Nathan.Rivard
Posts: 37
Joined: Tue Sep 10, 2019 3:28 pm

Pandas inserting random columns

Thu Nov 07, 2019 4:18 pm

So I am using pandas to calibrate a probe I have. I am putting the values of certain voltages and their corresponding value in a .csv file. For this to work I am using DF.iloc to index my values within my python code. My issue is that after a proper calibration, it works fine, the issue is that for some reason when writing back to the .csv the program shifts everything over a column and adds 0's and Unnamed: 0. No idea why it does this but it ruins the index.Any help?

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B.Goode
Posts: 10082
Joined: Mon Sep 01, 2014 4:03 pm
Location: UK

Re: Pandas inserting random columns

Thu Nov 07, 2019 7:01 pm

Nathan.Rivard wrote:
Thu Nov 07, 2019 4:18 pm
So I am using pandas to calibrate a probe I have. I am putting the values of certain voltages and their corresponding value in a .csv file. For this to work I am using DF.iloc to index my values within my python code. My issue is that after a proper calibration, it works fine, the issue is that for some reason when writing back to the .csv the program shifts everything over a column and adds 0's and Unnamed: 0. No idea why it does this but it ruins the index.Any help?


Is there any evidence to suggest that this is a Raspberry Pi or Raspbian specific problem?

If it is just about this Python Data Analysis Library perhaps you would get more knowledgeable advice from its own community support: https://pandas.pydata.org/community.html




For anyone, like me, who didn't have a clue about pandas -
Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.

Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate.

pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.
Ref: https://pandas.pydata.org/

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