Summary

The article outlines four advanced techniques for loading data into Pandas, including using the clipboard, reading multiple Excel sheets simultaneously, loading data from multiple workbooks, and combining columns to create a date column.

Abstract

The guide titled "4 Ways to Load Data in Pandas You Need to Know" explores lesser-known methods for data import within the Pandas library, which is a powerful tool for data manipulation and analysis. It begins by discussing the pd.read_clipboard() function for quick data transfer from the clipboard to a DataFrame. The article then delves into the ability to read all sheets from an Excel workbook into a dictionary of DataFrames using pd.read_excel() with sheet_name=None, followed by concatenation for a single DataFrame. It also covers loading multiple workbooks from a directory into a single DataFrame using list comprehension and pd.concat(). Lastly, it demonstrates how to parse and combine separate columns containing date components into a single datetime column during the data import process. These techniques are presented as hidden gems that can streamline data loading tasks for users working with diverse data sources.

Opinions

The author believes that the described functionalities of Pandas extend beyond the basic usage and can significantly simplify data loading processes.
The use of pd.read_clipboard() is presented as a convenient feature for quickly loading small datasets from the clipboard, which is particularly useful for ad-hoc analysis.
Reading all Excel sheets at once is highlighted as a time-saving feature, especially when dealing with multiple sheets in a workbook.
The ability to load multiple workbooks from a directory is portrayed as a powerful method for aggregating data from several sources into a single DataFrame.
Combining multiple columns into a date column is suggested as an efficient way to handle datasets where date information is split across different columns.
The author provides downloadable datasets for readers to practice the techniques discussed, indicating a hands-on approach to learning and a commitment to practical application.
The guide concludes with an optimistic view that readers will find these methods useful and that they will enhance their data analysis workflows with Pandas.

4 Ways to Load Data in Pandas You Need to Know

Hidden gems of the Pandas library

Pandas makes leading data easy! We can easily load CSV or Excel files using the pd.read_csv() and pd.read_excel() functions. But we can extend their functionality to make loading data easier and more straightforward!

In this guide, you’ll learn:

How to load data from the clipboard,
How to read all Excel sheets in a workbook at once,
How to load multiple workbooks in a directory, and
How to combine multiple columns into a date column

Let’s get started!

Load Data From the Clipboard

If you’re working with small data you found online or in a spreadsheet at work, you can load the data using the pd.read_clipboard() function. Let’s see what this process looks like. Copy the text from below onto your clipboard:

Name  Age  Score
0  Evan   33     85
1  Kate   34     90
2   Nik   32     85
3  Kyra   35     95

Then run the following commands, which should output the structured DataFrame:

Cool, right? In the next section, you’ll learn how to read all Excel sheets in a file at once.

Read All Excel Sheets at Once

Pandas provides a very helpful way of loading all worksheets contained in an Excel workbook at once. This can be done by using the pd.read_excel() function and loading in sheet_name=None. Want to follow along? You can download the dataset here. (The dataset is entirely fictional and provided by the author.)

When None is used as the argument, Pandas will load a dictionary of DataFrames, where the key is the sheet name and the DataFrame is the value.

From there, we can use the pd.concat() function to pass in the values of our dictionary. Let’s see what this looks like:

When we access the values of our dictionary, a list-like structure is returned. We can pass this into the pd.concat() function, which appends the DataFrames one after another.

Load Multiple Workbooks at Once

In this section, you’ll learn how to use Pandas to load multiple workbooks in a directory at the same time. You can download the files here and load them into a folder. (A quick note on the dataset: it’s created for fictional purposes by the author).

Let’s take a look at what we’re doing in the code above:

We import both Pandas and the os library
We load our directory to a variable, file_path
We then use a list comprehension to create a list of paths to our files
We then use the pd.concat() function to append all of our DataFrames

In the final section below, you’ll learn how to improve reading dates from multiple columns.

Improve Reading Dates From Multiple Columns

Pandas makes it easy to parse certain columns as dates. However, what may surprise you is that you can parse date components from different columns.

Our worksheets contain the Month, Day, and Year split out across separate columns. We can combine them together to read them as a single datetime column. This can be done by passing a list containing a list of columns to parse, such as [['Month', 'Day', 'Year']].

However, we can take this one step further by assigning a name to the column by using the code below:

By passing in the dictionary, we can assign a name to the resulting column. This also prevents reading in the individual columns, making the DataFrame quite a bit smaller.

Conclusion

In this post, you learned four hidden gems in reading data when working with Pandas! Pandas provides a ton of functionality and it can be hard to find exactly what you’re looking for. Hopefully, you learning something new!