Programming Articles - Page 666 of 3363

The “axes” is an attribute of the pandas series object, this attribute is used to access the group of index labels in the given Series. It will return a python list of index labels.The axes attribute collects all the index labels and returns a list object with all index labels in it.Example 1import pandas as pd # create a sample series s = pd.Series({'A':123, 'B':458, "C":556, "D": 238}) print(s) print("Output: ") print(s.axes)ExplanationIn the following example, we initialized a Series with some data. Then we called the axes property on the series object.OutputA   123 B   458 ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned.The 2nd parameter, ord is the order of the norm. The inf means numpy’s inf object. The default is None. The "fro" set as a parameter is the Frobenius norm. Both the Frobenius and nuclear norm orders are only defined for matricesStepsAt first, import the required ... Read More

The pandas.Series.at attribute is used to access the single labeled element from a Series object and It is very similar to the loc in pandas. The “at” attribute takes label data to get or set the series value in that particular label position.It will return a single value based on the index label, and it also uploads the data at that particular label position.Example 1import pandas as pd # create a sample series s = pd.Series({'A':12, 'B':78, "C":56}) print(s) print(s.at['A'])ExplanationIn this following example, we have created a series using a python dictionary and the index will be ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned. The 2nd parameter, ord is the order of the norm. The inf means numpy’s inf object. The default is None.The 3rd parameter axis, if an integer, specifies the axis of x along which to compute the vector norms. If axis is a 2-tuple, it specifies ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned.The 2nd parameter, ord is the order of the norm. The inf means numpy’s inf object. The default is None. The 3rd parameter axis, if an integer, specifies the axis of x along which to compute the vector norms. If axis is a 2-tuple, it specifies ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned. The 2nd parameter, ord is the order of the norm. The inf means numpy’s inf object. The default is None.The 3rd parameter axis, if an integer, specifies the axis of x along which to compute the vector norms. If axis is a 2-tuple, it specifies ... Read More

To return the Norm of the matrix or vector in Linear Algebra, use the LA.norm() method in Python Numpy. The 1st parameter, x is an input array. If axis is None, x must be 1-D or 2-D, unless ord is None. If both axis and ord are None, the 2-norm of x.ravel will be returned. The 2nd parameter, ord is the order of the norm. The inf means numpy’s inf object. The default is None.The 3rd parameter axis, if an integer, specifies the axis of x along which to compute the vector norms. If axis is a 2-tuple, it specifies ... Read More

The “.array” is one of the pandas series attributes. it will return a pandas ExtensionArray with the values stored in the series. The “.array” is used to get a zero-copy reference to the underlying data.The resultant array is not like a NumPy array it is an ExtensionArray, and it has different array types based on the data present in the series (dtype).Example 1import pandas as pd # create pandas series with numerical values s1 = pd.Series([1, 2, 3, 4]) print(s1) print(s1.array)ExplanationThe “s1” is the pandas series object which is created by using integer values with length 4. ... Read More

To integrate a Legendre series, use the polynomial.legendre.legint() method in Python. The method returns the Legendre series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable.The 1st parameter, c is an array of Legendre series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. The 2nd parameter, m is an order of integration, must be positive. (Default: ... Read More

To integrate a Legendre series, use the polynomial.legendre.legint() method in Python. The method returns the Legendre series coefficients c integrated m times from lbnd along axis. At each iteration the resulting series is multiplied by scl and an integration constant, k, is added. The scaling factor is for use in a linear change of variable.The 1st parameter, c is an array of Legendre series coefficients. If c is multidimensional the different axis correspond to different variables with the degree in each axis given by the corresponding index. The 2nd parameter, m is an order of integration, must be positive. (Default: ... Read More