一、数据导入

In [144]: 
#读取数据
import matplotlib.pyplot as plt
%matplotlib notebook
import seaborn as sns #要注意的是一旦导入了seaborn，matplotlib的默认作图风格就会被覆盖成seaborn的格式
import pandas
users=pandas.read_csv("./train_users_2.csv/train_users_2.csv")
查看数据信息
In [145]: 
users.info() # 查看数据整体信息
users.head() # 查看前5个数据信息

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null object
timestamp_first_active     213451 non-null int64
date_first_booking         88908 non-null object
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: float64(1), int64(2), object(13)
memory usage: 15.5+ MB
Out[145]:
  id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 20090319043255 NaN -unknown- NaN facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 20090523174809 NaN MALE 38.0 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 20090609231247 2010-08-02 FEMALE 56.0 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 20091031060129 2012-09-08 FEMALE 42.0 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 20091208061105 2010-02-18 -unknown- 41.0 basic 0 en direct direct untracked Web Mac Desktop Chrome US

二、格式转换

转换时间
In [146]: 
users["date_account_created"] = pandas.to_datetime(users["date_account_created"],format="%Y-%m-%d")
users["timestamp_first_active"] = pandas.to_datetime(users["timestamp_first_active"],format="%Y%m%d%H%M%S")
users["date_first_booking"] = pandas.to_datetime(users["date_first_booking"],format="%Y-%m-%d")
users.info()
users.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 17.1+ MB
Out[146]:
  id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 2009-03-19 04:32:55 NaT -unknown- NaN facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 2009-05-23 17:48:09 NaT MALE 38.0 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 2009-06-09 23:12:47 2010-08-02 FEMALE 56.0 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 2009-10-31 06:01:29 2012-09-08 FEMALE 42.0 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 2009-12-08 06:11:05 2010-02-18 -unknown- 41.0 basic 0 en direct direct untracked Web Mac Desktop Chrome US
转换数字
In [147]: 
users["age"] = users["age"].astype(dtype='str',errors="ignore")
users.info()
users.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        213451 non-null object
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), int64(1), object(12)
memory usage: 16.3+ MB
Out[147]:
  id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 2009-03-19 04:32:55 NaT -unknown- nan facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 2009-05-23 17:48:09 NaT MALE 38.0 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 2009-06-09 23:12:47 2010-08-02 FEMALE 56.0 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 2009-10-31 06:01:29 2012-09-08 FEMALE 42.0 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 2009-12-08 06:11:05 2010-02-18 -unknown- 41.0 basic 0 en direct direct untracked Web Mac Desktop Chrome US
In [148]: 
users["age"] = users["age"].astype(dtype='float',errors="ignore")
users.info()
users.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 17.1+ MB
Out[148]:
  id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 2009-03-19 04:32:55 NaT -unknown- NaN facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 2009-05-23 17:48:09 NaT MALE 38.0 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 2009-06-09 23:12:47 2010-08-02 FEMALE 56.0 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 2009-10-31 06:01:29 2012-09-08 FEMALE 42.0 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 2009-12-08 06:11:05 2010-02-18 -unknown- 41.0 basic 0 en direct direct untracked Web Mac Desktop Chrome US

三、缺失处理

1.直接丢弃
In [149]: 
users["age"].dropna()
Out[149]: 
1         38.0
2         56.0
3         42.0
4         41.0
6         46.0
7         47.0
8         50.0
9         46.0
10        36.0
11        47.0
13        37.0
14        36.0
15        33.0
17        31.0
19        29.0
21        30.0
22        40.0
24        40.0
25        26.0
27        32.0
28        35.0
29        37.0
30        42.0
31        31.0
32        31.0
33        29.0
34        59.0
35        49.0
36        31.0
37        30.0
          ... 
213390    30.0
213391    29.0
213393    26.0
213395    33.0
213398    39.0
213400    53.0
213401    45.0
213402    32.0
213403    27.0
213405    23.0
213406    35.0
213407    21.0
213408    69.0
213409    28.0
213410    33.0
213412    50.0
213415    55.0
213417    46.0
213423    20.0
213424    32.0
213425    30.0
213430    19.0
213432    31.0
213439    43.0
213440    24.0
213441    34.0
213443    36.0
213445    23.0
213446    32.0
213448    32.0
Name: age, Length: 125461, dtype: float64
2.填充数值（均值、中位数、众数或者经验数值）
In [150]: 
users["age"].describe()
Out[150]: 
count    125461.000000
mean         49.668335
std         155.666612
min           1.000000
25%          28.000000
50%          34.000000
75%          43.000000
max        2014.000000
Name: age, dtype: float64
In [151]: 
users.fillna(users["age"].mean())  # 填充平均值
Out[151]:
  id date_account_created timestamp_first_active date_first_booking gender age signup_method signup_flow language affiliate_channel affiliate_provider first_affiliate_tracked signup_app first_device_type first_browser country_destination
0 gxn3p5htnn 2010-06-28 2009-03-19 04:32:55 1970-01-01 00:00:00.000000049 -unknown- 49.668335 facebook 0 en direct direct untracked Web Mac Desktop Chrome NDF
1 820tgsjxq7 2011-05-25 2009-05-23 17:48:09 1970-01-01 00:00:00.000000049 MALE 38.000000 facebook 0 en seo google untracked Web Mac Desktop Chrome NDF
2 4ft3gnwmtx 2010-09-28 2009-06-09 23:12:47 2010-08-02 00:00:00.000000000 FEMALE 56.000000 basic 3 en direct direct untracked Web Windows Desktop IE US
3 bjjt8pjhuk 2011-12-05 2009-10-31 06:01:29 2012-09-08 00:00:00.000000000 FEMALE 42.000000 facebook 0 en direct direct untracked Web Mac Desktop Firefox other
4 87mebub9p4 2010-09-14 2009-12-08 06:11:05 2010-02-18 00:00:00.000000000 -unknown- 41.000000 basic 0 en direct direct untracked Web Mac Desktop Chrome US
5 osr2jwljor 2010-01-01 2010-01-01 21:56:19 2010-01-02 00:00:00.000000000 -unknown- 49.668335 basic 0 en other other omg Web Mac Desktop Chrome US
6 lsw9q7uk0j 2010-01-02 2010-01-02 01:25:58 2010-01-05 00:00:00.000000000 FEMALE 46.000000 basic 0 en other craigslist untracked Web Mac Desktop Safari US
7 0d01nltbrs 2010-01-03 2010-01-03 19:19:05 2010-01-13 00:00:00.000000000 FEMALE 47.000000 basic 0 en direct direct omg Web Mac Desktop Safari US
8 a1vcnhxeij 2010-01-04 2010-01-04 00:42:11 2010-07-29 00:00:00.000000000 FEMALE 50.000000 basic 0 en other craigslist untracked Web Mac Desktop Safari US
9 6uh8zyj2gn 2010-01-04 2010-01-04 02:37:58 2010-01-04 00:00:00.000000000 -unknown- 46.000000 basic 0 en other craigslist omg Web Mac Desktop Firefox US
10 yuuqmid2rp 2010-01-04 2010-01-04 19:42:51 2010-01-06 00:00:00.000000000 FEMALE 36.000000 basic 0 en other craigslist untracked Web Mac Desktop Firefox US
11 om1ss59ys8 2010-01-05 2010-01-05 05:18:12 1970-01-01 00:00:00.000000049 FEMALE 47.000000 basic 0 en other craigslist untracked Web iPhone -unknown- NDF
12 k6np330cm1 2010-01-05 2010-01-05 06:08:59 2010-01-18 00:00:00.000000000 -unknown- 49.668335 basic 0 en direct direct 49.6683 Web Other/Unknown -unknown- FR
13 dy3rgx56cu 2010-01-05 2010-01-05 08:32:59 1970-01-01 00:00:00.000000049 FEMALE 37.000000 basic 0 en other craigslist linked Web Mac Desktop Firefox NDF
14 ju3h98ch3w 2010-01-07 2010-01-07 05:58:20 1970-01-01 00:00:00.000000049 FEMALE 36.000000 basic 0 en other craigslist untracked Web iPhone Mobile Safari NDF
15 v4d5rl22px 2010-01-07 2010-01-07 20:45:55 2010-01-08 00:00:00.000000000 FEMALE 33.000000 basic 0 en direct direct untracked Web Windows Desktop Chrome CA
16 2dwbwkx056 2010-01-07 2010-01-07 21:51:25 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en other craigslist 49.6683 Web Other/Unknown -unknown- NDF
17 frhre329au 2010-01-07 2010-01-07 22:46:25 2010-01-09 00:00:00.000000000 -unknown- 31.000000 basic 0 en other craigslist 49.6683 Web Other/Unknown -unknown- US
18 cxlg85pg1r 2010-01-08 2010-01-08 01:56:41 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en seo facebook 49.6683 Web Other/Unknown -unknown- NDF
19 gdka1q5ktd 2010-01-10 2010-01-10 01:08:17 2010-01-10 00:00:00.000000000 FEMALE 29.000000 basic 0 en direct direct untracked Web Mac Desktop Chrome FR
20 qdubonn3uk 2010-01-10 2010-01-10 15:21:20 2010-01-18 00:00:00.000000000 -unknown- 49.668335 basic 0 en direct direct 49.6683 Web Other/Unknown -unknown- US
21 qsibmuz9sx 2010-01-10 2010-01-10 22:09:41 2010-01-11 00:00:00.000000000 MALE 30.000000 basic 0 en direct direct linked Web Mac Desktop Chrome US
22 80f7dwscrn 2010-01-11 2010-01-11 03:14:38 2010-01-11 00:00:00.000000000 -unknown- 40.000000 basic 0 en seo google untracked Web iPhone -unknown- US
23 jha93x042q 2010-01-11 2010-01-11 22:40:15 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en other craigslist untracked Web Mac Desktop Safari NDF
24 7i49vnuav6 2010-01-11 2010-01-11 23:08:08 1970-01-01 00:00:00.000000049 FEMALE 40.000000 basic 0 en seo google untracked Web Mac Desktop Firefox NDF
25 al8bcetz0g 2010-01-12 2010-01-12 13:14:44 2010-01-15 00:00:00.000000000 FEMALE 26.000000 basic 0 en other craigslist untracked Web Mac Desktop Chrome FR
26 bjg0m5otl3 2010-01-12 2010-01-12 15:54:20 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en other other untracked Web Other/Unknown -unknown- NDF
27 hfrl5gle36 2010-01-12 2010-01-12 20:59:49 2010-01-22 00:00:00.000000000 FEMALE 32.000000 basic 0 en other craigslist untracked Web Desktop (Other) Chrome US
28 tp6x3md0n4 2010-01-13 2010-01-13 04:46:50 2010-01-13 00:00:00.000000000 -unknown- 35.000000 basic 0 en direct direct 49.6683 Web Other/Unknown -unknown- FR
29 hql77nu2lk 2010-01-13 2010-01-13 06:43:33 2010-01-19 00:00:00.000000000 -unknown- 37.000000 basic 0 en direct direct untracked Web Android Tablet -unknown- US
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
213421 c98s3h7kgj 2014-06-30 2014-06-30 23:11:37 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct linked Web Mac Desktop Firefox NDF
213422 ytmpiwb8hj 2014-06-30 2014-06-30 23:12:46 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct untracked Web Windows Desktop IE NDF
213423 3dx1jk6yk2 2014-06-30 2014-06-30 23:15:48 1970-01-01 00:00:00.000000049 FEMALE 20.000000 facebook 25 en direct direct untracked iOS iPhone -unknown- NDF
213424 hcfj07iowv 2014-06-30 2014-06-30 23:18:59 1970-01-01 00:00:00.000000049 FEMALE 32.000000 facebook 0 en direct direct linked Web Windows Desktop Chrome NDF
213425 l1f71f9vsj 2014-06-30 2014-06-30 23:21:19 1970-01-01 00:00:00.000000049 FEMALE 30.000000 facebook 0 en direct direct linked Web Windows Desktop Chrome NDF
213426 15bj4ahmhf 2014-06-30 2014-06-30 23:23:31 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct untracked Moweb Android Phone Chrome Mobile NDF
213427 qwpybxfjdl 2014-06-30 2014-06-30 23:25:39 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct linked Web Desktop (Other) Chrome NDF
213428 k4t61wuvyq 2014-06-30 2014-06-30 23:26:34 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 23 en direct direct untracked Android Android Phone -unknown- NDF
213429 mhh7b52z44 2014-06-30 2014-06-30 23:27:12 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 25 en direct direct untracked iOS iPhone -unknown- NDF
213430 79wk7k2k5t 2014-06-30 2014-06-30 23:31:32 1970-01-01 00:00:00.000000049 -unknown- 19.000000 basic 0 en direct direct linked Web Mac Desktop Chrome NDF
213431 ftwmocvwlq 2014-06-30 2014-06-30 23:32:03 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct untracked Web Windows Desktop Firefox NDF
213432 rg7ayg1tob 2014-06-30 2014-06-30 23:32:24 1970-01-01 00:00:00.000000049 MALE 31.000000 facebook 0 en direct direct tracked-other Web Mac Desktop Safari NDF
213433 2f24umzkuv 2014-06-30 2014-06-30 23:34:27 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en sem-brand google untracked Web iPad Mobile Safari NDF
213434 or77n2ojuj 2014-06-30 2014-06-30 23:36:40 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en seo facebook product Web Mac Desktop Chrome NDF
213435 0a5bnb9bs4 2014-06-30 2014-06-30 23:38:51 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en seo google untracked Web Windows Desktop Chrome NDF
213436 6fzrn49sfn 2014-06-30 2014-06-30 23:41:13 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 25 en direct direct untracked iOS iPhone -unknown- NDF
213437 r0jq0devgy 2014-06-30 2014-06-30 23:42:43 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 23 en direct direct untracked Android Android Tablet -unknown- NDF
213438 v5lq9bj8gv 2014-06-30 2014-06-30 23:44:29 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 25 en direct direct untracked iOS iPhone -unknown- NDF
213439 msucfwmlzc 2014-06-30 2014-06-30 23:47:29 2015-03-16 00:00:00.000000000 MALE 43.000000 basic 0 en direct direct untracked Web Windows Desktop Firefox US
213440 04y8115avm 2014-06-30 2014-06-30 23:49:33 1970-01-01 00:00:00.000000049 FEMALE 24.000000 basic 25 en direct direct untracked iOS iPhone Mobile Safari NDF
213441 omlc9iku7t 2014-06-30 2014-06-30 23:51:51 2014-08-13 00:00:00.000000000 FEMALE 34.000000 basic 0 en direct direct linked Web Mac Desktop Chrome ES
213442 rf0ay567js 2014-06-30 2014-06-30 23:53:09 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en sem-brand google omg Web Mac Desktop Chrome NDF
213443 0k26r3mir0 2014-06-30 2014-06-30 23:53:40 2014-07-13 00:00:00.000000000 FEMALE 36.000000 basic 0 en sem-brand google linked Web Mac Desktop Safari US
213444 40o1ivh6cb 2014-06-30 2014-06-30 23:53:52 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct linked Web Windows Desktop Chrome NDF
213445 qbxza0xojf 2014-06-30 2014-06-30 23:55:47 2014-07-02 00:00:00.000000000 FEMALE 23.000000 basic 0 en sem-brand google omg Web Windows Desktop IE US
213446 zxodksqpep 2014-06-30 2014-06-30 23:56:36 1970-01-01 00:00:00.000000049 MALE 32.000000 basic 0 en sem-brand google omg Web Mac Desktop Safari NDF
213447 mhewnxesx9 2014-06-30 2014-06-30 23:57:19 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 0 en direct direct linked Web Windows Desktop Chrome NDF
213448 6o3arsjbb4 2014-06-30 2014-06-30 23:57:54 1970-01-01 00:00:00.000000049 -unknown- 32.000000 basic 0 en direct direct untracked Web Mac Desktop Firefox NDF
213449 jh95kwisub 2014-06-30 2014-06-30 23:58:22 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 25 en other other tracked-other iOS iPhone Mobile Safari NDF
213450 nw9fwlyb5f 2014-06-30 2014-06-30 23:58:24 1970-01-01 00:00:00.000000049 -unknown- 49.668335 basic 25 en direct direct untracked iOS iPhone -unknown- NDF

213451 rows × 16 columns

四、重复数据处理

In [152]: 
users.info()
users["id"] = users["id"].drop_duplicates()  # 去除重复数据
users.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 17.1+ MB
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 17.1+ MB

五、异常数据处理

In [156]: 
import seaborn
seaborn.distplot(users["age"].dropna())

Out[156]: 
<matplotlib.axes._subplots.AxesSubplot at 0x406f0ef0>
In [157]: 
seaborn.boxplot(users["age"].dropna())

Out[157]: 
<matplotlib.axes._subplots.AxesSubplot at 0x40746b90>
正常人的年龄很明显不会大于120
In [158]: 
users.info()
users=users[users["age"]<120]
users.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 213451 entries, 0 to 213450
Data columns (total 16 columns):
id                         213451 non-null object
date_account_created       213451 non-null datetime64[ns]
timestamp_first_active     213451 non-null datetime64[ns]
date_first_booking         88908 non-null datetime64[ns]
gender                     213451 non-null object
age                        125461 non-null float64
signup_method              213451 non-null object
signup_flow                213451 non-null int64
language                   213451 non-null object
affiliate_channel          213451 non-null object
affiliate_provider         213451 non-null object
first_affiliate_tracked    207386 non-null object
signup_app                 213451 non-null object
first_device_type          213451 non-null object
first_browser              213451 non-null object
country_destination        213451 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 17.1+ MB
<class 'pandas.core.frame.DataFrame'>
Int64Index: 124680 entries, 1 to 213448
Data columns (total 16 columns):
id                         124680 non-null object
date_account_created       124680 non-null datetime64[ns]
timestamp_first_active     124680 non-null datetime64[ns]
date_first_booking         68156 non-null datetime64[ns]
gender                     124680 non-null object
age                        124680 non-null float64
signup_method              124680 non-null object
signup_flow                124680 non-null int64
language                   124680 non-null object
affiliate_channel          124680 non-null object
affiliate_provider         124680 non-null object
first_affiliate_tracked    122682 non-null object
signup_app                 124680 non-null object
first_device_type          124680 non-null object
first_browser              124680 non-null object
country_destination        124680 non-null object
dtypes: datetime64[ns](3), float64(1), int64(1), object(11)
memory usage: 10.9+ MB
六、git
git: https://coding.net/u/RuoYun/p/Python-of-machine-learning/git/tree/master
# 数据分析 # python
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