I have a notebook which will process the file and creates a data frame in structured format.
Now I need to import that data frame created in another notebook, but the problem is before running the notebook I need to validate that only for some scenarios I need to run.
Usually to import all data structures, we use %run. But in my case it should be combinations of if clause and then notebook run
if "dataset" in path": %run ntbk_path
its giving an error " path not exist"
if "dataset" in path": dbutils.notebook.run(ntbk_path)
this one I cannot get all the data structures.
Can someone help me to resolve this error?
To implement it correctly you need to understand how things are working:
%run is a separate directive that should be put into the separate notebook cell, you can't mix it with the Python code. Plus, it can't accept the notebook name as variable. What %run is doing - it's evaluating the code from specified notebook in the context of the current Spark session, so everything that is defined in that notebook - variables, functions, etc. is available in the caller notebook.
dbutils.notebook.run is a function that may take a notebook path, plus parameters and execute it as a separate job on the current cluster. Because it's executed as a separate job, then it doesn't share the context with current notebook, and everything that is defined in it won't be available in the caller notebook (you can return a simple string as execution result, but it has a relatively small max length). One of the problems with dbutils.notebook.run is that scheduling of a job takes several seconds, even if the code is very simple.
How you can implement what you need?
if you use dbutils.notebook.run, then in the called notebook you can register a temp view, and caller notebook can read data from it (examples are adopted from this demo)
Called notebook (Code1 - it requires two parameters - name for view name & n - for number of entries to generate):
name = dbutils.widgets.get("name")
n = int(dbutils.widgets.get("n"))
df = spark.range(0, n)
df.createOrReplaceTempView(name)
Caller notebook (let's call it main):
if "dataset" in "path":
view_name = "some_name"
dbutils.notebook.run(ntbk_path, 300, {'name': view_name, 'n': "1000"})
df = spark.sql(f"select * from {view_name}")
... work with data
it's even possible to do something like with %run, but it could require a kind of "magic". The foundation of it is the fact that you can pass arguments to the called notebook by using the $arg_name="value", and you can even refer to the values specified in the widgets. But in any case, the check for value will happen in the called notebook.
The called notebook could look as following:
flag = dbutils.widgets.get("generate_data")
dataframe = None
if flag == "true":
dataframe = ..... create datarame
and the caller notebook could look as following:
------ cell in python
if "dataset" in "path":
gen_data = "true"
else:
gen_data = "false"
dbutils.widgets.text("gen_data", gen_data)
------- cell for %run
%run ./notebook_name $generate_data=$gen_data
------ again in python
dbutils.widgets.remove("gen_data") # remove widget
if dataframe: # dataframe is defined
do something with dataframe
Related
I am still a beginnner in R. For a project I am trying to fit a gam model on a simple dataset with a timeset and year. I am doing it in R and I keep getting an error message that claims an argument is unused, even though I specify it in the code.
It concerns a dataset which includes a categorical variable of "Year", with only two levels. 2020 and 2022. I want to investigate if there is a peak in the hourly rate of visitors ("H1") in a nature reserve. For each observation period the average time was taken, which is the predictor variable used here ("T"). I want to use a Gam model for this, and have the smoothing applied differently for the two years.
The following is the line of code that I tried to use
`gam1 <- gam(H1~Year+s(T,by=Year),data = d)`
When I try to run this code, I get the following error message
`Error in s(T, by = Year) : unused argument (by = Year)`
I also tried simply getting rid of the "by" argument
`gam1 <- gam(H1~Year+s(T,Year),data = d)`
This allows me to run the code, but when trying to summon the output using summary(gam1), I get
Error in [<-(tmp, snames, 2, value = round(nldf, 1)) : subscript out of bounds
Since I feel like both errors are probably related to the same thing that I'm doing wrong, I decided to combine the question.
Did you load the {mgcv} package or the {gam} package? The latter doesn't have factor by smooths and as such the first error message is what I would expect if you did library("gam") and then tried to fit the model you showed.
To fit the model you showed, you should restart R and try in a clean session:
library("mgcv")
# load you data
# fit model
gam1 <- gam(H1 ~ Year + s(T, by = Year), data = d)
It could well be that you have both {gam} and {mgcv} loaded, in which case whichever you loaded last will be earlier on the function search path. As both packages have functions gam() and s(), R might just be finding the wrong versions (masking), so you might also try
gam1 <- mgcv::gam(H1 ~ Year + mgcv::s(T, by = Year), data = d)
But you would be better off only loading {mgcv} if you wan factor by smooths.
#Gavin Simpson
I did have both loaded, and I tried just using mgcv as you suggested. However, then I get the following error.
Error in names(dat) <- object$term :
'names' attribute [1] must be the same length as the vector [0]
I am assuming this is simply because it's not actually trying to use the "gam" function, but rather it attempts to name something gam1. So I would assume I actually need the package of 'gam' before I could do this.
The second line of code also doesn't work. I get the following error
Error in model.frame.default(formula = H1 ~ Year + mgcv::s(T, by = Year), :
invalid type (list) for variable 'mgcv::s(T, by = Year)'
This happens no matter the order I download the two packages in. And if I don't download 'gam', I get the error as described above.
i'am trying to call a DataFrame that i created in notebook1 to use it in my notebook2 in Databricks Community addition with pyspark and i tried this code dbutils.notebook.run("notebook1", 60, {"dfnumber2"})
but it shows this error.
py4j.Py4JException: Method _run([class java.lang.String, class java.lang.Integer, class java.util.HashSet, null, class java.lang.String]) does not exist
any help please?
The actual problem is that you pass last parameter ({"dfnumber2"}) incorrectly - with this syntax it's a set, not the map type. You need to use syntax: {"table_name": "dfnumber2"} to represent it as a dict/map.
But if you look into documentation of dbutils.notebook.run, you will see following phrase:
To implement notebook workflows, use the dbutils.notebook.* methods. Unlike %run, the dbutils.notebook.run() method starts a new job to run the notebook.
But jobs aren't supported on the Community Edition, so it won't work anyway.
Create a global temp view and pass the table name as argument to your next notebook.
Drnumber2.createOrReplaceGlobalTempView("dfnumber2")
dbutils.notebook.run("notebook1", 60, {table_name:"dfnumber2"})
In your notebook1 you can do
table_name= dbutils.widgets.get("table_name")
Dfnumber2 = spark.sql("select * from global_temp."+table_name)
Will I have to use the str.split() for an exercise. I have a column called title and it looks like this:
and i need to split it into two columns Name and Season, the following code does not through an error but it doesn't seem to be doing anything as well when i'm testing it with df.head()
df[['Name', 'Season']] = df['title'].str.split(':',n=1, expand=True)
Any help as to why?
The code you have in your question is correct, and should be working. The issue could be coming from the execution order of your code though, if you're using Jupyter Notebook or some method that allows for unclear ordering of code execution.
I recommend starting a fresh kernel/terminal to clear all variables from the namespace, then executing those lines in order, e.g.:
# perform steps to load data in and clean
print(df.columns)
df[['Name', 'Season']] = df['title'].str.split(':',n=1, expand=True)
print(df.columns)
Alternatively you could add an assertion step in your code to ensure it's working as well:
df[['Name', 'Season']] = df['title'].str.split(':',n=1, expand=True)
assert {'Name', 'Season'}.issubset(set(df.columns)), "Columns were not added"
hope you are doing well !
I was following tutorials for process mining using 'PM4PY', but I found difficulties in the csv file ,
in my csv file I have this columns : 'id', 'status', 'mailID', 'date'.... ('status' is same as 'activity' that contain some specific choises )
my csv file contains a lot of data.
to follow process mining tutorial I must have in my columns something like 'case:concept:name' ... but I don't know how can I make it
In your case, I assume 'id' would be the same as the Case ID in normal process mining terminology. Similarly, 'status' corresponds to Activity ID and 'date' would correspond to the timestamp.
The best option is to first read into a pandas dataframe before feeding into PM4Py.
For a detailed understanding of how to do this, here is an example below. As you have not mentioned all the columns that you have in your csv file, let us assume that currently you only have [ 'id', 'status', 'date' ] as your column list. The following code can be adapted to any number of columns you have (by adding them to the list named cols) :
import pandas as pd
from pm4py.objects.conversion.log import converter as log_converter
path = '' # Enter path to the csv file
data = pd.read_csv(path)
cols = ['case:concept:name','concept:name','time:timestamp']
data.columns = cols
data['time:timestamp'] = pd.to_datetime(data['time:timestamp'])
data['concept:name'] = data['concept:name'].astype(str)
log = log_converter.apply(data, variant=log_converter.Variants.TO_EVENT_LOG)
Here we have changed the column names and their datatypes as required by the PM4Py package. Convert this dataframe into an event log using the log_converter function. Now you can perform your regular process mining tasks on this event log object. For instance, if you wish to create a Directly-Follows Graph from the event log, you can use the following line of code :
from pm4py.algo.discovery.dfg import algorithm as dfg_algorithm
dfg = dfg_algorithm.apply(log)
first you need import your csv file using pandas, then convert to an event log object, finally you can use in pm4py.
reference:
https://pm4py.fit.fraunhofer.de/documentation
So what I'm trying to do is the following:
I have 300+ CSVs in a certain folder. What I want to do is open each CSV and take only the first row of each.
What I wanted to do was the following:
import os
list_of_csvs = os.listdir() # puts all the names of the csv files into a list.
The above generates a list for me like ['file1.csv','file2.csv','file3.csv'].
This is great and all, but where I get stuck is the next step. I'll demonstrate this using pseudo-code:
import pandas as pd
for index,file in enumerate(list_of_csvs):
df{index} = pd.read_csv(file)
Basically, I want my for loop to iterate over my list_of_csvs object, and read the first item to df1, 2nd to df2, etc. But upon trying to do this I just realized - I have no idea how to change the variable being assigned when doing the assigning via an iteration!!!
That's what prompts my question. I managed to find another way to get my original job done no problemo, but this issue of doing variable assignment over an interation is something I haven't been able to find clear answers on!
If i understand your requirement correctly, we can do this quite simply, lets use Pathlib instead of os which was added in python 3.4+
from pathlib import Path
csvs = Path.cwd().glob('*.csv') # creates a generator expression.
#change Path(your_path) with Path.cwd() if script is in dif location
dfs = {} # lets hold the csv's in this dictionary
for file in csvs:
dfs[file.stem] = pd.read_csv(file,nrows=3) # change nrows [number of rows] to your spec.
#or with a dict comprhension
dfs = {file.stem : pd.read_csv(file) for file in Path('location\of\your\files').glob('*.csv')}
this will return a dictionary of dataframes with the key being the csv file name .stem adds this without the extension name.
much like
{
'csv_1' : dataframe,
'csv_2' : dataframe
}
if you want to concat these then do
df = pd.concat(dfs)
the index will be the csv file name.