Apache log file data analysis with python pandas - python

The problem with me is bit hard to explain. I'm analyzing a Apache log file which following is one line from it.
112.135.128.20 - [13/May/2013:23:55:04 +0530] "GET /SVRClientWeb/ActionController HTTP/1.1" 302 2 "https://www.example.com/sample" "Mozilla/5.0 (iPhone; CPU iPhone OS 6_1_3 like Mac OS X) AppleWebKit/536.26 (KHTML, like Gecko) Mobile/10B329" GET /SVRClientWeb/ActionController - HTTP/1.1 www.example.com
Some parts from my code:
df = df.rename(columns={'%>s': 'Status', '%b':'Bytes Returned',
'%h':'IP', '%l':'Username', '%r': 'Request', '%t': 'Time', '%u': 'Userid', '%{Referer}i': 'Referer', '%{User-Agent}i': 'Agent'})
df.index = pd.to_datetime(df.pop('Time'))
test = df.groupby(['IP', 'Agent']).size()
test.sort()
print test[-20:]
I read log file to a data frame and get the following output with hit counts and user agents.
IP Agent
74.86.158.106 Mozilla/5.0+(compatible; UptimeRobot/2.0; http://www.uptimerobot.com/) 369
203.81.107.103 Mozilla/5.0 (Windows NT 6.1; rv:21.0) Gecko/20100101 Firefox/21.0 388
173.199.120.155 Mozilla/5.0 (compatible; AhrefsBot/4.0; +http://ahrefs.com/robot/) 417
124.43.84.242 Mozilla/5.0 (Windows NT 6.2) AppleWebKit/537.31 (KHTML, like Gecko) Chrome/26.0.1410.64 Safari/537.31 448
112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.94 Safari/537.36 454
124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) Gecko/20100101 Firefox/21.0 461
124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20100101 Firefox/21.0 467
Then I want to get the
Most highest 3 hit counts(their IPs) and find the frequency of their occurrence?(like time difference between each hit occurrence
of the IP)
How to find whether there are different agents for one single IP?
At least please explain me how to solve above problems?


To do the first part you could just sort the DataFrame (by count) and take the top three rows:
In [11]: df.sort('Count', ascending=False).head(3)
Out[11]:
IP Agent Count
6 124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20... 467
5 124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) G... 461
4 112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.3... 454
To test whether there are multiple rows (Agents) for a single IP you can use groupby:
In [12]: g = df.groupby('IP')
In [13]: repeated = g.count().Count != 1
In [14]: repeated
Out[14]:
IP
112.135.196.223 False
124.43.104.198 False
124.43.155.138 False
124.43.84.242 False
173.199.120.155 False
203.81.107.103 False
74.86.158.106 False
Name: Count, dtype: bool
In [15]: repeated[repeated]
Out[15]: Series([], dtype: bool)
There are none in this example.
In order to avoid sorting the entire DataFrame, it's possible and it could be more efficient (update: IT'S NOT) to use heapq (I don't think there is an nlargest in pandas):
In [21]: from heapq import nlargest
In [22]: top_3 = nlargest(3, df.iterrows(), key=lambda x: x[1]['Count'])
In [23]: pd.DataFrame.from_items(top_3).T
Out[23]:
IP Agent Count
6 124.43.104.198 Mozilla/5.0 (Windows NT 5.1; rv:21.0) Gecko/20... 467
5 124.43.155.138 Mozilla/5.0 (Windows NT 6.1; WOW64; rv:21.0) G... 461
4 112.135.196.223 Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.3... 454

Related

Create batches of pandas dataframe based on timestamp

I have a dataframe of the following form:
#timestamp ISP cache_result client_ip client_request_host client_request_method client_ua client_url client_user content_type ... http_response_code major os os_name querystring reply_length_bytes ts_process_time ts_timestamp type ua_name
2018-04-17T08:12:32.000Z cuaerH c rt,nlEIrnii.cec TCP_REFRESH_MISS 25.204.184.124 testhost.net GET Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... /wp-content/themes/Avada/includes/lib/assets/m... - application/javascript ... 200 65.0 Windows 10 Windows 10 ?ver=2.2.3 25204 321 17/Apr/2018:08:12:32 -0000 testdata Chrome
2018-04-17T08:12:32.000Z HeE iclirueIc rat,nrncc. TCP_REFRESH_MISS 8.157.89.174 testhost.net GET Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... /wp-content/plugins/fusion-core/js/min/avada-p... - application/javascript ... 200 65.0 Windows 10 Windows 10 ?ver=1 2825 177 17/Apr/2018:08:12:32 -0000 testdata Chrome
2018-04-17T08:12:33.000Z ,rrnI EnH.ceeiuclcicrat TCP_REFRESH_MISS 37.151.22.36 testhost.net GET Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... /wp-content/themes/Avada/includes/lib/assets/m... - application/javascript ... 200 65.0 Windows 10 Windows 10 ?ver=1 267 275 17/Apr/2018:08:12:33 -0000 testdata Chrome
2018-04-17T08:12:34.000Z tn.cHer uE,lecnir aircIc TCP_REFRESH_MISS 202.165.110.43 testhost.net GET Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... /wp-content/themes/Avada/includes/lib/assets/m... - application/javascript ... 200 65.0 Windows 10 Windows 10 ?ver=1 341 172 17/Apr/2018:08:12:34 -0000 testdata Chrome
2018-04-17T08:12:34.000Z rneecHuraci ctInir cl.,E TCP_REFRESH_MISS 174.201.44.32 testhost.net GET Mozilla/5.0 (Windows NT 10.0; Win64; x64) Appl... /wp-content/plugins/fusion-builder/assets/js/m... - application/javascript ... 200 65.0 Windows 10 Windows 10 ?ver=1 302 180 17/Apr/2018:08:12:34 -0000 testdata Chrome
Is it possible to somehow split it to 2 minutes intervals? Let's say a function that takes the whole dataframe and outputs a df with the rows of the first 2 minutes, then if called again, it outputs the df with the rows of the next 2 minutes and so on.
EDIT: A larger portion of my data is the following:
{"#timestamp":"2018-04-17T08:12:32.000Z","ISP":"cuaerH c rt,nlEIrnii.cec","cache_result":"TCP_REFRESH_MISS","client_ip":"25.204.184.124","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.ilightbox.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"ecftdl1e","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=2.2.3","reply_length_bytes":25204,"ts_process_time":321,"ts_timestamp":"17\/Apr\/2018:08:12:32 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:32.000Z","ISP":"HeE iclirueIc rat,nrncc.","cache_result":"TCP_REFRESH_MISS","client_ip":"8.157.89.174","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/fusion-core\/js\/min\/avada-portfolio.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"ced1tlef","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":2825,"ts_process_time":177,"ts_timestamp":"17\/Apr\/2018:08:12:32 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:33.000Z","ISP":" ,rrnI EnH.ceeiuclcicrat","cache_result":"TCP_REFRESH_MISS","client_ip":"37.151.22.36","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/general\/fusion-waypoints.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"lde1ftce","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":267,"ts_process_time":275,"ts_timestamp":"17\/Apr\/2018:08:12:33 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:34.000Z","ISP":"tn.cHer uE,lecnir aircIc","cache_result":"TCP_REFRESH_MISS","client_ip":"202.165.110.43","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.requestAnimationFrame.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"cl1etefd","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":341,"ts_process_time":172,"ts_timestamp":"17\/Apr\/2018:08:12:34 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:34.000Z","ISP":"rneecHuraci ctInir cl.,E","cache_result":"TCP_REFRESH_MISS","client_ip":"174.201.44.32","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/fusion-builder\/assets\/js\/min\/general\/fusion-countdown.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"ctl1fdee","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":302,"ts_process_time":180,"ts_timestamp":"17\/Apr\/2018:08:12:34 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:35.000Z","ISP":"ri enuaHccecrcnl,.tir EI","cache_result":"TCP_REFRESH_MISS","client_ip":"170.122.151.169","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/fusion-builder\/assets\/js\/min\/general\/fusion-flip-boxes.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"cl1feted","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":376,"ts_process_time":178,"ts_timestamp":"17\/Apr\/2018:08:12:35 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:36.000Z","ISP":"earr ec,ulIriccnH.ci ntE","cache_result":"TCP_REFRESH_MISS","client_ip":"177.120.159.58","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.appear.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"t1lceedf","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":1331,"ts_process_time":179,"ts_timestamp":"17\/Apr\/2018:08:12:36 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:36.000Z","ISP":"a, uEr.cnIlHeictrecrcni ","cache_result":"TCP_REFRESH_MISS","client_ip":"94.247.12.106","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/fusion-builder\/assets\/js\/min\/general\/fusion-tabs.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"fetel1dc","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":1154,"ts_process_time":86,"ts_timestamp":"17\/Apr\/2018:08:12:36 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:37.000Z","ISP":"rlcEt.icree ncaI uHi,crn","cache_result":"TCP_REFRESH_MISS","client_ip":"149.218.159.35","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.hoverintent.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"lecte1df","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":463,"ts_process_time":172,"ts_timestamp":"17\/Apr\/2018:08:12:37 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:38.000Z","ISP":"e,ir ctuE iccnanrceIHlr.","cache_result":"TCP_REFRESH_MISS","client_ip":"138.228.110.199","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.cycle.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"e1ftlecd","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=3.0.3","reply_length_bytes":7523,"ts_process_time":179,"ts_timestamp":"17\/Apr\/2018:08:12:38 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:39.000Z","ISP":"nirEei,latnu.cr cIH recc","cache_result":"TCP_REFRESH_MISS","client_ip":"117.81.45.92","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/library\/jquery.placeholder.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"cte1efdl","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=2.0.7","reply_length_bytes":874,"ts_process_time":178,"ts_timestamp":"17\/Apr\/2018:08:12:39 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:39.000Z","ISP":"Eic,e rlHccacrnuntI .rie","cache_result":"TCP_REFRESH_MISS","client_ip":"62.189.164.148","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/general\/fusion-tooltip.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"fe1eltdc","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":452,"ts_process_time":89,"ts_timestamp":"17\/Apr\/2018:08:12:39 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:40.000Z","ISP":"It.crue,lare rHiic cncnE","cache_result":"TCP_REFRESH_MISS","client_ip":"136.44.153.177","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/includes\/lib\/assets\/min\/js\/general\/fusion-ie1011.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"1dcetlef","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=1","reply_length_bytes":526,"ts_process_time":89,"ts_timestamp":"17\/Apr\/2018:08:12:40 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:41.000Z","ISP":"nIr,erecluiiHac cr.Ec nt","cache_result":"TCP_REFRESH_MISS","client_ip":"228.104.233.205","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/assets\/min\/js\/library\/bootstrap.scrollspy.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"ec1edltf","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=3.3.2","reply_length_bytes":1060,"ts_process_time":172,"ts_timestamp":"17\/Apr\/2018:08:12:41 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:42.000Z","ISP":"lrne,tEcuc eircIHc.air n","cache_result":"TCP_REFRESH_MISS","client_ip":"168.41.158.162","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/themes\/Avada\/assets\/min\/js\/library\/jquery.sticky-kit.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"d1efctle","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=5.4.2","reply_length_bytes":1208,"ts_process_time":185,"ts_timestamp":"17\/Apr\/2018:08:12:42 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:27.000Z","ISP":".cccti a neuleEc,rriHnrI","cache_result":"TCP_REFRESH_MISS","client_ip":"113.202.240.119","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/revslider\/public\/assets\/js\/jquery.themepunch.tools.min.js","client_user":"-","content_type":"application\/javascript","device":"Other","dnet":"d1eflcet","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":"?ver=5.4.7","reply_length_bytes":38335,"ts_process_time":313,"ts_timestamp":"17\/Apr\/2018:08:12:27 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:28.000Z","ISP":"lnniueeiIH.ca rtrc ,ccEr","cache_result":"TCP_REFRESH_HIT","client_ip":"190.220.94.243","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/uploads\/2017\/10\/viettan.png","client_user":"-","content_type":"image\/png","device":"Other","dnet":"delcfet1","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":1549,"ts_process_time":170,"ts_timestamp":"17\/Apr\/2018:08:12:28 -0000","type":"testdata","ua_name":"Chrome"}
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{"#timestamp":"2018-04-17T08:12:43.000Z","ISP":"tariirs oftpooorCoMcn","cache_result":"ERR_CLIENT_ABORT","client_ip":"173.38.196.130","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (iPhone; CPU iPhone OS 7_0 like Mac OS X) AppleWebKit\/537.51.1 (KHTML, like Gecko) Version\/7.0 Mobile\/11A465 Safari\/9537.53 BingPreview\/1.0b","client_url":"\/amp_preconnect_polyfill_404_or_other_error_expected._Do_not_worry_about_it","client_user":"-","content_type":"text\/html","device":"Spider","dnet":"1ceetlfd","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":404,"major":1.0,"os":"iOS","os_name":"iOS","querystring":"?1523952720000","reply_length_bytes":43261,"ts_process_time":1075,"ts_timestamp":"17\/Apr\/2018:08:12:43 -0000","type":"testdata","ua_name":"BingPreview"}
{"#timestamp":"2018-04-17T08:12:44.000Z","ISP":"i.nae,crHntc uiEcrlr ecI","cache_result":"TCP_REFRESH_HIT","client_ip":"217.198.69.197","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/uploads\/2018\/04\/dong-tam-bat-giu-cong-an-640x360.jpg","client_user":"-","content_type":"image\/jpeg","device":"Other","dnet":"1teedfcl","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":38627,"ts_process_time":228,"ts_timestamp":"17\/Apr\/2018:08:12:44 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:45.000Z","ISP":"c rcEn.,er reHciulitcanI","cache_result":"TCP_MISS","client_ip":"204.99.48.109","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/uploads\/2018\/04\/TMDuc.jpg","client_user":"-","content_type":"image\/jpeg","device":"Other","dnet":"ceteldf1","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":206,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":141770,"ts_process_time":512,"ts_timestamp":"17\/Apr\/2018:08:12:45 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:47.000Z","ISP":"Ht, eri enaErurcIcc.ciln","cache_result":"TCP_REFRESH_HIT","client_ip":"20.204.32.235","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/uploads\/2018\/03\/f1-13.jpg","client_user":"-","content_type":"image\/jpeg","device":"Other","dnet":"tecfe1dl","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":161573,"ts_process_time":593,"ts_timestamp":"17\/Apr\/2018:08:12:47 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:47.000Z","ISP":"Ei .ne,cHncrterarilccu I","cache_result":"TCP_REFRESH_HIT","client_ip":"224.60.44.234","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/uploads\/2018\/04\/f1-9-177x142.jpg","client_user":"-","content_type":"image\/jpeg","device":"Other","dnet":"1fecldet","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":10410,"ts_process_time":170,"ts_timestamp":"17\/Apr\/2018:08:12:47 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:12:48.000Z","ISP":"irre,n Iec.rciu ntlcHacE","cache_result":"TCP_REFRESH_HIT","client_ip":"68.18.239.120","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/65.0.3325.181 Safari\/537.36","client_url":"\/wp-content\/plugins\/contact-form-7\/images\/ajax-loader.gif","client_user":"-","content_type":"image\/gif","device":"Other","dnet":"fcledet1","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":65.0,"os":"Windows 10","os_name":"Windows 10","querystring":null,"reply_length_bytes":847,"ts_process_time":89,"ts_timestamp":"17\/Apr\/2018:08:12:48 -0000","type":"testdata","ua_name":"Chrome"}
{"#timestamp":"2018-04-17T08:21:23.000Z","ISP":"nnH rGmelbeen iOHzt","cache_result":"TCP_MISS","client_ip":"234.197.117.162","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit\/534.58.2 (KHTML, like Gecko) Version\/5.1.8 Safari\/534.58.2","client_url":"\/Nhin-Thay-Gi-Tu-Mot-Hoi-Nghi.html","client_user":"-","content_type":"text\/html","device":"Other","dnet":"1ecfeldt","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":301,"major":5.0,"os":"Mac OS X","os_name":"Mac OS X","querystring":null,"reply_length_bytes":0,"ts_process_time":523,"ts_timestamp":"17\/Apr\/2018:08:21:23 -0000","type":"testdata","ua_name":"Safari"}
{"#timestamp":"2018-04-17T08:22:03.000Z","ISP":"Tx osoy1bcy dPdx hreah ia nOo ra-et51XsiaPttt","cache_result":"ERR_CLIENT_ABORT","client_ip":"218.202.132.77","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Linux; Android 5.1; A1601 Build\/LMY47I; wv) AppleWebKit\/537.36 (KHTML, like Gecko) Version\/4.0 Chrome\/64.0.3282.137 Mobile Safari\/537.36 [FB_IAB\/FB4A;FBAV\/166.0.0.66.95;]","client_url":"\/bat-binh-voi-toa-an-len-lut-giao-hat-van-hanh-noi-lua-hiep-thong-voi-tu-nhan-luong-tam\/","client_user":"-","content_type":"text\/html","device":"A1601","dnet":"ftee1dlc","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":301,"major":166.0,"os":"Android","os_name":"Android","querystring":null,"reply_length_bytes":17707,"ts_process_time":31255,"ts_timestamp":"17\/Apr\/2018:08:22:03 -0000","type":"testdata","ua_name":"Facebook"}
{"#timestamp":"2018-04-17T08:21:25.000Z","ISP":"ne z briltHmOnHGeen","cache_result":"TCP_MISS","client_ip":"69.10.61.78","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit\/534.58.2 (KHTML, like Gecko) Version\/5.1.8 Safari\/534.58.2","client_url":"\/nhin-thay-gi-tu-mot-hoi-nghi\/","client_user":"-","content_type":"text\/html","device":"Other","dnet":"cfdt1lee","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":5.0,"os":"Mac OS X","os_name":"Mac OS X","querystring":null,"reply_length_bytes":19351,"ts_process_time":1302,"ts_timestamp":"17\/Apr\/2018:08:21:25 -0000","type":"testdata","ua_name":"Safari"}
{"#timestamp":"2018-04-17T08:21:29.000Z","ISP":"gooLLeG lC","cache_result":"TCP_HIT","client_ip":"167.182.156.107","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (compatible; Google-Apps-Script)","client_url":"\/-","client_user":"-","content_type":"text\/html","device":"Other","dnet":"cee1tfld","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":null,"os":"Other","os_name":"Other","querystring":null,"reply_length_bytes":16962,"ts_process_time":0,"ts_timestamp":"17\/Apr\/2018:08:21:29 -0000","type":"testdata","ua_name":"Other"}
{"#timestamp":"2018-04-17T08:21:28.000Z","ISP":"eLLol GCgo","cache_result":"TCP_HIT","client_ip":"207.89.148.171","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (compatible; Google-Apps-Script)","client_url":"\/-","client_user":"-","content_type":"text\/html","device":"Other","dnet":"c1dleeft","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":200,"major":null,"os":"Other","os_name":"Other","querystring":null,"reply_length_bytes":16962,"ts_process_time":0,"ts_timestamp":"17\/Apr\/2018:08:21:28 -0000","type":"testdata","ua_name":"Other"}
{"#timestamp":"2018-04-17T08:28:51.000Z","ISP":"oeClL LgoG","cache_result":"TCP_IMS_HIT","client_ip":"98.217.204.182","client_request_host":"testhost.net","client_request_method":"GET","client_ua":"Mozilla\/5.0 (Linux; Android 6.0.1; Nexus 5X Build\/MMB29P) AppleWebKit\/537.36 (KHTML, like Gecko) Chrome\/41.0.2272.96 Mobile Safari\/537.36 (compatible; Googlebot\/2.1; +http:\/\/www.google.com\/bot.html)","client_url":"\/wp-content\/plugins\/accelerated-mobile-pages\/templates\/design-manager\/design-3\/fonts\/ptserif\/PT_Serif-Web-Regular.ttf","client_user":"-","content_type":"-","device":"Spider","dnet":"efd1etlc","host":"testhost.deflect.ca","http_request_scheme":"http","http_request_version":"HTTP\/1.1","http_response_code":304,"major":2.0,"os":"Android","os_name":"Android","querystring":null,"reply_length_bytes":0,"ts_process_time":0,"ts_timestamp":"17\/Apr\/2018:08:28:51 -0000","type":"testdata","ua_name":"Googlebot"}

With the following toy dataframe:
import pandas as pd
df = pd.DataFrame(
{
"timestamp": [
"2018-04-17T08:12:32.000Z",
"2018-04-17T08:11:33.000Z",
"2018-04-17T08:14:31.000Z",
"2018-04-17T08:25:35.000Z",
"2018-04-17T08:16:36.000Z",
"2018-04-17T08:10:42.000Z",
"2018-04-17T08:18:38.000Z",
"2018-04-17T08:09:29.000Z",
"2018-04-17T08:30:40.000Z",
"2018-04-17T08:21:21.000Z",
],
"value": [9, 2, 3, 4, 7, 8, 1, 2, 0, 3],
}
)
Here is one way to do it by defining a generator function:
def chunk(df, delta_in_min):
"""Helper function.
Args:
df: dataframe to split in chunks.
delta_in_min: size of chunk in minute (at least one).
Yields:
Chunk of input dataframe of the given size.
"""
start = df.index[0]
while True:
if delta_in_min <= 0:
yield df
break
end = start + pd.Timedelta(value=delta_in_min, unit="m")
if end > df.index[-1]:
yield df.loc[(df.index >= start), :]
break
yield df.loc[(df.index >= start) & (df.index < end), :]
start = end
if start > df.index[-1]:
break
And then:
df["timestamp"] = pd.to_datetime(df["timestamp"], infer_datetime_format=True)
df = df.set_index("timestamp").sort_index()
From here, you can call print(next(chunk(df, 2))) repeatedly to get each chunk or use a for loop, like this:
for s in chunk(df, 2):
print(s)
# Output
value
timestamp
2018-04-17 08:09:29+00:00 2
2018-04-17 08:10:42+00:00 8
value
timestamp
2018-04-17 08:11:33+00:00 2
2018-04-17 08:12:32+00:00 9
value
timestamp
2018-04-17 08:14:31+00:00 3
value
timestamp
2018-04-17 08:16:36+00:00 7
value
timestamp
2018-04-17 08:18:38+00:00 1
value
timestamp
2018-04-17 08:21:21+00:00 3
Empty DataFrame
Columns: [value]
Index: []
Empty DataFrame
Columns: [value]
Index: []
value
timestamp
2018-04-17 08:25:35+00:00 4
Empty DataFrame
Columns: [value]
Index: []
value
timestamp
2018-04-17 08:30:40+00:00 0

How to get correct date format from JSON string in Python?

I am trying to get some data from a JSON url using Python and convert in Pandas PD. Everything is working OK. Only there is a column for date. It is coming weired. How can I format it into correct date format? My code is given below:
sym_1 = 'NIFTY'
headers_gen = {"accept-encoding": "gzip, deflate, br",
"accept-language": "en-US,en;q=0.9",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36"}
def PCR(sym_1):
url_pcr = "https://opstra.definedge.com/api/futures/pcr/chart/" + sym_1
req_pcr = requests.get(url_pcr, headers=headers_gen)
text_data_pcr= req_pcr.text
json_dict_pcr= json.loads(text_data_pcr)
df_pcr = pd.DataFrame.from_dict(json_dict_pcr['data'])
print(df_pcr)
return df_pcr

pd.to_datetime(..., unit="ms") fixes things.
I also simplified the requests code a tiny bit and added error handling.
import pandas as pd
import requests
headers_gen = {
"accept-encoding": "gzip, deflate",
"accept-language": "en-US,en;q=0.9",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36",
}
def PCR(sym_1):
req_pcr = requests.get(f"https://opstra.definedge.com/api/futures/pcr/chart/{sym_1}", headers=headers_gen)
req_pcr.raise_for_status()
data = req_pcr.json()
df_pcr = pd.DataFrame.from_dict(data['data'])
df_pcr[0] = pd.to_datetime(df_pcr[0], unit='ms')
return df_pcr
if __name__ == '__main__':
print(PCR('NIFTY'))
outputs
0 1 2 ... 6 7 8
0 2019-04-26 05:30:00 11813.50 1.661348 ... NaN NaN NaN
1 2019-04-30 05:30:00 11791.55 1.587803 ... NaN NaN NaN
2 2019-05-02 05:30:00 11765.40 1.634619 ... NaN NaN NaN
.. ... ... ... ... ... ... ...
735 2022-04-18 00:00:00 17229.60 1.169555 ... 0.963420 0.771757 1.328892
736 2022-04-19 00:00:00 16969.35 1.014768 ... 1.385167 0.980847

sym_1 = 'NIFTY'
headers_gen = {"accept-encoding": "gzip, deflate, br",
"accept-language": "en-US,en;q=0.9",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.54 Safari/537.36"}
def PCR(sym_1):
url_pcr = "https://opstra.definedge.com/api/futures/pcr/chart/" + sym_1
req_pcr = requests.get(url_pcr, headers=headers_gen)
text_data_pcr= req_pcr.text
json_dict_pcr= json.loads(text_data_pcr)
df_pcr = pd.DataFrame.from_dict(json_dict_pcr['data'])
df_pcr[0] = df_pcr[0].apply(lambda x: datetime.utcfromtimestamp(x / 1000).astimezone(pytz.timezone('Asia/Kolkata')))
print(df_pcr)
return df_pcr
Updated to use apply and return datetime instead of string but AKX's answer is much more elegant.
Updated to use IST

Formatting Python beautifulsoup data and remove duplicates first columns values

I have the following snippet that already works however, I wanted to clean up a bit in the formatting by removing some duplicates 1st column data and make it more readable.
from urllib.request import Request, urlopen
from bs4 import BeautifulSoup
import re, random, ctypes
import requests
from time import sleep
url = 'https://bscscan.com/tokentxns'
user_agent_list = [
"header = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:86.0Gecko/20100101 Firefox/86.0'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Safari/605.1.15'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36'}"
]
header = random.choice(user_agent_list)
pausesleep = float(random.randint(10000,30000)) / 10000 #orig
req = requests.get(url,header, timeout=10)
soup = BeautifulSoup(req.content, 'html.parser')
rows = soup.findAll('table')[0].findAll('tr')
for row in rows[1:]:
tds = row.find_all('td')
txnhash = tds[1].text[0:]
age = tds[2].text[0:]
value = tds[7].text[0:]
token = tds[8].text[0:]
link = urljoin(url, tds[8].find('a')['href'])
print (str(txnhash) + " " + str(value) + " " + str(token))
Current Output:
0x70e16e1cbcd30d1c3a2abb03a3d3c43fc324aa794c45b10cd5ef1001e9af0915 899.885819768 TrusterCoin (TSC)
0x70e16e1cbcd30d1c3a2abb03a3d3c43fc324aa794c45b10cd5ef1001e9af0915 0.62679168 Wrapped BNB (WBNB)
0x52d862d3f920370d84039f2dccb40edc7343699310d3436b71738d4176997398 388,214,984,514.909719227 WoofCoin (WOOF)
0x52d862d3f920370d84039f2dccb40edc7343699310d3436b71738d4176997398 0.003 Wrapped BNB (WBNB)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 26.737674146727101117 Binance-Peg ... (BUSD)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 1.251364193609566793 Binance-Peg ... (ADA)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 0.03997685638568537 Binance-Peg ... (ADA)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 0.041171860015645402 Binance-Peg ... (ADA)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 0.089939749761843203 Wrapped BNB (WBNB)
Wanted Improvement:
0x70e16e1cbcd30d1c3a2abb03a3d3c43fc324aa794c45b10cd5ef1001e9af0915 899.885819768 TrusterCoin (TSC)
0.62679168 Wrapped BNB (WBNB)
0x52d862d3f920370d84039f2dccb40edc7343699310d3436b71738d4176997398 388,214,984,514.909719227 WoofCoin (WOOF)
0.003 Wrapped BNB (WBNB)
0x4fe83f2ebad772b4292e81f418a6f54572f7462934358a356787f8d777c58c8b 26.737674146727101117 Binance-Peg ... (BUSD)
1.251364193609566793 Binance-Peg ... (ADA)
0.03997685638568537 Binance-Peg ... (ADA)
0.041171860015645402 Binance-Peg ... (ADA)
0.089939749761843203 Wrapped BNB (WBNB)

Try this:
from urllib.request import Request, urlopen,urljoin
from bs4 import BeautifulSoup
import re, random, ctypes
import requests
from time import sleep
url = 'https://bscscan.com/tokentxns'
user_agent_list = [
"header = {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:86.0Gecko/20100101 Firefox/86.0'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.1.1 Safari/605.1.15'}",
"header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.2; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1667.0 Safari/537.36'}",
"header = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.67 Safari/537.36'}"
]
header = random.choice(user_agent_list)
pausesleep = float(random.randint(10000,30000)) / 10000
req = requests.get(url,header, timeout=10)
soup = BeautifulSoup(req.content, 'html.parser')
rows = soup.findAll('table')[0].findAll('tr')
ne=[]
for row in rows[1:]:
tds = row.find_all('td')
txnhash = tds[1].text[0:]
age = tds[2].text[0:]
value = tds[7].text[0:]
token = tds[8].text[0:]
link = urljoin(url, tds[8].find('a')['href'])
if str(txnhash) not in ne:
ne.append(str(txnhash))
print (str(txnhash),end=" ")
else:# If you want those tab also then. Otherwise remove else
print("\t\t\t",end=" ")
print(str(value) + " " + str(token))
We are creating list of txnhash in ne and then checking everytime if new txnhash is in that list or not.

Download Access Denied

Introduction
Hi! Im trying to download application content-types from Microsoft TLU Delivery. Upon downloading, its always resulted with corrupted files. The downloaded files just have 1kb size, meanwhile the original ones have 200kb+. I already trying to use custom headers and cookies on the Download Module but its still the same. Here's my network connection that i get from chrome console :
Request URL: http://tlu.dl.delivery.mp.microsoft.com/filestreamingservice/files/f7ded4c4-f468-4bdc-96f0-03fd60c5ae81?p1=1604760658&p2=402&p3=2&p4=hcdsxul%2f8qkk8gjjjyomq0ki%2bxwjxulxxeninyf1jqrp3%2bsnzk%2fpwk4dgsnbkfzkzoor4%2bvaixilmxk6r%2bz6%2ba%3d%3d
Request Method: GET
Status Code: 200 OK
Remote Address: 8.241.131.254:80
Referrer Policy: strict-origin-when-cross-origin
#Response Header
Accept-Ranges: bytes
Age: 240667
Cache-Control: public, max-age=17280000
Connection: keep-alive
Content-Disposition: attachment; filename=Microsoft.Services.Store.Engagement_10.0.19011.0_x64__8wekyb3d8bbwe.Appx
Content-Length: 281728
Content-Type: application/octet-stream
Date: Sat, 21 Nov 2020 22:49:40 GMT
ETag: "cP6LQFFTB9bLaXCyN/YHr8kWbqM="
Expires: Wed, 09 Jun 2021 22:49:58 GMT
g: g
Last-Modified: Wed, 30 Jan 2019 14:28:15 GMT
MS-CorrelationId: c93312f7-dc52-41ef-b2d2-9294c1deda19
MS-CV: /24yhmSg0EivGZOl.0.3.8.2.1.0.0.22.2.6.2.1.1.0
MS-RequestId: 9378576c-a791-4636-93bf-7b8ef57c85a3
MSRegion: APAC
Server: Microsoft-IIS/10.0
X-AspNet-Version: 4.0.30319
X-AspNetMvc-Version: 5.2
x-ccc: SG
x-cid: 3
X-MS-Ref-OriginShield: Ref A: 4127806A5D794419ACF51D29DD883F50 Ref B: BAYEDGE0319 Ref C: 2019-01-30T14:32:41Z
X-MSEdge-Ref: Ref A: 6F76857D59174A4BA37F3076DE6671DC Ref B: SJCEDGE0406 Ref C: 2019-01-30T14:32:41Z
X-Powered-By: ASP.NET
#Request header
Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9
Accept-Encoding: gzip, deflate
Accept-Language: en-US,en;q=0.9,id;q=0.8,pt;q=0.7
Connection: keep-alive
Cookie: _ga=GA1.2.282065737.1604285823; MUID=07B92E6562A266AE0A83211366A260EF; AAMC_mscom_0=REGION%7C3; aam_uuid=33272659623977483402062764826775668547; mbox=PC#f8f94dce007c4a3c88788082771689c3.38_0#1668947906|session#af45a09527ce4486be93aff7c2a9adf1#1605704965; _cs_c=0; _cs_id=cd07a4b3-19b2-a8c7-95e4-7b8fd7c286f9.1605703107.1.1605703107.1605703107.1594299326.1639867107380.Lax.0; _uetvid=c457b5101e8a11eb956e2f93309fcf0c; IR_PI=f62c6991-299a-11eb-980c-0abbe301118c%7C1605789507507
DNT: 1
Host: tlu.dl.delivery.mp.microsoft.com
Upgrade-Insecure-Requests: 1
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36
#Query String
p1: 1604760658
p2: 402
p3: 2
p4: hcdsxul/8qkk8gjjjyomq0ki+xwjxulxxeninyf1jqrp3+snzk/pwk4dgsnbkfzkzoor4+vaixilmxk6r+z6+a==
Problematic
Here's several download module that i've try:
from clint import textui
from clint.textui import progress
import urllib.request
import requests
from tqdm import tqdm
from urllib.request import FancyURLopener
from tqdm import tqdm
class DownloadProgressBar(tqdm):
def update_to(self, b=1, bsize=1, tsize=None):
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)
def CookDown(url, filename):
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'}
cookies = {'_ga': 'GA1.2.282065737.1604285823', 'MUID': '07B92E6562A266AE0A83211366A260EF','aam_uuid': '33272659623977483402062764826775668547','AAMC_mscom_0': 'REGION%7C3','_cs_c': '0','_cs_id': 'cd07a4b3-19b2-a8c7-95e4-7b8fd7c286f9.1605703107.1.1605703107.1605703107.1594299326.1639867107380.Lax.0','_uetvid': 'c457b5101e8a11eb956e2f93309fcf0c','IR_PI': 'f62c6991-299a-11eb-980c-0abbe301118c%7C1605789507507','mbox': 'PC#f8f94dce007c4a3c88788082771689c3.38_0#1668947906|session#af45a09527ce4486be93aff7c2a9adf1#1605704965'}
try:
read = requests.get(url, headers=headers, cookies=cookies)
with open(filename, 'wb') as w:
for chunk in read.iter_content(chunk_size=None):
if chunk:
w.write(chunk)
print(filename + ' downloaded successfully!!!')
except urllib.request.URLError as e:
print("Error :%s" % e)
def Cookdone(url, filename):
r = requests.get(url, stream=True)
with open(filename, "wb") as f:
for data in tqdm(response.iter_content()):
f.write(data)
def Cookdown(url, filename):
#headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'}
#cookies = {'_ga': 'GA1.2.282065737.1604285823', 'MUID': '07B92E6562A266AE0A83211366A260EF','aam_uuid': '33272659623977483402062764826775668547','AAMC_mscom_0': 'REGION%7C3','_cs_c': '0','_cs_id': 'cd07a4b3-19b2-a8c7-95e4-7b8fd7c286f9.1605703107.1.1605703107.1605703107.1594299326.1639867107380.Lax.0','_uetvid': 'c457b5101e8a11eb956e2f93309fcf0c','IR_PI': 'f62c6991-299a-11eb-980c-0abbe301118c%7C1605789507507','mbox': 'PC#f8f94dce007c4a3c88788082771689c3.38_0#1668947906|session#af45a09527ce4486be93aff7c2a9adf1#1605704965'}
import shutil
req = requests.get(url, stream=True)
print(req.headers)
with open(filename, "wb") as f:
req.raw.decode_content = False
shutil.copyfileobj(req.raw, f)
def download(url, path):
opener = FancyURLopener({})
opener.version = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'
opener.retrieve(url, path)
def down(url, filename):
proxy = ProxyHandler({})
opener = build_opener(proxy)
opener.addheaders = [('User-Agent','Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_4) AppleWebKit/603.1.30 (KHTML, like Gecko) Version/10.1 Safari/603.1.30')]
install_opener(opener)
urlretrieve(url, filename)
def DownLoad(url, filename):
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'}
r = requests.get(url, allow_redirects=True, headers=headers)
with open(filename, 'wb') as f:
for chunk in r.iter_content(1024):
f.write(chunk)
def DoneLoad():
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36'}
r = requests.get(url, allow_redirects=True, headers=headers)
with open(filename, 'wb') as f:
total_length = int(r.headers.get('content-length'))
for chunk in progress.bar(r.iter_content(chunk_size=1024), expected_size=(total_length/1024) + 1):
if chunk:
f.write(chunk)
f.flush()
def Download(url, path):
hdr = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36' }
req = urllib.request.Request(url, headers=hdr)
urllib.request.urlretrieve(req, path)
def downLoader(url, output_path):
with DownloadProgressBar(unit='B', unit_scale=True,
miniters=1, desc=url.split('/')[-1]) as t:
opener = urllib.request.build_opener()
opener.addheaders = [('User-agent', 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36')]
urllib.request.install_opener(opener)
urllib.request.urlretrieve(url, filename=output_path, reporthook=t.update_to)
def downLoad(url, output_path):
hdr = { 'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36' }
r = requests.get(url, headers=hdr, verify=False, stream=True)
with open(output_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=None):
f.write(chunk)
Here's piece of code to execute every each module :
CookDown('http://tlu.dl.delivery.mp.microsoft.com/filestreamingservice/files/f7ded4c4-f468-4bdc-96f0-03fd60c5ae81?p1=1604760658&p2=402&p3=2&p4=hcdsxul%2f8qkk8gjjjyomq0ki%2bxwjxulxxeninyf1jqrp3%2bsnzk%2fpwk4dgsnbkfzkzoor4%2bvaixilmxk6r%2bz6%2ba%3d%3d','Microsoft.Services.Store.Engagement_10.0.19011.0_x64__8wekyb3d8bbwe.appx')
Using each module, resulted on the same corrupted file. Any answers will hugely appreciated because im stuck and doesnt have any ideas anymore. Please stackoverflow gods...Come here :D

How to get a string formatted JSON into a table

I have the following string formatted JSON data. How can I convert data into a table format in R or Python?
I've tried df = pd.DataFrame(data), but that doesn't work, because data is a string.
data = '{"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20, "CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh", "OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0, "UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36", "ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily", "TimeZone":"E. South America Standard Time","Days":["All"], "Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]}, "IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
Desired Output:
output =
------------------------------------------------------------------
ID | RecordType | CreationTime
048f7de7-81a4-464d-bd6d-df3be3b1e7e8 | 20 | 2019-10-08T12:12:32
Error:
ValueError Traceback (most recent call last)
<ipython-input-26-039b238b38ef> in <module>
----> 1 df = pd.DataFrame(data)
e:\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
483 )
484 else:
--> 485 raise ValueError("DataFrame constructor not properly called!")
486
487 NDFrame.__init__(self, mgr, fastpath=True)
ValueError: DataFrame constructor not properly called!

In Python:
Given data
str.replace true with True
Use ast.literal_eval to convert data from a str to dict
pandas.io.json.json_normalize to convert the json to a pandas dataframe
import pandas as pd
from ast import literal_eval
from pandas.io.json import json_normalize
data = '{"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20, "CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh", "OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0, "UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36", "ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily", "TimeZone":"E. South America Standard Time","Days":["All"], "Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]}, "IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
data = data.replace('true', 'True')
data = literal_eval(data)
{'ActivityId': '4e8b4514-24be-4ba5-a7d3-a69e8cb8229e',
'CreationTime': '2019-10-08T12:12:32',
'Id': '048f7de7-81a4-464d-bd6d-df3be3b1e7e8',
'IsSuccess': True,
'ItemName': 'ASO Daily Statistics',
'Operation': 'SetScheduledRefresh',
'OrganizationId': '39b03722-b836-496a-85ec-850f0957ca6b',
'RecordType': 20,
'Schedules': {'Days': ['All'],
'RefreshFrequency': 'Daily',
'Time': ['07:30:00',
'10:30:00',
'13:30:00',
'16:30:00',
'19:30:00',
'22:30:00'],
'TimeZone': 'E. South America Standard Time'},
'UserAgent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 '
'(KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
'UserType': 0}
Create the dataframe:
df = json_normalize(data)
Id RecordType CreationTime Operation OrganizationId UserType UserAgent ItemName IsSuccess ActivityId Schedules.RefreshFrequency Schedules.TimeZone Schedules.Days Schedules.Time
048f7de7-81a4-464d-bd6d-df3be3b1e7e8 20 2019-10-08T12:12:32 SetScheduledRefresh 39b03722-b836-496a-85ec-850f0957ca6b 0 Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36 ASO Daily Statistics True 4e8b4514-24be-4ba5-a7d3-a69e8cb8229e Daily E. South America Standard Time [All] [07:30:00, 10:30:00, 13:30:00, 16:30:00, 19:30:00, 22:30:00]

You will need the reticulate library: You will need to change all true to True. Look at the code below
a <- 'string = {"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20,
"CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh",
"OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0,
"UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36",
"ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily",
"TimeZone":"E. South America Standard Time","Days":["All"],
"Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]},
"IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
data.frame(reticulate::py_eval(gsub('true','True',sub('.*=\\s+','',a))))

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