implementing np.histogram when dealing with large datasets and custom bins
I've been struggling with this for a few days now and could really use some help. I recently switched to I've been struggling with this for a few days now and could really use some help. I'm currently using NumPy to analyze a large dataset consisting of over 5 million samples and trying to create a histogram with custom bins. When I run the following code, I expect to get a histogram with specified bins, but instead, I'm working with discrepancies in the results: ```python import numpy as np import matplotlib.pyplot as plt # Simulated dataset np.random.seed(0) data = np.random.randn(5000000) # Custom bins bins = np.linspace(-5, 5, 21) # 20 bins from -5 to 5 # Computing the histogram hist, edges = np.histogram(data, bins=bins) # Plotting the results plt.hist(data, bins=bins, alpha=0.5, label='Data') plt.plot(edges[:-1], hist, marker='o', linestyle='-', color='r', label='Histogram') plt.legend() plt.show() ``` The histogram generated seems to misrepresent the distribution, particularly in the tails. I checked the shape of `data` and it confirms it's a 1D array with 5 million entries. I also verified the `bins`, and they should align correctly with the range of values in `data`. However, the frequencies in my histogram appear much lower than expected, particularly for the extreme values corresponding to the bins at -5 and 5. I initially thought this could be a question with the binning process, so I tested with the default bins: ```python hist_default, edges_default = np.histogram(data) ``` The default histogram returned results that were much clearer and more representative of the dataset, which leads me to believe there might be an scenario with how the custom bins are being defined or used. Is there something specific I might be overlooking with `np.histogram` and custom bin definitions? Is it possible that the size of the dataset is affecting the computation? I'm using NumPy version 1.21.0, and I would appreciate any insights or suggestions to resolve this scenario. What's the best practice here? I'm working with Python in a Docker container on CentOS. Is there a simpler solution I'm overlooking?