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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

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

The loc is an attribute in the pandas DataFrame constructor that is used to access the elements of the DataFrame based on row/column label indexing.The attribute .loc takes the labels of the DataFrame row and column to access the group of elements.The “.loc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc. And it will raise a KeyError if the specified label is not found in the DataFrame.Example 1In this following example, we have applied the slicing indexer to the loc attribute to access the values from ... Read More

The “.loc” is an attribute of the pandas.DataFrame. it is used to access elements from DataFrame based on row/column label indexing. And It works similar to pandas.DataFrame “at” attribute but the difference is, the “at” attribute is used to access only a single element whereas the “loc” attribute can access a group of elements.The “.loc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc. And it will raise a KeyError if the specified label is not found in the DataFrame.Example 1In this following example, we created a ... Read More

The DataFrame is a pandas two-dimension data structure that is used to store the labeled data in a table format, a DataFrame has row index labels and column index labels which are used to represent the element (a value) address.By using these row/column labels we can access elements of a DataFrame and we can do data manipulations too.If you want to get the column labels from a DataFrame separately then we can use the pandas.DataFrame “columns” attribute.Example 1In this example, we have applied the columns attribute to the pandas DataFrame to get the column labels.# importing pandas package import pandas ... Read More