Introduction to Bar Plots in Plotly

contrib/plotting-visualization/index.md

@@ -8,3 +8,4 @@
 - [Scatter Plots in Matplotlib](matplotlib-scatter-plot.md)
 - [Introduction to Seaborn and Installation](seaborn-intro.md)
 - [Getting started with Seaborn](seaborn-basics.md)
+- [Bar Plots in Plotly](plotly-bar-plots.md)

contrib/plotting-visualization/plotly-bar-plots.md

@@ -0,0 +1,348 @@
+# Bar Plots in Plotly
+
+A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally.
+
+It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes.
+
+Plotly is a very powerful library for creating modern visualizations and it provides a very easy and intuitive method to create highly customized bar plots.
+
+## Prerequisites
+
+Before creating bar plots in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
+
+## Introduction
+
+There are various ways to create bar plots in `plotly`. One of the prominent and easiest one is using `plotly.express`. Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures. On the other hand you can also use `plotly.graph_objects` to create various plots.
+
+Here, we'll be using `plotly.express` to create the bar plots. Also we'll be converting our datasets into pandas DataFrames which makes it extremely convenient to create plots.
+
+Also, note that when you execute the codes in a simple python file, the output plot will be shown in your **browser**, rather than a pop-up window like in matplotlib. If you do not want that, it is **recommended to create the plots in a notebook (like jupyter)**. For this, install an additional library `nbformat`. This way you can see the output on the notebook itself, and can also render its format to png, jpg, etc.
+
+## Creating a simple bar plot using `plotly.express.bar`
+
+With `plotly.express.bar`, each row of the DataFrame is represented as a rectangular mark.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold')
+
+# Showing plot
+fig.show()
+```
+![Basic Bar Plot](images/plotly-basic-bar-plot.png)
+
+Here, we are first creating the dataset and converting it into Pandas DataFrames using dictionaries, with its keys being DataFrame columns. Next, we are plotting the bar chart by using `px.bar`. In the `x` and `y` parameters, we have to specify a column name in the DataFrame.
+
+**Note:** When you generate the image using above code, it will show you an **interactive plot**, if you want image, you can download it from their itself.
+
+## Customizing Bar Plots
+
+### Adding title to the graph
+
+Let us create an imaginary graph of number of cars sold in a various years. Simply pass the title of your graph as a parameter in `px.bar`.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years')
+
+# Showing plot
+fig.show()
+```
+![Bar Plot Title](images/plotly-bar-title.png)
+
+### Adding bar colors and legends
+
+To add different colors to different bars, simply pass the column name of the x-axis or a custom column which groups different bars in `color` parameter.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Years')
+
+# Showing plot
+fig.show()
+```
+![Bar Colors Basic](images/plotly-bar-colors-basic.png)
+
+Now, let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation.
+
+The easiest way to achieve this is to add a new column to the dataframe and then pass it to the `color` parameter.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+# Creating the relevant colors dataset
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Century')
+
+# Showing plot
+fig.show()
+```
+![Bar Colors](images/plotly-bar-colors.png)
+
+### Adding labels to bars
+
+We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by setting `text_auto` parameter to `True`. If you want custom text then you can pass a column name to the `text` parameter.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Century',
+            text_auto=True)
+
+# Showing plot
+fig.show()
+```
+![Bar Labels-1](images/plotly-bar-labels-1.png)
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Century',
+            text='Century')
+
+# Showing plot
+fig.show()
+```
+![Bar Labels-2](images/plotly-bar-labels-2.png)
+
+You can also change the features of text (or any other element of your plot) using `fig.update_traces`.
+
+Here, we are changing the position of text to position it outside the bars.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Century',
+            text_auto=True)
+
+# Updating bar text properties
+fig.update_traces(textposition="outside", cliponaxis=False)
+
+# Showing plot
+fig.show()
+```
+![Bar Labels-3](images/plotly-bar-labels-3.png)
+
+### Rounded Bars
+
+You can create rounded by specifying the radius value to `barcornerradius` in `fig.update_layout`.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Years', y='Number of Cars sold', 
+            title='Number of cars sold in various years',
+            color='Century',
+            text_auto=True)
+
+# Updating bar text properties
+fig.update_traces(textposition="outside", cliponaxis=False)
+
+# Updating figure layout
+fig.update_layout({
+'plot_bgcolor': 'rgba(255, 255, 255, 1)',
+'paper_bgcolor': 'rgba(255, 255, 255, 1)',
+'barcornerradius': 15
+})
+
+# Showing plot
+fig.show()
+```
+
+![Rounded Bars](images/plotly-rounded-bars.png)
+
+## Horizontal Bar Plot
+
+To create a horizontal bar plot, you just have to interchange your `x` and `y` DataFrame columns. Plotly takes care of the rest!
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+years = ['1998', '1999', '2000', '2001', '2002']
+num_of_cars_sold = [200, 300, 500, 700, 1000]
+colors = ['1900s','1900s','2000s','2000s','2000s']
+
+# Converting dataset to pandas DataFrame
+dataset = {"Years":years, "Number of Cars sold":num_of_cars_sold, "Century":colors}
+df = pd.DataFrame(dataset)
+
+# Creating bar plot
+fig = px.bar(df, x='Number of Cars sold', y='Years', 
+            title='Number of cars sold in various years',
+            color='Century',
+            text_auto=True)
+
+# Updating bar text properties
+fig.update_traces(textposition="outside", cliponaxis=False)
+
+# Updating figure layout
+fig.update_layout({
+'barcornerradius': 30
+})
+
+# Showing plot
+fig.show()
+```
+![Horizontal Bar Plot](images/plotly-horizontal-bar-plot.png)
+
+## Plotting Long Format and Wide Format Data
+
+Long-form data has one row per observation, and one column per variable. This is suitable for storing and displaying multivariate data i.e. with dimension greater than 2.
+
+```Python
+# Plotting long format data
+
+import plotly.express as px
+
+# Long format dataset
+long_df = px.data.medals_long()
+
+# Creating Bar Plot
+fig = px.bar(long_df, x="nation", y="count", color="medal", title="Long-Form Input")
+
+# Showing Plot
+fig.show()
+```
+![Long format bar plot](images/plotly-long-format-bar-plot.png)
+
+```Python
+print(long_df)
+
+# Output
+        nation   medal  count
+0  South Korea    gold     24
+1        China    gold     10
+2       Canada    gold      9
+3  South Korea  silver     13
+4        China  silver     15
+5       Canada  silver     12
+6  South Korea  bronze     11
+7        China  bronze      8
+8       Canada  bronze     12
+```
+
+Wide-form data has one row per value of one of the first variable, and one column per value of the second variable. This is suitable for storing and displaying 2-dimensional data.
+
+```Python
+# Plotting wide format data
+import plotly.express as px
+
+# Wide format dataset
+wide_df = px.data.medals_wide()
+
+# Creating Bar Plot
+fig = px.bar(wide_df, x="nation", y=["gold", "silver", "bronze"], title="Wide-Form Input")
+
+# Showing Plot
+fig.show()
+```
+![Wide format bar plot](images/plotly-wide-format-bar-plot.png)
+
+```Python
+print(wide_df)
+
+# Output
+        nation  gold  silver  bronze
+0  South Korea    24      13      11
+1        China    10      15       8
+2       Canada     9      12      12