TL;DR Summary: Creating a Pandas DataFrame from a dictionary is straightforward, using methods like the DataFrame constructor and from_dict(). These approaches allow users to create DataFrames with specified column names and varying orientations.
- Two methods for DataFrame creation from dictionaries are detailed.
- Compatibility with the appropriate Python and Pandas versions is crucial.
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.
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:
Create dataframe with Pandas from_dict() Method
Pandas also has a Pandas.DataFrame.from_dict() method. If that sounds repetitious, since the regular constructor works with dictionaries, you can see from the example below that the from_dict() method supports parameters unique to dictionaries.
In the code, the keys of the dictionary are columns. The row indexes are numbers. That is default orientation, which is orient='columns' meaning take the dictionary keys as columns and put the values in rows.
pd.DataFrame.from_dict(dict)
Now we flip that on its side. We will make the rows the dictionary keys.
pd.DataFrame.from_dict(dict,orient='index')
Notice that the columns have no names, only numbers. That's not very useful, so below we use the columns parameter, which was introduced in Pandas 0.23.
It's as simple as putting the column names in an array and passing it as the columns parameter. One wonders why the earlier versions of Pandas did not have that.
pd.DataFrame.from_dict(dict,orient='index',columns=idx)
hamlet lear falstaff puck
scene foul murder drunken intrigue
facade fair beaten fat elf
Related reading
- BMC Machine Learning & Big Data Blog
- Pandas: How To Read CSV & JSON Files
- Apache Spark Guide, a series of tutorials
- Snowflake Guide
- Creating Redshift User Defined Function (UDF) in Python
Pandas Guide
- Pandas Introduction & Tutorials for Beginners
- Pandas: How To Read CSV & JSON Files
- How To Create a Pandas Dataframe from a Dictionary
- How To Group, Concatenate & Merge Data in Pandas
- Pandas Data Types
- Handling Missing Data in Pandas: NaN Values Explained
- NumPy Introduction with Examples
- Using StringIO to Read Delimited Text Files into NumPy
- Using the NumPy Bincount Statistical Function
- Top NumPy Statistical Functions & Distributions
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