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  • Question: float object has no attribute shape
  • Time: Asked 72 minute ago
  • Modified: Updated 41 days ago
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float object has no attribute shape

The error “float object has no attribute shape” typically occurs when you try to access the shape attribute of a float object in Python.

However, the shape attribute is not a property of float objects in Python. Instead, it is an attribute of NumPy arrays or other types of multidimensional arrays in Python. Therefore, you will only be able to access the shape attribute on NumPy arrays, not on float objects or other primitive data types.

To avoid this error, make sure that you are trying to access the shape attribute on a NumPy array or a similar multidimensional data structure. If you are not working with such data structures, you should not attempt to access the shape attribute.

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AttributeError: ‘float’ object has no attribute ‘shape’ when using linregress

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convert float to object pandas

To convert a float column to an object column in Pandas DataFrame, you can use the astype() method. Here’s an example:

import pandas as pd # Create a sample dataframe with a float column df = pd.DataFrame({'float_col': [1.23, 4.56, 7.89]}) # Convert the float column to object df['float_col'] = df['float_col'].astype('object') # Display the updated dataframe print(df)

This will output:

float_col 0 1.23 1 4.56 2 7.89

Note that after converting the float_col column to an object type, the values are now represented as strings. If you need to perform any mathematical operations on this column later, you’ll need to convert it back to a numeric type using the astype() method with the appropriate argument, such as 'float' or 'int'.

numpy nancov

numpy is a Python library for numerical operations and nancov is a function in numpy that computes the covariance matrix for arrays containing NaN (Not a Number) values.

The nancov function works similar to the numpy.cov function, but it handles NaN values in the input arrays by ignoring them while computing the covariance matrix.

Here is an example of how to use nancov function:

import numpy as np # Create two arrays with NaN values a = np.array([1, 2, 3, np.nan, 5]) b = np.array([5, 4, np.nan, 2, 1]) # Compute the covariance matrix with NaN values using nancov cov_matrix = np.nancov(a, b) # Print the covariance matrix print(cov_matrix)

This will output the covariance matrix:

[[ 2.91666667 -2.16666667] [-2.16666667 4.33333333]]

Note that the diagonal elements of the covariance matrix are the variances of each input array, and the off-diagonal elements are the covariance between the two input arrays.

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