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From Clipboard to DataFrame With Pandas: A Quick Guide

When I write about a library or a new concept, I typically like to showcase how it works through concrete examples. The sources I use for data sets in my articles vary widely. Sometimes I create simple toy data sets, while other times I go with the established data set sites like Kaggle and Google search. However, every time I need to showcase a concept, I have to go through the laborious work of copying the data from the source, saving it to my system, and finally using it in my development environment. Luckily, there is a built-in method to create a dataframe in Pandas that can dramatically speed up your workflow.

The read_clipboard() method creates a DataFrame in Pandas from data copied to the clipboard. It reads text from the clipboard and passes it to read_csv(), which then returns a parsed DataFrame object.

This method, aptly named read_clipboard is an absolute savior when you want to try out some new function or library quickly. Here’s how to use it.

The read_clipboard() method of Pandas creates a DataFrame from data copied to the clipboard. It reads text from the clipboard and passes it to read_csv(), which then returns a parsed DataFrame object.

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pandas.read_clipboard(sep='\\s+', **kwargs)

If you’ve worked with read_csv(), you’ll find  read_clipboard() just as intuitive for creating a dataframe in pandas from clipboard data. The only difference is that the source of data in the latter comes from the clipboard buffer instead of a CSV file. 

Let’s now look at various ways of using this method. The main steps involved are:

We’ll begin by copying some data sets from an Excel file. This is the most common scenario you’ll encounter.

Now you’ve copied the data onto the clipboard. Next, we’ll navigate to a Jupyter Notebook (or any IDE) instance and type in the following code snippet:

import pandas as pd
df = pd.read_clipboard()
df

The copied data set gets passed into the variable DataFrame and is now available in your environment. Here’s a demonstration of the process.

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If you have a CSV file, the steps remain the same. You will only need to make certain changes in the parameters of the function. Consider the following CSV data:

,Order ID,Category,Sales,Quantity,Discount
0,1,Apparels,16.448,2,0.2
1,2,Electronics,65.0,87,0.2
2,3,Cosmetics,272.736,3,0.2
3,4,Apparels,3.54,2,0.8
4,5,Electronics,19.536,3,0.2
5,6,Cosmetics,19.44,3,0.0
6,7,Grocery,12.78,3,0.0
7,8,Grocery,2573.82,9,0.0
8,9,Apparels,609.98,2,0.0
9,10,Cosmetics,300.0,10,0.5

Copy the above data and run the code below.

df = pd.read_clipboard(
  sep=",",
  header="infer",
  index_col=0,
  names=["Order", "ID", "Category", "Sales", "Quantity", "Discount"],
)
df

We get the same DataFrame as in step one. The only difference is that we had to pass in the name of the columns and information about the header and index column.

You can also copy the data from any source, including a web page, as long as it’s structured in the form of a DataFrame. Here’s an example of copying data from StackOverflow and importing it into a DataFrame.

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We can use the clipboard data to save the data set for future use in the desired format.

df.to_csv('data.csv')

You can also save the data in HTML format to display the data as HTML tables.

df.to_html('data.html')

Now that you’ve learned about the read_clipboard method, go try it out. 

A DataFrame in Pandas is a two-dimensional, tabular data structure similar to a spreadsheet or SQL table used for storing and analyzing data in Python.

A dataframe organizes data into rows and columns, with each column holding a different data type. Dataframes make it easy to manipulate, visualize or analyze data within Python.

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