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Articles on Trending Technologies
Technical articles with clear explanations and examples
How to scale the R data frame by excluding a particular column?
To scale the R data frame by excluding a particular column, we can follow the below steps −First of all, create a data frame.Then, subset the data frame with single square brackets and scale function.Create the data frameLet’s create a data frame as shown below −Group
Read MoreHow to create scatterplot by standardizing the columns of a data frame using ggplot2 R?
To create scatterplot by standardizing the columns of a data frame using ggplot2 R, we can follow the below steps −First of all, create a data frame.Then, create the scatterplot using ggplot2 with raw values.After that, create the scatterplot with scale function.Create the data frameLet’s create a data frame as shown below −x
Read MoreNode.js – hash.digest() Method
The Hash class is one of the many utility classes that is used for creating the hash digests of data. The hash.digest() method calculates all the data that needs to be hashed passed inside the hash function and returns them. If an encoding is defined, a string will be returned, else a buffer is returned.Syntaxhash.digest([encoding])ParametersIt takes a single parameter −encoding − This input parameter takes input for the encoding to be applied while calculating the hash.Example 1Create a file with the name "hashDigest.js" and copy the following code snippet. After creating the file, use the command "node hashDigest.js" to run ...
Read MoreHow to iterate over rows in a DataFrame in Pandas?
To iterate rows in a DataFrame in Pandas, we can use the iterrows() method, which will iterate over DataFrame rows as (index, Series) pairs.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Iterate df using df.iterrows() method.Print each row with index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Given DataFrame:", df for index, row in df.iterrows(): print "Row ", index, "contains: " print row["x"], row["y"], row["z"]OutputGiven DataFrame: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 Row 0 contains: 5 4 4 Row 1 contains: 2 1 1 Row 2 contains: 1 5 5 Row 3 contains: 9 10 0
Read MoreSelect rows from a Pandas DataFrame based on column values
To select rows from a DataFrame based on column values, we can take the following Steps −Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Use df.loc[df["x"]==2] to print the DataFrame when x==2.Similarly, print the DataFrame when (x >= 2) and (x < 2).Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Given DataFrame is:", df print "When column x value == 2:", df.loc[df["x"] == 2] ...
Read MoreHow to assign a value to a base R plot?
To assign a value to a base R plot, we can follow the below steps −First of all, create a vector and its histogram then record it with recordPlot function in an object.Then, use dev.off function to remove the plot.After that, read the plot with object name.Create the vector and histogram then save it in an objectLet’s create a vector of normal distribution and create its histogram then save it in an object called Histogram using recordPlot as shown below −x
Read MoreHow to rename column names in a Pandas DataFrame?
To rename columns in a Pandas DataFrame, we can override df.columns with the new column names.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Override the columns with new list of column names.Print the DataFrame again with the renamed column names.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print("Input DataFrame is:", df) df.columns = ["a", "b", "c"] print("After renaming, DataFrame is:", df)OutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 After renaming, DataFrame is: a b c 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0
Read MoreSelect multiple columns in a Pandas DataFrame
To select multiple columns in a Pandas DataFrame, we can create new a DataFrame from the existing DataFrameStepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Create a new DataFrame, df1, with selection of multiple columns.Print the new DataFrame with multiple selected columns.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Input DataFrame is:", df df1 = df[['x', 'y']] print "After selecting multiple columns:", df1OutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 After selecting multiple columns: x y 0 5 4 1 2 1 2 1 5 3 9 10
Read MoreHow to get the row count of a Pandas DataFrame?
To get the row count of a Pandas DataFrame, we can use the length of DataFrame index.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the length of the DataFrame index list, len(df.index).Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 1, 9], "y": [4, 1, 5, 10], "z": [4, 1, 5, 0] } ) print "Input DataFrame is:", df print "Row count of DataFrame is: ", len(df.index)OutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 Row count of DataFrame is: 4
Read MoreHow to convert a correlation matrix into a logical matrix based on correlation coefficient in R?
To convert a correlation matrix into a logical matrix based on correlation coefficient in R, we can follow the below steps −First of all, create a matrix.Then, find the correlation matrix.After that, convert the correlation matrix into logical matrix based on coefficient value using greater than or less than sign.Example 1Let’s create a matrix as shown below −M1
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