Plotting Histogram in Python using Matplotlib
A histogram is basically used to represent data provided in a form of some groups.It is accurate method for the graphical representation of numerical data distribution.It is a type of bar plot where X-axis represents the bin ranges while Y-axis gives information about frequency.
Creating a Histogram
To create a histogram the first step is to create bin of the ranges, then distribute the whole range of the values into a series of intervals, and count the values which fall into each of the intervals.Bins are clearly identified as consecutive, non-overlapping intervals of variables.The matplotlib.pyplot.hist() function is used to compute and create histogram of x.
The following table shows the parameters accepted by matplotlib.pyplot.hist() function :
| Attribute | parameter |
|---|---|
| x | array or sequence of array |
| bins | optional parameter contains integer or sequence or strings |
| density | optional parameter contains boolean values |
| range | optional parameter represents upper and lower range of bins |
| histtype | optional parameter used to create type of histogram [bar, barstacked, step, stepfilled], default is “bar” |
| align | optional parameter controls the plotting of histogram [left, right, mid] |
| weights | optional parameter contains array of weights having same dimensions as x |
| bottom | location of the baseline of each bin |
| rwidth | optional parameter which is relative width of the bars with respect to bin width |
| color | optional parameter used to set color or sequence of color specs |
| label | optional parameter string or sequence of string to match with multiple datasets |
| log | optional parameter used to set histogram axis on log scale |
Let’s create a basic histogram of some random values. Below code creates a simple histogram of some random values:
How to plot a histogram using Matplotlib in Python with a list of data?
How do I plot a histogram using matplotlib.pyplot.hist ? I have a list of y-values that correspond to bar height, and a list of x-value strings. Related: matplotlib.pyplot.bar .
5 Answers 5
If you want a histogram, you don’t need to attach any ‘names’ to x-values because:
- on x -axis you will have data bins
- on y -axis counts (by default) or frequencies ( density=True )
import matplotlib.pyplot as plt import numpy as np %matplotlib inline np.random.seed(42) x = np.random.normal(size=1000) plt.hist(x, density=True, bins=30) # density=False would make counts plt.ylabel('Probability') plt.xlabel('Data'); 
Note, the number of bins=30 was chosen arbitrarily, and there is Freedman–Diaconis rule to be more scientific in choosing the «right» bin width:
, where IQR is Interquartile range and n is total number of datapoints to plot
So, according to this rule one may calculate number of bins as:
q25, q75 = np.percentile(x, [25, 75]) bin_width = 2 * (q75 - q25) * len(x) ** (-1/3) bins = round((x.max() - x.min()) / bin_width) print("Freedman–Diaconis number of bins:", bins) plt.hist(x, bins=bins); Freedman–Diaconis number of bins: 82 
And finally you can make your histogram a bit fancier with PDF line, titles, and legend:
import scipy.stats as st plt.hist(x, density=True, bins=82, label="Data") mn, mx = plt.xlim() plt.xlim(mn, mx) kde_xs = np.linspace(mn, mx, 300) kde = st.gaussian_kde(x) plt.plot(kde_xs, kde.pdf(kde_xs), label="PDF") plt.legend(loc="upper left") plt.ylabel("Probability") plt.xlabel("Data") plt.title("Histogram"); 
If you’re willing to explore other opportunities, there is a shortcut with seaborn :
# !pip install seaborn import seaborn as sns sns.displot(x, bins=82, kde=True); 
If you have limited number of data points, a bar plot would make more sense to represent your data. Then you may attach labels to x-axis:
x = np.arange(3) plt.bar(x, height=[1,2,3]) plt.xticks(x, ['a','b','c']); 
@Toad22222 This is an excerpt from Ipython notebook cell. Try to execute it without semicolon and see the difference. All the code snippets I post on SO run perfectly on my computer.
If you are wondering about the semi-colon used by Sergey, see here and #16 here for how semi-colon is used in Jupyter notebooks (formerly IPython notebooks) cells when plotting to suppress the text about the plot object.
If you are getting OverflowError: cannot convert float infinity to integer just change .25 to 25 and .75 to 75
If you haven’t installed matplotlib yet just try the command.
Library import
import matplotlib.pyplot as plot The histogram data:
plot.hist(weightList,density=1, bins=20) plot.axis([50, 110, 0, 0.06]) #axis([xmin,xmax,ymin,ymax]) plot.xlabel('Weight') plot.ylabel('Probability') Display histogram
And the output is like :
The plot.axis([50, 110, 0, 0.06])’ line is useless for the example. Besides, as it hard codes the area of the plot to show, if your data does not fit entirely inside it you may be confused why it doesn’t show correctly.
This is an old question but none of the previous answers has addressed the real issue, i.e. that fact that the problem is with the question itself.
First, if the probabilities have been already calculated, i.e. the histogram aggregated data is available in a normalized way then the probabilities should add up to 1. They obviously do not and that means that something is wrong here, either with terminology or with the data or in the way the question is asked.
Second, the fact that the labels are provided (and not intervals) would normally mean that the probabilities are of categorical response variable — and a use of a bar plot for plotting the histogram is best (or some hacking of the pyplot’s hist method), Shayan Shafiq’s answer provides the code.
However, see issue 1, those probabilities are not correct and using bar plot in this case as «histogram» would be wrong because it does not tell the story of univariate distribution, for some reason (perhaps the classes are overlapping and observations are counted multiple times?) and such plot should not be called a histogram in this case.
Histogram is by definition a graphical representation of the distribution of univariate variable (see Histogram | NIST/SEMATECH e-Handbook of Statistical Methods & Histogram | Wikipedia) and is created by drawing bars of sizes representing counts or frequencies of observations in selected classes of the variable of interest. If the variable is measured on a continuous scale those classes are bins (intervals). Important part of histogram creation procedure is making a choice of how to group (or keep without grouping) the categories of responses for a categorical variable, or how to split the domain of possible values into intervals (where to put the bin boundaries) for continuous type variable. All observations should be represented, and each one only once in the plot. That means that the sum of the bar sizes should be equal to the total count of observation (or their areas in case of the variable widths, which is a less common approach). Or, if the histogram is normalised then all probabilities must add up to 1.
If the data itself is a list of «probabilities» as a response, i.e. the observations are probability values (of something) for each object of study then the best answer is simply plt.hist(probability) with maybe binning option, and use of x-labels already available is suspicious.
Then bar plot should not be used as histogram but rather simply
import matplotlib.pyplot as plt probability = [0.3602150537634409, 0.42028985507246375, 0.373117033603708, 0.36813186813186816, 0.32517482517482516, 0.4175257731958763, 0.41025641025641024, 0.39408866995073893, 0.4143222506393862, 0.34, 0.391025641025641, 0.3130841121495327, 0.35398230088495575] plt.hist(probability) plt.show() 
matplotlib in such case arrives by default with the following histogram values
(array([1., 1., 1., 1., 1., 2., 0., 2., 0., 4.]), array([0.31308411, 0.32380469, 0.33452526, 0.34524584, 0.35596641, 0.36668698, 0.37740756, 0.38812813, 0.39884871, 0.40956928, 0.42028986]), ) the result is a tuple of arrays, the first array contains observation counts, i.e. what will be shown against the y-axis of the plot (they add up to 13, total number of observations) and the second array are the interval boundaries for x-axis.
One can check they they are equally spaced,
x = plt.hist(probability)[1] for left, right in zip(x[:-1], x[1:]): print(left, right, right-left) 
Or, for example for 3 bins (my judgment call for 13 observations) one would get this histogram
with the plot data «behind the bars» being
The author of the question needs to clarify what is the meaning of the «probability» list of values — is the «probability» just a name of the response variable (then why are there x-labels ready for the histogram, it makes no sense), or are the list values the probabilities calculated from the data (then the fact they do not add up to 1 makes no sense).