In [47]:
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
%matplotlib inline
import pandas as pd
surveys_df = pd.read_csv("surveys.csv")
surveys_1 = surveys_df[surveys_df.month == 1]
surveys_2 = surveys_df[surveys_df.month == 2]
surveys_2
Out[47]:
record_id
month
day
year
plot_id
species_id
sex
hindfoot_length
weight
583
584
2
18
1978
3
OT
M
NaN
24.0
584
585
2
18
1978
2
DS
M
53.0
157.0
585
586
2
18
1978
13
DM
M
35.0
51.0
586
587
2
18
1978
2
OX
NaN
NaN
NaN
587
588
2
18
1978
2
NL
M
NaN
218.0
588
589
2
18
1978
20
DM
F
39.0
38.0
589
590
2
18
1978
2
PF
F
16.0
7.0
590
591
2
18
1978
6
DM
F
38.0
NaN
591
592
2
18
1978
22
DM
M
39.0
45.0
592
593
2
18
1978
2
DM
M
38.0
52.0
593
594
2
18
1978
9
DM
M
38.0
50.0
594
595
2
18
1978
2
DM
M
37.0
51.0
595
596
2
18
1978
9
DS
F
NaN
126.0
596
597
2
18
1978
20
DM
F
33.0
39.0
597
598
2
18
1978
2
OT
F
NaN
24.0
598
599
2
18
1978
3
OT
F
15.0
23.0
599
600
2
18
1978
2
DM
F
37.0
40.0
600
601
2
18
1978
2
DM
M
36.0
41.0
601
602
2
18
1978
13
DM
M
37.0
46.0
602
603
2
18
1978
2
OT
M
NaN
25.0
603
604
2
18
1978
2
PE
M
22.0
25.0
604
605
2
18
1978
19
NaN
NaN
NaN
NaN
605
606
2
18
1978
23
NaN
NaN
NaN
NaN
606
607
2
19
1978
5
DM
F
36.0
39.0
607
608
2
19
1978
17
DM
F
34.0
42.0
608
609
2
19
1978
4
DS
F
49.0
NaN
609
610
2
19
1978
17
DM
M
37.0
39.0
610
611
2
19
1978
21
OT
F
20.0
23.0
611
612
2
19
1978
4
DS
NaN
NaN
NaN
612
613
2
19
1978
5
DS
M
49.0
NaN
...
...
...
...
...
...
...
...
...
...
33540
33541
2
10
2002
13
PB
M
29.0
49.0
33541
33542
2
10
2002
13
PP
F
21.0
15.0
33542
33543
2
10
2002
14
DM
M
36.0
48.0
33543
33544
2
10
2002
14
AH
NaN
NaN
NaN
33544
33545
2
10
2002
14
DM
F
35.0
39.0
33545
33546
2
10
2002
14
OT
F
21.0
21.0
33546
33547
2
10
2002
14
DM
F
36.0
44.0
33547
33548
2
10
2002
14
DM
F
36.0
46.0
33548
33549
2
10
2002
14
NL
M
33.0
222.0
33549
33550
2
10
2002
15
PB
F
25.0
31.0
33550
33551
2
10
2002
15
RM
F
17.0
7.0
33551
33552
2
10
2002
15
AH
NaN
NaN
NaN
33552
33553
2
10
2002
15
AH
NaN
NaN
NaN
33553
33554
2
10
2002
15
RM
F
17.0
10.0
33554
33555
2
10
2002
15
PB
M
27.0
45.0
33555
33556
2
10
2002
5
RO
M
15.0
9.0
33556
33557
2
10
2002
5
RM
F
17.0
9.0
33557
33558
2
10
2002
16
PB
F
26.0
25.0
33558
33559
2
10
2002
16
DM
M
36.0
38.0
33559
33560
2
10
2002
16
DO
F
36.0
51.0
33560
33561
2
10
2002
10
RM
F
17.0
8.0
33561
33562
2
10
2002
10
DO
F
35.0
50.0
33562
33563
2
10
2002
10
DO
M
34.0
52.0
33563
33564
2
10
2002
10
DO
M
38.0
51.0
33564
33565
2
10
2002
10
RO
F
16.0
8.0
33565
33566
2
10
2002
7
DM
M
35.0
42.0
33566
33567
2
10
2002
7
DO
M
36.0
62.0
33567
33568
2
10
2002
7
DO
F
37.0
55.0
33568
33569
2
10
2002
7
DO
F
38.0
47.0
33569
33570
2
10
2002
7
DO
F
35.0
54.0
2796 rows × 9 columns
In [62]:
plt.hist(surveys_1['plot_id'])
plt.plot(surveys_1['plot_id'].mean(),'--')
plt.show()
In [63]:
plt.figure(1)
plt.subplot(1,1,1)
plt.plot(surveys_1['plot_id'])
plt.xlabel('record')
plt.ylabel('plot_id')
plt.title('oh')
plt.plot(surveys_2['plot_id'])
plt.show()
In [ ]:
Content source: GT-IDEaS/SkillsWorkshop2017
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