Latest Code Tutorials
Python is a popular programming language for various purposes, including web development, data analysis, machine learning, and scientific computing.
One of the main benefits of Python is its simplicity and readability. It uses indentation to define code blocks, which makes it easy to understand and write. Python also has a large standard library, meaning many common programming tasks can be performed without installing additional libraries.
There are many ways to get started with Python. You can install it on your own computer and use a text editor or integrated development environment (IDE) to write and run Python code. There are also many online platforms, such as Repl.it, that allows you to write and run Python code in a web browser.
The numpy.select() function “allows you to create a new array by applying a list of conditions” and corresponding actions (or values) to an input array. It returns a new array with the same shape as the input array, and the values of the new array are determined by the first condition, True, at a given … Read more
To replace a substring in a Pandas DataFrame column, you can use the “str.replace()” method. The str.replace() method enables you to replace a specified substring with another substring in a specific column. Example import pandas as pd # Sample DataFrame data = { “A”: [“bmw_car”, “volvo_bus”, “airbus_plane”] } df = pd.DataFrame(data) print(“Original DataFrame:”) print(df) # … Read more
To convert the Pandas column to a list, you can use the “tolist()” method on a specific column. Example import pandas as pd # Sample DataFrame data = { “A”: [1, 2, 3], “B”: [4, 5, 6] } df = pd.DataFrame(data) # Convert column A to a list column_a_list = df[“A”].tolist() # Convert column B … Read more
To convert the column type to a string in a Pandas DataFrame, you can use the “astype(str)” method. The astype() method allows you to change the data type of a specific column or multiple columns. Example import pandas as pd # Sample DataFrame data = { “A”: [1.0, 2.0, 3.0], “B”: [“4”, “5”, “6”] } df … Read more
To convert a column to an integer data type in a Pandas DataFrame, you can use the “astype(int)” method. This method allows you to change the data type of a specific column or multiple columns. Example import pandas as pd # Sample DataFrame data = { “A”: [1.0, 2.0, 3.0], “B”: [“4”, “5”, “6”] } … Read more
To group by multiple columns in Pandas, you can use the “groupby()” function and “pass a list of column names” as the argument. Example import pandas as pd data = { “Category”: [“Fruit”, “Fruit”, “Vegetable”, “Fruit”, “Vegetable”, “Vegetable”], “Item”: [“Apple”, “Banana”, “Carrot”, “Apple”, “Carrot”, “Pea”], “Price”: [1, 1.5, 0.5, 1.2, 0.6, 0.4], “Quantity”: [10, 20, … Read more
Pandas DataFrame sum() method calculates the sum of the elements in each column (by default) or along the rows. You can specify the axis along which you want to compute the sum. Syntax dataframe.sum(axis=None, skipna=True, level=None, numeric_only=None, min_count=0, **kwargs) Parameters axis: {0 or ‘index’, 1 or ‘columns’}, default 0. Axis along which the sum is … Read more
Pandas DataFrame to_excel() method “exports an Excel file”. It provides a convenient way to save your data in Excel format for further analysis or sharing with others. Multiple sheets may be written by specifying the unique sheet_name. With all data written to a file, it is necessary to save the changes. Note that creating the ExcelWriter … Read more
To transpose a matrix in Python, you can use the “list comprehension” or“numpy.transpose()” function. Method 1: Using the list comprehension def transpose(matrix): return [[row[i] for row in matrix] for i in range(len(matrix[0]))] # Example usage: matrix = [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ] transposed_matrix = transpose(matrix) print(transposed_matrix) Output [[1, 4, … Read more
To fix the RuntimeError: expected scalar type double but found float error, you can convert the input tensor’s data type to match the model’s expected data type using the double() or to() method. The RuntimeError: expected scalar type double but found float error occurs when there is a mismatch between the data types of the input tensor … Read more
Python raises ValueError: ‘Object arrays cannot be loaded when allow_pickle=False’ when you try to load a NumPy file (.npy) containing object arrays, but the ‘allow_pickle’ parameter is set to False. By default, this parameter is set to False for security reasons, as loading pickled object arrays can lead to arbitrary code execution. There are two ways … Read more
