Exercise 3.3: 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]:

%load_ext blackcellmagic

[8]:

df = df.assign(
    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)

[8]:

	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

[9]:

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

Python implementation: CPython
Python version       : 3.11.9
IPython version      : 8.20.0

pandas    : 2.2.1
jupyterlab: 4.0.13