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Programming Articles
Page 1107 of 2547
How 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
Read MoreNode.js – util.debuglog() Method
The util.debuglog() method creates a function that can be used to write the desired error/debug messages to stderr. These error messages are written only upon the existence of the NODE_DEBUG environment variable.Syntaxutil.debuglog(section, [callback])ParametersThe parameters are described below −section − This parameter takes the portion of the application for which the debug log is being created.callback − This is the callback function which will receive the pointer if any error occurs during the execution of method.Example 1Create a file with the name "debuglog.js" and copy the following code snippet -// util.debuglog() demo example // Importing the util module const util ...
Read MoreHow to get the list of column headers from a Pandas DataFrame?
To get a list of Pandas DataFrame column headers, we can use df.columns.values.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the list of df.columns.values output.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 "List of headers are: ", list(df.columns.values)OutputInput DataFrame is: x y z 0 5 4 4 1 2 1 1 2 1 5 5 3 9 10 0 List of headers are: ['x', 'y', 'z']
Read MoreHow to change the order of Pandas DataFrame columns?
To change the order of DataFrame columns, we can take the following Steps −StepsMake two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Get the list of DataFrame columns, using df.columns.tolist()Change the order of DataFrame columns.Modify the order of columns of the DataFrame.Print the DataFrame after changing the columns order.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 cols = df.columns.tolist() cols = cols[-1:] + cols[:-1] ...
Read MoreCreate a Pandas Dataframe by appending one row at a time
To create a Pandas DataFrame by appending one row at a time, we can iterate in a range and add multiple columns data in it.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Iterate in a range of 10.Assign values at different index with numbers.Print the created DataFrame.Exampleimport pandas as pd import random df = pd.DataFrame( { "x": [], "y": [], "z": [] } ) print "Input DataFrame:", df for i in range(10): df.loc[i] = [i, random.randint(1, 10), random.randint(1, 10)] print "After appending ...
Read MoreHow are iloc and loc different in Python Pandas?
Let's take an example to understand the difference between iloc and loc. Basically loc[0] returns the value present at 0 index, whereas iloc[0] returns the value present at the first location of a series.StepsCreate a one-dimensional ndarray with axis labels (including time series).Print the input series.Use loc[0] to print the value present at 0th index.Use iloc[0] to print the value present at the first location of the series table.Exampleimport pandas as pd s = pd.Series(list("AEIOU"), index=[2, 1, 0, 5, 8]) print "Input series is:", s print "Value at index=0:", s.loc[0] print "Value at the 1st location of the series:", s.iloc[0]OutputInput ...
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