How to Create a Pandas Dataframe from a Dictionary

Here is yet another example of how useful and powerful Pandas is. Pandas can create dataframes from many kinds of data structures—without you having to write lots of lengthy code. One of those data structures is a dictionary.

In this tutorial, we show you two approaches to doing that.

(This tutorial is part of our Pandas Guide. Use the right-hand menu to navigate.)

A word on Pandas versions

Before you start, upgrade Python to at least 3.7. With Python 3.4, the highest version of Pandas available is 0.22, which does not support specifying column names when creating a dictionary in all cases.

If you are running virtualenv, create a new Python environment and install Pandas like this:

virtualenv py37  --python=python3.7
pip install pandas

You can check the Pandas version with:

import pandas as pd
pd.__version__

Create dataframe with Pandas DataFrame constructor

Here we construct a Pandas dataframe from a dictionary. We use the Pandas constructor, since it can handle different types of data structures.

The dictionary below has two keys, scene and facade. Each value has an array of four elements, so it naturally fits into what you can think of as a table with 2 columns and 4 rows.

Pandas is designed to work with row and column data. Each row has a row index. By default, it is the numbers 0, 1, 2, 3, … But it also lets you use names.

So, let’s use the same in the array idx.

import pandas as pd
dict =  {'scene': ["foul", "murder", "drunken", "intrigue"],
'facade': ["fair", "beaten", "fat", "elf"]}
idx = ['hamlet', 'lear', 'falstaff','puck']
dp = pd.DataFrame(dict,index=idx)

Here is the resulting dataframe: