Double check if there's a space in the column name. 'Survived ' vs 'Survived' It happens more often than you'd think especially with CSV data source.

• The error occurs because pandas.Series.cat.add_categories is a Series method, and df[['embarked', 'sex', 'pclass']] is a DataFrame.
• Use pd.Categorical
• pandas: Categorical data
• Some of the titanic dataset columns contain NaNs, which can't be categories.
  • Use .dropna() when creating the categories.

single column

df['embarked'] = pd.Categorical(df['embarked'], categories=df['embarked'].dropna().unique())

multiple columns

# looping through the columns
for col in ['embarked', 'sex', 'pclass']:
    df[col] = pd.Categorical(df[col], categories=df[col].dropna().unique())

# alternatively with .apply
df[['embarked', 'sex', 'pclass']] = df[['embarked', 'sex', 'pclass']].apply(lambda x: pd.Categorical(x, x.dropna().unique(), ordered=True))

• Appending new categories

# create a sample series
s = pd.Series(["a", "b", "c", "a"], dtype="category")

# add a category
s = s.cat.add_categories([4])

Check your DataFrame with data.columns

It should print something like this

Index([u'regiment', u'company',  u'name',u'postTestScore'], dtype='object')

Check for hidden white spaces..Then you can rename with

data = data.rename(columns={'Number ': 'Number'})

The .cat is not applicable for Dataframe, so you have to apply for each column separately as series. You can use .apply() and apply cat as a lambda function

df[cat_columns] = df[cat_columns].apply(lambda x: x.cat.codes)

Or loop through the columns and use the cat funtion

for col in cat_columns:
    df[col] = df[col].cat.codes

"sklearn.datasets" is a scikit package, where it contains a method load_iris().

load_iris(), by default return an object which holds data, target and other members in it. In order to get actual values you have to read the data and target content itself.

Whereas 'iris.csv', holds feature and target together.

FYI: If you set return_X_y as True in load_iris(), then you will directly get features and target.

from sklearn import datasets
data,target = datasets.load_iris(return_X_y=True)

value_counts is a Series method rather than a DataFrame method (and you are trying to use it on a DataFrame, clean). You need to perform this on a specific column:

clean[column_name].value_counts()

It doesn't usually make sense to perform value_counts on a DataFrame, though I suppose you could apply it to every entry by flattening the underlying values array:

pd.value_counts(df.values.flatten())

The function pd.read_csv() is already a DataFrame and thus that kind of object does not support calling .to_dataframe().

You can check the type of your variable ds using print(type(ds)), you will see that it is a pandas DataFrame type.

[...] The author of this link (kaggle.com/dstuerzer/optimized-logistic-regression) has used it and it is working fine with his code.

In the link you mentioned, example, the author's database has a column named "Class" but the database that you have shown does not. As a result, the Class attribute does not exist in your database and therefore cannot be accessed.

Dominik Stuerzer:

   Time        V1        V2        V3        V4        V5        V6        V7  \
0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   
1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   
2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   

         V8        V9  ...         V21       V22       V23       V24  \
0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   
1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   
2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   

        V25       V26       V27       V28  Amount  Class  
0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  
1  0.167170  0.125895 -0.008983  0.014724    2.69      0  
2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  

[3 rows x 31 columns]

A class of 0 means that the transaction was in order, and a class of 1 means that the transaction was fraudulent. From personal experience we expect frauds to make up only a tiny fraction of all transactions. Indeed, in this dataset, for every fraud there are almost 600 non-fraudulent transactions: [...]