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How can I align columns if rows have different number of values?

I am scraping data with python. I get a csv file and can split it into columns in excel later. But I am encountering an issue I have not been able to solve. Sometimes the scraped items have two statuses and sometimes just one. The second status is thus moving the other values in the columns to the right and as a result the dates are not all in the same column which would be useful to sort the rows.
Do you have any idea how to make the columns merge if there are two statuses for example or other solutions?
Maybe is is also an issue that I still need to separate the values into columns manually with excel.
Here is my code
#call packages
import random
import time
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
import pandas as pd
# define driver etc.
service_obj = Service("C:\\Users\\joerg\\PycharmProjects\\dynamic2\\chromedriver.exe")
browser = webdriver.Chrome(service=service_obj)
# create loop
initiative_list = []
for i in range(0, 2):
url = 'https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives_de?page='+str(i)
browser.get(url)
time.sleep(random.randint(5, 10))
initiative_item = browser.find_elements(By.CSS_SELECTOR, "initivative-item")
initiatives = [item.text for item in initiative_item]
initiative_list.extend(initiatives)
df = pd.DataFrame(initiative_list)
#create csv
print(df)
df.to_csv('Initiativen.csv')
df.columns = ['tosplit']
new_df = df['tosplit'].str.split('\n', expand=True)
print(new_df)
new_df.to_csv('Initiativennew.csv')
I tried to merge the columns if there are two statuses.

make the columns merge if there are two statuses for example or other solutions
[If by "statuses" you mean the yellow labels ending in OPEN/UPCOMING/etc, then] it should be taken care of by the following parts of the getDetails_iiaRow (below the dividing line):
labels = cssSelect(iiaEl, 'div.field span.label')
and then
'labels': ', '.join([l.text.strip() for l in labels])
So, multiple labels will be separated by commas (or any other separator you apply .join to).
initiative_item = browser.find_elements(By.CSS_SELECTOR, "initivative-item")
initiatives = [item.text for item in initiative_item]
Instead of doing it like this and then having to split and clean things, you should consider extracting each item in a more specific manner and have each "row" be represented as a dictionary (with the column-names as the keys, so nothing gets mis-aligned later). If you wrap it as a function:
def cssSelect(el, sel): return el.find_elements(By.CSS_SELECTOR, sel)
def getDetails_iiaRow(iiaEl):
title = cssSelect(iiaEl, 'div.search-result-title')
labels = cssSelect(iiaEl, 'div.field span.label')
iiarDets = {
'title': title[0].text.strip() if title else None,
'labels': ', '.join([l.text.strip() for l in labels])
}
cvSel = 'div[translate]+div:last-child'
for c in cssSelect(iiaEl, f'div:has(>{cvSel})'):
colName = cssSelect(c, 'div[translate]')[0].text.strip()
iiarDets[colName] = cssSelect(c, cvSel)[0].text.strip()
link = iiaEl.get_attribute('href')
if link[:1] == '/':
link = f'https://ec.europa.eu/{link}'
iiarDets['link'] = iiaEl.get_attribute('href')
return iiarDets
then you can simply loop through the pages like:
initiative_list = []
for i in range(0, 2):
url = f'https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives_de?page={i}'
browser.get(url)
time.sleep(random.randint(5, 10))
initiative_list += [
getDetails_iiaRow(iia) for iia in
cssSelect(browser, 'initivative-item>article>a ')
]
and the since it's all cleaned already, you can directly save the data with
pd.DataFrame(initiative_list).to_csv('Initiativen.csv', index=False)
The output I got for the first 3 pages looks like:

I think it is worth working a little bit harder to get your data rationalised before putting it in the csv rather than trying to unpick the damage once ragged data has been exported.
A quick look at each record in the page suggests that there are five main items that you want to export and these correspond to the five top-level divs in the a element.
The complexity (as you note) comes because there are sometimes two statuses specified, and in that case there is sometimes a separate date range for each and sometimes a single date range.
I have therefore chosen to put the three ever present fields as the first three columns, followed next by the status + date range columns as pairs. Finally I have removed the field names (these should effectively become the column headings) to leave only the variable data in the rows.
initiatives = [processDiv(item) for item in initiative_item]
def processDiv(item):
divs = item.find_elements(By.XPATH, "./article/a/div")
if "\n" in divs[0].text:
statuses = divs[0].text.split("\n")
if len(divs) > 5:
return [divs[1].text, divs[2].text.split("\n")[1], divs[3].text.split("\n")[1], statuses[0], divs[4].text.split("\n")[1], statuses[1], divs[5].text.split("\n")[1]]
else:
return [divs[1].text, divs[2].text.split("\n")[1], divs[3].text.split("\n")[1], statuses[0], divs[4].text.split("\n")[1], statuses[1], divs[4].text.split("\n")[1]]
else:
return [divs[1].text, divs[2].text.split("\n")[1], divs[3].text.split("\n")[1], divs[0].text, divs[4].text.split("\n")[1]]
The above approach sticks as close to yours as I can. You will clearly need to rework the pandas code to reflect the slightly altered data structure.
Personally, I would invest even more time in clearly identifying the best definitions for the fields that represent each piece of data that you wish to retrieve (rather than as simply divs 0-5), and extract the text directly from them (rather than messing around with split). In this way you are far more likely to create robust code that can be maintained over time (perhaps not your goal).

How to apply dask method to apply functions on files in list?

first of all, thanks for this community and all advice we can retrieve, it's really appreciate!
This is my first venture into parallel processing and I have been looking into Dask by my own but I am having trouble actually coding it... to be honest I am really lost
In on of my project, I want to trigger URL and retrieve observations data (meteorological station) from xml files.
For each URL, I run some different process in order to: retreive data from URL, parsing XML information to dataframe, apply a filter and store data in MySQL database.
So i need to loop these process over thousands of URL (station)...
I wrote a sequential code , and it take 300s to finish computation which is really to long and not efficient.
As we are applying the same process for each station, I think I can speed-up all the computations, but I don't know where to start. I used delayed from dask but I don't think it's the best approach.
This is my code so far:
First I have some functions.
def xml_to_dataframe(ood_xml):
tmp_file = wget.download(ood_xml)
prstree = ETree.parse(tmp_file)
root = prstree.getroot()
################ Section to retrieve data for one station and apply parameter
all_obs = []
for obs in root.iter('observations'):
ood_observation = []
for n, param in enumerate(list_parameters):
x=obs.find(variable_to_check).text
ood_observation.append(x)
all_obs.append(ood_observation)
return(pd.DataFrame(all_obs, columns=list_parameters))
def filter_criteria(df,threshold,criteria):
if criteria in df.columns:
result = []
for index, row in df.iterrows():
if pd.to_numeric(row[criteria],errors='coerce') >= threshold:
result.append(index)
return result
else:
#print(criteria + ' parameter does not exist for this station !!! ')
return([])
def get_and_filter_data(filename,criteria,threshold):
try:
xmlToDf = xml_to_dataframe(filename)
final_df = xmlToDf.loc[filter_criteria(xmlToDf,threshold,criteria)]
some msql connection and instructions....
except:
pass
and then the main code I want to parallelise:
criteria = 'temperature'
threshold = 22
filenames =[url1.html, url2.html, url3.html]
for file in filenames:
get_and_filter_data(file,criteria,threshold)
Do you have any advice to do it ?
Many thanks for your help !
Guillaume

Not 100% sure this is what you are after, but one way is via delayed:
from dask import delayed, compute
delayeds = [delayed(get_and_filter_data)(file,criteria,threshold) for file in filenames]
results = compute(delayeds)

How to save results from an API call that uses a pandas column for the requests before the whole thing times out when using apply?

I have a pandas dataframe with strings that I'm using to query an API and return the results.
I'm trying to call the API using a function and .apply and then save the results from the api call into a csv file. The problem is that I'm trying to do 10000+ requests and my kernel/notebook crashes. Basically I'm trying to do a big operation and I'm guessing I'm running out of memory. So I'm trying to think of a way I can do these api calls and save the results and not have it all crash. My version with .apply works with a small amount of data but not once it gets larger.
So my notebook code currently looks something like this.
df = pd.read_csv('bigstringlist.csv')
df = df.loc[0:3000]
My function looks something like this.
def api_fetch_func(address):
sleep(.2)
API_PRIVATE = 'awewaefawefawef'
encoded = urllib.parse.quote(address)
query ='https://apitocall' + str(encoded) + \
'.json?limit=1&key=' \
+ API_PRIVATE
response = requests.get(query)
while True:
try:
jsonResponse = response.json()
break
except:
response = requests.get(query)
try:
return jsonResponse['results']
except:
return
else:
return
Then I'm calling the function like so
df['response_col'] = df['string_col'].apply(api_fetch_func)
Something tells me that .apply isn't the right thing to do here. Would be better if I just push the api responses into an array or another dataframe?
Should I just use .iterrows to loop over the list of strings and call the function? Something tells me .apply tries to jam too much into memory and that's why this doesn't work.
So I was going to try
results = []
for index, row in df.iterrows():
# call API
# push results to array
Or is there another way to do this?

If it's a memory issue, what I'd do is write the API calling function as a generator with the yield statement. Then, you can loop through the api_fetch_function generator and save smaller data frames for the csv files rather than holding everything in memory in one go.
for idx, response in api_fetch_generator():
if idx % 500 == 0:
df = create_df() # create a fresh df as you did above with 'string_col'.
df['response_col'] = df['string_col'].apply(response)
if (idx % 500 == 0) and idx != 0:
# Save the df using idx to control the file name
df.to_csv(f"response_batch_{idx / 500}.csv")
# Combine the csv's after everything is saved.

How can you show progress bar while iterating over a pandas dataframe

I am trying to iterate over a Pandas data frame with close to a million entries. I am using a for loop to iterate over them. Consider the following code as an example
import pandas as pd
import os
from requests_html import HTMLSession
from tqdm import tqdm
import time
df = pd.read_csv(os.getcwd()+'/test-urls.csv')
df = df.drop('Unnamed: 0', axis=1 )
new_df = pd.DataFrame(columns = ['pid', 'orig_url', 'hosted_url'])
refused_df = pd.DataFrame(columns = ['pid', 'refused_url'])
tic = time.time()
for idx, row in df.iterrows():
img_id = row['pid']
url = row['image_url']
#Let's do scrapping
session = HTMLSession()
r = session.get(url)
r.html.render(sleep=1, keep_page=True, scrolldown=1)
count = 0
link_vals = r.html.find('.zoomable')
if len(link_vals) != 0 :
attrs = link_vals[0].attrs
# print(attrs['src'])
embed_link = attrs['src']
else:
while count <=7:
link_vals = r.html.find('.zoomable')
count += 1
else:
print('Link refused connection for 7 tries. Adding URL to Refused URLs Data Frame')
ref_val = [img_id,URL]
len_ref = len(refused_df)
refused_df.loc[len_ref] = ref_val
print('Refused URL added')
continue
print('Got 1 link')
#Append scraped data to new_df
len_df = len(new_df)
append_value = [img_id,url, embed_link]
new_df.loc[len_df] = append_value
I wanted to know how could I use a progress bar to add a visual representation of how far along I am. I will appreciate any help. Please let me know if you need any clarification.

You could try out TQDM
from tqdm import tqdm
for idx, row in tqdm(df.iterrows()):
do something
This is primarily for a command-line progress bar. There are other solutions if you're looking for more of a GUI. PySimpleGUI comes to mind, but is definitely a little more complicated.

Would comment, but the reason you might want a progress bar is because it is taking a long time because iterrows() is a slow way to do operations in pandas.
I would suggest you use apply/ avoid using iterrows().
If you want to continue using iterrows just include a counter that counts up to the number of rows, df.shape[0]

PySimpleGUI makes this about as simple of a problem to solve as possible, assuming you know ahead of time time how items you have in your list. Indeterminate progress meters are possible, but a little more complicated.
There is no setup required before your loop. You don't need to make a special iterator. The only need you have to do is add 1 line of code inside your loop.
Inside your loop add a call to - one_line_progress_meter. The name sums up what it is. Add this call to the top of your loop, the bottom, it doesn't matter... just add it somewhere that's looped.
There 4 parameters you pass are:
A title to put on the meter (any string will do)
Where you are now - current counter
What the max counter value is
A "key" - a unique string, number, anything you want.
Here's a loop that iterates through a list of integers to demonstrate.
import PySimpleGUI as sg
items = list(range(1000))
total_items = len(items)
for index, item in enumerate(items):
sg.one_line_progress_meter('My meter', index+1, total_items, 'my meter' )
The list iteration code will be whatever your loop code is. The line of code to focus on that you'll be adding is this one:
sg.one_line_progress_meter('My meter', index+1, total_items, 'my meter' )
This line of code will show you the window below. It contains statistical information like how long you've been running the loop and an estimation on how much longer you have to go.

How to do that in pandas apply?
I do this
def some_func(a,b):
global index
some function involve a and b
index+=1
sg.one_line_progress_meter('My meter', index, len(df), 'my meter' )
return c
index=0
df['c'] = df[['a','b']].apply(lambda : some_func(*x),axis=1)

Most efficient way to update Dataframe with JSON array from WebService

I have a code column which I would like to pass to a web service and update two fields in the dataframe (dfMRD1['Cache_Ticker']and dfMRD1['Cache_Product'] with two values from the returned JSON (RbcSecurityDescription and RbcSecurityType1). I have achieved this by iteration but I'd like to know if there is a more efficient way to do it?
# http://postgre01:5002/bond/912828XU9
import requests
url = 'http://postgre01:5002/bond/'
def fastquery(code):
response = requests.get(url + code)
return response.json()
Here is the sample return call:
Here is the update of dfMRD1['Cache_Ticker']anddfMRD1['Cache_Product']
dfMRD1 = df[['code']].drop_duplicates()
dfMRD1['Cache_Ticker'] = ""
dfMRD1['Cache_Product'] = ""
for index, row in dfMRD1.iterrows():
result = fastquery(row['code'])
row['Cache_Ticker'] = result['RbcSecurityDescription']
row['Cache_Product'] = result['RbcSecurityType1']
display(dfMRD1.head(5))
Would it be best to just return the json array, unest it and dump all fields in its contents to another df which I can be join with dfMRD1? Best way to achieve this?

The most time-consuming part of your code is likely to be in making synchronous requests. Instead, you could leverage requests-futures to make asynchronous requests, construct the columns as lists of results and assign back to the DF. We have nothing to test with but the approach would look like this:
from requests_futures.sessions import FuturesSession
session = FuturesSession(max_workers = 10)
codes = df[['code']].drop_duplicates().values.tolist() # Take out of DF
url = 'http://postgre01:5002/bond/'
fire_requests = [session.get(url + code) for code in codes] # Async requests
responses = [item.result() for item in fire_requests] # Grab the results
dfMRD1['Cache_Ticker'] = [result['RbcSecurityDescription']
for result in responses]
dfMRD1['Cache_Product'] = [result['RbcSecurityType1']
for result in responses]
Depending on the size of the DF, you may get a lot of data in memory. If that becomes an issue, you'll want a background callback trimming your JSON responses as they come back.