And because I fixed the parameter of the random generator (with the np.random.seed() line), you’ll get the very same numpy arrays with the very same data points that I have. Just use the .hist() or the .plot.hist() functions on the dataframe that contains your data points and you’ll get beautiful histograms that will show you the distribution of your data. What is a Histogram? ), Python libraries and packages for Data Scientists. Gallery generated by Sphinx-Gallery. If you plot() the gym dataframe as it is: On the y-axis, you can see the different values of the height_m and height_f datasets. For this dataset above, a histogram would look like this: It’s very visual, very intuitive and tells you even more than the averages and variability measures above. For instance, let’s imagine that you measure the heights of your clients with a laser meter and you store first decimal values, too. To make this highly specialized plot, we can't use the standard hist method. For instance when you have way too many unique values in your dataset. The histograms for all the samples are The Astropy docs have a great section on how to But if you plot a histogram, too, you can also visualize the distribution of your data points. In that case, it’s handy if you don’t put these histograms next to each other — but on the very same chart. © Copyright 2002 - 2012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team; 2012 - 2018 The Matplotlib development team. Like this: This is the very same dataset as it was before… only one decimal more accurate. If you plot the output of this, you’ll get a much nicer line chart: This is closer to what we wanted… except that line charts are to show trends. Python has a lot of different options for building and plotting histograms. And don’t stop here, continue with the pandas tutorial episode #5 where I’ll show you how to plot a scatter plot in pandas. Additionally, the histograms are plotted to be symmetrical about their x-position, thus making them very similar to violin plots. But because of that tiny difference, now you have not ~25 but ~150 unique values. Plotting a histogram in Python is easier than you’d think! The And of course, if you have never plotted anything in pandas before, creating a simpler line chart first can be handy. A histogram divides the variable into bins, counts the data points in each bin, and shows the bins on the x-axis and the counts on the y-axis. If you use multiple data along with histtype as a bar, then those values are arranged side by side. Preparing your data is usually more than 80% of the job…. You can make this complicated by adding more parameters to display everything more nicely. So in this tutorial, I’ll focus on how to plot a histogram in Python that’s: The tool we will use for that is a function in our favorite Python data analytics library — pandas — and it’s called .hist()… But more about that in the article! Anyway, since these histograms are overlapping each other, I recommend setting their transparency to 70% by using the alpha parameter: This is it!Just as I promised: plotting a histogram in Python is easy… as long as you want to keep it simple. In that case, it’s handy if you don’t put these histograms next to each other — but on the very same chart. And in this article, I’ll show you how. But in this simpler case, you don’t have to worry about data cleaning (removing duplicates, filling empty values, etc.). barstacked: When you use the multiple data, those values stacked on top of each other. If you simply counted the unique values in the dataset and put that on a bar chart, you would have gotten this: But when you plot a histogram, there’s one more initial step: these unique values will be grouped into ranges. In the height_m dataset there are 250 height values of male clients. np.histogram function. To get what we wanted to get (plot the occurrence of each unique value in the dataset), we have to work a bit more with the original dataset. Anyway, these were the basics. grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3) From this we can specify subplot locations and extents using the familiary Python slicing syntax: In : plt.subplot(grid[0, 0]) plt.subplot(grid[0, 1:]) plt.subplot(grid[1, :2]) plt.subplot(grid[1, 2]); This type of flexible grid alignment has a wide range of uses. On the back end, Pandas will group your data into bins, or buckets. be symmetrical about their x-position, thus making them very similar When is this grouping-into-ranges concept useful? You have the individual data points – the height of each and every client in one big Python list: Looking at 250 data points is not very intuitive, is it? To put your data on a chart, just type the .plot() function right after the pandas dataframe you want to visualize. I have a strong opinion about visualization in Python, which is: it should be useful and not pretty. If you want to learn more about how to become a data scientist, take my 50-minute video course. These ranges are called bins or buckets — and in Python, the default number of bins is 10. The Junior Data Scientist’s First Month video course. In the height_f dataset you’ll get 250 height values of female clients of our hypothetical gym. Yepp, compared to the bar chart solution above, the .hist() function does a ton of cool things for you, automatically: So plotting a histogram (in Python, at least) is definitely a very convenient way to visualize the distribution of your data. The default .histogram() function will take care of most of your needs. We have the heights of female and male gym members in one big 250-row dataframe. A histogram shows the number of occurrences of different values in a dataset.