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The app.mountpath property contains those path patterns on which a sub-app was mounted. A sub-app can be defined as an instance of Express that may be used for handling the request to a route.This property is similar to the baseUrl property of the req object; the only difference is that req.baseUrl returns the matched URL path instead of the matched patterns.Syntaxapp.mountpathExample 1Create a file "appMountpath.js" and copy the following code snippet. After creating the file, use the command "node appMountpath" to run this code.// app.mountpath code Demo Example // Importing the express module var express = require('express'); // ... Read More
The app.locals object defines the properties that are local variables inside an application. Once the value of app.locals property is set, it persists throughout the life of the application. The res.locals property is valid only for the lifetime of the request.Syntaxapp.localsExample 1Create a file "appLocals.js" and copy the following code snippet. After creating the file, use the command "node appLocals.js" to run this code.// app.locals code Demo Example // Importing the express module var express = require('express'); // Initializing the express and port number var app = express(); // Setting the below email throught out the application ... Read More
The app.listen() method binds itself with the specified host and port to bind and listen for any connections. If the port is not defined or 0, an arbitrary unused port will be assigned by the operating system that is mainly used for automated tasks like testing, etc.The app object returned by express() is a JavaScript function, that is passed to Node’s HTTP servers as a callback which handles the requests. This makes the application to provide both HTTP and HTTPS versions of the same app with the same code base, as the app does not inherit from these.Syntaxapp.listen([port], [host], [backlog], ... Read More
Histogram is a representation of the distribution of data. To plot a Histogram, use the hist() method. At first, import both the libraries −import pandas as pd import matplotlib.pyplot as pltCreate a DataFrame with 2 columns −dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] })Plot a Histogram for Registration Price column −plt.hist(dataFrame["Reg_Price"])ExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as plt # creating dataframe dataFrame = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] }) # plot a ... Read More
We can plot Line Graph, Pie Chart, Histogram, etc. with a Pandas DataFrame using Matplotlib. For this, we need to import Pandas and Matplotlib libraries −import pandas as pd import matplotlib.pyplot as pltLet us begin plotting −Line GraphExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as plt # creating a DataFrame with 2 columns dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Reg_Price": [2000, 2500, 2800, 3000, 3200, 3500], "Units": [100, 120, 150, 170, 180, 200] ... Read More
Point Plot in Seaborn is used to show point estimates and confidence intervals using scatter plot glyphs. The seaborn.pointplot() is used for this. Display Standard Deviation of Observations using confidence interval ci parameter value "sd" in the pointplot() method.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv")Plotting point plot with “Academy” and “Age”. Display Standard Deviation of Observations using confidence interval parameter value "sd"sb.pointplot( ... Read More
Box Plot in Seaborn is used to draw a box plot to show distributions with respect to categories. The seaborn.boxplot() is used for this. Use the "orient” parameter for orientation of each numeric variable.Let’s say the following is our dataset in the form of a CSV file − Cricketers.csvAt first, import the required libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv")Plotting box plot using the orient parameter for orientation of each numeric variable −sb.boxplot( data = dataFrame, orient="h")ExampleFollowing is the code −import seaborn ... Read More
We will group Pandas DataFrame using the groupby(). Select the column to be used using the grouper function. We will group year-wise and calculate sum of Registration Price with year interval for our example shown below for Car Sale Records.At first, let’s say the following is our Pandas DataFrame with three columns −# dataframe with one of the columns as Date_of_Purchase dataFrame = pd.DataFrame( { "Car": ["Audi", "Lexus", "Tesla", "Mercedes", "BMW", "Toyota", "Nissan", "Bentley", "Mustang"], "Date_of_Purchase": [pd.Timestamp("2021-06-10"), pd.Timestamp("2019-07-11"), pd.Timestamp("2016-06-25"), pd.Timestamp("2021-06-29"), ... Read More
Bar Plot in Seaborn is used to show point estimates and confidence intervals as rectangular bars. The seaborn.barplot() is used for this. Display Standard Deviation of Observations using confidence interval ci parameter value sd.Let’s say the following is our dataset in the form of a CSV file − Cricketers2.csvAt first, import the required libraries −import seaborn as sb import pandas as pd import matplotlib.pyplot as pltLoad data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers2.csv")Plotting bar plot with Academy and Matches. Display Standard Deviation of Observations using confidence interval parameter value "sd" −sb.barplot(x = "Academy", y = ... Read More
To select a subset of rows and columns, use the loc. Use the index operator i.e. the square bracket and set conditions in the loc.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")Select a subset of rows and columns combined. Right column displays the column you want to display i.e. Cars column here −dataFrame.loc[dataFrame["Units"] > 100, "Car"]ExampleFollowing is the code −import pandas as pd # Load data from a CSV file into a Pandas DataFrame: dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv") ... Read More