I have found that pandas v13.0 for Python 2.7 win32 works for most codes I have written in which I want to use both arcpy and pandas. I put that pandas version into the C:\Python27\ArcGIS10.2\Lib\site-packages directory. I tried other versions, but got miscellaneous errors when trying to run them.
I wrote a new code today, however, that manages to not work. It gives the error:
Access violation at address 1E0ACF39 in module 'python27.dll'. Read of
address 9807D3AF.
with the following code:
cond = dfDSS['OBSERVATION NAME']=='A413011CC1'
dfDSS['GROUP'][cond]='HA273UTheads'
All the code before this to create dfDSS using pd.read_csv and inserting the column 'GROUP' with a value of 'other' everywhere is fine. Only when I try to reset the values using the conditional statement.
The code to this point was written in iPython Notebook using Anaconda, but I now want to do some arcpy stuff with it.
Any suggestions in getting the different versions of Python to work together are appreciated.
Related
I am trying to use pandas_bokeh to create a line graph with a pandas dataframe I call bucketed_df
import pandas_bokeh
pandas_bokeh.output_notebook()
bucketed_df.plot_bokeh(kind='line')
for some reason I get the error
AttributeError: unexpected attribute 'plot_width' to figure, similar attributes are outer_width, width or min_width
Im not sure what this is or how toi fix it. I am using python 3.9 and jupyter notebooks
I have a matplotlib linegraph in a cell above it. I am thinking there could be some issue with that.
Also if anyone knows any interactive graphs that are better I am open to switching to a different library.
I'm getting a similar error on a recent install of bokeh==3.0.1.
The error goes away if I install version 2.4.3 (last release) with pip install --upgrade bokeh==2.4.3
It seems there was a breaking change in 3.0.0 that hasn't been addressed yet. If you have time, post this issue at https://github.com/bokeh/bokeh/issues.
temp way:
File ".../lib/python3.10/site-packages/pandas_bokeh/plot.py", line 439, in plot
Add
figure_options['width'] = figure_options['plot_width']
figure_options['height'] = figure_options['plot_height']
del figure_options['plot_width']
del figure_options['plot_height']
can be done like
p=figure(min_width=500, height=500)
or simply,
p=figure(width=500, height=500)
this may help.
I am currently looking for a way to directly pass in a pandas dataframe or csv file to zipline for simple backtesting WITHOUT having to ingest a data bundle. The reason is that I am planning to generate new data outside of the existing bundle during a backtest and it seems very inefficient to ingest a new bundle for every handle_data call.
I have been looking for this everywhere, including the source codes of zipline. I found that an older version of zipline has a 'data' param in the run_algo function call where you could pass in a df directly, but I can't find that old version at the moment. Is anyone attempting the same thing? Is there any way other than ingesting data bundles in the command line everytime?
I'm using zipline 1.3.0 and it actually does have a data param. This comment is from run_algo.py file of zipline:
data : pd.DataFrame, pd.Panel, or DataPortal, optional
The ohlcv data to run the backtest with.
This argument is mutually exclusive with:
``bundle``
``bundle_timestamp``
Hope it helped
I am referring TensorFlow speciliazation from Coursera where a certain piece of code works absolutely fine in Google Colab, whereas when I try to run it locally on PyCharm, it gives following error:
Failed to find data adapter that can handle input
Any suggestions?
Can you tell me the code where the error occurred?
It should be available in logs under your PyCharm console.
Looking at your comments, it seems that the model is expecting an array while you provided a list.
I was facing the same issue. Turns out it was a in the form of a list. I had to convert the fields into a numpy array like:
training_padded = np.array(training_padded)
training_labels = np.array(training_labels)
testing_padded = np.array(testing_padded)
testing_labels = np.array(testing_labels)
thats it!
Try it out and let me know if it works.
I am having trouble using the google bigquery package in pandas. I have installed the google-api-python-client as well as the pandas-gbq packages. But for some reason when I go to query a table I get a DistributionNotFound: The 'google-api-python-client' distribution was not found and is required by the application error. Here is a snippet of my code:
import pandas as pd
from pandas.io import gbq
count_block = gbq.read_gbq('SELECT count(int64_field_0) as count_blocks FROM Data.bh', projectid)
Using a virtual environment in this scenario can allow you to rule out problems with your library installations
I use Julia 0.4.3 and I have updated all packages using Pkg.update().
From the "ControlSystems" documentation, it is explicitly stated that plotting requires extra care in that the user is free to choose plotting back-end (I guess back-end means which plotting-package is used by "ControlSystems"). I have installed and like to use pyplot - hence I try the following code:
using ControlSystems
Plots.pyplot()
s = tf("s");
G = 1/(s+1);
stepplot(G);
Gives the error-message
ERROR: UndefVarError: subplot not defined
in stepplot at C:\folder\.julia\v0.4\ControlSystems\src\plotting.jl81
in stepplot at C:\folder\.julia\v0.4\ControlSystems\src\plotting.jl103
I have also tried the same code without the "Plots.pyplot()"-command.