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Articles by Gireesha Devara
Page 7 of 18
How to wrap python object in C/C++?
To wrap existing C or C++ functionality in Python, there are number of options available, which are: Manual wrapping using PyMethodDef and Py_InitModule, SWIG, Pyrex, ctypes, SIP, Boost.Python, and pybind1. Using the SWIG Module Let’s take a C function and then tune it to python using SWIG. The SWIG stands for “Simple Wrapper Interface Generator”, and it is capable of wrapping C in a large variety of languages like python, PHP, TCL etc. Example Consider simple factorial function fact() in example.c file. /* File : example.c */ #include // calculate factorial int fact(int n) { ...
Read MoreConversion Functions in Pandas DataFrame
Pandas is one of the most potent libraries in python that provide high-performance data manipulation and analysis tools, it allows us to work with tabular data like spreadsheets, CSV, and SQL data using DataFrame. A DataFrame is a 2-dimensional labeled data structure it represents the data in rows and columns format. Data present in each column may have different data types. DataFrame: Integers Floats Strings Dates 0 1.0 1.300 p 2023-05-07 1 2.0 NaN y 2023-05-14 2 5.0 4.600 t 2023-05-21 3 3.0 1.020 h 2023-05-28 4 6.0 0.300 o 2023-06-04 5 NaN 0.001 n 2023-06-11 The DataFrame demonstrated above is having 6 rows and 4 columns and the data present in each row has different datatypes. And Conversions functions ...
Read MoreConvert a NumPy array to a Pandas series
A Numpy array is an N-dimensional array also called a ndarray, it is a main object of the NumPy library. In the same way, the pandas series is a one-dimensional data structure of the pandas library. Both pandas and NumPy are validly used open-source libraries in python. Below we can see the one-dimensional numpy array. NumPy array array([1, 2, 3, 4]) The pandas Series is a one-dimensional data structure with labeled indices and it is very similar to a one-dimensional NumPy array. Pandas Series: 0 1 1 2 2 3 ...
Read MoreConvert a NumPy array to Pandas dataframe with headers
Both pandas and NumPy are validly used open-source libraries in python. Numpy stands for Numerical Python. This is the core library for scientific computing. A Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. NumPy array array([[1, 2], [3, 4]]) Pandas provide high-performance data manipulation and analysis tools in Python, it allows us to work with tabular data like spreadsheets, CSV, and SQL data. And it has data structures like DataFrame and Series that are mainly used for analyzing the data. DataFrame is a 2-dimensional labeled data structure used to ...
Read MoreHow to remove NaN from a Pandas Series?
In the pandas series constructor, the method called dropna() is used to remove missing values from a series object. And it does not update the original series object with removed NaN values instead of updating the original series object, it will return another series object with updated values.The parameters of the dropna() method are axis, inplace, and how.Example 1# importing packages import pandas as pd import numpy as np # Creating Series objects sr = pd.Series([42, np.nan, 55, 42, np.nan, 73, np.nan, 55, 76, 87], index=list("ABCDEFGHIJ")) print('Series object:', sr) # Remove missing elements result = sr.dropna() ...
Read MoreHow to remove a specified row From the Pandas Series Using Drop() method?
The pandas series.drop() method is used to remove a specific row from the pandas series object. And It will return a series object with the removed row.The drop() method can be applied to both labeled-based and position index abased series objects. The parameters of this drop() method are labels, axis, level, inplace, and raise.It will raise a Key error if the specified row label is not found in the index of the series object. We can suppress the errors by setting the errors parameter from raise to ignore.Example 1# import pandas package import pandas as pd # Creating Series ...
Read MoreHow can we give the scalar value to the pandas series.divmod() method?
The Series.divmod() method in the pandas series constructor is used to perform both integer division and modular division operations on series objects with a scalar, or we can apply this divmod() method on two series also.The method performs an element-wise division operation of its two input objects. And it returns a python tuple with two series objects, the first series of the tuple is representing the integer division output, and the second series object of the tuple representing the modulo division output.Example 1import pandas as pd # create pandas Series series = pd.Series([25, 48, 18, 99, 61]) print("Series ...
Read MoreHow to find the dot product of two pandas series objects?
The series.dot() method in pandas series is used to perform the dot product operation of two pandas series objects. In mathematics, a dot product of two sequences is given by Sum of multiplication of values at each sequence.The series.dot() takes only one parameter which is another object, it takes a series or an array-like object to perform dot product between elements of each object.Example 1# import pandas packages import pandas as pd # Creating Series objects series1 = pd.Series([1, 0, 5, 2]) print('First series object:', series1) series2 = pd.Series([3, 7, 2, 9]) print('second series object:', series2) ...
Read MoreHow to divide a pandas series with scalar using series.div() method?
In the pandas series constructor, the div() or divide() method is used to perform element-wise floating division operation between the two series objects or between a series and a scalar.The method returns a series with resultant floating division values. We can also do the division operation between series and scalar.Here we will see some examples for dividing a series with a scalar.Example 1import pandas as pd # create pandas Series series = pd.Series([13, 48, 6, 72, 8]) print("Series object:", series) # divide print("combined series:", series.div(2))ExplanationIn this example, we will divide the Series with a scalar value “2”. ...
Read MoreHow does pandas series div() method work?
In the pandas series constructor, the div() or divide() method is used to perform floating division of two series objects or division of a series with a scalar value. And performs element-wise division operation.The method returns a series with the result of floating division values. It has 3 parameters, which are fill_value, other, and level. The other parameter is nothing but 2nd input series or a scalar value.The fill_value parameter is used to fill the missing value. If the index is missed at any one of the series objects, then we can fill that missing index value with a specified ...
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