column level, as we’ve been exploring so far. Lists of lists behave a little differently, as you’re essentially adding data at, what appears to be, a row level, rather than. For example, a list of lists may look like this: data =, ] They are also often called multi-dimensional lists. Lists of lists are simply lists that contain other lists. There may be many times you encounter lists of lists, such as when you’re working with web scraping data. In the next section, you’ll learn how to turn lists of lists into a Pandas dataframe! Create a Pandas Dataframe from a List of Lists Want to learn more about the zip() function? Check out my in-depth tutorial on zipping two or more lists in Python and pick up some fun tips and tricks along the way! We then passed this zipped object into our DataFrame() class, along with a list of our column names to create our dataframe.We then applied the list() function to turn this zip object into a list of tuples We then created a Python zip() object, which contained the zips of names, ages, and locations.We created three lists, containing names, ages, and locations, holding our ordered data.Let’s also break down what we’ve done here: Let’s see what this dataframe looks like by printing it out: Name Age Location Zipped = list(zip(names, ages, locations))ĭf = pd.DataFrame(zipped, columns=) Let’s see how this can work by creating a Pandas dataframe from two or more lists: # Create a Pandas Dataframe from Multiple Lists using zip() The function takes two or more iterables, like lists, and combines them into an object, in the same way that a zipper does! The easiest way to do this is to use the built-in zip() function. Because of this, we need to combine our lists in order. Simply passing in multiple lists, unfortunately, doesn’t work. Let’s say you have more than a single list and want to pass them in. Create a Pandas Dataframe from Multiple Lists with Zip In the next section, you’ll learn how to create a Pandas dataframe from multiple lists, by using the zip() function. This now returns a clearly-labelled dataframe that looks like the below: Name Let’s re-create our dataframe and specify a column name: import pandas as pdĭf = pd.DataFrame(names, columns=) The columns= argument takes a list-like object, passing in column headers in the order in which the columns are created. Since Pandas doesn’t actually know what to call the column, we need to more explicit and use the columns= argument. ![]() We can see that Pandas has successfully created our dataframe, but that our column is unnamed. ![]() ![]() This returns a dataframe that looks like this: 0ģ Evan Specifying Column Names when Creating a Pandas Dataframe Let’s take a look at passing in a single list to create a Pandas dataframe: import pandas as pd Because the data= parameter is the first parameter, we can simply pass in a list without needing to specify the parameter. Recall, that the data= parameter is the parameter used to pass in data. Now that you have an understanding of what the pandas DataFrame class is, lets take a look at how we can create a Pandas dataframe from a single list. Because of these many options, lets see how you can create a dataframe from Pandas lists! Create a Pandas Dataframe from a Single List ![]() The data= parameter can contain an ndarray, a dictionary, a list, or a list-like object. Instead, you can use the data= parameter, which, positionally is the first argument. But in this tutorial, you won’t be creating an empty dataframe. We’ve covered creating an empty dataframe before, and how to append data to it. You can create an empty dataframe by simply writing df = pd.DataFrame(), which creates an empty dataframe object.
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