Shape of Passed Values is (x,y), Indices Imply (w,z): A Deep Dive into Pandas DataFrame Behavior

When working with Pandas DataFrames, it’s common to encounter a frustrating error: “Shape of passed values is (x,y), indices imply (w,z)”. This issue arises when dealing with mixed-type DataFrames, where the number of columns in the result does not match the index. In this article, we’ll delve into the world of Pandas and explore the underlying reasons behind this behavior.

Introduction to Mixed-Type DataFrames

A mixed-type DataFrame is a DataFrame that contains columns with different data types. For instance:

import pandas as pd

df = pd.DataFrame({
    'one': pd.Series([1, 2, 3, 4], dtype=int),
    'two': pd.Series([20, 30, 40, 50], dtype=float)
})

In this example, the ‘one’ column has an integer data type, while the ’two’ column has a floating-point data type.

The Problem: Zip() Throws an Error

When we try to add two new columns to our DataFrame using the zip() function, we encounter an error:

df.apply(lambda row: (row.one + row.two,), axis=1)

The error message is:

ValueError: Shape of passed values is (4, 2), indices imply (4, 3)

This error occurs because the zip() function tries to align two DataFrames with different numbers of columns. In this case, the resulting DataFrame has only two columns, but the original DataFrame has three columns.

The Solution: Returning a Series

To fix this issue, we need to return a Series from our function instead of trying to use the zip() function directly. Here’s an example:

df.apply(lambda row: pd.Series((row.one + row.two, row.one * row.two)), axis=1)

By returning a Series, we ensure that the resulting DataFrame has the correct number of columns.

The Underlying Reason: `_is_mixed_type` and `_apply_standard`

When dealing with mixed-type DataFrames, Pandas uses a different function to apply the calculation: _apply_standard. This function returns a dict where each key is a column name and each value is the result of the calculation for that column.

Here’s an excerpt from the DataFrame._apply_standard method:

def _apply_standard(self, func, axis=0):
    if self._is_mixed_type:
        results = {}
        index = []
        for col in self.columns:
            if col not in results:
                results[col] = pd.Series(func(col), name=col)
            else:
                # align columns with different data types
                raise ValueError("Shape of passed values is (x,y), indices imply (w,z)")
    return DataFrame(results, index=index)

As you can see, when dealing with mixed-type DataFrames, Pandas tries to align the columns by using a dictionary where each key is a column name and each value is the result of the calculation for that column.

Conclusion

In conclusion, the “Shape of passed values is (x,y), indices imply (w,z)” error occurs when dealing with mixed-type DataFrames. By returning a Series from our function instead of trying to use the zip() function directly, we can fix this issue and get the desired result.

Additionally, understanding the underlying reasons behind this behavior, such as _is_mixed_type and _apply_standard, can help us write more efficient and effective code when working with Pandas DataFrames.

Example Use Cases

Adding two new columns to a mixed-type DataFrame:

df = pd.DataFrame({
    'one': pd.Series([1, 2, 3, 4], dtype=int),
    'two': pd.Series([20, 30, 40, 50], dtype=float)
})

df['three'] = df['one'] + df['two']
df['four'] = df['one'] * df['two']

print(df)

Using the zip() function to add two new columns:

df.apply(lambda row: (row.one + row.two,), axis=1)

This will throw an error because of the mismatch in column numbers.

Tips and Variations

When dealing with mixed-type DataFrames, make sure to return a Series from your function instead of trying to use the zip() function directly.
Use the _is_mixed_type and _apply_standard methods to understand how Pandas handles mixed-type DataFrames.
Consider using the pd.merge() function to concatenate two DataFrames with different numbers of columns.

Last modified on 2023-08-11