I want to check a YouTube video's views and keep track of them over time. I wrote a script that works great:
import requests
import re
import pandas as pd
from datetime import datetime
import time
def check_views(link):
todays_date = datetime.now().strftime('%d-%m')
now_time = datetime.now().strftime('%H:%M')
#get the site
r = requests.get(link)
text = r.text
tag = re.compile('\d+ views')
views = re.findall(tag,text)[0]
#get the digit number of views. It's returned in a list so I need to get that item out
cleaned_views=re.findall('\d+',views)[0]
print(cleaned_views)
#append to the df
df.loc[len(df)] = [todays_date, now_time, int(cleaned_views)]
#df = df.append([todays_date, now_time, int(cleaned_views)],axis=0)
df.to_csv('views.csv')
return df
df = pd.DataFrame(columns=['Date','Time','Views'])
while True:
df = check_views('https://www.youtube.com/watch?v=gPHgRp70H8o&t=3s')
time.sleep(1800)
But now I want to use this function for multiple links. I want a different CSV file for each link. So I made a dictionary:
link_dict = {'link1':'https://www.youtube.com/watch?v=gPHgRp70H8o&t=3s',
'link2':'https://www.youtube.com/watch?v=ZPrAKuOBWzw'}
#this makes it easy for each csv file to be named for the corresponding link
The loop then becomes:
for key, value in link_dict.items():
df = check_views(value)
That seems to work passing the value of the dict (link) into the function. Inside the function, I just made sure to load the correct csv file at the beginning:
#Existing csv files
df=pd.read_csv(k+'.csv')
But then I'm getting an error when I go to append a new row to the df (“cannot set a row with mismatched columns”). I don't get that since it works just fine as the code written above. This is the part giving me an error:
df.loc[len(df)] = [todays_date, now_time, int(cleaned_views)]
What am I missing here? It seems like a super messy way using this dictionary method (I only have 2 links I want to check but rather than just duplicate a function I wanted to experiment more). Any tips? Thanks!
Figured it out! The problem was that I was saving the df as a csv and then trying to read back that csv later. When I saved the csv, I didn't use index=False with df.to_csv() so there was an extra column! When I was just testing with the dictionary, I was just reusing the df and even though I was saving it to a csv, the script kept using the df to do the actual adding of rows.
Related
I'm trying to create a metadata scraper to enrich my e-book collection, but am experiencing some problems. I want to create a dict (or whatever gets the job done) to store the index (only while testing), the path and the series name. This is the code I've written so far:
from bs4 import BeautifulSoup
def get_opf_path():
opffile=variables.items
pathdict={'index':[],'path':[],'series':[]}
safe=[]
x=0
for f in opffile:
x+=1
pathdict['path']=f
pathdict['index']=x
with open(f, 'r') as fi:
soup=BeautifulSoup(fi, 'lxml')
for meta in soup.find_all('meta'):
if meta.get('name')=='calibre:series':
pathdict['series']=meta.get('content')
safe.append(pathdict)
print(pathdict)
print(safe)
this code is able to go through all the opf files and get the series, index and path, I'm sure of this, since the console output is this:
However, when I try to store the pathdict to the safe, no matter where I put the safe.append(pathdict) the output is either:
or
or
What do I have to do, so that the safe=[] has the data shown in image 1?
I have tried everything I could think of, but nothing worked.
Any help is appreciated.
I believe this is the correct way:
from bs4 import BeautifulSoup
def get_opf_path():
opffile = variables.items
pathdict = {'index':[], 'path':[], 'series':[]}
safe = []
x = 0
for f in opffile:
x += 1
pathdict['path'] = f
pathdict['index'] = x
with open(f, 'r') as fi:
soup = BeautifulSoup(fi, 'lxml')
for meta in soup.find_all('meta'):
if meta.get('name') == 'calibre:series':
pathdict['series'] = meta.get('content')
print(pathdict)
safe.append(pathdict.copy())
print(safe)
For two main reasons:
When you do:
pathdict['series'] = meta.get('content')
you are overwriting the last value in pathdict['series'] so I believe this is where you should save.
You also need to make a copy of it, if you don´t it will change also in the list. When you store the dict you really are storing a reeference to it (in this case, a reference to the variable pathdict.
Note
If you want to print the elements of the list in separated lines you can do something like this:
print(*save, sep="\n")
I'm trying to read data from https://download.bls.gov/pub/time.series/bp/bp.measure using pandas, like this:
import pandas as pd
url = 'https://download.bls.gov/pub/time.series/bp/bp.measure'
df = pd.read_csv(url, sep='\t')
However, I just need to get the dataset with the two columns: measure_code and measure_text. As this dataset as a title BP measure I also tried to read it like:
url = 'https://download.bls.gov/pub/time.series/bp/bp.measure'
df = pd.read_csv(url, sep='\t', skiprows=1)
But in this case it returns a dataset with just one column and I'm not being able to slipt it:
>>> df.columns
Index([' measure_code measure_text'], dtype='object')
Any suggestion/idea on a better approach to get this dataset?
It's definitely possible, but the format has a few quirks.
As you noted, the column headers start on line 2, so you need skiprows=1.
The file is space-separated, not tab-separated.
Column values are continued across multiple lines.
Issues 1 and 2 can be fixed using skiprows and sep. Problem 3 is harder, and requires you to preprocess the file a little. For that reason, I used a slightly more flexible way of fetching the file, using the requests library. Once I have the file, I use regular expressions to fix problem 3, and give the file back to pandas.
Here's the code:
import requests
import re
import io
import pandas as pd
url = 'https://download.bls.gov/pub/time.series/bp/bp.measure'
# Get the URL, convert the document from DOS to Unix linebreaks
measure_codes = requests.get(url) \
.text \
.replace("\r\n", "\n")
# If there's a linebreak, followed by at least 7 spaces, combine it with
# previous line
measure_codes = re.sub("\n {7,}", " ", measure_codes)
# Convert the string to a file-like object
measure_codes = io.BytesIO(measure_codes.encode('utf-8'))
# Read in file, interpreting 4 spaces or more as a delimiter.
# Using a regex like this requires using the slower Python engine.
# Use skiprows=1 to skip the header
# Use dtype="str" to avoid converting measure code to integer.
df = pd.read_csv(measure_codes, engine="python", sep=" {4,}", skiprows=1, dtype="str")
print(df)
I am new to python.
After researching some code based on my idea which is extracting historical stock data,
I have now working code(see below) when extracting individual name and exporting it to a csv file
import investpy
import sys
sys.stdout = open("extracted.csv", "w")
df = investpy.get_stock_historical_data(stock='JFC',
country='philippines',
from_date='25/11/2020',
to_date='18/12/2020')
print(df)
sys.stdout.close()
Now,
I'm trying to make it more advance.
I want to run this code multiple times automatically with different stock name(about 300 plus name) and export it respectively.
I know it is possible but I cannot search the exact terminology to this problem.
Hoping for your help.
Regards,
you can store the stock's name as a list and then iterate through the list and save all the dataframes into separate files.
import investpy
import sys
stocks_list = ['JFC','AAPL',....] # your stock lists
for stock in stocks_list:
df = investpy.get_stock_historical_data(stock=stock,
country='philippines',
from_date='25/11/2020',
to_date='18/12/2020')
print(df)
file_name = 'extracted_'+stock+'.csv'
df.to_csv(file_name,index=False)
I am trying to convert .pdf data to a spreadsheet. Based on some research, some guys recommended transforming it into csv first in order to avoid errors.
So, I made the below coding which is giving me:
"TypeError: cannot concatenate object of type ''; only Series and DataFrame objs are valid"
Error appears at 'pd.concat' command.
'''
import tabula
import pandas as pd
import glob
path = r'C:\Users\REC.AC'
all_files = glob.glob(path + "/*.pdf")
print (all_files)
df = pd.concat(tabula.read_pdf(f1) for f1 in all_files)
df.to_csv("output.csv", index = False)
'''
Since this might be a common issue, I am posting the solution I found.
"""
df = []
for f1 in all_files:
df = pd.concat(tabula.read_pdf(f1))
"""
I believe that breaking the item iteration in two parts would generate the dataframe it needed and therefore would work.
I've written a script to parse data from the first table of a website. I've used xpath to parse the table. Btw, I didn't use "tr" tag cause without using it I can still see the results in the console when printed. When I run my script, the data are getting scraped but being printed in a single line in a csv file. I can't find out the mistake I'm making. Any input on this will be highly appreciated. Here is what I've tried with:
import csv
import requests
from lxml import html
url="https://fantasy.premierleague.com/player-list/"
response = requests.get(url).text
outfile=open('Data_tab.csv','w', newline='')
writer=csv.writer(outfile)
writer.writerow(["Player","Team","Points","Cost"])
tree = html.fromstring(response)
for titles in tree.xpath("//table[#class='ism-table']")[0]:
# tab_r = titles.xpath('.//tr/text()')
tab_d = titles.xpath('.//td/text()')
writer.writerow(tab_d)
You might want to add a level of looping, examining each table row in turn.
Try this:
for titles in tree.xpath("//table[#class='ism-table']")[0]:
for row in titles.xpath('./tr'):
tab_d = row.xpath('./td/text()')
writer.writerow(tab_d)
Or, perhaps this:
table = tree.xpath("//table[#class='ism-table']")[0]
for row in table.xpath('.//tr'):
items = row.xpath('./td/text()')
writer.writerow(items)
Or you could have the first XPath expression find the rows for you:
rows = tree.xpath("(.//table[#class='ism-table'])[1]//tr")
for row in rows:
items = row.xpath('./td/text()')
writer.writerow(items)