Typeerror: '<=' Not Supported Between Instances of 'Str' and 'Int'

TypeError: '=' not supported between instances of 'str' and 'int'

Change

vote = input('Enter the name of the player you wish to vote for')

to

vote = int(input('Enter the name of the player you wish to vote for'))

You are getting the input from the console as a string, so you must cast that input string to an int object in order to do numerical operations.

TypeError: '' not supported between instances of 'str' and 'int' after converting string to float

There are two Gls columns in your dataframe. I think you converted only one "Gls" column to float, and when you do dfstandard['Gls'] = dfstandard['Gls'] / dfstandard['90s'], the other "Gls" column is getting considered?...

Try stripping whitespace from the column names too

df = df.rename(columns=lambda x: x.strip())
df['90s'] = pd.to_numeric(df['90s'], errors='coerce')
df['Gls'] = pd.to_numeric(df['Gls'], errors='coerce')

Thus the error.

=' not supported between instances of 'str' and 'int' in functions and using lambda in apply

Your point is in string so, you're getting this error. But you can solve this by changing that string into integer. And in function you are sending wrongly, it should be points and country rather country and points.

def stars(points,country):
    if country == 'Canada':
        return 3
    elif int(points) >= 95:
        return 3
    elif int(points) >= 85:
        return 2
    else:
        return 1

star_ratings = reviews.apply(lambda x: stars(x['points'], x['country']), axis=1)

' is not supported between instances of 'str' and 'int'

As the traceback said, age is a string because it has just been "inputted" by the user. Unlike C, there is no method to do something like scanf("%d", &age), so you need to manually cast age to an integer with age = int(age).

name = input("Enter your name:")
print("Hello, " +name)
age = input("Please enter your age:")
# do exception handling to make sure age is in integer format
age = int(age)

What features are causing TypeError: '' not supported between instances of 'str' and 'int'

I see that your label column is of type object, meaning it is a string. But in your class weights, you use an integer in class_weight = {1: 3.5, 0: 1} , so you to specify the classes correctly, or labelEncode.

With an example dataset, where my labels are "yes" or "no" :

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder, Normalizer, LabelEncoder
from sklearn.compose import ColumnTransformer
import pandas as pd
import numpy as np

df = pd.DataFrame({'f1':np.random.uniform(0,1,100),
'f2':np.random.choice(['a','b','c'],100),
'label':np.random.choice(['yes','no'],100)})

df.dtypes
f1       float64
f2        object
label     object

If we set up the pipeline like you did, I get a similar error:

columns_for_encoding = ['f2']
columns_for_scaling = ['f1']

transformerVectoriser = ColumnTransformer(
transformers=[('Vector Cat', OneHotEncoder(handle_unknown = "ignore"), columns_for_encoding),
('Normalizer', Normalizer(), columns_for_scaling)],
remainder='passthrough') 

pipeline = Pipeline([('transformer', transformerVectoriser),
                         ('classifier', RandomForestClassifier(class_weight = {1: 3.5, 0: 1}))])

pipeline.fit(df[['f1','f2']],df['label'])

Let's define the weights properly and it works:

pipeline = Pipeline([('transformer', transformerVectoriser),
                             ('classifier', RandomForestClassifier(class_weight = {'yes': 3.5, 'no': 1}))])

pipeline.fit(df[['f1','f2']],df['label'])

Pipeline(steps=[('transformer',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('Vector Cat',
                                                  OneHotEncoder(handle_unknown='ignore'),
                                                  ['f2']),
                                                 ('Normalizer', Normalizer(),
                                                  ['f1'])])),
                ('classifier',
                 RandomForestClassifier(class_weight={'no': 1, 'yes': 3.5}))])

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