Pandas Timestamp Get Day
This was a short tutorial depicting how to extract the day and day name from a timestamp object. You can do this using the timestamp() function.
Pandas Describe
This discussed how to use the describe() function in Pandas which allows you to get the statistical summary of the data within your Pandas DataFrame.
Create a List of Lists in Python
To create a list of lists in Python, use the bracket notation used for list initialization or use the append() method to add lists into a list.
Pandas Combine Date and Time
This article discussed how you could combine date and time objects in Pandas to create a timestamp object. It covered how you can combine date and time columns.
Numpy Np.Apply_Along_Axis
This article discusses how apply_along_axis() function is used to apply a specific function to a 1D slice along a specified axis.
Numpy Np.Add.At
This short article discussed the basics of using the ufunc at() function in NumPy; the add.at a function in NumPy allows you to perfrom an in-place operation.
Numpy Fromfile
NumPy tofile() function allows you to save an array to a text or binary file. This will discuss saving and reading a NumPy array to and from a binary file.
NumPy Count True
This article discusses how to use the count_nonzero() function to determine the number of True elements in an array with examples.
Find Strings in Pandas
This will help you understand methods we can use to search for a string in Pandas DataFrame or for a substring using the contains() method.
Pandas Cumsum()
The cumsum() function in Pandas allows you to calculate the cumulative sum over a given axis. Pandas Cumsum() function is discussed in this article.
Pandas Check Column Type
The most straightforward way to get the column’s data type in Pandas is to use the dtypes attribute. Pandas also provide us with the info() method.
Pandas Column Type to String
The astype() function in Pandas allows you to cast an object to a specific data type. Pandas column type to string is discussed in this article.
NumPy np.power()
The power() function in NumPy allows you to raise the elements from the first array to the power of the elements in the second array.
NumPy np.identity()
One of the most practical functions in NumPy is the identity() function. This function allows you to generate an identity array in a simple step.
NumPy np.absolute()
The absolute() function in NumPy allows you to determine the distance between an element and 0, also known as an absolute value in a given array.
NumPy np.roll
NumPy roll function is used to roll elements in an input array along a specified axis. Rolling refers to the process of shifting the position of the elements.
NumPy np.zeros_like()
The NumPy zeros_like() function generates an array of the same shape and data type specified but populated with zeros is discussed in this article.
NumPy np.flatten()
The NumPy package provides a flatten() function that allows you to return a copy of an array collapsed into a one-dimension array is discussed in this article.
NumPy np.outer()
In NumPy, the outer() function allows us to calculate the outer product of two vectors. NumPy np.outer() function is discussed in this article.
NumPy np.c_
The NumPy np.c_ is part of NumPy’s indexing routines that allow you to concatenate an array along the second axis. NumPy np.c_ is discussed in this article.