pydata | Code and details for pydata 2013 Bayesian Tutorial | Machine Learning library

im trying to make a connection to an as400 with db2 using pyodbc and the ibm db2 odbc driver.

I have the following problem: in a df, I want to select specific rows and a specific column and in this selection take the first n elements and assign a new value to them. Naively, I thought that the following code should do the job:

I've tried the code below to change 'new_col's value from 3 to 1. First of all, the random matrix was generated with an index of ['a','b'] and column name [x1~x5] I then additionally added 'new_col'.

I needed to call the row through column 'x1', therefore I randf[df['x1']==val], then I thought I could simply convert 'new_col's value by running df[df['x1']==val]['new_col'] = 1

Physical Background

I'm working on a function that calculates some metrics for each vertical profile in an up to four dimensional temperature field (time, longitude, latitude, pressure as height measure). I have a working function that takes the pressure and temperature at a single location and returns the metrics (tropopause information). I want to wrap it with a function that applies it to every vertical profile in the data passed.

Technical Description of the Problem

I want my function to apply another function to every 1D array corresponding to the last dimension in my N-dimensional array, where N <= 4. So I need an efficient loop over all dimensions but the last one without knowing the number of dimensions beforehand.

Why I Open a New Question

I am aware of several questions (e.g., iterating over some dimensions of a ndarray, Iterating over the last dimensions of a numpy array, Iterating over 3D numpy using one dimension as iterator remaining dimensions in the loop, Iterating over a numpy matrix with unknown dimension) asking how to iterate over a specific dimension or how to iterate over an array with unknown dimensions. The combination of these two problems is new as far as I know. Using numpy.nditer for example I haven't found out how to exclude just the last dimension regardless of the number of dimensions left.

EDIT

I tried to do a minimal, reproducible example:

To continue my research on how to plot a xml file and continue checking my code, I first applied a division to signal.attrib ["Value"], since it shows some string values ​​and what I'm interested in is the numeric values.

And as you can see below, I relied on the documentation for Pandas and SQL Compare.

Pandas Docs for pandas.Series.map says that:

"When arg is a dictionary, values in Series that are not in the dictionary (as keys) are converted to NaN. However, if the dictionary is a dict subclass that defines missing (i.e. provides a method for default values), then this default is used rather than NaN."

How do you actually do that? I cannot get it to work..

I assign some value to a dataframe and I got following warning message:

I have this dataframe

I've seen that on Pandas version 1.3.0.dev0+1779.gdcc2a8f801 there is a new implemented method (read_xml) and I would like to use it. The problem is that I have not found a way to install a development version of Pandas. i am currently using Python3 and pip and have tried from its source repository (Pandas-dev page)

How could I deal with that? Maybe using Conda or compiling it myself? I don't know exactcly how to do it...