In [3]: records =[json.loads(line) for line in open(path)] # line JSON python # records JSON In [16]: len(records) Out[16]: 3560 In [17]: records[0] O
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- しょうすけ にかどり
- 5 years ago
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1 Chapter 1 json 2) path - path='-----.txt' 3) open(path).readline() 4) list records = [json.loads(x) for x in open(path)] 5) records[i]: i+1, records[i]['x']: i 'x' 6) time_zones = [y['tz'] for y in records if 'tz' in y] # y 'tz' tz tz >>if 'tz' in y In [1]: import json path =' txt' #1.6M EXECUTION/ Chapter 2 page 16 In [2]: #JSON: JavaScript Object Notation WEB ERROR: Line magic function `%json` not found. In [2]: open(path).readline()#json Out[2]: '{ "a": "Mozilla\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\/ (KHTML, like Gecko) Chrome\\/ Safari\\/535.11", "c": "US", "nk": 1, "tz": "America\\/New_York", "gr": "MA", "g": "A6qOVH", "h ": "wflqtf", "l": "orofrog", "al": "en-us,en;q=0.8", "hh": "1.usa.go v", "r": " wflqtf", "u": " "t": , "hc": , "cy": "Danvers", "ll": [ , ] }\n'
2 In [3]: records =[json.loads(line) for line in open(path)] # line JSON python # records JSON In [16]: len(records) Out[16]: 3560 In [17]: records[0] Out[17]: {'a': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/ (KHTML, like Gecko) Chrome/ Safari/535.11', 'al': 'en-us,en;q=0.8', 'c': 'US', 'cy': 'Danvers', 'g': 'A6qOVH', 'gr': 'MA', 'h': 'wflqtf', 'hc': , 'hh': '1.usa.gov', 'l': 'orofrog', 'll': [ , ], 'nk': 1, 'r': ' 't': , 'tz': 'America/New_York', 'u': ' JSON Python dict obj JSON module In [4]: records[0]['tz'] #timezone Out[4]: 'America/New_York' In [5]: time_zones = [rec['tz'] for rec in records if 'tz' in rec] # rec
3 {'': 521, 'Africa/Cairo': 3, In [6]: time_zones[:10]# 'tz' Out[6]: ['America/New_York', 'America/Denver', 'America/New_York', 'America/Sao_Paulo', 'America/New_York', 'America/New_York', 'Europe/Warsaw', '', '', ''] get_counts(list) 1) counts = {} 2) for x in list:, if x in counts: counts[x] +=1, else: counts[x] = 1, return counts else 1 +=1 counts[x] +1 >>>> {'x1':1, 'x2':2,... } In [7]: def get_counts(sequence): counts ={} for x in sequence: if x in counts: counts[x]+=1 else: counts[x] = 1 return counts #tz In [8]: get_counts(time_zones) Out[8]:
4 'Africa/Casablanca': 1, 'Africa/Ceuta': 2, 'Africa/Johannesburg': 1, 'Africa/Lusaka': 1, 'America/Anchorage': 5, 'America/Argentina/Buenos_Aires': 1, 'America/Argentina/Cordoba': 1, 'America/Argentina/Mendoza': 1, 'America/Bogota': 3, 'America/Caracas': 1, 'America/Chicago': 400, 'America/Chihuahua': 2, 'America/Costa_Rica': 1, 'America/Denver': 191, 'America/Edmonton': 6, 'America/Guayaquil': 2, 'America/Halifax': 4, 'America/Indianapolis': 20, 'America/La_Paz': 1, 'America/Lima': 1, 'America/Los_Angeles': 382, 'America/Managua': 3, 'America/Mazatlan': 1, 'America/Mexico_City': 15, 'America/Monterrey': 1, 'America/Montevideo': 1, 'America/Montreal': 9, 'America/New_York': 1251, 'America/Phoenix': 20, 'America/Puerto_Rico': 10, 'America/Rainy_River': 25, 'America/Recife': 2, 'America/Santo_Domingo': 1, 'America/Sao_Paulo': 33, 'America/St_Kitts': 1, 'America/Tegucigalpa': 1, 'America/Vancouver': 12, 'America/Winnipeg': 4, 'Asia/Amman': 2, 'Asia/Bangkok': 6, 'Asia/Beirut': 4, 'Asia/Calcutta': 9, 'Asia/Dubai': 4, 'Asia/Harbin': 3, 'Asia/Hong_Kong': 10, 'Asia/Istanbul': 9, 'Asia/Jakarta': 3, 'Asia/Jerusalem': 3, 'Asia/Karachi': 3, 'Asia/Kuala_Lumpur': 3, 'Asia/Kuching': 1, 'Asia/Manila': 1, 'Asia/Nicosia': 1, 'Asia/Novosibirsk': 1, 'Asia/Pontianak': 1, 'Asia/Riyadh': 1, 'Asia/Seoul': 5,
5 'Asia/Tokyo': 37, 'Asia/Yekaterinburg': 1, 'Australia/NSW': 6, 'Australia/Queensland': 1, 'Chile/Continental': 6, 'Europe/Amsterdam': 22, 'Europe/Athens': 6, 'Europe/Belgrade': 2, 'Europe/Berlin': 28, 'Europe/Bratislava': 3, 'Europe/Brussels': 4, 'Europe/Bucharest': 4, 'Europe/Budapest': 5, 'Europe/Copenhagen': 5, 'Europe/Dublin': 3, 'Europe/Helsinki': 10, 'Europe/Lisbon': 8, 'Europe/Ljubljana': 1, 'Europe/London': 74, 'Europe/Madrid': 35, 'Europe/Malta': 2, 'Europe/Moscow': 10, 'Europe/Oslo': 10, 'Europe/Paris': 14, 'Europe/Prague': 10, 'Europe/Riga': 2, 'Europe/Rome': 27, 'Europe/Skopje': 1, 'Europe/Sofia': 1, 'Europe/Stockholm': 14, 'Europe/Uzhgorod': 1, 'Europe/Vienna': 6, 'Europe/Vilnius': 2, 'Europe/Volgograd': 1, 'Europe/Warsaw': 16, 'Europe/Zurich': 4, 'Pacific/Auckland': 11, 'Pacific/Honolulu': 36} In [9]: counts = get_counts(time_zones) In [10]: counts['america/new_york'] #counts 'America/New_York' Out[10]: 1251
6 In [11]: len(time_zones) Out[11]: 3440 top_counts(count_dict, n=10) (top_counts(dict obj, n=10)) top_counts(tupple list) 10 list.items() list.sort() counts.sort()[-10:] [:10]
7 In [12]: counts.items() Out[12]: dict_items([('', 521), ('Asia/Istanbul', 9), ('America/Montevideo', 1), ('Chile/Continental', 6), ('Asia/Manila', 1), ('America/Rainy_Ri ver', 25), ('Asia/Novosibirsk', 1), ('Europe/Uzhgorod', 1), ('Pacifi c/honolulu', 36), ('America/Sao_Paulo', 33), ('America/La_Paz', 1), ('Europe/Paris', 14), ('Africa/Ceuta', 2), ('America/Mazatlan', 1), ('Europe/Helsinki', 10), ('Asia/Nicosia', 1), ('Asia/Bangkok', 6), ( 'America/Anchorage', 5), ('Africa/Lusaka', 1), ('Europe/Stockholm', 14), ('America/Mexico_City', 15), ('America/Costa_Rica', 1), ('Europ e/belgrade', 2), ('America/Puerto_Rico', 10), ('America/New_York', 1 251), ('Africa/Casablanca', 1), ('Europe/Madrid', 35), ('America/Chi cago', 400), ('America/Recife', 2), ('Asia/Yekaterinburg', 1), ('Ame rica/guayaquil', 2), ('Africa/Johannesburg', 1), ('America/Denver', 191), ('America/Caracas', 1), ('Europe/Warsaw', 16), ('Europe/Athens ', 6), ('America/St_Kitts', 1), ('America/Argentina/Buenos_Aires', 1 ), ('Europe/Bucharest', 4), ('Europe/Amsterdam', 22), ('Asia/Riyadh', 1), ('America/Tegucigalpa', 1), ('America/Argentina/Cordoba', 1), ('America/Edmonton', 6), ('Europe/Sofia', 1), ('Europe/Moscow', 10), ('Europe/Dublin', 3), ('Europe/Brussels', 4), ('Europe/Malta', 2), ( 'Asia/Calcutta', 9), ('Europe/London', 74), ('Europe/Riga', 2), ('Am erica/phoenix', 20), ('Europe/Zurich', 4), ('Europe/Lisbon', 8), ('A sia/jakarta', 3), ('America/Halifax', 4), ('America/Vancouver', 12), ('Asia/Pontianak', 1), ('Europe/Berlin', 28), ('America/Chihuahua', 2), ('America/Winnipeg', 4), ('Europe/Skopje', 1), ('Asia/Amman', 2), ('Africa/Cairo', 3), ('America/Indianapolis', 20), ('Asia/Seoul', 5), ('Asia/Jerusalem', 3), ('Europe/Budapest', 5), ('Australia/Queen sland', 1), ('Asia/Beirut', 4), ('Asia/Kuala_Lumpur', 3), ('America/ Lima', 1), ('America/Monterrey', 1), ('Europe/Copenhagen', 5), ('Asi a/karachi', 3), ('Europe/Ljubljana', 1), ('Asia/Hong_Kong', 10), ('E urope/volgograd', 1), ('America/Montreal', 9), ('Australia/NSW', 6), ('America/Santo_Domingo', 1), ('Europe/Rome', 27), ('Asia/Tokyo', 37 ), ('Europe/Vienna', 6), ('Asia/Harbin', 3), ('America/Managua', 3), ('Europe/Bratislava', 3), ('Europe/Oslo', 10), ('Asia/Kuching', 1), ('Pacific/Auckland', 11), ('America/Argentina/Mendoza', 1), ('Europe /Vilnius', 2), ('America/Bogota', 3), ('America/Los_Angeles', 382), ('Europe/Prague', 10), ('Asia/Dubai', 4)]) In [13]: def top_counts(count_dict,n=10): value_key_pairs =[(count,tz) for tz, count in count_dict.items()]#(count, tz) value_key_pairs.sort()# return value_key_pairs[-n:]# #count count tz (count, tz)
8 In [14]: top_counts(counts) Out[14]: [(33, 'America/Sao_Paulo'), (35, 'Europe/Madrid'), (36, 'Pacific/Honolulu'), (37, 'Asia/Tokyo'), (74, 'Europe/London'), (191, 'America/Denver'), (382, 'America/Los_Angeles'), (400, 'America/Chicago'), (521, ''), (1251, 'America/New_York')] JSON (NewYork ) NY pandas pandas In [15]: from pandas import DataFrame, Series In [16]: import pandas as pd import numpy as np In [17]: frame = DataFrame(records)
9 In [38]: frame Out[38]: _heartbeat_ a al c cy g gr 0 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... en-us,en;q=0.8 US Danvers A6qOVH MA 1 NaN GoogleMaps/RochesterNY NaN US Provo mwszks UT 2 NaN 3 NaN 4 NaN In [18]: Mozilla/4.0 (compatible; MSIE 8.0; Windows NT... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... en-us US Washington xxr3qb DC pt-br BR Braz zcalwp 27 en-us,en;q=0.8 US Shrewsbury 9b6kNl MA frame['tz'][:10] #frame['tz'] (Series) # Series value_counts Out[18]: 0 America/New_York 1 America/Denver 2 America/New_York 3 America/Sao_Paulo 4 America/New_York 5 America/New_York 6 Europe/Warsaw Name: tz, dtype: object In [23]: tz_counts = frame.tz.value_counts()[:10]#list['x'].value_counts() #tz_counts = frame['tz'].value_counts() pandas DataFrame(records)['tz'].value_counts() [:10] pandas tz
10 In [25]: tz_counts Out[25]: America/New_York America/Chicago 400 America/Los_Angeles 382 America/Denver 191 Europe/London 74 Asia/Tokyo 37 Pacific/Honolulu 36 Europe/Madrid 35 America/Sao_Paulo 33 Name: tz, dtype: int64 In [26]: clean_tz =frame['tz'].fillna('missing')#timezone missing Na In [27]: clean_tz[clean_tz == '']='Unknown'#timezone Unknown In [28]: tz_counts = clean_tz.value_counts() In [29]: tz_counts[:10] Out[29]: America/New_York 1251 Unknown 521 America/Chicago 400 America/Los_Angeles 382 America/Denver 191 Missing 120 Europe/London 74 Asia/Tokyo 37 Pacific/Honolulu 36 Europe/Madrid 35 Name: tz, dtype: int64
11 In [30]: #Graph import matplotlib.pyplot as plt %matplotlib inline tz_counts[:10].plot(kind='barh',rot=0) Out[30]: <matplotlib.axes._subplots.axessubplot at 0x117d641d0> tz In [31]: frame['a'][50] Out[31]: 'Mozilla/5.0 (Windows NT 5.1; rv:10.0.2) Gecko/ Firefox/10.0.2' In [32]: results = Series([x.split()[0] for x in frame.a.dropna()]) # x[0] M,G,M,M,M x.split()[0] #x[0] 1 Series[list one parametar]
12 In [33]: results[:5]#series Out[33]: 0 Mozilla/5.0 1 GoogleMaps/RochesterNY 2 Mozilla/4.0 3 Mozilla/5.0 4 Mozilla/5.0 dtype: object In [34]: results.value_counts()[:8]#value_count sort Out[34]: Mozilla/ Mozilla/ GoogleMaps/RochesterNY 121 Opera/ TEST_INTERNET_AGENT 24 GoogleProducer 21 Mozilla/6.0 5 BlackBerry8520/ dtype: int64 In [35]: cframe = frame[frame.a.notnull()] # frame 3543 a NaN In [36]: cframe Out[36]: _heartbeat_ a al c cy g gr 0 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... en-us,en;q=0.8 US Danvers A6qOVH MA 1 NaN GoogleMaps/RochesterNY NaN US Provo mwszks UT 2 NaN 3 NaN 4 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT... Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)... Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... en-us US Washington xxr3qb DC pt-br BR Braz zcalwp 27 en-us,en;q=0.8 US Shrewsbury 9b6kNl MA
13 In [37]: # Windows operating_system = np.where(cframe['a'].str.contains('windows'),'windows','not Wi In [38]: Series(operating_system[:5])#operating_system array (np) Out[38]: 0 Windows 1 Not Windows 2 Windows 3 Not Windows 4 Windows dtype: object In [39]: by_tz_os = cframe.groupby(['tz',operating_system]) In [44]: agg_counts = by_tz_os.size().fillna(0) In [45]: agg_counts[:10] Out[45]: tz Not Windows 245 Windows 276 Africa/Cairo Windows 3 Africa/Casablanca Windows 1 Africa/Ceuta Windows 2 Africa/Johannesburg Windows 1 Africa/Lusaka Windows 1 America/Anchorage Not Windows 4 Windows 1 America/Argentina/Buenos_Aires Not Windows 1 dtype: int64 In [49]: agg_counts = by_tz_os.size().unstack().fillna(0)#unstack() In [50]: agg_counts Out[50]: Not Windows Windows
14 tz Africa/Cairo Africa/Casablanca Africa/Ceuta Africa/Johannesburg Africa/Lusaka America/Anchorage America/Argentina/Buenos_Aires America/Argentina/Cordoba America/Argentina/Mendoza America/Bogota America/Caracas America/Chicago America/Chihuahua America/Costa_Rica America/Denver America/Edmonton America/Guayaquil America/Halifax America/Indianapolis America/La_Paz America/Lima America/Los_Angeles America/Managua America/Mazatlan America/Mexico_City America/Monterrey America/Montevideo America/Montreal America/New_York Europe/Berlin
15 Europe/Bratislava Europe/Brussels Europe/Bucharest Europe/Budapest Europe/Copenhagen Europe/Dublin Europe/Helsinki Europe/Lisbon Europe/Ljubljana Europe/London Europe/Madrid Europe/Malta Europe/Moscow Europe/Oslo Europe/Paris Europe/Prague Europe/Riga Europe/Rome Europe/Skopje Europe/Sofia Europe/Stockholm Europe/Uzhgorod Europe/Vienna Europe/Vilnius Europe/Volgograd Europe/Warsaw Europe/Zurich Pacific/Auckland Pacific/Honolulu rows 2 columns In [51]: indexer = agg_counts.sum(0).argsort()
16 In [54]: indexer[:10] Out[54]: Not Windows 0 Windows 1 dtype: int64 In [58]: indexer = agg_counts.sum(1).argsort() In [59]: indexer[:10] Out[59]: tz 24 Africa/Cairo 20 Africa/Casablanca 21 Africa/Ceuta 92 Africa/Johannesburg 87 Africa/Lusaka 53 America/Anchorage 54 America/Argentina/Buenos_Aires 57 America/Argentina/Cordoba 26 America/Argentina/Mendoza 55 dtype: int64 In [60]: indexer[:50] Out[60]: tz 24 Africa/Cairo 20 Africa/Casablanca 21 Africa/Ceuta 92 Africa/Johannesburg 87 Africa/Lusaka 53 America/Anchorage 54 America/Argentina/Buenos_Aires 57 America/Argentina/Cordoba 26 America/Argentina/Mendoza 55 America/Bogota 62 America/Caracas 34 America/Chicago 60 America/Chihuahua 36 America/Costa_Rica 37 America/Denver 27 America/Edmonton 76 America/Guayaquil 56
17 America/Halifax 89 America/Indianapolis 2 America/La_Paz 4 America/Lima 5 America/Los_Angeles 7 America/Managua 8 America/Mazatlan 9 America/Mexico_City 86 America/Monterrey 11 America/Montevideo 14 America/Montreal 52 America/New_York 84 America/Phoenix 17 America/Puerto_Rico 91 America/Rainy_River 40 America/Recife 66 America/Santo_Domingo 13 America/Sao_Paulo 33 America/St_Kitts 3 America/Tegucigalpa 79 America/Vancouver 51 America/Winnipeg 45 Asia/Amman 48 Asia/Bangkok 50 Asia/Beirut 23 Asia/Calcutta 73 Asia/Dubai 10 Asia/Harbin 1 Asia/Hong_Kong 68 Asia/Istanbul 49 Asia/Jakarta 69 Asia/Jerusalem 70 dtype: int64 In [61]: count_subset = agg_counts.take(indexer)[-10:]
![In [62]: count_subset Out[62]: Not Windows Windows tz America/Sao_Paulo 13.0 20.0 Europe/Madrid 16.0 19.0 Pacific/Honolulu 0.0 36.0 Asia/Tokyo 2.0 35.0 Europe/London 43.0 31.0 America/Denver 132.0 59.](/docs-images/91/106733108/images/18-1.jpg "0 America/Los_Angeles 130.0 252.0 America/Chicago 115.0 285.0 245.0 276.0 America/New_York 339.0 912.0 In [63]: count_subset.plot(kind='barh',stacked = True) Out[63]: <matplotlib.axes._subplots.")
18 In [62]: count_subset Out[62]: Not Windows Windows tz America/Sao_Paulo Europe/Madrid Pacific/Honolulu Asia/Tokyo Europe/London America/Denver America/Los_Angeles America/Chicago America/New_York In [63]: count_subset.plot(kind='barh',stacked = True) Out[63]: <matplotlib.axes._subplots.axessubplot at 0x11b062630> In [64]: normed_subset = count_subset.div(count_subset.sum(1),axis = 0)
![In [65]: normed_subset.plot(kind='barh',stacked = True) Out[65]: <matplotlib.axes._subplots.axessubplot at 0x11b0e2d68> 2.3 p35 In [66]:!head -n 10 yob1880.](/docs-images/91/106733108/images/19-0.jpg "txt # 10 Mary,F,7065 Anna,F,2604 Emma,F,2003 Elizabeth,F,1939 Minnie,F,1746 Margaret,F,1578 Ida,F,1472 Alice,F,1414 Bertha,F,1320 Sarah,F,1288 In [67]: import pandas as pd In [68]: names1881 = pd.")
19 In [65]: normed_subset.plot(kind='barh',stacked = True) Out[65]: <matplotlib.axes._subplots.axessubplot at 0x11b0e2d68> 2.3 p35 In [66]:!head -n 10 yob1880.txt # 10 Mary,F,7065 Anna,F,2604 Emma,F,2003 Elizabeth,F,1939 Minnie,F,1746 Margaret,F,1578 Ida,F,1472 Alice,F,1414 Bertha,F,1320 Sarah,F,1288 In [67]: import pandas as pd In [68]: names1881 = pd.read_csv('names/yob1881.txt',names=['name','sex','births']) # cd EXECUTION/ names In [64]: names1881 Out[64]:
20 name sex births 0 Mary F Anna F Emma F Elizabeth F Margaret F Minnie F Ida F Annie F Bertha F Alice F Clara F Sarah F Ella F Nellie F Grace F Florence F Martha F Cora F Laura F Carrie F Maude F Bessie F Mabel F Gertrude F Ethel F Jennie F Edith F Hattie F Mattie F Julia F Mercer M 5
21 1906 Monte M Montgomery M Nolan M Okey M Orley M Page M Philo M Primus M Prosper M Pryor M Rene M Robin M Roll M Rolland M Seward M Shannon M Talmage M Urban M Vaughn M Verner M Waverly M Webster M Weldon M Wells M Wiliam M Wilton M Wing M Wood M Wright M rows 3 columns
22 In [69]: names1881.groupby('sex').births.sum()#sex Out[69]: sex F M Name: births, dtype: int64 In [70]: years=range(1880,2011) pieces =[] columns=['name','sex','births'] #'names/yob1881.txt',names=['name','sex','births'],cd EXECUTION/ names # yob.year.txt 1881 'yob%d.txt' % year #years = range(1880,2011) for year in years: path = 'names/yob%d.txt'%year frame = pd.read_csv(path,names=columns) frame['year'] = year pieces.append(frame) #pieces #year names = pd.concat(pieces,ignore_index=true) In [72]: #frame? length name, sex, briths, year In [67]: #pd.concat ingore_index=true In [71]: total_births = names.groupby('year').births.sum() #pivot_table
![In [72]: total_births.tail() Out[72]: year 2006 3946702 2007 3986130 2008 3915955 2009 3801002 2010 3657392 Name: births, dtype: int64 In [73]: import matplotlib.](/docs-images/91/106733108/images/23-1.jpg "pyplot as plt %matplotlib inline total_births.plot(title='total births by sex and year') plt.show() In [74]: def add_prop(group): births = group.births.astype(float)#integer group['prop'] = births/births.")
23 In [72]: total_births.tail() Out[72]: year Name: births, dtype: int64 In [73]: import matplotlib.pyplot as plt %matplotlib inline total_births.plot(title='total births by sex and year') plt.show() In [74]: def add_prop(group): births = group.births.astype(float)#integer group['prop'] = births/births.sum() return group names = names.groupby(['year','sex']).apply(add_prop) In [78]: names Out[78]: name sex births year prop
24 0 Mary F Anna F Emma F Elizabeth F Minnie F Margaret F Ida F Alice F Bertha F Sarah F Annie F Clara F Ella F Florence F Cora F Martha F Laura F Nellie F Grace F Carrie F Maude F Mabel F Bessie F Jennie F Gertrude F Julia F Hattie F Edith F Mattie F Rose F Zaviyon M Zaybrien M
25 Zayshawn M Zayyan M Zeal M Zealan M Zecharia M Zeferino M Zekariah M Zeki M Zeriah M Zeshan M Zhyier M Zildjian M Zinn M Zishan M Ziven M Zmari M Zoren M Zuhaib M Zyeire M Zygmunt M Zykerion M Zylar M Zylin M Zymaire M Zyonne M Zyquarius M Zyran M Zzyzx M rows 5 columns
26 In [75]: def get_top1000(group): return group.sort_index(by='births',ascending=false)[:1000] grouped = names.groupby(['year','sex']) top1000 = grouped.apply(get_top1000) /Users/junyamamoto/anaconda/lib/python3.5/site-packages/ipykernel/ main.py:2: FutureWarning: by argument to sort_index is deprecated, pls use.sort_values(by=...) from ipykernel import kernelapp as app In [76]: get_top1000(names) /Users/junyamamoto/anaconda/lib/python3.5/site-packages/ipykernel/ main.py:2: FutureWarning: by argument to sort_index is deprecated, pls use.sort_values(by=...) from ipykernel import kernelapp as app Out[76]: name sex births year prop Linda F Linda F James M Michael M Robert M Linda F Michael M Michael M James M Michael M Michael M John M James M James M James M Michael M James M Robert M James M
27 Robert M Charles M Robert M David M James M Robert M David M James M Robert M Michael M Michael M Michael M Robert M Jeffrey M Joseph M Cynthia F John M Joseph M Jeffrey M Andrew M Joseph M Joseph M Joseph M Joseph M Robert M Gary M Nancy F Matthew M Charles M Brian M Michael M Jason M Ronald M
28 Charles M Ryan M Daniel M Joseph M Donna F Joseph M John M Karen F rows 5 columns In [77]: top1000 Out[77]: name sex births year prop year sex 0 Mary F Anna F Emma F Elizabeth F Minnie F Margaret F Ida F Alice F Bertha F Sarah F Annie F Clara F Ella F Florence F F 14 Cora F Martha F Laura F Nellie F
29 18 Grace F Carrie F Maude F Mabel F Bessie F Jennie F Gertrude F Julia F Hattie F Edith F Mattie F Rose F Yair M Talan M Keyon M Kael M Demarion M Gibson M Reagan M Cristofer M Daylen M Jordon M Dashawn M Masen M Rowen M Yousef M M Thaddeus M Kadin M Dillan M Clarence M Slade M Clinton M Sheldon M
30 Keshawn M Menachem M Joziah M Bailey M Camilo M Destin M Jaquan M Jaydan M Maxton M rows 5 columns In [78]: boys=top1000[top1000.sex == 'M'] In [79]: girls = top1000[top1000.sex == 'F'] In [80]: total_births = top1000.pivot_table('births', 'year', 'name', aggfunc = sum) In [81]: total_births Out[81]: name Aaden Aaliyah Aarav Aaron Aarush Ab Abagail Abb Abbey Abbie... year 1880 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 94.0 NaN NaN NaN NaN NaN NaN NaN NaN 85.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 97.0 NaN NaN NaN NaN NaN NaN NaN NaN 88.0 NaN 6.0 NaN NaN NaN NaN NaN NaN 86.0 NaN NaN NaN NaN NaN NaN NaN NaN 78.0 NaN NaN NaN NaN NaN NaN NaN NaN 90.0 NaN NaN NaN NaN NaN
31 1889 NaN NaN NaN 85.0 NaN NaN NaN NaN NaN NaN NaN NaN 96.0 NaN NaN NaN 6.0 NaN NaN NaN NaN 69.0 NaN NaN NaN NaN NaN NaN NaN NaN 95.0 NaN NaN NaN NaN NaN NaN NaN NaN 81.0 NaN NaN NaN NaN NaN NaN NaN NaN 79.0 NaN NaN NaN NaN NaN NaN NaN NaN 94.0 NaN NaN NaN NaN NaN NaN NaN NaN 69.0 NaN NaN NaN NaN NaN NaN NaN NaN 87.0 NaN NaN NaN NaN NaN NaN NaN NaN 89.0 NaN NaN NaN NaN NaN NaN NaN NaN 71.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 80.0 NaN NaN NaN NaN NaN NaN NaN NaN 78.0 NaN NaN NaN NaN NaN NaN NaN NaN 93.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 96.0 NaN NaN NaN NaN NaN NaN NaN NaN 96.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
32 1993 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN rows 6868 columns In [82]: subset = total_births[['john','harry','mary','marilyn']]
33 In [83]: subset.plot() Out[83]: <matplotlib.axes._subplots.axessubplot at 0x11b36d208> In [85]: table = top1000.pivot_table('prop','year','sex',aggfunc=sum) In [87]: table.plot(title='top1000 over total births') Out[87]: <matplotlib.axes._subplots.axessubplot at 0x124abc5c0> In [ ]:
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