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Mastering Histogram Creation and Customization using Plotly in Python

Creating a Histogram using Plotly in Python

Histograms are one of the most useful tools for visualizing data distribution in numerical data. Understanding the distribution of data is key to drawing relevant insights and making informed decisions.

Python has various libraries that enable the creation of histograms, one of which is Plotly. In this article, we will take a look at how to use Plotly to create histograms, adjust their properties and customize their color and pattern sequences.

We will also explore other ways to adjust various properties of the histogram, such as hiding the legend and adding text, as well as adding a line around each bin.

Functionality of a Histogram

The functionality of a histogram is primarily based on the distribution of numerical data into bins, with the height of each bin representing the frequency of data falling within that particular range. The distribution pattern – whether data is normally distributed, skewed, or bimodal – can be seen visually on the histogram.

Using the histogram() function

The histogram() function simplifies the creation of histograms in Plotly. The function will require data, a DataFrame, or Series as input, along with axes and a figure size:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, y=’y’, width=500, height=500, color=’color’)

“`

The code above uses the ‘x’ and ‘y’ values from the DataFrame ‘data’ to generate a histogram.

The width and height of the figure are set to 500 pixels, and the histogram bars are colored by the ‘color’ column in the DataFrame.

Adjusting the Histogram Properties

We can adjust several histogram properties, including changing bin patterns and spacings, plotting a distribution plot above a histogram, changing opacity and axis scale, and setting the number of bins and histogram function.

Changing Bin Patterns and Spacings

The pattern_shape attribute can be used to change the pattern of bars in the histogram. Additionally, facet_row and facet_col can be used to display the histograms in rows and columns.

The subplot attribute can be used to specify the numbers of rows and columns, and spacing can be used to adjust the gaps between the subplots:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, pattern_shape=’|’, facet_row=’week’, facet_col=’month’,

subplot_titles=True, spacing=0.1)

“`

The code above creates a histogram with | shaped bar patterns, with subplots displayed for weeks and months with spacing of 0.1.

Plotting Distribution Plot Above Histogram

To visualize the distribution of data on top of a histogram, the marginal argument can be used:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, y=’y’, marginal=’box’)

“`

The code above creates a distribution plot on top of the histogram, with a boxplot displayed for ‘y’ values.

Changing Opacity and Axis Scale

The opacity parameter sets the transparency level of the histogram bars. The log_x parameter scales the x-axis logarithmically, and log_y does the same for the y-axis:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, opacity=0.7, log_x=True, log_y=True)

“`

The code above creates a histogram with transparent bars, using logarithmic scales for the x and y axis.

Setting Number of Bins and Histogram Function

The nbins attribute sets the number of data bins to be used in the histogram. The histfunc attribute can be used to set the type of histogram function that will be used.

Options include count, sum, min, max, avg, and median:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, nbins=25, histfunc=’avg’)

“`

The code above creates a histogram with 25 bins and displaying the average histogram function.

Customizing Color and Pattern Sequence

You can customize the color and pattern sequence using color_discrete_sequence and pattern_shape_sequence:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, nbins=25, color_discrete_sequence=[‘#00FF00’], pattern_shape_sequence=[‘|’])

“`

Overriding Default Colors with Specific Colors

You can also use color_discrete_map to specify default colors to override, in this example using red and green for representation:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, nbins=25, color_discrete_map={‘Female’: ‘red’, ‘Male’: ‘green’})

“`

Adjusting Other Properties of the Histogram

Hiding Legend and Adding Text

By default, the legend is displayed on the histogram; however, you can hide the legend by setting the ‘showlegend’ property to ‘False.’ You can also add text to the histogram by utilizing the ‘text’ and ‘textposition’ properties. “`

import plotly.express as px

fig = px.histogram(data, x=’x’, nbins=25, color=’gender’, showlegend=False,

text=data[‘id’], textposition=’inside top left’)

“`

Adding Line Around Each Bin

You can add a line around each histogram bar using the marker argument with color and width properties as shown below:

“`

import plotly.express as px

fig = px.histogram(data, x=’x’, nbins=25, color=’gender’, showlegend=True,

marker=dict(color=’red’, line=dict(color=’black’, width=1)))

“`

Conclusion

In conclusion, Python’s Plotly library provides an easy-to-use way of creating histograms, adjusting their properties, and customizing their color and pattern sequences. Additionally, we explored how to adjust other properties of the histogram, including hiding the legend and adding text, as well as adding a line around each bin.

With the knowledge gained in this article, you’ll be able to hone your data visualization skills and develop better, more meaningful insights from data. In summary, creating histograms with Plotly in Python is an essential skill for visualizing numerical data.

By adjusting properties such as the bin patterns and spacings, axis scale, opacity, number of bins, and the histogram’s color and pattern sequence, you can create more informative visualizations. Other properties, such as hiding the legend, adding text, and adding a line around each bin, allow for better customization.

The main takeaway is that mastering the creation and customization of histograms can lead to better insights and data-driven decisions.

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