I want to index a table in MATLAB on a particular column. In Python we can use set_index(column_name) using pandas library. I want an equivalent code that can do in MATLAB. To be more precise I want to look at the internal code of set_index() in Python. Can someone help me?
Code in MATLAB:
T = readtable('filename.csv');
I want to set an index on T.column_name here.
Related
[What I am starting out withWhat I want to end up with](https://i.stack.imgur.com/xW8Zf.jpg)I am having trouble writing the code to transform this dataset into what you see below. I am a beginner and am just practicing using Python.
So far, I have tried the str.split, but it didn’t produce the results I was hoping for.
Is it possible to convert excel formulas to python code? For example:
"=TEXT(SORT(PROPER(UNIQUE(FILTER("
"ws_1!A:A,ws_2!B:B=ws_3!C3"
')))), "")'
Or it is not possible. I was looking into Pycel, xlcalculator, formulas module. But unfortunately i cannot find more complicated example than sum(A,B).
Probably i could do it with pandas, but it won't work constantly in spreadsheet. Or can i save some python script instead formula to cell?
if you have any idea how to translate easier formulas eg. or any library that can do it, I would be grateful for the tips :
'=IFERROR(VLOOKUP(C2,ws!A2:B3,2,0), "Invalid")'
My motivation is to avoid a long excel formula in python code. And make it testable
I am coming from SQL world and we are using pandas for ETL this time. We use DIFFERENCE and SOUNDEX for the string comparison. But its not giving expected results lately. Is there any way to achieve this in python?
Currently we are using code like below which will return a score for the match.
SELECT difference(soundex('string'),soundex(Col)) from table
Looking for a similar solution here. Thanks in advance
I am an elementary Python programmer and have been using this module called "Pybaseball" to analyze sabermetrics data. When using this module, I came across a problem when trying to retrieve information from the program. The program reads a CSV file from any baseball stats site and outputs it onto a program for ease of use but the problem is that some of the information is not shown and is instead all replaced with a "...". An example of this is shown:
from pybaseball import batting_stats_range
data = batting_stats_range('2017-05-01', '2017-05-08')
print(data.head())
I should be getting:
https://github.com/jldbc/pybaseball#batting-stats-hitting-stats-for-players-within-seasons-or-during-a-specified-time-period
But the information is cutoff from 'TM' all the way to 'CS' and is replaced with a ... on my code. Can someone explain to me why this happens and how I can prevent it?
As the docs states, head() is meant for "quickly testing if your object has the right type of data in it." So, it is expected that some data may not show because it is collapsed.
If you need to analyze the data with more detail you can access specific columns with other methods.
For example, using iloc(). You can read more about it here, but essentially you can "ask" for a slice of those columns and then apply a new slice to get only nrows.
Another example would be loc(), docs here. The main difference being that loc() uses labels (column names) to filter data instead of numerical order of columns. You can filter a subset of specific columns and then get a sample of rows from that.
So, to answer your question "..." is pandas's way of collapsing data in order to get a prettier view of the results.
I am trying to replicate a paper whose code was written in Stata for my course project using Python. I have difficulty replicating the results from a collapse command in their do-file. The corresponding line in the do-file is
collapse lexptot, by(clwpop right)
while I have
df.groupby(['cwpop', 'right'])['lexptot'].agg(['mean'])
The lexptot variable is the logarithm of a variable 'exptot' which I calculated previously using np.log(dfs['exptot]).
Does anyone have an idea what is going wrong here? The means I calculate are typically around 1.5 higher than the means calculated in Stata.
Once you update the question with more relevant details maybe I can answer more. But this is what I think might help you!
df.groupby(['cwpop', 'right']).mean()['lexptot']