Bar Plot in Seaborn is used to show point estimates and confidence intervals as rectangular bars. The seaborn.barplot() is used for this. Plot vertical bar plots grouped by a categorical variable, by passing the variable as x or y coordinates in the barplot() method.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 vertical bar plots grouped by a categorical variable −sb.barplot(x = dataFrame["Role"], y ... Read More

Swarm Plot in Seaborn is used to draw a categorical scatterplot with non-overlapping points. The seaborn.swarmplot() is used for this. Draw swarms of observations on top of a violin plot using the violinplot().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")Draw swarms of observations on top of a violin plot −sb.violinplot(x = dataFrame["Role"], y = dataFrame["Matches"]) sb.swarmplot(x = dataFrame["Role"], y = dataFrame["Matches"], color="white")ExampleFollowing is ... Read More

To read CSV file without header, use the header parameter and set it to “None” in the read_csv() method.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −At first, import the required library −import pandas as pdLoad data from a CSV file into a Pandas DataFrame. This will display the headers as well −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")While loading, use the header parameter and set None to load the CSV without header −pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv", header=None)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

Scatter Plot is a data visualization technique. Use the plot.scatter() to plot the Scatter Plot. At first, Let us import the required libraries −We have our data with Team Records. Set it in the Pandas DataFrame −data = [["Australia", 2500], ["Bangladesh", 1000], ["England", 2000], ["India", 3000], ["Srilanka", 1500]] dataFrame = pd.DataFrame(data, columns=["Team", "Rank_Points"]) Let us plot now with the columns −dataFrame.plot.scatter(x="Team", y="Rank_Points")ExampleFollowing is the code −import pandas as pd import matplotlib.pyplot as mp # our data data = [["Australia", 2500], ["Bangladesh", 1000], ["England", 2000], ["India", 3000], ["Srilanka", 1500]] # dataframe dataFrame = pd.DataFrame(data, columns=["Team", "Rank_Points"]) ... Read More

Using columns.values(), we can easily rename column name with index number of a CSV file.Let’s say the following are the contents of our CSV file opened in Microsoft Excel −We will rename the column names. At first, load data from a CSV file into a Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\SalesData.csv")Display all the column names from the CSV −dataFrame.columnsNow, rename column names −dataFrame.columns.values[0] = "Car Names" dataFrame.columns.values[1] = "Registration Cost" dataFrame.columns.values[2] = "Units Sold"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") print("Reading the CSV file...", dataFrame) ... Read More

To select rows that contain specific text, use the contains() method. Let’s say the following is our CSV file path −C:\Users\amit_\Desktop\SalesRecords.csvAt first, let us read the CSV file and create Pandas DataFrame −dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv")Now, let us select rows that contain specific text “BMW” −dataFrame = dataFrame[dataFrame['Car'].str.contains('BMW')]ExampleFollowing is the code −import pandas as pd # reading csv file dataFrame = pd.read_csv("C:\Users\amit_\Desktop\CarRecords.csv") print("DataFrame...", dataFrame) # select rows containing text "BMW" dataFrame = dataFrame[dataFrame['Car'].str.contains('BMW')] print("Fetching rows with text BMW ...", dataFrame)OutputThis will produce the following output −DataFrame ...            Car       Place   UnitsSold ... Read More

The process.connected property returns True if an IPC channel is connected and will return False after the process.disconnect() method is called. This happens only when the node process is spawned with an IPC channel (i.e., Child process and Cluster).Once the process.connected property is false, no messages can be sent over the IPC channel.Syntaxprocess.connectedExample 1Create two files "parent.js" and "child.js" as follows −parent.js// process.connected Property Demo Example // Importing the child_process modules const fork = require('child_process').fork; // Attaching the child process file const child_file = 'util.js'; // Spawning/calling child process const child = fork(child_file);child.jsconsole.log('In Child') // Check ... Read More

The timers module contains functions that can execute code after a certain period of time. You do not need to import the timer module explicitly, as it is already globally available across the emulated browser JavaScript API.Timers module is mainly categorised into two categoriesScheduling Timers − This timer schedules the task to take place after a certain instant of time.setImmediate()setInterval()setTimeout()Cancelling Timers − This type of timers cancels the scheduled tasks which is set to take place.   ClearImmediate()clearInterval()clearTimeout()Scheduling Timers1. setTimeout() MethodThe setTimeout() method schedules the code execution after a designated number of milliseconds. Only after the timeout has occurred, the code ... Read More

To merge Pandas DataFrame, use the merge() function. The many-to-one relation is implemented on both the DataFrames by setting under the “validate” parameter of the merge() function i.e. −validate = “many-to-one” or validate = “m:1”The many-to-one relation checks if merge keys are unique in right dataset.At first, let us create our 1st DataFrame −dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 110, 80, 110, 90] } ) Now, let us create our 2nd DataFrame −dataFrame2 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', ... Read More

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")To select multiple column records, use the square brackets. Mention the columns in the brackets and fetch multiple columns from the entire dataset −dataFrame[['Reg_Price', 'Units']] 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") print("Reading the CSV file...", dataFrame) # displaying two columns res = dataFrame[['Reg_Price', 'Units']]; print("Displaying two columns : ", res)OutputThis will produce the ... Read More