+With the standard Python functions and the <b><code>numpy</code></b> library, you already have access to powerful tools to process data. However, you'll find that organizing data using them might still be confusing and messy... so let us introduce you to <b><code>pandas</code></b>: a Python library specialized in data organization. Its functions are simple to use, and they achieve a lot. Furthermore, <b><code>pandas</code></b> was built on top of the <b><code>numpy</code></b> library, using some of their functions and data structures. This makes <b><code>pandas</code></b> fast. The <b><code>pandas</code></b> library is often used in Data Science and Machine Learning to organize data that are used as input in other functions, of other libraries. For example, you store and organize an Excel file using <b><code>pandas</code></b> data structures, apply statistical analysis using <b><code>SciPy</code></b>, and then plot the result using <b><code>matplotlib</code></b>.<br><br> In this section, we'll introduce you to the basic <b><code>pandas</code></b> data structures: the <b><code>Series</code></b> and <b><code>DataFrame</code></b> objects; and how to store data in them. In <b><code>pandas</code></b>, a <b><code>Series</code></b> represents a list, and <b><code>DataFrame</code></b> represents a table.
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