Using df.apply

pd.Series.str.replace is a series method not for dataframes. You can use apply on each column (series) instead.

dataFrame[headers] = dataFrame[headers].apply(lambda x: x.str.replace(',', ''))

Another option is to use apply on each row (series) with axis=1.

Using df.applymap

Or, you can use applymap and treat each cell as a string and use replace directly on them.

dataFrame[headers] = dataFrame[headers].applymap(lambda x: x.replace(',', ''))

Using df.replace

You can also use df.replace which is a method available to replace values in df directly across all columns selected. But, for this purpose you will have to set regex=True.

dataFrame[headers] = dataFrame[headers].replace(',', '', regex=True)

In pandas the object type is used when there is not a clear distinction between the types stored in the column.

So, I guess that in your column, some objects are float type and some objects are str type. Or maybe, you are also dealing with NaN objects, NaN objects are float objects.

a) Convert the column to string: Are you getting your DataFrame from a CSV or XLS format file? Then at the moment of reading the file, you can specify that that column is an str type or just make the type conversion of the column you are dealing with.

b) After that, you can apply the string changes and/or deal with the NaN objects.

c) Finally, you transform your column into float type`.

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 replace method was added in version 0.9.0 (see release notes).

Note: You can inspect the docs for a specific version of pandas by selecting that version on the right-hand-side of the webpage. But do consider updating to the latest stable version.

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'})

"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)

What you are trying to do is not practical - in any language.

First, as others have said, there is no replace() method on a file object.

More importantly you don't seem to understand how text files work. You say: "I saw some answers when they create a new file everytime". Did you consider why they do that?

"I just need to add +1 to an specific line"

Let's take a couple of lines in a text file that you think looks like this:

onetwomumble9
threefourmumble3

But a text file is nothing special, it just goes from the first byte to the last with newlines showing where lines end. It really looks like this:

onetwomumble9\nthreefourmumble3\n

Where \n represents the single newline character.

Let's say that you did replace 9 with 10 and 3 with 4. The problem is that 10 is wider than 9 and the extra character will overwrite the newline (or any other character) which follows. So you end-up with this:

onetwomumble10threefourmumble4\n

You lost the newline!

That's why you have to copy the file to update it! The alternative is to use a database (like SQLite) which handles these issues.

There are a few work-arounds. The simplest is to decide on an absolute maximum number of characters, and pad with zeros, for example 0009 gets updated to 0010.

Let's say the file looks like this:

onetwomumble0009
threefourmumble0005
sixsevenmumble0067
eightnonemumber0042

For reading and writing to the same file, you have to maintain the file position yourself. When you read a line it puts the current position to the next line, so to rewrite a line you have to move it back. Example code:

import re
import sys

# Could be done as a one-line lambda, but would be difficult to read
def addit(m):
    num = m.groups()[0]
    new_num = "%04d" % (int(num) + 1)
    return new_num

line_number = 1

with open('gash.txt', 'r+') as uf:
    while True:
        start_pos = uf.tell()  # Get start of line position
        line = uf.readline()
        if not line : break
        end_pos = uf.tell()  # Get end of line position - needed if updating more than one line

        # Let's say we update line 2, and we decided on 4 chars
        if line_number == 2: 
            # Do the add
            newline = re.sub(r'(\d{4})$', addit, line)


            # Sanity check
            if len(newline) != len(line):
                print("line length changed!", line, file=sys.stderr)
                sys.exit(1)

            # Seek to start of line just read and write
            uf.seek(start_pos)
            uf.write(newline)
            # Seek to start of next line
            uf.seek(end_pos)

            # If we only wanted to update one line, we can:
            break

        line_number += 1

You tried to use .Values with a capital v instead of .values. Changing the capital v to a lowercase v should solve fix the error you're getting.

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.