org/pandas-docs/stable/categorical.html. Also,
> check out http://www.pytables.org.
>
> Albert-Jan
> --
> *From:* Python-list hotmail@python.org> on behalf of Bhaskar Dhariyal <
> dhariyalbhas...@gmail.com>
> *Sent:* Thursday, June 29, 2
___
> From: Python-list on
> behalf of Paul Barry
> Sent: Wednesday, June 28, 2017 12:30:25 PM
> To: Bhaskar Dhariyal
> Cc: python-list@python.org
> Subject: Re: Combining 2 data series into one
>
> Maybe look at using .concat instead of +
>
> See:
> http://
Paul Barry wrote:
>
> >
> > Maybe try your code on a sub-set of your data - perhaps 1000 lines of
> > data? - to see if that works.
> >
> > Anyone else on the list suggest anything to try here?
> >
> > On 28 June 2017 at 12:50, Bhaskar Dhariyal
> > wro
first_name
> 0bhaskar
> 1 Rohit
> >>> df2 = pd.DataFrame(['dhariyal', 'Gavval'], columns=['last_name'])
> >>> df2
> last_name
> 0 dhariyal
> 1Gavval
> >>> df = pd.DataFrame()
> >>> df['na
Hi!
I have 2 dataframe i.e. df1['first_name'] and df2['last_name']. I want to make
it as df['name']. How to do it using pandas dataframe.
first_name
--
bhaskar
Rohit
last_name
---
dhariyal
Gavval
should appear as
name
--
bhaskar d
You can't train a model on words. You need to convert it into numerical
form(vector form). For this there are some packages like word2vec and doc2vec.
Thanks for replying
On Tuesday, 27 June 2017 16:37:56 UTC+5:30, Ben Bacarisse wrote:
> Bhaskar Dhariyal writes:
>
> > I a
Hi,
I am doing a project on data science. I need to convert sentences to vectorial
form. I have already written code for cleansing data and already removed stop
words and performed stemming. Please help in converting kickdesc and kickkey to
vectorial form.
Link to code and dataset:
https://dr
Hi everyone!
I have a dataset which I want to make model trainable. I ahve been trying to do
some thing for past 2-3 days.
Actually I wanted to clean 'desc' and 'keywords' column from the dataset. I am
using NLTK to vectorize, than remove stopwords & alpha numeric values and do
stemming. More
Int64Index: 171594 entries, 0 to 63464
Data columns (total 7 columns):
project_id 171594 non-null object
desc171594 non-null object
goal171594 non-null float64
keywords171594 non-null object
diff_creat_laun 171594 non-null int64
diff_laun_st
