5 Best Ways to Create Logarithmic Bins in a Python Histogram

π‘ Problem Formulation: When visualizing data with a broad range of values, a common issue in histograms is that some bins may have too many counts while others have too few. To manage the wide distribution of data points, logarithmic bins can present a clearer picture of the dataset distribution. The input for our case is a numeric dataset with a large range of values. The desired output is a histogram with bins that increase in size logarithmically.

Method 1: Using NumPy’s logspace

NumPy’s `logspace` function is ideal for creating logarithmic bins. This function generates bins that increase exponentially from a start value to a stop value. By inputting the logarithmically spaced values into the `bins` parameter of a histogram plotting function (like `matplotlib.pyplot.hist`), one can generate a histogram with logarithmic bins.

Here’s an example:

```import numpy as np
import matplotlib.pyplot as plt

data = np.random.exponential(scale=1.0, size=1000)
bins = np.logspace(np.log10(min(data)), np.log10(max(data)), 20)

plt.hist(data, bins=bins, edgecolor='black')
plt.xscale('log')
plt.show()```

The output is a pyplot histogram with logarithmically spaced bins.

This code snippet first generates a dataset following an exponential distribution, which often necessitates logarithmic binning due to its range. It then uses `np.logspace` to produce bins sized appropriately for a logarithmic scale. Finally, it plots the histogram with these bins and sets the x-axis to a logarithmic scale.

Method 2: Matplotlib Pyplot hist with log

Matplotlib’s `hist` function has a `log` parameter which can be set to `True` to plot the y-axis on a logarithmic scale. This does not change the bins to be logarithmic, but it helps to visualize the data with large range discrepancies.

Here’s an example:

```import matplotlib.pyplot as plt

data = np.random.exponential(scale=1.0, size=1000)

plt.hist(data, bins=50, log=True)
plt.show()```

The output is a pyplot histogram with a logarithmic y-axis.

The code generates a histogram where each bin has the same size but uses a logarithmic scale for the y-axis. This means the count in each bin is transformed to a logarithmic scale, which can sometimes offer a better visualization of data with vastly differing bin counts.

Method 3: Using Matplotlib’s Axes set_xscale

With matplotlib, create the histogram normally and then set the x-axis to logarithmic scale using an Axes instance’s `set_xscale('log')` method. This adjusts the axis after plotting, providing flexibility for customizing plot appearance.

Here’s an example:

```import matplotlib.pyplot as plt

data = np.random.power(a=5, size=1000)  # Generating skewed data
fig, ax = plt.subplots()
ax.hist(data, bins=50)
ax.set_xscale('log')
plt.show()```

The output is a histogram with a logarithmic x-axis.

This code generates skewed data suitable for a logarithmic scale, plots a histogram with the data using a specified number of bins, and then sets the x-axis to logarithmic. This method allows for greater control after the plot has been generated.

Method 4: Custom Log Bin Function

For complete control over bin generation, one can write a custom function to create logarithmically spaced bins. The function takes in the desired number of bins and the data range to produce a list of bin edges.

Here’s an example:

```def custom_log_binning(data, num_bins):
log_min = np.log10(min(data))
log_max = np.log10(max(data))
bins = np.logspace(log_min, log_max, num_bins+1)
return bins

data = np.random.exponential(scale=1.0, size=1000)
bins = custom_log_binning(data, 15)

plt.hist(data, bins=bins, edgecolor='black')
plt.xscale('log')
plt.show()```

The output is a histogram with custom logarithmic bins based on data range.

This method is very flexible as it allows for a custom number of bins and can be easily adjusted for different data sets. The custom_log_binning function calculates logarithmic bins that best represent the data.

Bonus One-Liner Method 5: Inline Logspace Bin Generation

For a quick, inline logarithmic binning, one can directly generate bins using `np.logspace` inside the histogram plotting command.

Here’s an example:

```plt.hist(np.random.rand(1000)*100, bins=np.logspace(0.1, 2, 20), edgecolor='black')
plt.xscale('log')
plt.show()```

The output is a histogram with 20 logarithmic bins plotted inline.

This succinct approach directly incorporates the creation of logarithmic bins into the histogram plotting step, reducing the code to a one-liner. It uses `np.logspace` to create bins from a minimum value of 0.1 to a maximum of 2 on a logarithmic scale, suitable for random data scaled up to 100.

Summary/Discussion

• Method 1: NumPy’s logspace. Most flexible. Allows for explicit control over bin spacing. Requires separate calculation.
• Method 2: Matplotlib hist log. Simple usage. Good for y-axis log scaling. Doesnβt change bin sizes.
• Method 3: Axes set_xscale. Post-plot customization. Good for adjusting visual scale after plotting. Doesn’t inherently create logarithmic bins.
• Method 4: Custom Log Bin Function. Complete control. Ideal for very specific bin needs. Requires a custom function.
• Method 5: Inline Logspace Bin Generation. Quick and concise. Suitable for fast, in-line calculations. Less flexible than other methods.