In any data science/data analysis work, the first step is to read CSV file (with pandas library). Pandas read_csv function is popular to load any CSV file in pandas. In this post we’ll explore various options of pandas read_csv function.
Transposing numpy array is extremely simple using
np.transpose function. Fundamentally, transposing numpy array only make sense when you have array of 2 or more than 2 dimensions.
In post, we’ll learn to create pandas dataframe from python lists and dictionary objects. Creating pandas dataframe is fairly simple and basic step for Data Analysis. There are also other ways to create dataframe (i.e. from csv, excel files or even from databases queries). But we’ll cover other steps in other posts.
In python, reshaping numpy array can be very critical while creating a matrix or tensor from vectors. In order to reshape numpy array of one dimension to n dimensions one can use
np.reshape() method. Let’s check out some simple examples.
This post will give you a better hands on with creating numpy array. At the end of the post, you will have clarity on different ways of creating numpy arrays with helpful visualizations. If you are a beginner in Data Analytics or Data Science field, you must have in depth understanding of numpy package of python.