DataFrame.hist()方法中bins参数的含义与取值方法咨询

Hey there! Let's break down the bins parameter in pandas.DataFrame.hist() clearly—no confusing jargon, just straightforward explanations.

At its core, bins defines how many equal-width intervals (or "buckets") your data gets split into when plotting the histogram.

For example, if you're working with housing price data ranging from $10k to $500k, setting bins=50 will slice that $490k range into 50 equal chunks (each ~$9.8k wide). The histogram then counts how many data points fall into each of these chunks and plots those counts as bars.

The number of bins directly impacts how you interpret your data's distribution:

• Too few bins (e.g., bins=5): You'll oversimplify the data, hiding important patterns like local peaks or gaps in the distribution.
• Too many bins (e.g., bins=200): Your histogram will become noisy—most bins will have very few or no data points, making it impossible to spot overall trends.
• The right number of bins: Balances detail and readability, letting you clearly see if your data is normally distributed, skewed, or has outliers.

There's no one-size-fits-all answer, but here are practical methods I use regularly:

• Square Root Rule: A quick starting point—calculate bins ≈ sqrt(total_number_of_data_points). For example, if you have 10,000 housing entries, sqrt(10000) = 100 gives you a reasonable starting bin count.
• Sturges' Formula: Works well for normally distributed data: bins = 1 + log2(total_number_of_data_points). For 10,000 entries, that's 1 + log2(10000) ≈ 15 bins.
• Freedman-Diaconis Criterion: More robust to outliers. Calculate it with this snippet:

  import numpy as np
  q25, q75 = np.percentile(housing, [25, 75])
  iqr = q75 - q25
  bin_width = 2 * iqr * len(housing) ** (-1/3)
  bins = round((housing.max().max() - housing.min().min()) / bin_width)
  

• Trial and Error: The most hands-on approach. Start with the 50 bins from your book, then try 30, 70, or 100. Pick the bin count that makes the distribution's shape easiest to understand.

The line housing.hist(bins=50, figsize=(20,15)) uses 50 bins because it's a middle ground—enough to show granular details in housing data without making the histogram messy. It's a common default for exploratory data analysis.

内容的提问来源于stack exchange，提问作者Aniruddh Sharma