I have recently been going through "Data Science From Scratch" which may be
interesting. There is a podcast with the author on talk python to me.
https://talkpython.fm/episodes/show/56/data-science-from-scratch
On Sat, May 14, 2016 at 10:33 AM, Michael Selik
wrote:
> You might also be interest
You might also be interested in "Python for Data Analysis" for a thorough
discussion of Pandas.
http://shop.oreilly.com/product/0636920023784.do
On Sat, May 14, 2016 at 10:29 AM Michael Selik
wrote:
> David, it sounds like you'll need a thorough introduction to the basics of
> Python.
> Check ou
David, it sounds like you'll need a thorough introduction to the basics of
Python.
Check out the tutorial: https://docs.python.org/3/tutorial/
On Sat, May 14, 2016 at 6:19 AM David Shi wrote:
> Hello, Michael,
>
> I discovered that the problem is "two columns of data are put together"
> and "are
Hello, Michael,
I discovered that the problem is "two columns of data are put together" and
"are recognised as one column".
This is very strange. I would like to understand the subject well.
And, how many ways are there to investigate into the nature of objects
dynamically?
Some object types onl
It looks like you're getting a Series. Apparently more that one row has the
same index.
On Fri, May 13, 2016 at 11:30 PM Michael Selik
wrote:
> What were you hoping to get from ``df[0]``?
> When you say it "yields nothing" do you mean it raised an error? What was
> the error message?
>
> Have yo
What were you hoping to get from ``df[0]``?
When you say it "yields nothing" do you mean it raised an error? What was
the error message?
Have you tried a Google search for "pandas set index"?
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html
On Fri, May 13, 201
Hello, Michael,
This is very weird.
55 145340.20
56 25.43
Name: 3, dtype: float64
It looks like two columns, but it shows one single object.
Any clue?
On Saturday, 14 May 2016, 4:15, David Shi wrote:
Hello, Michael,
I tried to discover the pro
Hello, Michael,
I do not understand this.
I tried list =df[3]
it worked. But, it does not behave like a list.
list[0] nothinglist[1] a valuelist[2] nothing
list[4] a value
It behaves like a dictionary.
On Saturday, 14 May 2016, 4:27, David Shi wrote:
Hello, Michael,
This is very weird.
Hello, Michael,
I tried to discover the problem.
df[0] yields nothingdf[1] yields nothingdf[2] yields nothing
However, df[3] gives the following:sid
-9223372036854775808 NaN
1 133738.70
4 295256.11
5 137733.09
6
What have code you tried? What error message are you receiving?
On Fri, May 13, 2016, 5:54 PM David Shi wrote:
> Hello, Michael,
>
> How to convert a float type column into an integer or label or string type?
>
>
> On Friday, 13 May 2016, 22:02, Michael Selik
> wrote:
>
>
> To clarify that you'
Hello, Michael,
How to convert a float type column into an integer or label or string type?
On Friday, 13 May 2016, 22:02, Michael Selik
wrote:
To clarify that you're specifying the index as a label, use df.iloc
>>> df = pd.DataFrame({'X': range(4)}, index=list('abcd')) >>> df
To clarify that you're specifying the index as a label, use df.iloc
>>> df = pd.DataFrame({'X': range(4)}, index=list('abcd'))
>>> df
X
a 0
b 1
c 2
d 3
>>> df.loc['a']
X0
Name: a, dtype: int64
>>> df.iloc[0]
X0
Name: a, dtype: int6
Dear Michael,
To avoid complication, I only groupby using one column.
It is OK now. But, how to refer to new row index? How do I use floating index?
Float64Index([ 1.0, 4.0, 5.0, 6.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 16.0,
17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
Here's an example.
>>> import pandas as pd
>>> df = pd.DataFrame({'group': list('AB') * 2, 'data': range(4)},
index=list('wxyz'))
>>> df
data group
w 0 A
x 1 B
y 2 A
z 3 B
>>> df = df.reset_index()
>>> df
index data
Hello, Michael,
I changed groupby with one column.
The index is different.
Index([ u'AL',u'AR',u'AZ',u'CA',u'CO',u'CT',u'DC',
u'DE',u'FL',u'GA',u'IA',u'ID',u'IL',u'IN',
u'KS',u'KY',u'LA',u'MA',u'MD',u'ME',
Hello, Michael,
I typed in df.index
I got the followingMultiIndex(levels=[[1.0, 4.0, 5.0, 6.0, 8.0, 9.0, 10.0,
11.0, 12.0, 13.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0,
39.0, 40.0, 41.0, 42.0, 44.0,
Dear Michael,
I have done a number of operation in between.
Providing that information does not help you
How to reset index after grouping and various operations is of interest.
How to type in a command to find out its current dataframe?
Regards.
David
On Friday, 13 May 2016, 20:58, Michael S
Dear MIchael,
I am very confused.
Can you send me a link to a working example?
Regards.
David
On Friday, 13 May 2016, 20:56, Michael Selik
wrote:
In order to preserve your index after the aggregation, you need to make sure
it is considered a data column (via reset_index) and then choos
Just in case I misunderstood, why don't you make a little example of before
and after the grouping? This mailing list does not accept attachments, so
you'll have to make do with pasting a few rows of comma-separated or
tab-separated values.
On Fri, May 13, 2016 at 3:56 PM Michael Selik
wrote:
>
In order to preserve your index after the aggregation, you need to make
sure it is considered a data column (via reset_index) and then choose how
your aggregation will operate on that column.
On Fri, May 13, 2016 at 3:29 PM David Shi wrote:
> Hello, Michael,
>
> Why reset_index before grouping?
Hello, Michael,
Why reset_index before grouping?
Regards.
David
On Friday, 13 May 2016, 17:57, Michael Selik
wrote:
On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list
wrote:
I lost my indexes after grouping in Pandas.
I managed to rest_index and got back the index column.
B
Hello, Michael,Thank you. Yes, aster grouping I lost my indexing in both x, y
directions.
How to convert a row, and a column into indexes or labels?
On Friday, 13 May 2016, 17:57, Michael Selik
wrote:
On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list
wrote:
I lost my ind
On Fri, May 13, 2016 at 12:27 PM David Shi via Python-list <
python-list@python.org> wrote:
> I lost my indexes after grouping in Pandas.
> I managed to rest_index and got back the index column.
> But How can I get back a index row?
>
Was the grouping an aggregation? If so, the original indexes a
I lost my indexes after grouping in Pandas.
I managed to rest_index and got back the index column.
But How can I get back a index row?
Regards.
David
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