How to Create a Pandas DataFrame from a Dictionary with Varying Array Lengths?

Creating DataFrames from Dictionaries with Uneven Entry Lengths

In Python, it is possible to create a DataFrame from a dictionary where each entry holds a Numpy array. However, challenges arise when the array lengths vary among entries. By default, Pandas requires arrays of uniform length, leading to errors like "ValueError: arrays must all be the same length."

Overcoming the Length Discrepancy

To address this issue, we can leverage the capability of Pandas to use NaN (Not-a-Number) values as placeholders for missing data. By utilizing this, we can effectively create a DataFrame with columns of different lengths.

To achieve this, we can convert each dictionary entry into a Pandas Series, a one-dimensional array that can seamlessly handle missing values. By wrapping the dictionary items in a generator expression and using the Series constructor, we can create a dictionary of Series objects.

import pandas as pd
import numpy as np

# Sample data with uneven array lengths
data = {
    'A': np.random.randn(5),
    'B': np.random.randn(8),
    'C': np.random.randn(4)
}

# Convert dictionary items to Series
series_dict = dict((k, pd.Series(v)) for k, v in data.items())

# Create DataFrame from the dictionary of Series
df = pd.DataFrame(series_dict)

Result:

In [1]: df
Out[1]:
        A         B         C
0  1.162543  1.681243  0.191287
1  0.459621  -0.141198 -0.109864
2  -0.866704 -0.128677  -0.511496
3  1.222436  -0.371449 -0.705894
4  -0.980584  1.255133       NaN
5        NaN -0.351051       NaN
6        NaN  0.443017       NaN
7        NaN -1.053693       NaN

As evident, the DataFrame contains missing values (NaN) where the array lengths differ, allowing us to create a DataFrame with different column lengths from a dictionary with varying array lengths.

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