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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).

Compare data between two csv files and count how many rows have the same data

Let's say I have list of all OUs (AllOU.csv):
NEWS
STORE
SPRINKLES
ICECREAM
I want to look through a csv file (samplefile.csv) on the third column called 'column3', and search through each row if it matches what is in the samplefile.csv.
Then I want to sort them and count how many rows each one has.
This is how the column looks:
column3
CN=Clark Kent,OU=news,dc=company,dc=com
CN=Mary Poppins,OU=ice cream, dc=company,dc=com
CN=Mary Jane,OU=news,OU=tv,dc=company,dc=com
CN=Pepper Jack,OU=store,OU=tv,dc=company,dc=com
CN=Monty Python,OU=store,dc=company,dc=com
CN=Anne Potts,OU=sprinkles,dc=company,dc=com
I want to sort them out like this (or a list):
CN=Clark Kent,OU=news,dc=company,dc=com
CN=Mary Jane,OU=news,OU=tv,dc=company,dc=com
CN=Pepper Jack,OU=tv,OU=store,dc=company,dc=com
CN=Monty Python,OU=store,dc=company,dc=com
CN=Mary Poppins,OU=ice cream, dc=company,dc=com
CN=Anne Potts,OU=sprinkles,dc=company,dc=com
This is what the final output should be:
2, news
2, store,
1, icecream
1, sprinkles
Maybe a list would be a good way of sorting them? Like this?
holdingList =['CN=Clark Kent,OU=news,dc=company,dc=com','CN=Mary Jane,OU=news,OU=tv,dc=company,dc=com'],
['CN=Pepper Jack,OU=tv,OU=store,dc=company,dc=com','CN=Monty Python,OU=store,dc=company,dc=com'],
['CN=Mary Poppins,OU=ice cream, dc=company,dc=com'],
['CN=Anne Potts,OU=sprinkles,dc=company,dc=com']
I had something like this so far:
file = open('samplefile.csv')
df = pd.read_csv(file, usecols=['column3'])
#file of all OUs
file2 = open('ALLOU.csv')
OUList = pd.read_csv(file2, header=None)
for OU in OUList[0]:
df_dept = df[df['column3'].str.contains(f'OU={OU }')].count()
print({OU}, df_dept)

Read your file first and create a list of objects.
[{CN:’Clark Kent’,OU:’news’,dc:’company’,dc:’com’},…{…}]
Once you have created the list you can convert it to data frame and then apply all the grouping, sorting and other abilities of pandas.
Now to achieve this, first read your file into a variable lets call var filedata=yourFileContents. Next split filedata. var lines = filedata.split(‘\n’)
Now loop over each lines
dataList = []
for line in lines:
item = dict()
elements = line.split(‘,’)
for element in elements:
key_value = element.split(‘=‘)
item[key_value[0]] = key_value[1]
dataList.append(item)
print(dataList)
Now you may load this onto a panda dataframe and apply sorting and grouping. Once you have structured the data frame, you can simply search the key from the other file in this dataframe and get your numbers

How to work with Rows/Columns from CSV files?

I have about 10 columns of data in a CSV file that I want to get statistics on using python. I am currently using the import csv module to open the file and read the contents. But I also want to look at 2 particular columns to compare data and get a percentage of accuracy based on the data.
Although I can open the file and parse through the rows I cannot figure out for example how to compare:
Row[i] Column[8] with Row[i] Column[10]
My pseudo code would be something like this:
category = Row[i] Column[8]
label = Row[i] Column[10]
if(category!=label):
difference+=1
totalChecked+=1
else:
correct+=1
totalChecked+=1
The only thing I am able to do is to read the entire row. But I want to get the exact Row and Column of my 2 variables category and label and compare them.
How do I work with specific row/columns for an entire excel sheet?

convert both to pandas dataframes and compare similarly as this example. Whatever dataset your working on using the Pandas module, alongside any other necessary relevant modules, and transforming the data into lists and dataframes, would be first step to working with it imo.
I've taken the liberty and time/ effort to delve into this myself as it will be useful to me going forward. Columns don't have to have the same lengths at all in his example, so that's good. I've tested the below code (Python 3.8) and it works successfully.
With only a slight adaptations can be used for your specific data columns, objects and purposes.
import pandas as pd
A = pd.read_csv(r'C:\Users\User\Documents\query_sequences.csv') #dropped the S fom _sequences
B = pd.read_csv(r'C:\Users\User\Documents\Sequence_reference.csv')
print(A.columns)
print(B.columns)
my_unknown_id = A['Unknown_sample_no'].tolist() #Unknown_sample_no
my_unknown_seq = A['Unknown_sample_seq'].tolist() #Unknown_sample_seq
Reference_Species1 = B['Reference_sequences_ID'].tolist()
Reference_Sequences1 = B['Reference_Sequences'].tolist() #it was Reference_sequences
Ref_dict = dict(zip(Reference_Species1, Reference_Sequences1)) #it was Reference_sequences
Unknown_dict = dict(zip(my_unknown_id, my_unknown_seq))
print(Ref_dict)
print(Unknown_dict)
Ref_dict = dict(zip(Reference_Species1, Reference_Sequences1))
Unknown_dict = dict(zip(my_unknown_id, my_unknown_seq))
print(Ref_dict)
print(Unknown_dict)
import re
filename = 'seq_match_compare2.csv'
f = open(filename, 'a') #in his eg it was 'w'
headers = 'Query_ID, Query_Seq, Ref_species, Ref_seq, Match, Match start Position\n'
f.write(headers)
for ID, seq in Unknown_dict.items():
for species, seq1 in Ref_dict.items():
m = re.search(seq, seq1)
if m:
match = m.group()
pos = m.start() + 1
f.write(str(ID) + ',' + seq + ',' + species + ',' + seq1 + ',' + match + ',' + str(pos) + '\n')
f.close()
And I did it myself too, assuming your columns contained integers, and according to your specifications (As best at the moment I can). Its my first try [Its my first attempt without webscraping, so go easy]. You could use my code below for a benchmark of how to move forward on your question.
Basically it does what you want (give you the skeleton) and does this : "imports csv in python using pandas module, converts to dataframes, works on specific columns only in those df's, make new columns (results), prints results alongside the original data in the terminal, and saves to new csv. It's as as messy as my python is , but it works! personally (& professionally) speaking is a milestone for me and I Will hopefully be working on it at a later date to improve it readability, scope, functionality and abilities [as the days go by (from next weekend).]
# This is work in progress, (although it does work and does a job), and its doing that for you. there are redundant lines of code in it, even the lines not hashed out (because im a self teaching newbie on my weekends). I was just finishing up on getting the results printed to a new csv file (done too). You can see how you could convert your columns & rows into lists with pandas dataframes, and start to do calculations with them in Python, and get your results back out to a new CSV. It a start on how you can answer your question going forward
#ITS FOR HER TO DO MUCH MORE & BETTER ON!! BUT IT DOES IN BASIC TERMS WHAT SHE ASKED FOR.
import pandas as pd
from pandas import DataFrame
import csv
import itertools #redundant now'?
A = pd.read_csv(r'C:\Users\User\Documents\book6 category labels.csv')
A["Category"].fillna("empty data - missing value", inplace = True)
#A["Blank1"].fillna("empty data - missing value", inplace = True)
# ...etc
print(A.columns)
MyCat=A['Category'].tolist()
MyLab=A['Label'].tolist()
My_Cats = A['Category1'].tolist()
My_Labs = A['Label1'].tolist()
#Ref_dict0 = zip(My_Labs, My_Cats) #good to compare whole columns as block, Enumerate ZIP 19:06 01/06/2020 FORGET THIS FOR NOW, WAS PART OF A LATTER ATTEMPT TO COMPARE TEXT & MISSED TEXT WITH INTERGER FIELDS. DOESNT EFFECT PROGRAM
Ref_dict = dict(zip(My_Labs, My_Cats))
Compareprep = dict(zip(My_Cats, My_Labs))
Ref_dict = dict(zip(My_Cats, My_Labs))
print(Ref_dict)
import re #this is for string matching & comparison. redundant in my example here but youll need it to compare tables if strings.
#filename = 'CATS&LABS64.csv' # when i got to exporting part, this is redundant now
#csvfile = open(filename, 'a') #when i tried to export results/output it first time - redundant
print("Given Dataframe :\n", A)
A['Lab-Cat_diff'] = A['Category1'].sub(A['Label1'], axis=0)
print("\nDifference of score1 and score2 :\n", A)
#YOU CAN DO OTHER MATCHES, COMPARISONS AND CALCULTAIONS YOURSELF HERE AND ADD THEM TO THE OUTPUT
result = (print("\nDifference of score1 and score2 :\n", A))
result2 = print(A) and print(result)
def result22(result2):
for aSentence in result2:
df = pd.DataFrame(result2)
print(str())
return df
print(result2)
print(result22) # printing out the function itself 'produces nothing but its name of course
output_df = DataFrame((result2),A)
output_df.to_csv('some_name5523.csv')
Yes, i know, its by no means perfect At all, but wanted to give you the heads up about panda's and dataframes for doing what you want moving forward.

Python Pandas replacing part of a string

I'm trying to filter data that is stored in a .csv file that contains time and angle values and save filtered data in an output .csv file. I solved the filtering part, but the problem is that time is recorded in hh:mm:ss:msmsmsms (12:55:34:500) format and I want to change that to hhmmss (125534) or in other words remove the : and the millisecond part.
I tried using the .replace function but I keep getting the KeyError: 'time' error.
Input data:
time,angle
12:45:55,56
12:45:56,89
12:45:57,112
12:45:58,189
12:45:59,122
12:46:00,123
Code:
import pandas as pd
#define min and max angle values
alpha_min = 110
alpha_max = 125
#read input .csv file
data = pd.read_csv('test_csv3.csv', index_col=0)
#filter by angle size
data = data[(data['angle'] < alpha_max) & (data['angle'] > alpha_min)]
#replace ":" with "" in time values
data['time'] = data['time'].replace(':','')
#display results
print data
#write results
data.to_csv('test_csv3_output.csv')

That's because time is an index. You can do this and remove the index_col=0:
data = pd.read_csv('test_csv3.csv')
And change this line:
data['time'] = pd.to_datetime(data['time']).dt.strftime('%H%M%S')
Output:
time angle
2 124557 112
4 124559 122
5 124600 123

What would print (data.keys()) or print(data.head()) yield? It seems like you have a stray character before\after the time index string, happens from time to time, depending on how the csv was created vs how it was read (see this question).
If it's not a bigger project and/or you just want the data, you could just do some silly workaround like: timeKeyString=list(data.columns.values)[0] (assuming time is the first one).

Python : Separating a .txt file into columns and finding the most frequent data item in one of the columns

I read from a file and stored into artists_tag with column names .
Now this file has multiple columns and I need to generate a new data structure which has 2 columns from the artists_tag as it is and the most frequent value from the 'Tag' column as the 3rd column value.
Here is what I have written as of now:
import pandas as pd
from collections import Counter
def parse_artists_tags(filename):
df = pd.read_csv(filename, sep="|", names=["ArtistID", "ArtistName", "Tag", "Count"])
return df
def parse_user_artists_matrix(filename):
df = pd.read_csv(filename)
return df
# artists_tags = parse_artists_tags(DATA_PATH + "\\artists-tags.txt")
artists_tags = parse_artists_tags("C:\\Users\\15-J001TX\\Documents\\ml_task\\artists-tags.txt")
#print(artists_tags)
user_art_mat = parse_user_artists_matrix("C:\\Users\\15-J001TX\\Documents\\ml_task\\userart-mat-training.csv")
#print ("Number of tags {0}".format(len(artists_tags))) # Change this line. Should be 952803
#print ("Number of artists {0}".format(len(user_art_mat))) # Change this line. Should be 17119
# TODO Implement this. You can change the function arguments if necessary
# Return a data structure that contains (artist id, artist name, top tag) for every artist
def calculate_top_tag(all_tags):
temp = all_tags.Tag
a = Counter(temp)
a = a.most_common()
print (a)
top_tags = all_tags.ArtistID,all_tags.ArtistName,a;
return top_tags
top_tags = calculate_top_tag(artists_tags)
# Print the top tag for Nirvana
# Artist ID for Nirvana is 5b11f4ce-a62d-471e-81fc-a69a8278c7da
# Should be 'Grunge'
print ("Top tag for Nirvana is {0}".format(top_tags)) # Complete this line
In the last method calculate_top_tag I don't understand how to choose the most frequent value from the 'Tag' column and put it as the third column for top_tags before returning it.
I am new to python and my knowledge of syntax and data structures is limited. I did try the various solutions mentioned for finding the most frequent value from the list but they seem to display the entire column and not one particular value. I know this is some trivial syntax issue but after having searched for long I still cannot figure out how to get this one.
edit 1 :
I need to find the most common tag for a particular artist and not the most common overall.
But again, I don't know how to.
edit 2 :
here is the link to the data files:
https://github.com/amplab/datascience-sp14/raw/master/hw2/hw2data.tar.gz

I'm sure there is a more succint way of doing it, but this should get you started:
# returns a df grouped by ArtistID and Tag
tag_counts = artists_tags.groupby(['ArtistID', 'Tag'])
# sum up tag counts and sort in descending order
tag_counts = tag_counts.sum().sort('Count', ascending=False).reset_index()
# keep only the top ranking tag per artist
top_tags = tag_counts.groupby('ArtistID').first()
# top_tags is now a dataframe which contains the top tag for every artist
# We can simply lookup the top tag for Nirvana via it's index:
top_tags.ix['5b11f4ce-a62d-471e-81fc-a69a8278c7da'][0]
# 'Grunge'