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I'm new to Pandas. I downloaded and installed Anaconda. Then I tried running the following code via the Spyder app:
import pandas as pd
import numpy as np
train = pd.read_csv('/Users/Ben/Documents/Kaggle/Titanic/train.csv')
train
Although this prints the dataframe as I expected, it also shows these errors
//anaconda/lib/python3.4/site-packages/pandas/core/format.py:1969: RuntimeWarning: invalid value encountered in greater
has_large_values = (abs_vals > 1e8).any()
//anaconda/lib/python3.4/site-packages/pandas/core/format.py:1970: RuntimeWarning: invalid value encountered in less
has_small_values = ((abs_vals < 10 ** (-self.digits)) &
//anaconda/lib/python3.4/site-packages/pandas/core/format.py:1971: RuntimeWarning: invalid value encountered in greater
(abs_vals > 0)).any()
Why am I getting these errors?
EDIT: I just tested the above code in an IPython notebook and it works without errors. So, is there something wrong with my Spyder installation? Any help would be appreciated.
EDIT2: After some testing, I can read the first 5 rows of the CSV without getting the warning. So, I suspect a NaN in the 6th row for a float64 type column is triggering the warning.
986
Why Is the Runtimewarning: Invalid Value Encountered in double_scalars Error Happening? The runtimewarning: invalid value encountered in double_scalars error happens when web developers try to perform certain mathematical operations that include numbers at the opposite end of the spectrum.
A double scalar is a value of type double . It is called scalar to differentiate it in numpy from double arrays.
Solution: We can fix this Runtime Warning by using seterr method which takes invalid as a parameter and assign ignore as a value to it. By that, it can hide the warning message which contains invalid in that.
I have the same error and have decided that it is a bug. It seems to be caused by the presence of NaN values in a DataFrame in Spyder. I have uninstalled and reinstalled all packages and nothing has effected it. NaN values are supported and are completely valid in DataFrames especially if they have a DateTime index.
In the end I have settled for suppressing this warnings as follows.
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