ValueError: Input contains NaN, infinity or a value too large for dtype('float64') while preprocessing Data

2.0K Asked by CaroleThom in Python , Asked on Jun 2, 2021

I have two CSV files(Training set and Test Set). Since there are visible NaN values in few of the columns (status, hedge_value, indicator_code, portfolio_id, desk_id, office_id).

I start the process by replacing the NaN values with some huge value corresponding to the column. Then I am doing LabelEncoding to remove the text data and convert them into Numerical data. Now, when I try to do OneHotEncoding on the categorical data, I get the error. I tried giving input one by one into the OneHotEncoding constructor, but I get the same error for every column.

Basically, my end goal is to predict the return values, but I am stuck in the data preprocessing part because of this. How do I solve this issue?

I am using Python3.6 with Pandas and Sklearn for data processing.

Code

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
test_data = pd.read_csv('test.csv')
train_data = pd.read_csv('train.csv')
# Replacing Nan values here
train_data['status']=train_data['status'].fillna(2.0)
train_data['hedge_value']=train_data['hedge_value'].fillna(2.0)
train_data['indicator_code']=train_data['indicator_code'].fillna(2.0)
train_data['portfolio_id']=train_data['portfolio_id'].fillna('PF99999999')
train_data['desk_id']=train_data['desk_id'].fillna('DSK99999999')
train_data['office_id']=train_data['office_id'].fillna('OFF99999999')
x_train = train_data.iloc[:, :-1].values
y_train = train_data.iloc[:, 17].values
# =============================================================================
# from sklearn.preprocessing import Imputer
# imputer = Imputer(missing_values="NaN", strategy="mean", axis=0)
# imputer.fit(x_train[:, 15:17])
# x_train[:, 15:17] = imputer.fit_transform(x_train[:, 15:17])
# 
# imputer.fit(x_train[:, 12:13])
# x_train[:, 12:13] = imputer.fit_transform(x_train[:, 12:13])
# =============================================================================
# Encoding categorical data, i.e. Text data, since calculation happens on numbers only, so having text like 
# Country name, Purchased status will give trouble
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
x_train[:, 0] = labelencoder_X.fit_transform(x_train[:, 0])
x_train[:, 1] = labelencoder_X.fit_transform(x_train[:, 1])
x_train[:, 2] = labelencoder_X.fit_transform(x_train[:, 2])
x_train[:, 3] = labelencoder_X.fit_transform(x_train[:, 3])
x_train[:, 6] = labelencoder_X.fit_transform(x_train[:, 6])
x_train[:, 8] = labelencoder_X.fit_transform(x_train[:, 8])
x_train[:, 14] = labelencoder_X.fit_transform(x_train[:, 14])
# =============================================================================
# import numpy as np
# x_train[:, 3] = x_train[:, 3].reshape(x_train[:, 3].size,1)
# x_train[:, 3] = x_train[:, 3].astype(np.float64, copy=False)
# np.isnan(x_train[:, 3]).any()
# =============================================================================
# =============================================================================
# from sklearn.preprocessing import StandardScaler
# sc_X = StandardScaler()
# x_train = sc_X.fit_transform(x_train)
# =============================================================================
onehotencoder = OneHotEncoder(categorical_features=[0,1,2,3,6,8,14])
x_train = onehotencoder.fit_transform(x_train).toarray() # Replace Country Names with One Hot Encoding.

Error

Traceback (most recent call last):
  File "", line 58, in 
    x_train = onehotencoder.fit_transform(x_train).toarray() # Replace Country Names with One Hot Encoding.
  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 2019, in fit_transform
    self.categorical_features, copy=True)
  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py", line 1809, in _transform_selected
    X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES)
  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py", line 453, in check_array
    _assert_all_finite(array)
  File "/Users/parthapratimneog/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py", line 44, in _assert_all_finite
    " or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

Answered by Chris Dyer

You can use the Pandas function to solve input contains nan, infinity or a value too large for dtype('float64')

pd.isnull(train_data).sum() > 0

After handling the new Nan the code will work fine and give the outcome:

Result

portfolio_id      False

desk_id           False

office_id         False

pf_category       False

start_date        False

sold               True

country_code      False

euribor_rate      False

currency          False

libor_rate         True

bought             True

creation_date     False

indicator_code    False

sell_date         False

type              False

hedge_value       False

status            False

return            False

dtype: bool

ValueError: Input contains NaN, infinity or a value too large for dtype('float64') while preprocessing Data

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