pandas 第1课

How do I read a tabular data file into pandas?¶

In [2]:

import pandas as pd

In [6]:

orders = pd.read_table('http://bit.ly/chiporders') #需要翻墙

In [8]:

orders.head()

Out[8]:

	order_id	quantity	item_name	choice_description	item_price
0	1	1	Chips and Fresh Tomato Salsa	NaN	$2.39
1	1	1	Izze	[Clementine]	$3.39
2	1	1	Nantucket Nectar	[Apple]	$3.39
3	1	1	Chips and Tomatillo-Green Chili Salsa	NaN	$2.39
4	2	2	Chicken Bowl	[Tomatillo-Red Chili Salsa (Hot), [Black Beans…	$16.98

In [11]:

pd.read_table('http://bit.ly/movieusers')

Out[11]:

	1|24|M|technician|85711
0	2|53|F|other|94043
1	3|23|M|writer|32067
2	4|24|M|technician|43537
3	5|33|F|other|15213
4	6|42|M|executive|98101
5	7|57|M|administrator|91344
6	8|36|M|administrator|05201
7	9|29|M|student|01002
8	10|53|M|lawyer|90703
9	11|39|F|other|30329
10	12|28|F|other|06405
11	13|47|M|educator|29206
12	14|45|M|scientist|55106
13	15|49|F|educator|97301
14	16|21|M|entertainment|10309
15	17|30|M|programmer|06355
16	18|35|F|other|37212
17	19|40|M|librarian|02138
18	20|42|F|homemaker|95660
19	21|26|M|writer|30068
20	22|25|M|writer|40206
21	23|30|F|artist|48197
22	24|21|F|artist|94533
23	25|39|M|engineer|55107
24	26|49|M|engineer|21044
25	27|40|F|librarian|30030
26	28|32|M|writer|55369
27	29|41|M|programmer|94043
28	30|7|M|student|55436
29	31|24|M|artist|10003
…	…
912	914|44|F|other|08105
913	915|50|M|entertainment|60614
914	916|27|M|engineer|N2L5N
915	917|22|F|student|20006
916	918|40|M|scientist|70116
917	919|25|M|other|14216
918	920|30|F|artist|90008
919	921|20|F|student|98801
920	922|29|F|administrator|21114
921	923|21|M|student|E2E3R
922	924|29|M|other|11753
923	925|18|F|salesman|49036
924	926|49|M|entertainment|01701
925	927|23|M|programmer|55428
926	928|21|M|student|55408
927	929|44|M|scientist|53711
928	930|28|F|scientist|07310
929	931|60|M|educator|33556
930	932|58|M|educator|06437
931	933|28|M|student|48105
932	934|61|M|engineer|22902
933	935|42|M|doctor|66221
934	936|24|M|other|32789
935	937|48|M|educator|98072
936	938|38|F|technician|55038
937	939|26|F|student|33319
938	940|32|M|administrator|02215
939	941|20|M|student|97229
940	942|48|F|librarian|78209
941	943|22|M|student|77841

942 rows × 1 columns

In [15]:

pd.read_table('http://bit.ly/movieusers',sep='|')    # 将数据按|进行分隔

Out[15]:

	1	24	M	technician	85711
0	2	53	F	other	94043
1	3	23	M	writer	32067
2	4	24	M	technician	43537
3	5	33	F	other	15213
4	6	42	M	executive	98101
5	7	57	M	administrator	91344
6	8	36	M	administrator	05201
7	9	29	M	student	01002
8	10	53	M	lawyer	90703
9	11	39	F	other	30329
10	12	28	F	other	06405
11	13	47	M	educator	29206
12	14	45	M	scientist	55106
13	15	49	F	educator	97301
14	16	21	M	entertainment	10309
15	17	30	M	programmer	06355
16	18	35	F	other	37212
17	19	40	M	librarian	02138
18	20	42	F	homemaker	95660
19	21	26	M	writer	30068
20	22	25	M	writer	40206
21	23	30	F	artist	48197
22	24	21	F	artist	94533
23	25	39	M	engineer	55107
24	26	49	M	engineer	21044
25	27	40	F	librarian	30030
26	28	32	M	writer	55369
27	29	41	M	programmer	94043
28	30	7	M	student	55436
29	31	24	M	artist	10003
…	…	…	…	…	…
912	914	44	F	other	08105
913	915	50	M	entertainment	60614
914	916	27	M	engineer	N2L5N
915	917	22	F	student	20006
916	918	40	M	scientist	70116
917	919	25	M	other	14216
918	920	30	F	artist	90008
919	921	20	F	student	98801
920	922	29	F	administrator	21114
921	923	21	M	student	E2E3R
922	924	29	M	other	11753
923	925	18	F	salesman	49036
924	926	49	M	entertainment	01701
925	927	23	M	programmer	55428
926	928	21	M	student	55408
927	929	44	M	scientist	53711
928	930	28	F	scientist	07310
929	931	60	M	educator	33556
930	932	58	M	educator	06437
931	933	28	M	student	48105
932	934	61	M	engineer	22902
933	935	42	M	doctor	66221
934	936	24	M	other	32789
935	937	48	M	educator	98072
936	938	38	F	technician	55038
937	939	26	F	student	33319
938	940	32	M	administrator	02215
939	941	20	M	student	97229
940	942	48	F	librarian	78209
941	943	22	M	student	77841

942 rows × 5 columns

In [18]:

pd.read_table('http://bit.ly/movieusers',sep='|',header=None)    # 告诉pandas第一行不是表头

Out[18]:

	0	1	2	3	4
0	1	24	M	technician	85711
1	2	53	F	other	94043
2	3	23	M	writer	32067
3	4	24	M	technician	43537
4	5	33	F	other	15213
5	6	42	M	executive	98101
6	7	57	M	administrator	91344
7	8	36	M	administrator	05201
8	9	29	M	student	01002
9	10	53	M	lawyer	90703
10	11	39	F	other	30329
11	12	28	F	other	06405
12	13	47	M	educator	29206
13	14	45	M	scientist	55106
14	15	49	F	educator	97301
15	16	21	M	entertainment	10309
16	17	30	M	programmer	06355
17	18	35	F	other	37212
18	19	40	M	librarian	02138
19	20	42	F	homemaker	95660
20	21	26	M	writer	30068
21	22	25	M	writer	40206
22	23	30	F	artist	48197
23	24	21	F	artist	94533
24	25	39	M	engineer	55107
25	26	49	M	engineer	21044
26	27	40	F	librarian	30030
27	28	32	M	writer	55369
28	29	41	M	programmer	94043
29	30	7	M	student	55436
…	…	…	…	…	…
913	914	44	F	other	08105
914	915	50	M	entertainment	60614
915	916	27	M	engineer	N2L5N
916	917	22	F	student	20006
917	918	40	M	scientist	70116
918	919	25	M	other	14216
919	920	30	F	artist	90008
920	921	20	F	student	98801
921	922	29	F	administrator	21114
922	923	21	M	student	E2E3R
923	924	29	M	other	11753
924	925	18	F	salesman	49036
925	926	49	M	entertainment	01701
926	927	23	M	programmer	55428
927	928	21	M	student	55408
928	929	44	M	scientist	53711
929	930	28	F	scientist	07310
930	931	60	M	educator	33556
931	932	58	M	educator	06437
932	933	28	M	student	48105
933	934	61	M	engineer	22902
934	935	42	M	doctor	66221
935	936	24	M	other	32789
936	937	48	M	educator	98072
937	938	38	F	technician	55038
938	939	26	F	student	33319
939	940	32	M	administrator	02215
940	941	20	M	student	97229
941	942	48	F	librarian	78209
942	943	22	M	student	77841

943 rows × 5 columns

In [22]:

#给pandas添加表头
user_cols = ['user_id','age','gender','occupation','zip_code'] #occupation n. 职业  zip code 邮编
pd.read_table('http://bit.ly/movieusers',sep='|',header=None, names=user_cols )    # 告诉pandas第一行不是表头

Out[22]:

	user_id	age	gender	occupation	zip_code
0	1	24	M	technician	85711
1	2	53	F	other	94043
2	3	23	M	writer	32067
3	4	24	M	technician	43537
4	5	33	F	other	15213
5	6	42	M	executive	98101
6	7	57	M	administrator	91344
7	8	36	M	administrator	05201
8	9	29	M	student	01002
9	10	53	M	lawyer	90703
10	11	39	F	other	30329
11	12	28	F	other	06405
12	13	47	M	educator	29206
13	14	45	M	scientist	55106
14	15	49	F	educator	97301
15	16	21	M	entertainment	10309
16	17	30	M	programmer	06355
17	18	35	F	other	37212
18	19	40	M	librarian	02138
19	20	42	F	homemaker	95660
20	21	26	M	writer	30068
21	22	25	M	writer	40206
22	23	30	F	artist	48197
23	24	21	F	artist	94533
24	25	39	M	engineer	55107
25	26	49	M	engineer	21044
26	27	40	F	librarian	30030
27	28	32	M	writer	55369
28	29	41	M	programmer	94043
29	30	7	M	student	55436
…	…	…	…	…	…
913	914	44	F	other	08105
914	915	50	M	entertainment	60614
915	916	27	M	engineer	N2L5N
916	917	22	F	student	20006
917	918	40	M	scientist	70116
918	919	25	M	other	14216
919	920	30	F	artist	90008
920	921	20	F	student	98801
921	922	29	F	administrator	21114
922	923	21	M	student	E2E3R
923	924	29	M	other	11753
924	925	18	F	salesman	49036
925	926	49	M	entertainment	01701
926	927	23	M	programmer	55428
927	928	21	M	student	55408
928	929	44	M	scientist	53711
929	930	28	F	scientist	07310
930	931	60	M	educator	33556
931	932	58	M	educator	06437
932	933	28	M	student	48105
933	934	61	M	engineer	22902
934	935	42	M	doctor	66221
935	936	24	M	other	32789
936	937	48	M	educator	98072
937	938	38	F	technician	55038
938	939	26	F	student	33319
939	940	32	M	administrator	02215
940	941	20	M	student	97229
941	942	48	F	librarian	78209
942	943	22	M	student	77841

943 rows × 5 columns

In [23]:

#给pandas添加表头
user_cols = ['user_id','age','gender','occupation','zip_code'] #occupation n. 职业  zip code 邮编
users = pd.read_table('http://bit.ly/movieusers',sep='|',header=None, names=user_cols )    # 告诉pandas第一行不是表头

In [25]:

users.head()

Out[25]:

	user_id	age	gender	occupation	zip_code
0	1	24	M	technician	85711
1	2	53	F	other	94043
2	3	23	M	writer	32067
3	4	24	M	technician	43537
4	5	33	F	other	15213

In [ ]: