Pythont AttributeError: 'str' Object Has No Attribute 'decode'

Pythont AttributeError: 'str' Object Has No Attribute 'decode'

  1. Binary Mode Fix Decode Error in Python
  2. encoding Parameter Fix Decode Error Without Binary Mode in Python
  3. LogisticRegression Raises Decode Error in Python

In this article, we’ll discuss the AttributeError: 'str' object has no attribute 'decode' and its solutions in Python.

Binary Mode Fix Decode Error in Python

One of the causes of the error is that the user has no idea how to open a file while decoding the data in python. An in-built method is called open(), and we can open the file using this method.

The main reason to open the file is two things it could be reading or writing. Inside the open() method, we need to declare several parameters; the first parameter is the file name.

Two other parameters are optional; the first one is mode, and the second one is encoding. If you do not declare the other parameters, Python sets default values.

If you try to decode the file in a specific decoding system, the open() method will use the default encoding, and Python handles either binary mode or text mode. It depends on which type of data you need to handle.

Let’s look at an example where we try to decode data using the ISO-8859-1 decoding system.

Code:

count=0
for File_Data in open('demo.txt','r'):

    Decoded_DATA = File_Data.decode('ISO-8859-1')

    Data_row = Decoded_DATA.split('\t')
    print(Data_row)
    if count<1:
        print(type(File_Data))
    count+=1

Output:

AttributeError: 'str' object has no attribute 'decode'

The error occurs because no encoding is used, and we are just trying to decode the data we are accessing from a text file without encoding data. We only read this file in the reading mode, which is why we failed to decode the data.

If we want to decode this file data, we have to specify binary mode because binary mode directly reads in byte object, and we do not need to encode it separately. For the reading mode, the scenario will be different.

Code:

count=0
for File_Data in open('demo.txt','rb'):

    Decoded_DATA = File_Data.decode('ISO-8859-1')

    Data_row = Decoded_DATA.split('\t')
    print(Data_row)
    if count<1:
        print(type(File_Data))
    count+=1

Output:

['This is sample text\n']
<class 'bytes'>
['This is sample text\n']
<class 'bytes'>
['This is sample text']
<class 'bytes'>

We put b in front of r mode and can see the data type.

encoding Parameter Fix Decode Error Without Binary Mode in Python

The encoding parameter supports multiple types of encoding. When we neither use the encoding parameter, we need to use the decode() method or binary mode.

Code:

count=0
for File_Data in open('demo.txt','r',encoding='ISO-8859-1'):

    # Decoded_DATA = File_Data.decode('ISO-8859-1')
    Data_row = File_Data.split('\t')
    print(Data_row)
    if count<1:
        print(type(File_Data))
    count+=1

Output:

['This is sample text\n']
<class 'str'>
['This is sample text\n']
['This is sample text']

LogisticRegression Raises Decode Error in Python

Now, we will switch to a problem to see if there is an approach to create a binary classification using the LogisticRegression model from the sklearn module.

First, we load some module packages and then read the soner data.

from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd

Son_DATA = pd.read_csv("Sonar.csv")
Son_DATA.head()

The below image shows 62 columns, and one is unnamed.

read csv data in python with sklearn module

After checking the dimension of the data set, we will do a bit of conversion to ensure this shows up as a factor or category type of data.

print(Son_DATA.shape)

DATA_X = Son_DATA.drop('Class', 1)
Son_DATA['Class'] = Son_DATA['Class'].astype('category')
DATA_Y = Son_DATA['Class']

Output:

(208, 62)

In a data frame, we will split up data using the test_size parameter for 0.2 as the 20% of the test data set.

Train_DATA_X, Test_DATA_X, Train_DATA_Y, TEST_DATA_Y = train_test_split( DATA_X, DATA_Y, test_size=0.2, random_state=4)

In the next line, we will feed the training data into the linear logistic regression model where most programmers get the AttributeError: 'str' object has no attribute 'decode'. This error occurs when we try to build a binary classifier because the binary classifier uses a decoding system.

Model = LogisticRegression(C=0.01).fit(Train_DATA_X,Train_DATA_Y)

We did not get this error because of using the upgrade version of the sklearn library. Most users use older versions, and they still get this error, so we need to use the latest version.

It may be possible you do not get this error, and the python community may have fixed it, but now we are not getting it. You can run the following command to upgrade the version of sklearn.

pip install scikit-learn -U

If you do not want to upgrade the version for any reason, then you can use the solver argument in the LogisticRegression() and pass the string value as liblinear.

Model = LogisticRegression(C=0.01,solver='liblinear').fit(Train_DATA_X,Train_DATA_Y)

Now we get the revenues model, predict the results, and print the confusion matrix and accuracy.

DATA_Y_prd = Model.predict(Test_DATA_X)
print("Accuracy: {0:0.3}".format(metrics.accuracy_score(TEST_DATA_Y, DATA_Y_prd)))
print(confusion_matrix(TEST_DATA_Y, DATA_Y_prd))

Output:

Accuracy: 1.0
[[25 0]
 [ 0 17]]

Complete Python Code:

from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd

Son_DATA = pd.read_csv("Sonar.csv")
Son_DATA.head()

print(Son_DATA.shape)

DATA_X = Son_DATA.drop('Class', 1)
Son_DATA['Class'] = Son_DATA['Class'].astype('category')
DATA_Y = Son_DATA['Class']

Train_DATA_X, Test_DATA_X, Train_DATA_Y, TEST_DATA_Y = train_test_split( DATA_X, DATA_Y, test_size=0.2, random_state=4)

Model = LogisticRegression(C=0.01,solver='liblinear').fit(Train_DATA_X,Train_DATA_Y)

DATA_Y_prd = Model.predict(Test_DATA_X)
print("Accuracy: {0:0.3}".format(metrics.accuracy_score(TEST_DATA_Y, DATA_Y_prd)))
print(confusion_matrix(TEST_DATA_Y, DATA_Y_prd))
Salman Mehmood avatar Salman Mehmood avatar

Hello! I am Salman Bin Mehmood(Baum), a software developer and I help organizations, address complex problems. My expertise lies within back-end, data science and machine learning. I am a lifelong learner, currently working on metaverse, and enrolled in a course building an AI application with python. I love solving problems and developing bug-free software for people. I write content related to python and hot Technologies.

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