are removed automatically to avoid errors:

df['A_rank'] = pd.qcut(df['A'], [0, 0.25, 0.5, 0.75, 1], duplicates="drop")

This adds a new column A_rank that labels each row with the quantile bin it falls into:

A         B          A_rank
0  0.459759  0.152645   (0.411, 0.678]
1  0.183613  0.756527  (-0.0464, 0.411]
2 -1.836027  0.032433  (-1.837, -0.883]
3  0.264336  0.170171  (-0.0464, 0.411]
4 -0.276347  0.208389  (-0.883, -0.0464]
5  0.677709  0.725274   (0.411, 0.678]
6 -0.547858  0.376683  (-0.883, -0.0464]
7 -0.994759 -0.750373  (-1.837, -0.883]
8  0.556593  1.282167   (0.411, 0.678]
9 -1.444533  0.589768  (-1.837, -0.883]

Quick Notes: #

• pd.qcut() divides data into bins such that each bin has roughly the same number of observations, which is perfect for quantile-based ranking.
• The list [0, 0.25, 0.5, 0.75, 1] defines our thresholds (25th, 50th, 75th percentiles).
• duplicates="drop" is handy when your dataset has repeated values that would cause overlapping bin edges.

内容的提问来源于stack exchange，提问作者SankMa