ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). site:stackoverflow.com
np.all(np.isfinite(mat))ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). site:stackoverflow.com
np.all(np.isfinite(mat))ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
# Loads your dataset, drops labels and response variable as usual...
df = pd.read_csv('train_test.csv', sep=',')
X = df.drop(["label_1","label_2","response_var"], axis=1)
y = df['response_var']
# this will print TRUE or FALSE for positions containing or not NaN
# values in your dataset
print(np.isnan(X))ValueError: Input contains NaN, infinity or a value too large for dtype('float32'). site:stackoverflow.com
np.any(np.isnan(mat))Input contains NaN, infinity or a value too large for dtype('float64').
import numpy as np
# generate example matrix
matrix = np.random.rand(5,5)
matrix[0,:] = np.inf
matrix[2,:] = -np.inf
>>> matrix
array([[ inf, inf, inf, inf, inf],
[0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
[ -inf, -inf, -inf, -inf, -inf],
[0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
[0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])
# find min and max values for each column, ignoring nan, -inf, and inf
mins = [np.nanmin(matrix[:, i][matrix[:, i] != -np.inf]) for i in range(matrix.shape[1])]
maxs = [np.nanmax(matrix[:, i][matrix[:, i] != np.inf]) for i in range(matrix.shape[1])]
# go through matrix one column at a time and replace + and -infinity
# with the max or min for that column
for i in range(matrix.shape[1]):
matrix[:, i][matrix[:, i] == -np.inf] = mins[i]
matrix[:, i][matrix[:, i] == np.inf] = maxs[i]
>>> matrix
array([[0.90272002, 0.37357483, 0.95222639, 0.37570528, 0.68779902],
[0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
[0.72877665, 0.06580068, 0.7427659 , 0.00833664, 0.20837798],
[0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
[0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])Copyright © 2021 Codeinu
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