How Can I Easily Share Complex DataFrames for Reproducible Code Examples?

Easy Sharing of Data Samples with df.to_dict()

Despite clear guidelines for good questions and the inclusion of reproducible data samples, many users often neglect to provide sufficient data for analysis. This article explores the use of the df.to_dict() function as a practical way to share sample dataframes that are more complex than random numbers.

Case 1: Dataframes from Local Sources

For dataframes obtained from local sources, this approach is straightforward:

1. Execute df.to_dict() to generate a dictionary representation of the dataframe.
2. Copy the output, including the dictionary structure.
3. Paste the content into pd.DataFrame(
   ) in your code snippet.

Case 2: Tables from Other Applications

If your table is located in an application like Excel, you can use the following steps:

1. Copy the table contents.
2. Execute df=pd.read_clipboard(sep='s ') to read the contents into a dataframe, where 's ' means any space.
3. Run df.to_dict() and include the result in df=pd.DataFrame(
   ).

Handling Larger Dataframes

For larger dataframes, consider the following approaches:

• Use df.head(20).to_dict() to include only the first 20 rows.
• Use df.to_dict('split') to reshape the output for improved readability on fewer lines.

Example Using Iris Dataset

Consider the iris dataset, known for being available in plotly express.

import plotly.express as px
import pandas as pd

df = px.data.iris().head(10)
sample = df.to_dict('split')

This will produce a dictionary with index, columns, and data keys, allowing for easy recreation of the dataframe using:

df = pd.DataFrame(index=sample['index'], columns=sample['columns'], data=sample['data'])

Edit

Note that df.to_dict() cannot read timestamps without explicitly including the necessary import (e.g., from pandas import Timestamp).

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