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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
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 More
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']
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 More
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 More
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 ... Read More
To delete a DataFrame row in Pandas based on column value, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Here, we will delete the row from the DataFrame that contains 0 in its Z-column, using df=df[df.z != 0]Print the updated DataFrame, after deleting row based on column value.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 = ... Read More
To count the NaN values in a column in a Pandas DataFrame, we can use the isna() method with sum.StepsCreate a series, s, one-dimensional ndarray with axis labels (including time series).Print the series, s.Count the number of NaN present in the series.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Find NaN count column wise.Print the count DataFrame.Exampleimport pandas as pd import numpy as np s = pd.Series([1, np.nan, 3, np.nan, 3, np.nan, 7, np.nan, 3]) print "Input series is:", s count = s.isna().sum() print "NAN count in series: ", count df = pd.DataFrame( { ... Read More
To convert a Pandas DataFrame to a NumPy array, we can use to_numpy().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the NumPy array of the given array, using df.to_numpy().Print the NumPy array of the given array for a specific column, using df['x'].to_numpy().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 "DataFrame to numpy is:", df.to_numpy() print "DataFrame to numpy is:", df['x'].to_numpy()OutputInput DataFrame ... Read More
To make a multi-index in Pandas, we can use groupby with list of columns.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the index of DataFrame count.Use groupby to get different levels of a hierarchical index and count it.Print the mulitindex set in step 4.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 "Default index: ", df.count().index df1 = df.groupby(["x", "y"]).count() print ... Read More