Merge pull request #1335 from piyush-poddar/introduction-to-line-charts-in-plotly

Introduction to Line Charts in Plotly

contrib/plotting-visualization/index.md

@@ -13,4 +13,5 @@
 - [Getting started with Seaborn](seaborn-basics.md)
 - [Bar Plots in Plotly](plotly-bar-plots.md)
 - [Pie Charts in Plotly](plotly-pie-charts.md)
+- [Line Charts in Plotly](plotly-line-charts.md)
 - [Scatter Plots in Plotly](plotly-scatter-plots.md)

contrib/plotting-visualization/plotly-line-charts.md

@@ -0,0 +1,300 @@
+# Line Charts in Plotly
+
+A line chart displays information as a series of data points connected by straight line segments. It represents the change in a quantity with respect to another quantity and helps us to see trends and patterns over time or across categories. It is a basic type of chart common in many fields. For example, it is used to represent the price of stocks with respect to time, among many others.
+
+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 line charts.
+
+## Prerequisites
+
+Before creating line charts in Plotly you must ensure that you have Python, Plotly and Pandas installed on your system.
+
+## Introduction
+
+There are various ways to create line charts 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 line charts. 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 line chart using `plotly.express.line`
+
+With `plotly.express.line`, each data point is represented as a vertex (which location is given by the x and y columns) of a polyline mark in 2D space.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold')
+
+# Showing plot
+fig.show()
+```
+
+![Basic Line Chart](images/plotly-basic-line-chart.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 line chart by using `px.line`. 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 Line Charts
+
+### Adding title to the chart
+
+Simply pass the title of your graph as a parameter in `px.line`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+# Showing plot
+fig.show()
+```
+
+![Line Chart Title](images/plotly-line-title.png)
+
+### Adding Markers to the lines
+
+The `markers` argument can be set to `True` to show markers on lines.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years',
+              markers=True)
+
+# Showing plot
+fig.show()
+```
+
+![Line Markers](images/plotly-line-markers.png)
+
+### Dashed Lines
+
+You can plot dashed lines by changing the `dash` property of `line` to `dash` or `longdash` and passing it as a dictionary to `patch` parameter in `fig.update_traces`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+fig.update_traces(patch={"line": {"dash": 'dash'}}) 
+
+# Showing plot
+fig.show()
+```
+
+![Dashed Line](images/plotly-line-dashed.png)
+
+### Dotted Lines
+
+You can plot dotted lines by changing the `dash` property of `line` to `dot` and passing it as a dictionary to `patch` parameter in `fig.update_traces`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+fig.update_traces(patch={"line": {"dash": 'dot'}}) 
+
+# Showing plot
+fig.show()
+```
+
+![Dotted Line](images/plotly-line-dotted.png)
+
+### Dashed and Dotted Lines
+
+You can plot dotted lines by changing the `dash` property of `line` to `dashdot` and passing it as a dictionary to `patch` parameter in `fig.update_traces`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+fig.update_traces(patch={"line": {"dash": 'dashdot'}}) 
+
+# Showing plot
+fig.show()
+```
+
+![Dotted Line](images/plotly-line-dasheddotted.png)
+
+### Changing line colors
+
+You can set custom colors to lines by changing the `color` property of `line` to `your_color` and passing it as a dictionary to `patch` parameter in `fig.update_traces`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+fig.update_traces(patch={"line": {"color": 'red'}}) 
+
+# Showing plot
+fig.show()
+```
+
+![Colored Line](images/plotly-line-color.png)
+
+### Changing line width
+
+You can set custom width to lines by changing the `width` property of `line` to `your_width` and passing it as a dictionary to `patch` parameter in `fig.update_traces`.
+
+```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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years')
+
+fig.update_traces(patch={"line": {"width": 7}}) 
+
+# Showing plot
+fig.show()
+```
+
+![Width Line](images/plotly-line-width.png)
+
+### Labeling Data Points
+
+You can label your data points by passing the relevant column name of your DataFrame to `text` parameter in `px.line`.
+
+```Python
+# 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 line chart
+fig = px.line(df, x='Years', y='Number of Cars sold', 
+              title='Number of cars sold in various years',
+              text='Number of Cars sold')
+
+fig.update_traces(textposition="bottom right")
+
+# Showing plot
+fig.show()
+```
+
+![Data Point Labelling](images/plotly-line-datapoint-label.png)
+
+## Plotting multiple lines
+
+There are several ways to plot multiple lines in plotly, like using `plotly.graph_objects`, using `fig.add_scatter`, having multiple columns in the DataFrame, etc.
+
+Here, we'll be creating a simple dataset of the runs scored by the end of each over by India and South Africa in recent T20 World Cup Final and plot it using plotly.
+
+```Python
+import plotly.express as px
+import pandas as pd
+
+# Creating dataset
+overs = list(range(0,21))
+runs_india = [0,15,23,26,32,39,45,49,59,68,75,82,93,98,108,118,126,134,150,167,176]
+runs_rsa = [0,6,11,14,22,32,42,49,62,71,81,93,101,109,123,147,151,155,157,161,169]
+
+# Converting dataset to pandas DataFrame
+dataset = {"overs":overs, "India":runs_india, "South Africa":runs_rsa}
+df = pd.DataFrame(dataset)
+
+# Creating line chart
+fig = px.line(df, x="overs", y=["India", "South Africa"])
+fig.update_layout(xaxis_title="Overs", yaxis_title="Runs", legend_title=None)
+
+# Showing plot
+fig.show()
+```
+
+![Multiple Lines](images/plotly-line-multiple-lines.png)
+
+To plot multiple lines, we have passed multiple columns of the DataFrame in the `y` parameter.