In this tutorial, you will learn different ways of optimizing loops in pandas. Pandas is one of the most popular python libraries among data scientists. While performing data analysis and data manipulation tasks in pandas, sometimes, you may want to loop/iterate over DataFrame and do some operation on each row. While this can be a simple task if the size of the data is small, it is cumbersome and very much time consuming if you have a larger data-set. So, we need to find an efficient way to loop through the pandas DataFrame.
Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation in python due to their intuitive syntax and high-performance matrix computation capabilities.
Merging two columns in Pandas can be a tedious task if you don't know the Pandas merging concept. You can easily merge two different data frames easily. But on two or more columns on the same data frame is of a different concept. In this entire post, you will learn how to merge two columns in Pandas using different approaches. Numpy and Pandas Packages are only required for this tutorial, therefore I am importing it.
First, Pandas is an open source Python library for data analysis. It contains data manipulation and data structures tools designed to make spreadsheet-like data for loading, manipulating, merging, cleaning, among other functions, fast and easy in Python. It is often used with analytical libraries like scikit-learn, data visualization libraries like matplotlib, and numerical computing tools like NumPy and SciPy. Pandas has introduced new data types to Python: Series and DataFrame. This two workhorse data structures are not a universal solution for every problem, but they provide a solid basis for most applications.
This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation. Anaconda is interested in scaling the scientific python ecosystem. My current focus is on out-of-core, parallel, and distributed machine learning. This series of posts will introduce those concepts, explore what we have available today, and track the community's efforts to push the boundaries. I am (or was, anyway) an economist, and economists like to think in terms of constraints.