A histogram provides a straightforward method for visually displaying data when you’ve organized it into groups. It effectively illustrates the distribution of numerical data. In essence, it functions as a type of bar plot where the X-axis indicates the ranges, or “bins,” and the Y-axis reveals the frequency of each bin. A histogram graphically represents data distribution. In Python, you can craft histograms with various libraries, but Matplotlib. It is one of the most commonly used libraries for this purpose. It offers a convenient way to create histograms and tailor their appearance.

1. HOW TO CREATE HISTOGRAM IN
PYTHON
BY – DEVELOPER HELPS

2. INTRODUCTION
• A HISTOGRAM PROVIDES A STRAIGHTFORWARD
METHOD FOR VISUALLY DISPLAYING DATA WHEN
YOU’VE ORGANIZED IT INTO GROUPS. IT
EFFECTIVELY ILLUSTRATES THE DISTRIBUTION OF
NUMERICAL DATA. IN ESSENCE, IT FUNCTIONS AS
A TYPE OF BAR PLOT WHERE THE X-AXIS
INDICATES THE RANGES, OR “BINS,” AND THE Y-
AXIS REVEALS THE FREQUENCY OF EACH BIN.

3. CREATING PYTHON HISTOGRAM USING
NUMPY AND MATPLOTLIB
• TO CREATE A HISTOGRAM IN PYTHON WITH ATTRIBUTES IN A
TABULAR FORM, YOU TYPICALLY USE A LIBRARY LIKE MATPLOTLIB
TO CUSTOMIZE VARIOUS ASPECTS OF THE HISTOGRAM. YOU CAN
SPECIFY ATTRIBUTES LIKE THE NUMBER OF BINS, COLORS, LABELS,
AND MORE.

4. DIFFERENT ATTRIBUTES

5. EXAMPLE OUTPUT

6. OTHER USES
• WE USE NECESSARY LIBRARIES: MATPLOTLIB’S PYPLOT FOR CREATING PLOTS AND
NUMPY FOR GENERATING RANDOM DATA.
• WE GENERATE RANDOM DATA USING NUMPY’S NUMPY.RANDOM.RANDN. FEEL FREE
TO REPLACE THIS WITH YOUR OWN DATASET OR LOAD DATA FROM A FILE FOR
SPECIFIC VISUALIZATION NEEDS.
• WE CONSTRUCT THE HISTOGRAMS USING PLT.HIST(). THE ‘BINS’ PARAMETER ALLOWS
YOU TO CONTROL THE NUMBER OF BARS IN THE HISTOGRAMS, PROVIDING
FLEXIBILITY TO ADJUST THE GRANULARITY.
• WE INCLUDE LABELS FOR THE X AND Y AXES AND ASSIGN A TITLE TO THE PLOT.

7. HOW TO CUSTOMIZE PYTHON HISTOGRAMS
IN MATPLOTLIB
• MATPLOTLIB OFFERS VARIOUS METHODS FOR CUSTOMIZING HISTOGRAMS.
NOTABLY, THE MATPLOTLIB.PYPLOT.HIST() FUNCTION OFFERS AN ARRAY
OF ATTRIBUTES THAT ALLOW US TO TAILOR A HISTOGRAM TO OUR
SPECIFIC NEEDS. THIS FUNCTION ALSO PROVIDES A ‘PATCHES’ OBJECT,
GRANTING ACCESS TO THE PROPERTIES OF THE CREATED OBJECTS.
UTILIZING THIS, WE CAN EASILY MAKE FURTHER ADJUSTMENTS TO THE
PLOT AS DESIRED.

8. EXAMPLE OUTPUT

9. HOW TO CREATE MULTIPLE
HISTOGRAMS IN PYTHON
• CONSTRUCTING MULTIPLE HISTOGRAMS IN PYTHON WITH
MATPLOTLIB PROVES TO BE A VALUABLE TECHNIQUE WHEN
COMPARING DISTRIBUTIONS OF VARIOUS DATASETS WITHIN A
SINGLE GRAPH.

10. EXAMPLE OUTPUT

11. CONCLUSION
• FIRST, WE IMPORT THE NECESSARY LIBRARIES, WHICH INCLUDE MATPLOTLIB AND NUMPY.
• NEXT, WE GENERATE TWO SETS OF RANDOM DATA USING NUMPY’S NP.RANDOM.NORMAL FUNCTION. SPECIFICALLY, WE
CREATE ‘DATA1’ BY SAMPLING FROM A NORMAL DISTRIBUTION WITH A MEAN OF 0 AND A STANDARD DEVIATION OF 1.
‘DATA2’ IS GENERATED FROM A NORMAL DISTRIBUTION WITH A MEAN OF 2 AND A STANDARD DEVIATION OF 1.
• MOVING ON, WE PROCEED TO CREATE HISTOGRAMS FOR BOTH DATASETS EMPLOYING PLT.HIST. WITHIN THIS STEP, WE
SPECIFY VARIOUS PARAMETERS SUCH AS THE NUMBER OF BINS, COLORS, AND ALPHA (WHICH CONTROLS TRANSPARENCY)
FOR EACH HISTOGRAMS. WE ALSO UTILIZE THE ‘LABEL’ PARAMETER TO ASSIGN LABELS FOR THE LEGEND, AIDING IN DATASET
DIFFERENTIATION.
• AFTERWARD, WE ENHANCE THE VISUALIZATION BY INCLUDING LABELS FOR THE X AND Y AXES, SETTING A PLOT TITLE, AND
ENABLING GRID LINES.
• TO DISTINGUISH BETWEEN THE TWO DATASETS IN OUR PLOT, WE EFFECTIVELY EMPLOY PLT.LEGEND() TO DISPLAY A LEGEND.
• FINALLY, TO SHOWCASE OUR HISTOGRAM, WE UTILIZE PLT.SHOW().
BY – DEVELOPERHELPS