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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
The pandas Series align method is used to align two pandas series objects on basics of the same row and/or column configuration, which is done by specifying the parameters like join, axis, etc.Instead of combining the two series of objects, the pandas series align method aligns them in a specific order. This method takes 10 parameters which are “other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None”. Out of these parameters other, join and axis parameters are very important. Based on these parameters the output series object alignment depends.Example 1import pandas as pd s1 = pd.Series([8, 4, 2, 1], ... 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
To differentiate a Legendre series, use the polynomial.laguerre.legder() method in Python. Returns the Legendre series coefficients c differentiated m times along axis. At each iteration the result is multiplied by scl. 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 the number of derivatives taken, must be non-negative. (Default: 1). The 3rd parameter, scl is a scalar. Each differentiation is multiplied by scl. The end result is multiplication by scl**m. This is ... Read More
To evaluate a 3D Hermite_e series at points (x, y, z), use the hermite.hermeval3d() method in Python Numpy. The method returns the values of the multidimensional polynomial on points formed with triples of corresponding values from x, y, and z.The 1st parameter is x, y, z. The three dimensional series is evaluated at the points (x, y, z), where x, y, and z must have the same shape. If any of x, y, or z is a list or tuple, it is first converted to an ndarray, otherwise it is left unchanged and if it isn’t an ndarray it is ... Read More
To evaluate a Hermite_e series at points x, use the hermite.hermeval() method in Python Numpy. The 1st parameter, x, if x is a list or tuple, it is converted to an ndarray, otherwise it is left unchanged and treated as a scalar. In either case, x or its elements must support addition and multiplication with themselves and with the elements of c.The 2nd parameter, C, an array of coefficients ordered so that the coefficients for terms of degree n are contained in c[n]. If c is multidimensional the remaining indices enumerate multiple polynomials. In the two dimensional case the coefficients ... Read More
Use the DataFrame.empty property to check if DataFrame contains the data or not (empty or not). The DataFrame.empty attribute returns a boolean value indicating whether this DataFrame is empty or not.If the DataFrame is empty, it will return True. and it will return False If the DataFrame is not empty.Example 1In the following example, we have initialized a DataFrame with some data and then applied the empty attribute to check if the empty attribute returns False or not.# importing pandas package import pandas as pd # create an empty DataFrame df = pd.DataFrame([['a', 'b', 'c'], ['b', 'c', 'd'], ['d', ... Read More
The ndim is an attribute in the pandas DataFrame which is used to get the integer/number representation of dimensions of the given DataFrame object.As we know, the pandas DataFrame is a two-dimensional data structure that is used to store the data in a tabular format. Regardless of the number of rows and columns lengths or type of data the dimensions of the DataFrame do not affect.The output for the ndim property of pandas DataFrame is always 2.Example 1In this following example, we have applied the ndim attribute to the pandas DataFrame object “df”, this DataFrame is created with a single ... Read More
The ndim is an attribute in the pandas series which is used to get the integer representation of dimensions of a series object.As we know, the pandas series is a 1-dimensional data structure so the output for this ndim property is always 1. It doesn’t take any input to get the dimensions. Regardless of the number of rows and columns, the ndim property always returns 1 for pandas Series.Example 1In this following example, we have applied the ndim attribute to the pandas series object “s”.# importing packages import pandas as pd import numpy as np # create pandas Series ... Read More