I think the column name that contains "Number" is something like " Number" or "Number ". I'm assuming you might have a residual space in the column name. Please run print "<{}>".format(data.columns[1]) and see what you get. If it's something like < Number>, it can be fixed with:

data.columns = data.columns.str.strip()

See pandas.Series.str.strip

In general, AttributeError: 'DataFrame' object has no attribute '...', where ... is some column name, is caused because . notation has been used to reference a nonexistent column name or pandas method.

pandas methods are accessed with a .. pandas columns can also be accessed with a . (e.g. data.col) or with brackets (e.g. ['col'] or [['col1', 'col2']]).

data.columns = data.columns.str.strip() is a fast way to quickly remove leading and trailing spaces from all column names. Otherwise verify the column or attribute is correctly spelled.

The Iris Dataset from Sklearn is in Sklearn's Bunch format:

print(type(iris))
print(iris.keys())

output:

<class 'sklearn.utils.Bunch'>
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])

So, that's why you can access it as:

x=iris.data
y=iris.target

But when you read the CSV file as DataFrame as mentioned by you:

iris = pd.read_csv('iris.csv',header=None).iloc[:,2:4]
iris.head()

output is:

    2   3
0   petal_length    petal_width
1   1.4 0.2
2   1.4 0.2
3   1.3 0.2
4   1.5 0.2

Here the column names are '1' and '2'.

First of all you should read the CSV file as:

df = pd.read_csv('iris.csv')

you should not include header=None as your csv file includes the column names i.e. the headers.

So, now what you can do is something like this:

X = df.iloc[:, [2, 3]] # Will give you columns 2 and 3 i.e 'petal_length' and 'petal_width'
y = df.iloc[:, 4] # Label column i.e 'species'

or if you want to use the column names then:

X = df[['petal_length', 'petal_width']]
y = df.iloc['species']

Also, if you want to convert labels from string to numerical format use sklearn LabelEncoder

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
y = le.fit_transform(y)

It's an issue with how you're calling the data and if it's actually there.

train.loc[:,['Survived','Sex']]

tells me that there's a DataFrame (which is from pandas, hence the error) called train and this line is trying to access parts of that dataframe (it's just a type of an array). Specifically, it's trying to access columns named Survived and Sex.

Similarly, this line tells me there's another dataframe (df) known as test with a column named Sex and this is access that data.

test.loc[:,['Sex']]

The error code also informs me of some things

predictions = pandas.DataFrame({'PassengerId': test.PassengerId,'Survived': hypothesis.Survived.tolist()})

There's another df called predictions that's of dict type which is trying to access information from the another hypothesis df. The attribute it's tryin to access in the second key of the dict is

hypothesis.Survived.tolist()

which is a way of calling a column from that df. That is, when the predictions line is executed, it's trying to pull all the values from the Survived column of the hypothesis df.

The error is that the df doesn't actually have a column named Survived. So either there's missing data, or you're calling it wrong, or there's a missing reference.

Without knowing more about your code and your question, I can't really extrapolate much more.

You can use df.rdd.map(), as DataFrame does not have map or flatMap, but be aware of the implications of using df.rdd:

Converting to RDD breaks Dataframe lineage, there is no predicate pushdown, no column prunning, no SQL plan and less efficient PySpark transformations.

What should you do instead?

Keep in mind that the high-level DataFrame API is equipped with many alternatives. First, you can use select or selectExpr.

Another example is using explode instead of flatMap(which existed in RDD):

df.select($"name",explode($"knownLanguages"))
    .show(false)

Result:

+-------+------+
|name   |col   |
+-------+------+
|James  |Java  |
|James  |Scala |
|Michael|Spark |
|Michael|Java  |
|Michael|null  |
|Robert |CSharp|
|Robert |      |
+-------+------+

You can also use withColumn or UDF, depending on the use-case, or another option in the DataFrame API.