trying to reproduce Ten's tutorial on Time Series Forecasting with fbprophet (https://xang1234.github.io/prophet/). Dataset can be downloaded from https://data.gov.sg/dataset/air-passenger-arrivals-total-by-region-and-selected-country-of-embarkation
Here's my code:
air=pd.read_csv(r'C:\Users\minri\Desktop\total-air-passenger-arrivals-by-country.csv')
air=air[air.level_3=='China']
air=air.drop(['level_1','level_2','level_3'],axis=1)
air=air[(air.value!='na') & (air.value!='-')]
air=air.rename(columns={'month':'ds','value':'y'})
model_air=Prophet()
model_air.fit(air)
below are my current versions:
pandas: 1.0.4
numpy: 1.18.5
fbprophet: 0.7.1
pystan: 2.18.0.0
INFO:numexpr.utils:NumExpr defaulting to 8 threads.
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
KeyError: 'metric_file'
Restarting kernel...
[SpyderKernelApp] WARNING | No such comm: d690f93a8e1511eb9fdc1063c874af38
Related
I want to run modules of Geopandas in Spyder. Apparently Geopandas is compatible with Sypder 4.2.5, (not with any higher version) and I could run code with this combination. However, in one of my code I had to use "input" command and the problem starts there. Sypder 4.2.5 crashes if I try to run input command. From the internet, I came to know that there was a bug in spyder and it was fixed in Spyder 5.3. Now I have no idea how to fix this problem. If I upgrade Spyder, Geopandas will not work. If I don't upgrade spyder, 'input' will not work.
I was trying to run something like the following code
def Coditions_R3():
print("This is R3")
def Coditions_R4():
print("This is R4")
System = input('Please Enter drone system: \n' )
print(System)
if (System == 'R3'):
Coditions_R3()
elif (System == 'R4'):
Coditions_R4()
Can anyone help? is there any way around to run geopandas with higher Spyder versions? or use something else in place of input?
I'm trying to reload another model to another jupyter notebook using this code:
import tensorflow as tf
reloaded = tf.saved_model.load('m_translator')
result = reloaded.tf_translate(input_text)
and I recently got this error:
KeyError Traceback (most recent call last)
File ~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py:4177, in Graph._get_op_def(self, type)
4176 try:
-> 4177 return self._op_def_cache[type]
4178 except KeyError:
KeyError: 'NormalizeUTF8'
FileNotFoundError: Op type not registered 'NormalizeUTF8' in binary running on LAPTOP-D3PPA576. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
You may be trying to load on a different device from the computational device. Consider setting the `experimental_io_device` option in `tf.saved_model.LoadOptions` to the io_device such as '/job:localhost'.
I had this issue before. To solve it, you need to install tensorflow_text. You should try to :
>>> tf.__version__
2.8.2
>>>!pip install tensorflow-text==2.8.2
In addition to installing tensorflow_text library, what helped me with a similar problem was importing the library at the top of the notebook:
import tensorflow_text
I have pandas data frame(sample program), converted koalas dataframe, now I am to execute on spark cluster(windows standalone), when i try from command prompt as
spark-submit --master local hello.py, getting error ModuleNotFoundError: No module named 'databricks'
import pandas as pd
from databricks import koalas as ks
workbook_loc = "c:\\2020\Book1.xlsx"
df = pd.read_excel(workbook_loc, sheet_name='Sheet1')
kdf = ks.from_pandas(df)
print(kdf)
What should I change so that I can make use of spark cluster features. My actual program written in pandas does many things, I want to make use of spark cluster to see performance improvements.
You should install koalas via the cluster's admin UI (Libraries/PyPI), if you run pip install koalas on the cluster, it won't work.
I'm running the following basic code:
dfMain.to_csv('./January_filtered_International_WE.csv')
which used to run normally until yesterday. This morning I upgraded to pandas 0.25.0 while running code and now I cannot write my 500k rows dataframe to a csv. I can mention that I left Jupyter Notebook running in order to do some processing, so this morning when I opened it I had the dataFrame already, processed.
Versions (using Windows 10)
Jupyter notebook : 5.7.8
Python : 3.6.7
Pandas : 0.25.0
I would like to save my DataFrame in a fast and efficient manner as I will load it several times in the future. I do not want to close the notebook as this will delete the dataFrame.
I tried:
downgrading to Pandas 0.24.2 (previous version used) but still getting the __init__() got an unexpected keyword argument 'tupleize_cols'
use pd.to_pickle but got a memoryError
use pd.to_hdf but got a memoryError
using msgbox instead but apparently it does not support DataFrames (got an error)
upgrade Jupyter notebook, but got the following error:
ERROR: ipython 5.8.0 has requirement prompt-toolkit<2.0.0,>=1.0.4, but
you'll have prompt-toolkit 2.0.9 which is incompatible
so naturally I did pip install prompt-toolkit 1.0.16 but then got this message:
ERROR: jupyter-console 6.0.0 has requirement prompt-toolkit<2.1.0,>=2.0.0, but you'll have prompt-toolkit 1.0.16 which is incompatible.
As an alternative I went into PyCharm and took a random DataFrame.to_csv and it worked. This makes me think the issue is with Jupyter Notebook.
Any help on how to save the DataFrame (~12 GB) is appreciated!
Re-installing Jupyter did the trick in my case
I kept getting the same error, but updating Jupyter fixed it
I'm using Google Bigquery ML for the first time and try to train a linear regression model using the following command:
%%bigquery
CREATE OR REPLACE MODEL `sandbox.sample_lr_model`
OPTIONS
(model_type='linear_reg',
data_split_method ='no_split',
max_iterations=1) AS
SELECT
y AS label,
x AS x
FROM
`sandbox.y2018m08d01_rh_sample_dataframe_to_bq_v01_v01`
this step fails with the following error message:
ValueError: Table has no schema: call 'client.get_table()'
However the model is created and can be viewed:
The model has a so-called "Model schema". Am I doing something wrong?
google-cloud-bigquery==1.4.0 Python 3.5 Ubuntu
My input table is a minimum example:
This issue was fixed in https://github.com/GoogleCloudPlatform/google-cloud-python/pull/5602, which was released in version 1.4.0 of BigQuery. To double-check your BigQuery version, run !pip freeze | grep bigquery in a notebook cell.
Note that Datalab does not include the latest version of the google-cloud-bigquery library. To upgrade the version, run !pip install --upgrade google-cloud-bigquery.