![]() Seeing that Series are in some ways, fancy dictionary objects, it would be no surprise that dictionaries can be used to create Series objects. This will create a series, where each row can be addressed with the letter index, like: continents_lĪnd behind the scenes, each row retains a numerical index – try addressing the same row with: continents_l.ilocīoth approaches should result in 'Americas'! It simply gives you more options. For example, you can create a Series with an explicit index (like a Python dictionary). So, why use a Series over a list? Well, there is far more you can do with a Series than you can with a list. ![]() ![]() Also, typing continents into the Python shell will show the contents. Inspecting the new object with type(continents) reveals to us that it is a object. Try creating a new Series object with: continents = pd.Series() Simply passing a list to the pd.Series function will convert that list to a Series. Import numpy as np Creating a Series From a listĬreating a Series is easy. The convention is to import pandas as pd to save our precious keystrokes (and numpy as np). We can’t do anything without importing the pandas module. For our dummy data, I will use continental data from the GapMinder dataset. Let’s begin to explore a few of the many ways that exist to create them. Another analogy would be to a spreadsheet, where a Series is essentially a single column of data, whereas a DataFrame is like an entire sheet. At its core, Pandas is built on top of Numpy, and if you are not familiar with them, it is probably easiest to think of Series as a Pandas equivalent of a one-dimensional array, and a DataFrame as a two-dimensional array, composed of multiple Series. Series and DataFrames are the core data types used in Pandas for data analysis. ![]() Just as a journey of a thousand miles begins with a single step, we actually need to successfully introduce data into Pandas in order to begin to manipulate and analyse data. (Well, as far as data is concerned, anyway.) Pandas is a very feature-rich, powerful tool, and mastering it will make your life easier, richer and happier, for sure. If you have been dabbling with data analysis, data science, or anything data-related in Python, you are probably not a stranger to Pandas. Pandas is the go-to tool for manipulating and analysing data in Python. In this article, we will take you through one of the most commonly used methods to create a DataFrame or Series – from a list or a dictionary, with clear, simple examples. Use Pandas Series or DataFrames to make your data life easier ![]()
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