Month: May 2015

Saving images from google search using Selenium and Python

Below is a short python script that allows users to save searched images to local drive using Image search on Google. It requires Selenium as Google requires users to press the “show more results” button and the scroll bar to move all the way to the bottom of page for more images to be displayed. Using Selenium will be an easier choice for this function.

The below python script will have the following:

  1. Enable users to input multiple search keywords either by entry or get from file. Users can leave the program to download on its own after creating a series of search keywords.
  2. Based on each keyword, form the google search url. Most of the parameters inside the google search url can be fixed. The only part that required changing is the search keyword as highlighted below in red.
  3. Run google search and obtain page source for the images. This is run using Selenium. To obtain the full set of images, Selenium will help to press the button and scroll the scrollbar to bottom of pages so that Google can load the remaining images. There seems to be a hard quota of 1000 pics for image search on Google.
  4. Use python pattern and xpath to retrieve the corresponding url for each image. The xpath will use the following tag:
    • tag_list = dom(‘a.rg_l’) #a tag with class = rg_l
  5. Based on each url, it will check the following before downloading the image file:
    • whether there is any redirect of site. This is done using Python Pattern redirect function.
    • check the extension whether it is a valid image file type.
  6. The image files are downloaded to a local folder (generated by date). Each image will be label according to the search key and a counter. There will be a corresponding text file mapping the image label to the image url for reference.
import re, os, sys, datetime, time
import pandas
from selenium import webdriver
from contextlib import closing
from selenium.webdriver import Firefox
from import WebDriverWait
from import By
from import expected_conditions as EC

from pattern.web import URL, extension, cache, plaintext, Newsfeed, DOM

class GoogleImageExtractor(object):

    def __init__(self, search_key = '' ):
        """ Google image search class
                search_key to be entered.

        if type(search_key) == str:
            ## convert to list even for one search keyword to standalize the pulling.
            self.g_search_key_list = [search_key]
        elif type(search_key) == list:
            self.g_search_key_list = search_key
            print 'google_search_keyword not of type str or list'

        self.g_search_key = ''

        ## user options
        self.image_dl_per_search = 200

        ## url construct string text
        self.prefix_of_search_url = ""
        self.postfix_of_search_url = '&source=lnms&tbm=isch&sa=X&ei=0eZEVbj3IJG5uATalICQAQ&ved=0CAcQ_AUoAQ&biw=939&bih=591'# non changable text
        self.target_url_str = ''

        ## storage
        self.pic_url_list = []
        self.pic_info_list = []

        ## file and folder path
        self.folder_main_dir_prefix = r'C:\data\temp\gimage_pic'

    def reformat_search_for_spaces(self):
            Method call immediately at the initialization stages
            get rid of the spaces and replace by the "+"
            Use in search term. Eg: "Cookie fast" to "Cookie+fast"

            strip any lagging spaces if present
            replace the self.g_search_key
        self.g_search_key = self.g_search_key.rstrip().replace(' ', '+')

    def set_num_image_to_dl(self, num_image):
        """ Set the number of image to download. Set to self.image_dl_per_search.
                num_image (int): num of image to download.
        self.image_dl_per_search = num_image

    def get_searchlist_fr_file(self, filename):
        """Get search list from filename. Ability to add in a lot of phrases.
            Will replace the self.g_search_key_list
                filename (str): full file path
        with open(filename,'r') as f:
            self.g_search_key_list = f.readlines()

    def formed_search_url(self):
        ''' Form the url either one selected key phrases or multiple search items.
            Get the url from the self.g_search_key_list
            Set to self.sp_search_url_list
        self.target_url_str = self.prefix_of_search_url + self.g_search_key +\

    def retrieve_source_fr_html(self):
        """ Make use of selenium. Retrieve from html table using pandas table.

        driver = webdriver.Firefox()

        ## wait for log in then get the page source.
            driver.execute_script("window.scrollTo(0, 30000)")
            self.temp_page_source = driver.page_source
            #driver.find_element_by_css_selector('ksb _kvc').click()#cant find the class
            driver.find_element_by_id('smb').click() #ok
            driver.execute_script("window.scrollTo(0, 60000)")
            driver.execute_script("window.scrollTo(0, 60000)")

            print 'not able to find'

        self.page_source = driver.page_source


    def extract_pic_url(self):
        """ extract all the raw pic url in list

        dom = DOM(self.page_source)
        tag_list = dom('a.rg_l')

        for tag in tag_list[:self.image_dl_per_search]:
            tar_str ='imgurl=(.*)&imgrefurl', tag.attributes['href'])
                print 'error parsing', tag

    def multi_search_download(self):
        """ Mutli search download"""
        for indiv_search in self.g_search_key_list:
            self.pic_url_list = []
            self.pic_info_list = []

            self.g_search_key = indiv_search

            self.downloading_all_photos() #some download might not be jpg?? use selnium to download??

    def downloading_all_photos(self):
        """ download all photos to particular folder

        pic_counter = 1
        for url_link in self.pic_url_list:
            print pic_counter
            pic_prefix_str = self.g_search_key  + str(pic_counter)
            self.download_single_image(url_link.encode(), pic_prefix_str)
            pic_counter = pic_counter +1

    def download_single_image(self, url_link, pic_prefix_str):
        """ Download data according to the url link given.
                url_link (str): url str.
                pic_prefix_str (str): pic_prefix_str for unique label the pic
        self.download_fault = 0
        file_ext = os.path.splitext(url_link)[1] #use for checking valid pic ext
        temp_filename = pic_prefix_str + file_ext
        temp_filename_full_path = os.path.join(self.gs_raw_dirpath, temp_filename )

        valid_image_ext_list = ['.png','.jpg','.jpeg', '.gif', '.bmp', '.tiff'] #not comprehensive

        url = URL(url_link)
        if url.redirect:
            return # if there is re-direct, return

        if file_ext not in valid_image_ext_list:
            return #return if not valid image extension

        f = open(temp_filename_full_path, 'wb') # save as test.gif
        print url_link
        self.pic_info_list.append(pic_prefix_str + ': ' + url_link )
            f.write( have problem skip
            #if self.__print_download_fault:
            print 'Problem with processing this data: ', url_link
            self.download_fault =1

    def create_folder(self):
            Create a folder to put the log data segregate by date

        self.gs_raw_dirpath = os.path.join(self.folder_main_dir_prefix, time.strftime("_%d_%b%y", time.localtime()))
        if not os.path.exists(self.gs_raw_dirpath):

    def save_infolist_to_file(self):
        """ Save the info list to file.

        temp_filename_full_path = os.path.join(self.gs_raw_dirpath, self.g_search_key + '_info.txt' )

        with  open(temp_filename_full_path, 'w') as f:
            for n in self.pic_info_list:

if __name__ == '__main__':

    choice =4

    if choice ==4:
        """test the downloading of files"""
        w = GoogleImageExtractor('')#leave blanks if get the search list from file
        searchlist_filename = r'C:\data\temp\gimage_pic\imgsearch_list.txt'
        w.get_searchlist_fr_file(searchlist_filename)#replace the searclist

Retrieving short sell qty for SG stocks from SGX using python

SGX usually releases short sell information for each stock at the end of each trading day. This information are found in their website. The daily short sell of all stocks are compiled into a  report classified by day. We are interested in getting the short qty ranked by stocks per day.

If we examine the link, each report is in the form of a table format. To extract the information, we can use python pattern for web content download and Pandas for table extraction. Pandas has a function “” that can retrieve table like data from the html string easily.

The following lists the steps to retrieve the short sell information.

  1. URL formation: As the link are joined by the date, need to retrieve the date str to join to the fixed url string. However, not all the date will be present, eg , during weekends. A better way is keep looping the the date back from current to get the latest date avaliable.
  2. HTML data download: This can be done using python pattern.
  3. Converting the table to data frame: This can be done using Pandas function “”. Also Pandas provides a rank function so that the results can be ranked accordingly. Converting into Pandas database make it easy.
  4. Ranking by absolute qty may tend to mislead as it will also depends on the shares relative volume. Combining with the actual shares traded will give  a more representative data. For this case, the data frame retrieved can be joined to the current price df created from the previous post “Retrieving stock news and Ex-date from SGX using python“.
  5. The last will be to set the alerts which can be done easily using PushBullet as describe as the following post “Sending alerts to iphone or Android phone using python“. You can customize to send the alert at the end of each trading day to determine the top 10 short sell stocks.

Below show the short sell info retrieval portion of the code found in the  “”  for retrieving the short sell qty for each stocks. The updated code is found in Github.

    def retrieve_shortsell_info(self):
        """ Retrieve the shortsell information.
            will form the url and retrieved the information using pandas to make into table.
            The function will set to self_shortsell_info_df.
            make it iterat over the days to get the latest data
        for last_effective_date in range(7):
            url = URL(self.shortsell_full_url)
                #see data is available for that current date
                url_data = = 50)
                shortsell_list =
                self.shortsell_info_df =shortsell_list[1]

            #continue if there is no data
            if len(self.shortsell_info_df) == 0: continue

            self.shortsell_info_df.rename(columns={0:'Security',1:'Short Sale Volume',
                                                  2:'Currency',3:'Short Sale Value',
                                                    },inplace =True)
            self.shortsell_info_df = self.shortsell_info_df[1:-3]
            #change type of the columns
            self.shortsell_info_df[['Short Sale Volume', 'Short Sale Value']] = self.shortsell_info_df[['Short Sale Volume', 'Short Sale Value']].astype(float)
            #need a rank on the short sell
            self.shortsell_info_df['ranked_shortsell'] = self.shortsell_info_df['Short Sale Volume'].rank(method='min',ascending=False)
            self.shortsell_info_df['shortsell_lastdate'] = self.set_last_desired_date(last_effective_date)
            #need percentage as well

            # have a sorting of data?

        print 'No suitable data found within time frame.'

    def form_shortsell_url(self, last_effective_date):
        """ Based on the current date to set the shorsell url.
            Set to self.shortsell_full_url
                last_effective_date (int): last desired date in yyyymmdd.
        #retrieve the current date in yyyymmdd format
        self.shortsell_date_url = self.set_last_desired_date(num_days = last_effective_date)
        self.shortsell_full_url = self.shortsell_info_start_url + self.shortsell_date_url + self.shortsell_end_url

    def set_last_desired_date(self, num_days = 0):
        """ Return the last date in which the results will be displayed.
            It is set to be the current date - num of days as set by users.
            Affect only self.print_feeds function.
                num_days (int): num of days prior to the current date.
                Setting to 0 will only retrieve the current date
                (int): datekey as yyyyymmdd.
        last_eff_date_list = list(( - datetime.timedelta(num_days)).timetuple()[0:3])

        if len(str(last_eff_date_list[1])) == 1:
            last_eff_date_list[1] = '0' + str(last_eff_date_list[1])

        if len(str(last_eff_date_list[2])) == 1:
            last_eff_date_list[2] = '0' + str(last_eff_date_list[2])

        return str(last_eff_date_list[0]) + str(last_eff_date_list[1]) + str(last_eff_date_list[2])

    def shortsell_notification(self):
        """ Use for alerts on shortsell information.
            Identify top ten short sell plus target stock short sell information.

        ## get the current price df so can combined with the shortsell info
        merged_shortsell_df = pandas.merge(self.shortsell_info_df,self.sgx_curr_price_df,left_on = 'Security', right_on = 'CompanyName' )

        ## add in additional columns
        merged_shortsell_df['shortsell_vol_per'] = merged_shortsell_df['Short Sale Volume']/merged_shortsell_df['DailyVolume']
        merged_shortsell_df['ranked_percent_vol_shortsell'] = merged_shortsell_df['shortsell_vol_per'].rank(method='min',ascending=False)

        top_shortsell_df = merged_shortsell_df[merged_shortsell_df['ranked_shortsell'].isin(range(1,16))]
        top_shortsell_df  = top_shortsell_df.sort(columns = 'ranked_shortsell', ascending =True)
        top_shortsell_df = top_shortsell_df[['Security','Short Sale Volume','shortsell_lastdate']]
        shortsell_top15_shtver = top_shortsell_df.to_string()

        api_key_path = r'C:\Users\356039\Desktop\running bat\pushbullet_api\key.txt'
        with open(api_key_path,'r') as f:
            apiKey =

        p = PushBullet(apiKey)

        if shortsell_top15_shtver:
            p.pushNote('all', 'Shortsell top10', shortsell_top15_shtver,recipient_type="random1")

        ## display for target watchlist
        tar_watchlist_shortsell_df = merged_shortsell_df[merged_shortsell_df['Security'].isin(self.companyname_watchlist)]
        tar_watchlist_shortsell_df = tar_watchlist_shortsell_df[['Security','Short Sale Volume','ranked_shortsell','shortsell_vol_per','ranked_percent_vol_shortsell']]
        tar_watchlist_shortsell_df =tar_watchlist_shortsell_df[tar_watchlist_shortsell_df['ranked_shortsell'].isin(range(1,100))]
        tar_watchlist_shortsell_df  = tar_watchlist_shortsell_df.sort(columns = 'ranked_shortsell', ascending =True)
        tar_watchlist_shortsell_shtver = tar_watchlist_shortsell_df.to_string()

        if tar_watchlist_shortsell_shtver:
            p.pushNote('all', 'Shortsell targetwatchlist', tar_watchlist_shortsell_shtver,recipient_type="random1")

Sample output as followed:
Security | Short Sale Volume|  ranked_shortsell | shortsell_vol_per | ranked_percent_vol_shortsell
Sembcorp Ind | 3529600 | 6 | 0.437422 | 4
CapitaLand | 3313300 | 7 | 0.354216|  7
SingTel | 2809000 | 8 | 0.276471 | 16
Lippo Malls Tr | 2073800 | 11 | 0.492531 | 2

  1. Ranked_shortsell –> rank according to the absolute volume
  2. Shortsell_vol_per –> short sell qty as ratio of transacted vol
  3. ranked_percent_vol_shortsell –> rank according to Shortsell_vol_per