The Plotly Python library is an interactive open-source library. This can be a very helpful tool for data visualization and understanding the data simply and easily. plotly graph objects are a high-level interface to plotly which are easy to use. It can plot various types of graphs and charts like scatter plots, line charts, bar charts, box plots, histograms, pie charts, etc.
So you all must be wondering why plotly over other visualization tools or libraries? Here’s the answer –
- Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
- It is visually attractive that can be accepted by a wide range of audiences.
- It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others.
Ok, enough theory let’s start.
Installation:
To install this module type the below command in the terminal.
pip install plotly
Getting Started
Let’s create various plots using this module
- Scatter Plot: Scatter plot represent values for two different numeric variables. They are mainly used for the representation of the relationship between two variables.
Python3
# import all required librariesimport numpy as npimport plotlyimport plotly.graph_objects as goimport plotly.offline as pyofrom plotly.offline import init_notebook_modeinit_notebook_mode(connected=True)# generating 150 random integers# from 1 to 50x = np.random.randint(low=1, high=50, size=150)*0.1# generating 150 random integers# from 1 to 50y = np.random.randint(low=1, high=50, size=150)*0.1# plotting scatter plotfig = go.Figure(data=go.Scatter(x=x, y=y, mode='markers'))fig.show() |
Output:
- Bar charts: Bar charts are used when we want to compare different groups of data and make inferences of which groups are highest and which groups are common and compare how one group is performing compared to others.
Python3
# import all required librariesimport numpy as npimport plotlyimport plotly.graph_objects as goimport plotly.offline as pyofrom plotly.offline import init_notebook_modeinit_notebook_mode(connected = True) # countries on x-axiscountries=['India', 'canada', 'Australia','Brazil', 'Mexico','Russia', 'Germany','Switzerland', 'Texas'] # plotting corresponding y for each # country in x fig = go.Figure([go.Bar(x=countries, y=[80,70,60,50, 40,50,60,70,80])]) fig.show() |
Output:
- Pie chart: A pie chart represents the distribution of different variables among total. In the pie chart each slice shows its contribution to the total amount.
Python3
# import all required librariesimport numpy as npimport plotlyimport plotly.graph_objects as goimport plotly.offline as pyofrom plotly.offline import init_notebook_modeinit_notebook_mode(connected = True) # different individual parts in # total chartcountries=['India', 'canada', 'Australia','Brazil', 'Mexico','Russia', 'Germany','Switzerland', 'Texas'] # values corresponding to each # individual country present in# countriesvalues = [4500, 2500, 1053, 500, 3200, 1500, 1253, 600, 3500] # plotting pie chartfig = go.Figure(data=[go.Pie(labels=countries, values=values)])fig.show() |
Output:
- Histogram: A histogram plots the continuous distribution of variable as series of bars and each bar indicates the frequency of the occurring value in a variable. In order to use a histogram, we simply require a variable that takes continuous numeric values
Python3
# import all required librariesimport numpy as npimport plotlyimport plotly.graph_objects as goimport plotly.offline as pyofrom plotly.offline import init_notebook_modeinit_notebook_mode(connected = True) # save the state of randomnp.random.seed(42) # generating 250 random numbersx = np.random.randn(250) # plotting histogram for xfig = go.Figure(data=[go.Histogram(x=x)]) fig.show() |
Output:
- Box plot: A box plot is the representation of a statistical summary. Minimum, First Quartile, Median, Third Quartile, Maximum.
Python3
# import all required librariesimport numpy as npimport plotlyimport plotly.graph_objects as goimport plotly.offline as pyofrom plotly.offline import init_notebook_modeinit_notebook_mode(connected = True) np.random.seed(42)# generating 50 random numbersy = np.random.randn(50) # generating 50 random numbersy1 = np.random.randn(50) fig = go.Figure() # updating the figure with yfig.add_trace(go.Box(y=y)) # updating the figure with y1fig.add_trace(go.Box(y=y1)) fig.show() |
Output:
