![]() This is because it’s a much more common data structure you’ll encounter in your day-to-day work. We’ll focus more on the Pandas DataFrame in this guide. Similarly, by providing two data structures, pandas makes it much easier to work with two-dimensional data. Understanding Pandas Series and DataFramesīecause the DataFrame is a container for the Series, they can also share a similar language for accessing, manipulating, and working with the data. ![]() Because of this, the DataFrame can be heterogeneous. Meanwhile, a pandas DataFrame contains multiple Series objects that share the same index. Pandas will make sure that the data in the Series is homogenous, meaning that it contains only a single data type. You can think of a pandas Series as a column in a tabular dataset. The DataFrame itself contains Series objects, while the Series contains individual scalar data points. The idea is that pandas opens up accessing lower-level data using simple, dictionary-like methods. The pandas DataFrame structure, which is a two-dimensional, mutable, and potentially heterogeneous structureĪt this point, you may be wondering why pandas provides more than one data structure.The pandas Series structure, which is a one-dimensional homogenous array, and.Pandas provides access to two data structures: Ok, now that we’re up and running, let’s take a look at the different data types that the library offers: Series and DataFrame. Let’s see how we can import the library in a Python script: # How to import the pandas library in a Python script While you can use any alias you want, following this convention will ensure that your code is more easily understood. If you’re using pip, use the command below: pip install pandasĪlternatively, if you’re using conda, use the command below: conda install pandasīy convention, pandas is imported using the alias pd. We can do this using either the pip or conda package managers.ĭepending on the package manager you use, use one of the commands below. Because of this, we need to install it before we can use it. Pandas isn’t part of the standard Python library. Let’s start diving into the library to better understand what it offers. This doesn’t even begin to cover off all of the functionality that Pandas provides but highlights a lot of the important pieces. Hierarchical axes to add additional depth to your data.Powerful time series functionality, such as frequency conversion, moving windows, and lagging.Simple and easy to understand merging and joining of datasets.Simple plotting interface for quick data visualization.Versatile reshaping of datasets, such as moving from wide to long or long to wide.Familiar ways of aggregating data using Pandas pivot_table and grouping data using the group_by method.Simple ways of working with missing data.Manipulating DataFrames to add, delete, and insert data.Reading, accessing, and viewing data in familiar tabular formats.Let’s take a look at some of the things the library does very well: It’s flexible, easy to understand, and incredibly powerful. Pandas is the quintessential data analysis library in Python (and arguable, in other languages, too). We’ll dive more into these in a second, but let’s take a little moment to dive into some of the many benefits the pandas library provides. Pandas provides two main data structures to work with: a one-dimension pandas Series and a two-dimensional pandas DataFrame. Other structured datasets, such as those coming from web data, like JSON files. ![]()
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