Exercise 5.3: Adding data to a data frame

[1]:

import pandas as pd

We continue working with the frog tongue data. Recall that the header comments in the data file contained information about the frogs.

[2]:

!head -20 data/frog_tongue_adhesion.csv

# These data are from the paper,
#   Kleinteich and Gorb, Sci. Rep., 4, 5225, 2014.
# It was featured in the New York Times.
#    http://www.nytimes.com/2014/08/25/science/a-frog-thats-a-living-breathing-pac-man.html
#
# The authors included the data in their supplemental information.
#
# Importantly, the ID refers to the identifites of the frogs they tested.
#   I:   adult, 63 mm snout-vent-length (SVL) and 63.1 g body weight,
#        Ceratophrys cranwelli crossed with Ceratophrys cornuta
#   II:  adult, 70 mm SVL and 72.7 g body weight,
#        Ceratophrys cranwelli crossed with Ceratophrys cornuta
#   III: juvenile, 28 mm SVL and 12.7 g body weight, Ceratophrys cranwelli
#   IV:  juvenile, 31 mm SVL and 12.7 g body weight, Ceratophrys cranwelli
date,ID,trial number,impact force (mN),impact time (ms),impact force / body weight,adhesive force (mN),time frog pulls on target (ms),adhesive force / body weight,adhesive impulse (N-s),total contact area (mm2),contact area without mucus (mm2),contact area with mucus / contact area without mucus,contact pressure (Pa),adhesive strength (Pa)
2013_02_26,I,3,1205,46,1.95,-785,884,1.27,-0.290,387,70,0.82,3117,-2030
2013_02_26,I,4,2527,44,4.08,-983,248,1.59,-0.181,101,94,0.07,24923,-9695
2013_03_01,I,1,1745,34,2.82,-850,211,1.37,-0.157,83,79,0.05,21020,-10239
2013_03_01,I,2,1556,41,2.51,-455,1025,0.74,-0.170,330,158,0.52,4718,-1381
2013_03_01,I,3,493,36,0.80,-974,499,1.57,-0.423,245,216,0.12,2012,-3975

So, each frog has associated with it an age (adult or juvenile), snout-vent-length (SVL), body weight, and species (either cross or cranwelli). For a tidy data frame, we should have a column for each of these values. Your task is to load in the data, and then add these columns to the data frame. For convenience, here is a data frame with data about each frog.

[3]:

df_frog = pd.DataFrame(
    data={
        "ID": ["I", "II", "III", "IV"],
        "age": ["adult", "adult", "juvenile", "juvenile"],
        "SVL (mm)": [63, 70, 28, 31],
        "weight (g)": [63.1, 72.7, 12.7, 12.7],
        "species": ["cross", "cross", "cranwelli", "cranwelli"],
    }
)

Note: There are lots of ways to solve this problem. This is a good exercise in searching through the Pandas documentation and other online resources, such as Stack Overflow. Remember, much of your programming efforts are spent searching through documentation and the internet.

Finally, as a fun challenge, see if you can highlight the strike with the highest impact force for each frog in the data frame.

Solution

The most direct way is to use built-in pd.merge() function. This function finds a common column between two DataFrames, and then uses that column to merge them, filling in values that match in the common column. This is exactly what we want.

[4]:

# Load the data
df = pd.read_csv('data/frog_tongue_adhesion.csv', comment='#')

# Perform merge
df = df.merge(df_frog)

Let’s look at the DataFrame to make sure it has what we expect.

[5]:

df.head()

[5]:
date ID trial number impact force (mN) impact time (ms) impact force / body weight adhesive force (mN) time frog pulls on target (ms) adhesive force / body weight adhesive impulse (N-s) total contact area (mm2) contact area without mucus (mm2) contact area with mucus / contact area without mucus contact pressure (Pa) adhesive strength (Pa) age SVL (mm) weight (g) species
0 2013_02_26 I 3 1205 46 1.95 -785 884 1.27 -0.290 387 70 0.82 3117 -2030 adult 63 63.1 cross
1 2013_02_26 I 4 2527 44 4.08 -983 248 1.59 -0.181 101 94 0.07 24923 -9695 adult 63 63.1 cross
2 2013_03_01 I 1 1745 34 2.82 -850 211 1.37 -0.157 83 79 0.05 21020 -10239 adult 63 63.1 cross
3 2013_03_01 I 2 1556 41 2.51 -455 1025 0.74 -0.170 330 158 0.52 4718 -1381 adult 63 63.1 cross
4 2013_03_01 I 3 493 36 0.80 -974 499 1.57 -0.423 245 216 0.12 2012 -3975 adult 63 63.1 cross

Now, we can highlight the strike with the highest impact force for each frog. One way to do this is to write a function that returns an entire data frame with the same index and columns as df where each entry contains a formatting string like 'background-color: lightgray', and then use df.style.apply() with the axis=None kwarg.

[6]:

def highlight_max(df):
    # Make full data frame with all strings being light gray bg color
    highlight_df = pd.DataFrame(
        "background-color: lightgray", index=df.index, columns=df.columns
    )

    # Which rows contain a maximum force
    has_max_force = (
        df.groupby("ID")["impact force (mN)"]
        .transform("max")
        .eq(df["impact force (mN)"])
    )

    # Color rows with maximum force green
    highlight_df.loc[has_max_force, :] = 'background-color: #7fc97f'

    return highlight_df

df.style.apply(highlight_max, axis=None)

[6]:
date ID trial number impact force (mN) impact time (ms) impact force / body weight adhesive force (mN) time frog pulls on target (ms) adhesive force / body weight adhesive impulse (N-s) total contact area (mm2) contact area without mucus (mm2) contact area with mucus / contact area without mucus contact pressure (Pa) adhesive strength (Pa) age SVL (mm) weight (g) species
0 2013_02_26 I 3 1205 46 1.950000 -785 884 1.270000 -0.290000 387 70 0.820000 3117 -2030 adult 63 63.100000 cross
1 2013_02_26 I 4 2527 44 4.080000 -983 248 1.590000 -0.181000 101 94 0.070000 24923 -9695 adult 63 63.100000 cross
2 2013_03_01 I 1 1745 34 2.820000 -850 211 1.370000 -0.157000 83 79 0.050000 21020 -10239 adult 63 63.100000 cross
3 2013_03_01 I 2 1556 41 2.510000 -455 1025 0.740000 -0.170000 330 158 0.520000 4718 -1381 adult 63 63.100000 cross
4 2013_03_01 I 3 493 36 0.800000 -974 499 1.570000 -0.423000 245 216 0.120000 2012 -3975 adult 63 63.100000 cross
5 2013_03_01 I 4 2276 31 3.680000 -592 969 0.960000 -0.176000 341 106 0.690000 6676 -1737 adult 63 63.100000 cross
6 2013_03_05 I 1 556 43 0.900000 -512 835 0.830000 -0.285000 359 110 0.690000 1550 -1427 adult 63 63.100000 cross
7 2013_03_05 I 2 1928 46 3.110000 -804 508 1.300000 -0.285000 246 178 0.280000 7832 -3266 adult 63 63.100000 cross
8 2013_03_05 I 3 2641 50 4.270000 -690 491 1.120000 -0.239000 269 224 0.170000 9824 -2568 adult 63 63.100000 cross
9 2013_03_05 I 4 1897 41 3.060000 -462 839 0.750000 -0.328000 266 176 0.340000 7122 -1733 adult 63 63.100000 cross
10 2013_03_12 I 1 1891 40 3.060000 -766 1069 1.240000 -0.380000 408 33 0.920000 4638 -1879 adult 63 63.100000 cross
11 2013_03_12 I 2 1545 48 2.500000 -715 649 1.150000 -0.298000 141 112 0.210000 10947 -5064 adult 63 63.100000 cross
12 2013_03_12 I 3 1307 29 2.110000 -613 1845 0.990000 -0.768000 455 92 0.800000 2874 -1348 adult 63 63.100000 cross
13 2013_03_12 I 4 1692 31 2.730000 -677 917 1.090000 -0.457000 186 129 0.310000 9089 -3636 adult 63 63.100000 cross
14 2013_03_12 I 5 1543 38 2.490000 -528 750 0.850000 -0.353000 153 148 0.030000 10095 -3453 adult 63 63.100000 cross
15 2013_03_15 I 1 1282 31 2.070000 -452 785 0.730000 -0.253000 290 105 0.640000 4419 -1557 adult 63 63.100000 cross
16 2013_03_15 I 2 775 34 1.250000 -430 837 0.700000 -0.276000 257 124 0.520000 3019 -1677 adult 63 63.100000 cross
17 2013_03_15 I 3 2032 60 3.280000 -652 486 1.050000 -0.257000 147 134 0.090000 13784 -4425 adult 63 63.100000 cross
18 2013_03_15 I 4 1240 34 2.000000 -692 906 1.120000 -0.317000 364 260 0.280000 3406 -1901 adult 63 63.100000 cross
19 2013_03_15 I 5 473 40 0.760000 -536 1218 0.870000 -0.382000 259 168 0.350000 1830 -2073 adult 63 63.100000 cross
20 2013_03_19 II 1 1612 18 3.790000 -655 3087 1.540000 -0.385000 348 15 0.960000 4633 -1881 adult 70 72.700000 cross
21 2013_03_19 II 2 605 55 1.420000 -292 1261 0.690000 -0.294000 248 24 0.900000 2441 -1177 adult 70 72.700000 cross
22 2013_03_19 II 3 327 51 0.770000 -246 1508 0.580000 -0.282000 130 34 0.740000 2517 -1894 adult 70 72.700000 cross
23 2013_03_19 II 4 946 59 2.230000 -245 1841 0.580000 -0.340000 106 26 0.760000 8893 -2301 adult 70 72.700000 cross
24 2013_03_21 II 1 541 33 1.270000 -553 3126 1.300000 -0.432000 276 16 0.940000 1959 -2004 adult 70 72.700000 cross
25 2013_03_21 II 2 1539 43 3.620000 -664 741 1.560000 -0.046000 85 24 0.720000 18073 -7802 adult 70 72.700000 cross
26 2013_03_21 II 3 529 28 1.240000 -261 2482 0.610000 -0.414000 325 33 0.900000 1627 -803 adult 70 72.700000 cross
27 2013_03_21 II 4 628 31 1.480000 -691 998 1.630000 -0.071000 242 67 0.720000 2600 -2860 adult 70 72.700000 cross
28 2013_03_25 II 1 1453 72 3.420000 -92 1652 0.220000 -0.008000 136 0 1.000000 10645 -678 adult 70 72.700000 cross
29 2013_03_25 II 2 297 42 0.700000 -566 936 1.330000 -0.084000 126 4 0.970000 2367 -4506 adult 70 72.700000 cross
30 2013_03_25 II 3 703 33 1.650000 -223 2152 0.520000 -0.209000 237 8 0.970000 2972 -942 adult 70 72.700000 cross
31 2013_03_25 II 4 269 57 0.630000 -512 189 1.200000 -0.055000 29 28 0.030000 9279 -17652 adult 70 72.700000 cross
32 2013_03_28 II 1 751 39 1.770000 -227 1195 0.530000 -0.026000 206 0 1.000000 3647 -1101 adult 70 72.700000 cross
33 2013_03_28 II 2 245 21 0.580000 -573 1466 1.350000 -0.215000 190 46 0.760000 1288 -3014 adult 70 72.700000 cross
34 2013_04_03 II 1 1182 28 2.780000 -522 1197 1.230000 -0.118000 281 0 1.000000 4213 -1860 adult 70 72.700000 cross
35 2013_04_03 II 2 515 29 1.210000 -599 1486 1.410000 -0.226000 217 0 1.000000 2369 -2757 adult 70 72.700000 cross
36 2013_04_08 II 1 435 26 1.020000 -364 1017 0.860000 -0.211000 189 89 0.530000 2302 -1927 adult 70 72.700000 cross
37 2013_04_08 II 2 383 31 0.900000 -469 974 1.100000 -0.260000 221 72 0.670000 1737 -2129 adult 70 72.700000 cross
38 2013_04_08 II 3 457 15 1.080000 -844 780 1.990000 -0.328000 171 106 0.380000 2665 -4925 adult 70 72.700000 cross
39 2013_04_12 II 1 730 42 1.720000 -648 786 1.520000 -0.121000 142 43 0.700000 5149 -4573 adult 70 72.700000 cross
40 2013_05_27 III 1 614 88 4.940000 -94 683 0.760000 -0.001000 97 15 0.830000 6326 -967 juvenile 28 12.700000 cranwelli
41 2013_05_27 III 2 414 143 3.330000 -163 245 1.310000 -0.032000 108 10 0.600000 3824 -1507 juvenile 28 12.700000 cranwelli
42 2013_05_27 III 3 324 105 2.610000 -172 619 1.380000 -0.079000 55 23 0.370000 5946 -3149 juvenile 28 12.700000 cranwelli
43 2013_06_11 III 1 776 35 6.240000 -225 1823 1.810000 -0.132000 124 17 0.770000 6272 -1818 juvenile 28 12.700000 cranwelli
44 2013_06_11 III 2 611 29 4.910000 -301 918 2.420000 -0.155000 128 43 0.020000 4770 -2354 juvenile 28 12.700000 cranwelli
45 2013_06_11 III 3 544 16 4.380000 -93 1351 0.750000 -0.110000 43 34 0.710000 12699 -2181 juvenile 28 12.700000 cranwelli
46 2013_06_14 III 1 538 38 4.320000 -131 1790 1.050000 -0.036000 130 74 1.000000 4130 -1005 juvenile 28 12.700000 cranwelli
47 2013_06_14 III 2 579 31 4.660000 -289 1006 2.330000 -0.073000 113 4 0.480000 5110 -2555 juvenile 28 12.700000 cranwelli
48 2013_06_18 III 1 806 29 6.490000 -104 883 0.840000 -0.055000 115 55 0.660000 6993 -902 juvenile 28 12.700000 cranwelli
49 2013_06_18 III 2 459 32 3.700000 -229 1218 1.850000 -0.137000 89 6 0.950000 5165 -2580 juvenile 28 12.700000 cranwelli
50 2013_06_18 III 3 458 30 3.690000 -259 910 2.080000 -0.194000 91 88 0.150000 5048 -2855 juvenile 28 12.700000 cranwelli
51 2013_06_18 III 4 626 16 5.040000 -231 550 1.860000 -0.042000 82 23 0.010000 7633 -2819 juvenile 28 12.700000 cranwelli
52 2013_06_21 III 1 621 27 4.990000 -267 2081 2.140000 -0.183000 120 58 0.900000 5152 -2213 juvenile 28 12.700000 cranwelli
53 2013_06_21 III 2 544 30 4.380000 -178 376 1.430000 -0.034000 19 17 0.050000 28641 -9364 juvenile 28 12.700000 cranwelli
54 2013_06_21 III 3 535 35 4.300000 -123 289 0.990000 -0.029000 21 29 0.050000 25471 -5843 juvenile 28 12.700000 cranwelli
55 2013_06_21 III 4 385 39 3.090000 -151 607 1.220000 -0.082000 31 126 0.030000 12409 -4882 juvenile 28 12.700000 cranwelli
56 2013_06_26 III 1 401 36 3.230000 -127 2932 1.020000 -0.215000 142 12 0.860000 2835 -896 juvenile 28 12.700000 cranwelli
57 2013_06_26 III 2 614 34 4.940000 -372 680 2.990000 -0.140000 72 1 0.420000 8475 -5136 juvenile 28 12.700000 cranwelli
58 2013_06_26 III 3 665 40 5.350000 -236 685 1.900000 -0.118000 129 0 0.160000 5171 -1834 juvenile 28 12.700000 cranwelli
59 2013_06_26 III 4 488 34 3.930000 -390 1308 3.140000 -0.208000 112 58 0.390000 4376 -3492 juvenile 28 12.700000 cranwelli
60 2013_05_27 IV 2 172 26 1.280000 -456 462 3.400000 -0.050000 133 0 0.880000 1297 -3440 juvenile 31 12.700000 cranwelli
61 2013_05_27 IV 3 142 20 1.050000 -193 250 1.440000 -0.047000 57 74 0.830000 2498 -3400 juvenile 31 12.700000 cranwelli
62 2013_05_27 IV 4 37 55 0.280000 -236 743 1.760000 -0.119000 51 44 0.540000 735 -4647 juvenile 31 12.700000 cranwelli
63 2013_05_30 IV 1 453 38 3.370000 -225 844 1.680000 -0.110000 142 108 0.480000 3177 -1581 juvenile 31 12.700000 cranwelli
64 2013_05_30 IV 2 355 31 2.640000 -217 728 1.610000 -0.023000 174 39 0.980000 2037 -1245 juvenile 31 12.700000 cranwelli
65 2013_05_30 IV 3 22 33 0.170000 -161 472 1.200000 -0.052000 56 4 0.020000 397 -2866 juvenile 31 12.700000 cranwelli
66 2013_06_03 IV 1 502 74 3.740000 -139 959 1.040000 -0.089000 65 77 0.910000 7713 -2141 juvenile 31 12.700000 cranwelli
67 2013_06_11 IV 1 273 26 2.030000 -264 844 1.970000 -0.160000 124 81 0.290000 2205 -2136 juvenile 31 12.700000 cranwelli
68 2013_06_11 IV 2 720 27 5.360000 -342 1515 2.550000 -0.226000 137 0 0.830000 5259 -2497 juvenile 31 12.700000 cranwelli
69 2013_06_11 IV 3 582 33 4.340000 -231 279 1.720000 -0.033000 60 4 0.030000 9705 -3847 juvenile 31 12.700000 cranwelli
70 2013_06_11 IV 4 198 23 1.470000 -209 1427 1.550000 -0.151000 110 69 0.840000 1793 -1889 juvenile 31 12.700000 cranwelli
71 2013_06_14 IV 1 198 6 1.470000 -292 2874 2.170000 -0.232000 145 50 0.990000 1369 -2018 juvenile 31 12.700000 cranwelli
72 2013_06_18 IV 1 597 29 4.440000 -339 4251 2.530000 -0.281000 191 12 1.000000 3116 -1772 juvenile 31 12.700000 cranwelli
73 2013_06_18 IV 2 516 31 3.840000 -371 626 2.760000 -0.094000 83 18 0.120000 6184 -4447 juvenile 31 12.700000 cranwelli
74 2013_06_18 IV 3 815 34 6.070000 -331 1254 2.470000 -0.077000 151 20 0.710000 5386 -2190 juvenile 31 12.700000 cranwelli
75 2013_06_18 IV 4 402 38 3.000000 -302 986 2.250000 -0.122000 117 30 0.070000 3446 -2591 juvenile 31 12.700000 cranwelli
76 2013_06_21 IV 1 605 39 4.500000 -216 1627 1.610000 -0.139000 123 20 1.000000 4928 -1759 juvenile 31 12.700000 cranwelli
77 2013_06_21 IV 2 711 76 5.300000 -163 2021 1.210000 -0.217000 129 42 0.970000 5498 -1257 juvenile 31 12.700000 cranwelli
78 2013_06_21 IV 3 614 33 4.570000 -367 1366 2.730000 -0.198000 128 108 0.460000 4776 -2857 juvenile 31 12.700000 cranwelli
79 2013_06_21 IV 4 468 36 3.480000 -218 1269 1.630000 -0.122000 129 68 0.610000 3617 -1688 juvenile 31 12.700000 cranwelli

While this works and does not require adding anything to the data frame, I think it is more concise to simply add a column that is a Boolean stating whether or not the row contains a maximum impact force and then using this column to determine how each row is shaded.

[7]:

df["max strike"] = (
    df.groupby("ID")["impact force (mN)"].transform("max").eq(df["impact force (mN)"])
)


def highlight_max(s):
    if s["max strike"]:
        return ["background-color: #7fc97f"] * len(s)
    else:
        return ["background-color: lightgray"] * len(s)


df.style.apply(highlight_max, axis=1)

[7]:
date ID trial number impact force (mN) impact time (ms) impact force / body weight adhesive force (mN) time frog pulls on target (ms) adhesive force / body weight adhesive impulse (N-s) total contact area (mm2) contact area without mucus (mm2) contact area with mucus / contact area without mucus contact pressure (Pa) adhesive strength (Pa) age SVL (mm) weight (g) species max strike
0 2013_02_26 I 3 1205 46 1.950000 -785 884 1.270000 -0.290000 387 70 0.820000 3117 -2030 adult 63 63.100000 cross False
1 2013_02_26 I 4 2527 44 4.080000 -983 248 1.590000 -0.181000 101 94 0.070000 24923 -9695 adult 63 63.100000 cross False
2 2013_03_01 I 1 1745 34 2.820000 -850 211 1.370000 -0.157000 83 79 0.050000 21020 -10239 adult 63 63.100000 cross False
3 2013_03_01 I 2 1556 41 2.510000 -455 1025 0.740000 -0.170000 330 158 0.520000 4718 -1381 adult 63 63.100000 cross False
4 2013_03_01 I 3 493 36 0.800000 -974 499 1.570000 -0.423000 245 216 0.120000 2012 -3975 adult 63 63.100000 cross False
5 2013_03_01 I 4 2276 31 3.680000 -592 969 0.960000 -0.176000 341 106 0.690000 6676 -1737 adult 63 63.100000 cross False
6 2013_03_05 I 1 556 43 0.900000 -512 835 0.830000 -0.285000 359 110 0.690000 1550 -1427 adult 63 63.100000 cross False
7 2013_03_05 I 2 1928 46 3.110000 -804 508 1.300000 -0.285000 246 178 0.280000 7832 -3266 adult 63 63.100000 cross False
8 2013_03_05 I 3 2641 50 4.270000 -690 491 1.120000 -0.239000 269 224 0.170000 9824 -2568 adult 63 63.100000 cross True
9 2013_03_05 I 4 1897 41 3.060000 -462 839 0.750000 -0.328000 266 176 0.340000 7122 -1733 adult 63 63.100000 cross False
10 2013_03_12 I 1 1891 40 3.060000 -766 1069 1.240000 -0.380000 408 33 0.920000 4638 -1879 adult 63 63.100000 cross False
11 2013_03_12 I 2 1545 48 2.500000 -715 649 1.150000 -0.298000 141 112 0.210000 10947 -5064 adult 63 63.100000 cross False
12 2013_03_12 I 3 1307 29 2.110000 -613 1845 0.990000 -0.768000 455 92 0.800000 2874 -1348 adult 63 63.100000 cross False
13 2013_03_12 I 4 1692 31 2.730000 -677 917 1.090000 -0.457000 186 129 0.310000 9089 -3636 adult 63 63.100000 cross False
14 2013_03_12 I 5 1543 38 2.490000 -528 750 0.850000 -0.353000 153 148 0.030000 10095 -3453 adult 63 63.100000 cross False
15 2013_03_15 I 1 1282 31 2.070000 -452 785 0.730000 -0.253000 290 105 0.640000 4419 -1557 adult 63 63.100000 cross False
16 2013_03_15 I 2 775 34 1.250000 -430 837 0.700000 -0.276000 257 124 0.520000 3019 -1677 adult 63 63.100000 cross False
17 2013_03_15 I 3 2032 60 3.280000 -652 486 1.050000 -0.257000 147 134 0.090000 13784 -4425 adult 63 63.100000 cross False
18 2013_03_15 I 4 1240 34 2.000000 -692 906 1.120000 -0.317000 364 260 0.280000 3406 -1901 adult 63 63.100000 cross False
19 2013_03_15 I 5 473 40 0.760000 -536 1218 0.870000 -0.382000 259 168 0.350000 1830 -2073 adult 63 63.100000 cross False
20 2013_03_19 II 1 1612 18 3.790000 -655 3087 1.540000 -0.385000 348 15 0.960000 4633 -1881 adult 70 72.700000 cross True
21 2013_03_19 II 2 605 55 1.420000 -292 1261 0.690000 -0.294000 248 24 0.900000 2441 -1177 adult 70 72.700000 cross False
22 2013_03_19 II 3 327 51 0.770000 -246 1508 0.580000 -0.282000 130 34 0.740000 2517 -1894 adult 70 72.700000 cross False
23 2013_03_19 II 4 946 59 2.230000 -245 1841 0.580000 -0.340000 106 26 0.760000 8893 -2301 adult 70 72.700000 cross False
24 2013_03_21 II 1 541 33 1.270000 -553 3126 1.300000 -0.432000 276 16 0.940000 1959 -2004 adult 70 72.700000 cross False
25 2013_03_21 II 2 1539 43 3.620000 -664 741 1.560000 -0.046000 85 24 0.720000 18073 -7802 adult 70 72.700000 cross False
26 2013_03_21 II 3 529 28 1.240000 -261 2482 0.610000 -0.414000 325 33 0.900000 1627 -803 adult 70 72.700000 cross False
27 2013_03_21 II 4 628 31 1.480000 -691 998 1.630000 -0.071000 242 67 0.720000 2600 -2860 adult 70 72.700000 cross False
28 2013_03_25 II 1 1453 72 3.420000 -92 1652 0.220000 -0.008000 136 0 1.000000 10645 -678 adult 70 72.700000 cross False
29 2013_03_25 II 2 297 42 0.700000 -566 936 1.330000 -0.084000 126 4 0.970000 2367 -4506 adult 70 72.700000 cross False
30 2013_03_25 II 3 703 33 1.650000 -223 2152 0.520000 -0.209000 237 8 0.970000 2972 -942 adult 70 72.700000 cross False
31 2013_03_25 II 4 269 57 0.630000 -512 189 1.200000 -0.055000 29 28 0.030000 9279 -17652 adult 70 72.700000 cross False
32 2013_03_28 II 1 751 39 1.770000 -227 1195 0.530000 -0.026000 206 0 1.000000 3647 -1101 adult 70 72.700000 cross False
33 2013_03_28 II 2 245 21 0.580000 -573 1466 1.350000 -0.215000 190 46 0.760000 1288 -3014 adult 70 72.700000 cross False
34 2013_04_03 II 1 1182 28 2.780000 -522 1197 1.230000 -0.118000 281 0 1.000000 4213 -1860 adult 70 72.700000 cross False
35 2013_04_03 II 2 515 29 1.210000 -599 1486 1.410000 -0.226000 217 0 1.000000 2369 -2757 adult 70 72.700000 cross False
36 2013_04_08 II 1 435 26 1.020000 -364 1017 0.860000 -0.211000 189 89 0.530000 2302 -1927 adult 70 72.700000 cross False
37 2013_04_08 II 2 383 31 0.900000 -469 974 1.100000 -0.260000 221 72 0.670000 1737 -2129 adult 70 72.700000 cross False
38 2013_04_08 II 3 457 15 1.080000 -844 780 1.990000 -0.328000 171 106 0.380000 2665 -4925 adult 70 72.700000 cross False
39 2013_04_12 II 1 730 42 1.720000 -648 786 1.520000 -0.121000 142 43 0.700000 5149 -4573 adult 70 72.700000 cross False
40 2013_05_27 III 1 614 88 4.940000 -94 683 0.760000 -0.001000 97 15 0.830000 6326 -967 juvenile 28 12.700000 cranwelli False
41 2013_05_27 III 2 414 143 3.330000 -163 245 1.310000 -0.032000 108 10 0.600000 3824 -1507 juvenile 28 12.700000 cranwelli False
42 2013_05_27 III 3 324 105 2.610000 -172 619 1.380000 -0.079000 55 23 0.370000 5946 -3149 juvenile 28 12.700000 cranwelli False
43 2013_06_11 III 1 776 35 6.240000 -225 1823 1.810000 -0.132000 124 17 0.770000 6272 -1818 juvenile 28 12.700000 cranwelli False
44 2013_06_11 III 2 611 29 4.910000 -301 918 2.420000 -0.155000 128 43 0.020000 4770 -2354 juvenile 28 12.700000 cranwelli False
45 2013_06_11 III 3 544 16 4.380000 -93 1351 0.750000 -0.110000 43 34 0.710000 12699 -2181 juvenile 28 12.700000 cranwelli False
46 2013_06_14 III 1 538 38 4.320000 -131 1790 1.050000 -0.036000 130 74 1.000000 4130 -1005 juvenile 28 12.700000 cranwelli False
47 2013_06_14 III 2 579 31 4.660000 -289 1006 2.330000 -0.073000 113 4 0.480000 5110 -2555 juvenile 28 12.700000 cranwelli False
48 2013_06_18 III 1 806 29 6.490000 -104 883 0.840000 -0.055000 115 55 0.660000 6993 -902 juvenile 28 12.700000 cranwelli True
49 2013_06_18 III 2 459 32 3.700000 -229 1218 1.850000 -0.137000 89 6 0.950000 5165 -2580 juvenile 28 12.700000 cranwelli False
50 2013_06_18 III 3 458 30 3.690000 -259 910 2.080000 -0.194000 91 88 0.150000 5048 -2855 juvenile 28 12.700000 cranwelli False
51 2013_06_18 III 4 626 16 5.040000 -231 550 1.860000 -0.042000 82 23 0.010000 7633 -2819 juvenile 28 12.700000 cranwelli False
52 2013_06_21 III 1 621 27 4.990000 -267 2081 2.140000 -0.183000 120 58 0.900000 5152 -2213 juvenile 28 12.700000 cranwelli False
53 2013_06_21 III 2 544 30 4.380000 -178 376 1.430000 -0.034000 19 17 0.050000 28641 -9364 juvenile 28 12.700000 cranwelli False
54 2013_06_21 III 3 535 35 4.300000 -123 289 0.990000 -0.029000 21 29 0.050000 25471 -5843 juvenile 28 12.700000 cranwelli False
55 2013_06_21 III 4 385 39 3.090000 -151 607 1.220000 -0.082000 31 126 0.030000 12409 -4882 juvenile 28 12.700000 cranwelli False
56 2013_06_26 III 1 401 36 3.230000 -127 2932 1.020000 -0.215000 142 12 0.860000 2835 -896 juvenile 28 12.700000 cranwelli False
57 2013_06_26 III 2 614 34 4.940000 -372 680 2.990000 -0.140000 72 1 0.420000 8475 -5136 juvenile 28 12.700000 cranwelli False
58 2013_06_26 III 3 665 40 5.350000 -236 685 1.900000 -0.118000 129 0 0.160000 5171 -1834 juvenile 28 12.700000 cranwelli False
59 2013_06_26 III 4 488 34 3.930000 -390 1308 3.140000 -0.208000 112 58 0.390000 4376 -3492 juvenile 28 12.700000 cranwelli False
60 2013_05_27 IV 2 172 26 1.280000 -456 462 3.400000 -0.050000 133 0 0.880000 1297 -3440 juvenile 31 12.700000 cranwelli False
61 2013_05_27 IV 3 142 20 1.050000 -193 250 1.440000 -0.047000 57 74 0.830000 2498 -3400 juvenile 31 12.700000 cranwelli False
62 2013_05_27 IV 4 37 55 0.280000 -236 743 1.760000 -0.119000 51 44 0.540000 735 -4647 juvenile 31 12.700000 cranwelli False
63 2013_05_30 IV 1 453 38 3.370000 -225 844 1.680000 -0.110000 142 108 0.480000 3177 -1581 juvenile 31 12.700000 cranwelli False
64 2013_05_30 IV 2 355 31 2.640000 -217 728 1.610000 -0.023000 174 39 0.980000 2037 -1245 juvenile 31 12.700000 cranwelli False
65 2013_05_30 IV 3 22 33 0.170000 -161 472 1.200000 -0.052000 56 4 0.020000 397 -2866 juvenile 31 12.700000 cranwelli False
66 2013_06_03 IV 1 502 74 3.740000 -139 959 1.040000 -0.089000 65 77 0.910000 7713 -2141 juvenile 31 12.700000 cranwelli False
67 2013_06_11 IV 1 273 26 2.030000 -264 844 1.970000 -0.160000 124 81 0.290000 2205 -2136 juvenile 31 12.700000 cranwelli False
68 2013_06_11 IV 2 720 27 5.360000 -342 1515 2.550000 -0.226000 137 0 0.830000 5259 -2497 juvenile 31 12.700000 cranwelli False
69 2013_06_11 IV 3 582 33 4.340000 -231 279 1.720000 -0.033000 60 4 0.030000 9705 -3847 juvenile 31 12.700000 cranwelli False
70 2013_06_11 IV 4 198 23 1.470000 -209 1427 1.550000 -0.151000 110 69 0.840000 1793 -1889 juvenile 31 12.700000 cranwelli False
71 2013_06_14 IV 1 198 6 1.470000 -292 2874 2.170000 -0.232000 145 50 0.990000 1369 -2018 juvenile 31 12.700000 cranwelli False
72 2013_06_18 IV 1 597 29 4.440000 -339 4251 2.530000 -0.281000 191 12 1.000000 3116 -1772 juvenile 31 12.700000 cranwelli False
73 2013_06_18 IV 2 516 31 3.840000 -371 626 2.760000 -0.094000 83 18 0.120000 6184 -4447 juvenile 31 12.700000 cranwelli False
74 2013_06_18 IV 3 815 34 6.070000 -331 1254 2.470000 -0.077000 151 20 0.710000 5386 -2190 juvenile 31 12.700000 cranwelli True
75 2013_06_18 IV 4 402 38 3.000000 -302 986 2.250000 -0.122000 117 30 0.070000 3446 -2591 juvenile 31 12.700000 cranwelli False
76 2013_06_21 IV 1 605 39 4.500000 -216 1627 1.610000 -0.139000 123 20 1.000000 4928 -1759 juvenile 31 12.700000 cranwelli False
77 2013_06_21 IV 2 711 76 5.300000 -163 2021 1.210000 -0.217000 129 42 0.970000 5498 -1257 juvenile 31 12.700000 cranwelli False
78 2013_06_21 IV 3 614 33 4.570000 -367 1366 2.730000 -0.198000 128 108 0.460000 4776 -2857 juvenile 31 12.700000 cranwelli False
79 2013_06_21 IV 4 468 36 3.480000 -218 1269 1.630000 -0.122000 129 68 0.610000 3617 -1688 juvenile 31 12.700000 cranwelli False

Computing environment

[8]:

%load_ext watermark
%watermark -v -p pandas,jupyterlab

Python implementation: CPython
Python version       : 3.8.10
IPython version      : 7.22.0

pandas    : 1.2.4
jupyterlab: 3.0.14