At this point, you’ve seen more than a handful of functions and methods to choose from for plotting a Python histogram. How do they compare? In short, there is no one-size-fits-all answer. Here’s a recap of the functions and methods you’ve covered so far, all of which relate to breaking down and representing distributions in Python:

You Have/Want To | Consider Using | Note(s) |
---|---|---|

Clean-cut integer data housed in a data structure such as a list, tuple, or set, and you want to create a Python histogram without importing any third party libraries. | `collections.Counter()` from the Python standard library offers a fast and straightforward way to get frequency counts from a container of data. |
This is a frequency table, so it doesn’t use the concept of binning as a “true” histogram does. |

Large array of data, and you want to compute the “mathematical” histogram that represents bins and the corresponding frequencies. | NumPy’s `np.histogram()` and `np.bincount()` are useful for computing the histogram values numerically and the corresponding bin edges. |
For more, check out `np.digitize()` . |

Tabular data in Pandas’ `Series` or `DataFrame` object. |
Pandas methods such as `Series.plot.hist()` , `DataFrame.plot.hist()` , `Series.value_counts()` , and `cut()` , as well as `Series.plot.kde()` and `DataFrame.plot.kde()` . |
Check out the Pandas visualization docs for inspiration. |

Create a highly customizable, fine-tuned plot from any data structure. | `pyplot.hist()` is a widely used histogram plotting function that uses `np.histogram()` and is the basis for Pandas’ plotting functions. |
Matplotlib, and especially its object-oriented framework, is great for fine-tuning the details of a histogram. This interface can take a bit of time to master, but ultimately allows you to be very precise in how any visualization is laid out. |

Pre-canned design and integration. | Seaborn’s `distplot()` , for combining a histogram and KDE plot or plotting distribution-fitting. |
Essentially a “wrapper around a wrapper” that leverages a Matplotlib histogram internally, which in turn utilizes NumPy. |

With that, best of luck creating histograms in the wild. Whatever you do, just don’t use a pie chart!

**Congratulations, you made it to the end of the course!** What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the *discussion* section and let us know.