Pandas Exercise 6 : Stats

The continuity of my practice on Pandas exercise from guisapmora.

US - Baby Names Dataset

Introduction:

We are going to use a subset of US Baby Names from Kaggle.
In the file it will be names from 2004 until 2014

Step 1. Import the necessary libraries

import pandas as pd

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called baby_names.

url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names/US_Baby_Names_right.csv'
baby_name = pd.read_csv(url)

Step 4. See the first 10 entries

baby_name.head(10)

	Unnamed: 0	Id	Name	Year	Gender	State	Count
0	11349	11350	Emma	2004	F	AK	62
1	11350	11351	Madison	2004	F	AK	48
2	11351	11352	Hannah	2004	F	AK	46
3	11352	11353	Grace	2004	F	AK	44
4	11353	11354	Emily	2004	F	AK	41
5	11354	11355	Abigail	2004	F	AK	37
6	11355	11356	Olivia	2004	F	AK	33
7	11356	11357	Isabella	2004	F	AK	30
8	11357	11358	Alyssa	2004	F	AK	29
9	11358	11359	Sophia	2004	F	AK	28

Step 5. Delete the column ‘Unnamed: 0’ and ‘Id’

baby_name = baby_name.drop(columns=['Unnamed: 0', 'Id'], axis=1)

Step 6. Is there more male or female names in the dataset?

baby_name['Gender'].value_counts()

F    558846
M    457549
Name: Gender, dtype: int64

Step 7. Group the dataset by name and assign to names

names = baby_name.groupby(['Name']).count()

Step 8. How many different names exist in the dataset?

names.shape[0]

17632

Step 9. What is the name with most occurrences?

names['Count'].sort_values(ascending=False).head(1)

Name
Riley    1112
Name: Count, dtype: int64

Step 10. How many different names have the least occurrences?

names[names['Count'] == names['Count'].min()].shape[0]

3682

Step 11. What is the median name occurrence?

names['Count'].median()

8.0

Step 12. What is the standard deviation of names?

names['Count'].std()

122.02996350814125

Step 13. Get a summary with the mean, min, max, std and quartiles.

names['Count'].describe()

count    17632.000000
mean        57.644907
std        122.029964
min          1.000000
25%          2.000000
50%          8.000000
75%         39.000000
max       1112.000000
Name: Count, dtype: float64

Wind Statistics Dateset

Introduction:

The data have been modified to contain some missing values, identified by NaN.
Using pandas should make this exercise easier, in particular for the bonus question.

You should be able to perform all of these operations without using a for loop or other looping construct.

1. The data in ‘wind.data’ has the following format:

"""
Yr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL
61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04
61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83
61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71
"""

'\nYr Mo Dy   RPT   VAL   ROS   KIL   SHA   BIR   DUB   CLA   MUL   CLO   BEL   MAL\n61  1  1 15.04 14.96 13.17  9.29   NaN  9.87 13.67 10.25 10.83 12.58 18.50 15.04\n61  1  2 14.71   NaN 10.83  6.50 12.62  7.67 11.50 10.04  9.79  9.67 17.54 13.83\n61  1  3 18.50 16.88 12.33 10.13 11.17  6.17 11.25   NaN  8.50  7.67 12.75 12.71\n'

The first three columns are year, month and day. The remaining 12 columns are average windspeeds in knots at 12 locations in Ireland on that day.

More information about the dataset go here.

Step 1. Import the necessary libraries

import pandas as pd

Step 2. Import the dataset from this address

Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index.

url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/Wind_Stats/wind.data'
data = pd.read_csv(url, sep='\s+', parse_dates=[0,1,2])

Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it.

data['Yr'] = data['Yr'].apply(lambda x : '19' + x)

Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns].

data.set_index(pd.to_datetime(data['Yr']+data['Mo']+data['Dy'], format='%Y%m%d'), inplace=True)
data.drop(columns=['Yr', 'Mo', 'Dy'], axis=1, inplace=True)

Step 6. Compute how many values are missing for each location over the entire record.

They should be ignored in all calculations below.

data.isna().sum()

RPT    6
VAL    3
ROS    2
KIL    5
SHA    2
BIR    0
DUB    3
CLA    2
MUL    3
CLO    1
BEL    0
MAL    4
dtype: int64

Step 7. Compute how many non-missing values there are in total.

(data.count() - data.isna().sum()).sum()

78826

Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.

A single number for the entire dataset.

data.mean().mean()

10.227982360836924

Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days

A different set of numbers for each location.

loc_stats = pd.DataFrame(data.describe()).T
loc_stats

	count	mean	std	min	25%	50%	75%	max
RPT	6568.0	12.362987	5.618413	0.67	8.12	11.71	15.92	35.80
VAL	6571.0	10.644314	5.267356	0.21	6.67	10.17	14.04	33.37
ROS	6572.0	11.660526	5.008450	1.50	8.00	10.92	14.67	33.84
KIL	6569.0	6.306468	3.605811	0.00	3.58	5.75	8.42	28.46
SHA	6572.0	10.455834	4.936125	0.13	6.75	9.96	13.54	37.54
BIR	6574.0	7.092254	3.968683	0.00	4.00	6.83	9.67	26.16
DUB	6571.0	9.797343	4.977555	0.00	6.00	9.21	12.96	30.37
CLA	6572.0	8.495053	4.499449	0.00	5.09	8.08	11.42	31.08
MUL	6571.0	8.493590	4.166872	0.00	5.37	8.17	11.19	25.88
CLO	6573.0	8.707332	4.503954	0.04	5.33	8.29	11.63	28.21
BEL	6574.0	13.121007	5.835037	0.13	8.71	12.50	16.88	42.38
MAL	6570.0	15.599079	6.699794	0.67	10.71	15.00	19.83	42.54

Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.

A different set of numbers for each day.

day_stats = pd.DataFrame(data.T.describe().T)
day_stats

	count	mean	std	min	25%	50%	75%	max
1961-01-01	11.0	13.018182	2.808875	9.29	10.5400	13.170	15.0000	18.50
1961-01-02	11.0	11.336364	3.188994	6.50	9.7300	10.830	13.2250	17.54
1961-01-03	11.0	11.641818	3.681912	6.17	9.3150	11.250	12.7300	18.50
1961-01-04	12.0	6.619167	3.198126	1.79	4.5700	5.855	9.1175	11.75
1961-01-05	12.0	10.630000	2.445356	6.17	9.8075	11.170	12.1700	13.33
...	...	...	...	...	...	...	...	...
1978-12-27	12.0	16.708333	7.868076	8.08	13.8025	15.025	17.3025	40.08
1978-12-28	12.0	15.150000	9.687857	5.00	9.0950	13.895	16.7000	41.46
1978-12-29	12.0	14.890000	5.756836	8.71	10.4775	14.210	17.0350	29.58
1978-12-30	12.0	15.367500	5.540437	9.13	12.3750	13.455	18.1850	28.79
1978-12-31	12.0	15.402500	5.702483	9.59	11.5300	12.080	19.5200	27.29

6574 rows × 8 columns

Step 11. Find the average windspeed in January for each location.

Treat January 1961 and January 1962 both as January.

data[data.index.month == 1].mean()

RPT    14.407735
VAL    12.362146
ROS    13.290470
KIL     6.926239
SHA    11.205064
BIR     7.827393
DUB    11.953120
CLA     9.377511
MUL     9.469915
CLO     9.880812
BEL    14.582350
MAL    18.332051
dtype: float64

Step 12. Downsample the record to a yearly frequency for each location.

data.groupby(data.index.year).mean()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
1961	12.299583	10.351796	11.362369	6.958227	10.881763	7.729726	9.733923	8.858788	8.647652	9.835577	13.502795	13.680773
1962	12.246923	10.110438	11.732712	6.960440	10.657918	7.393068	11.020712	8.793753	8.316822	9.676247	12.930685	14.323956
1963	12.813452	10.836986	12.541151	7.330055	11.724110	8.434712	11.075699	10.336548	8.903589	10.224438	13.638877	14.999014
1964	12.363661	10.920164	12.104372	6.787787	11.454481	7.570874	10.259153	9.467350	7.789016	10.207951	13.740546	14.910301
1965	12.451370	11.075534	11.848767	6.858466	11.024795	7.478110	10.618712	8.879918	7.907425	9.918082	12.964247	15.591644
1966	13.461973	11.557205	12.020630	7.345726	11.805041	7.793671	10.579808	8.835096	8.514438	9.768959	14.265836	16.307260
1967	12.737151	10.990986	11.739397	7.143425	11.630740	7.368164	10.652027	9.325616	8.645014	9.547425	14.774548	17.135945
1968	11.835628	10.468197	11.409754	6.477678	10.760765	6.067322	8.859180	8.255519	7.224945	7.832978	12.808634	15.017486
1969	11.166356	9.723699	10.902000	5.767973	9.873918	6.189973	8.564493	7.711397	7.924521	7.754384	12.621233	15.762904
1970	12.600329	10.726932	11.730247	6.217178	10.567370	7.609452	9.609890	8.334630	9.297616	8.289808	13.183644	16.456027
1971	11.273123	9.095178	11.088329	5.241507	9.440329	6.097151	8.385890	6.757315	7.915370	7.229753	12.208932	15.025233
1972	12.463962	10.561311	12.058333	5.929699	9.430410	6.358825	9.704508	7.680792	8.357295	7.515273	12.727377	15.028716
1973	11.828466	10.680493	10.680493	5.547863	9.640877	6.548740	8.482110	7.614274	8.245534	7.812411	12.169699	15.441096
1974	13.643096	11.811781	12.336356	6.427041	11.110986	6.809781	10.084603	9.896986	9.331753	8.736356	13.252959	16.947671
1975	12.008575	10.293836	11.564712	5.269096	9.190082	5.668521	8.562603	7.843836	8.797945	7.382822	12.631671	15.307863
1976	11.737842	10.203115	10.761230	5.109426	8.846339	6.311038	9.149126	7.146202	8.883716	7.883087	12.332377	15.471448
1977	13.099616	11.144493	12.627836	6.073945	10.003836	8.586438	11.523205	8.378384	9.098192	8.821616	13.459068	16.590849
1978	12.504356	11.044274	11.380000	6.082356	10.167233	7.650658	9.489342	8.800466	9.089753	8.301699	12.967397	16.771370

Step 13. Downsample the record to a monthly frequency for each location.

data.groupby(data.index.month).mean()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
1	14.407735	12.362146	13.290470	6.926239	11.205064	7.827393	11.953120	9.377511	9.469915	9.880812	14.582350	18.332051
2	13.710906	12.111122	12.879132	6.942411	11.551772	7.633858	11.206024	9.341437	9.313169	9.518051	13.728898	17.156142
3	13.158687	11.505842	12.648118	7.265907	11.554516	7.959409	11.310179	9.635896	9.700324	10.096953	13.810609	16.909317
4	12.555648	10.429759	12.204815	6.898037	10.677667	7.441389	10.221315	8.909056	8.930870	9.158019	12.664759	14.937611
5	11.724032	10.145619	11.550394	6.307487	10.224301	6.942061	8.797738	8.452903	8.040806	8.524857	12.767258	13.736039
6	10.451317	8.949704	10.361315	5.652278	9.529926	6.410093	8.009556	7.920796	7.639796	7.729185	12.246407	12.861818
7	9.992007	8.357778	9.349642	5.416935	9.302634	5.972348	7.843501	7.262760	7.544480	7.321416	11.676505	12.800789
8	10.213411	8.415143	9.993441	5.270681	8.901559	5.891057	7.772312	6.842025	7.240573	7.002783	11.110090	12.565943
9	11.458519	9.981002	10.756883	5.615176	9.766315	6.566222	8.609722	7.745677	7.610556	7.689278	12.686389	14.761963
10	12.660610	11.010681	11.453943	6.065215	10.550251	7.159910	9.387778	8.726308	8.347181	8.850376	14.155323	16.697151
11	13.778291	12.140912	12.663775	6.567949	10.952411	7.447849	11.137247	8.753590	8.874886	9.258917	14.044117	18.066268
12	14.486328	12.456597	13.100194	6.903722	11.354931	7.959569	11.711028	9.246833	9.439875	9.721625	14.257306	18.476042

Step 14. Downsample the record to a weekly frequency for each location.

data.groupby(data.index.isocalendar().week).mean()

	RPT	VAL	ROS	KIL	SHA	BIR	DUB	CLA	MUL	CLO	BEL	MAL
week
1	13.920000	11.710880	12.853016	6.617302	10.473175	7.578492	11.623651	9.123600	9.272222	9.870635	14.241746	18.249841
2	15.444717	13.504528	14.104717	7.094906	12.230566	8.184717	12.113585	9.939245	9.692264	9.559434	14.948302	18.242830
3	13.676154	11.921538	13.295385	6.330769	11.052308	8.214615	11.654615	9.586154	9.337692	9.860769	14.151538	19.094615
4	16.208333	14.823333	16.127500	7.858333	13.040833	8.395000	13.055833	10.645833	10.765000	11.393333	16.794167	20.674167
5	14.640098	12.991569	13.281667	7.487647	11.969118	8.234902	12.148333	9.689216	9.834118	10.153431	14.735392	17.584608
6	13.203730	11.508492	12.142619	6.469127	11.025476	7.166746	11.115317	8.894206	9.323413	9.298175	13.903254	17.921587
7	14.517460	12.681349	13.219524	7.148160	12.141032	7.906190	11.390238	9.749524	9.417778	9.605476	13.776667	17.130159
8	13.422857	11.822937	13.367381	7.142400	11.480635	7.729683	11.329048	9.329762	9.247063	9.541984	13.132302	16.882540
9	12.761360	11.837460	12.627440	6.620000	10.937143	7.457778	10.622778	9.387778	8.864160	9.611508	13.724048	16.602080
10	13.526720	11.758095	13.151667	7.317063	11.477143	7.965397	10.751111	9.151825	9.367302	9.761429	13.055000	16.047381
11	13.597143	11.495079	13.032698	7.446270	11.689206	7.990238	11.444365	10.038968	9.783730	10.398730	13.823968	17.331280
12	13.006825	11.621032	12.579048	7.390476	11.850476	8.288095	12.008413	10.136746	10.176270	10.259048	14.389524	17.859365
13	12.772540	11.076349	12.104444	7.386640	11.664286	8.042063	11.306587	9.494365	9.977760	10.038571	14.241667	16.486508
14	13.903968	11.025238	14.149286	7.798968	11.482857	8.006905	11.525238	9.579921	9.894762	9.948175	13.386270	16.691349
15	12.599444	9.966667	11.654286	6.758175	10.642540	7.377778	10.677143	9.328413	9.248333	9.338413	12.842460	15.900476
16	12.753968	11.111587	11.966587	7.110000	10.983730	7.660794	9.981905	8.763571	8.802063	9.326905	12.472857	14.229524
17	10.966349	9.647698	11.272619	6.019286	9.627857	6.687063	8.870714	8.091825	7.836032	8.238333	11.971667	13.097460
18	12.897222	11.095079	11.256825	6.745794	11.035952	7.405238	9.318730	8.674206	8.340952	8.952857	12.928968	14.523810
19	13.061429	11.275159	12.630397	7.005238	10.944683	7.559603	9.624320	9.111349	8.604683	9.243254	13.721984	14.288016
20	11.338571	9.769200	11.566746	6.195238	9.967937	6.811429	8.732857	8.495317	7.984365	8.507540	12.740952	13.859286
21	11.282698	9.673889	11.223968	5.975680	10.051905	6.730476	8.435397	8.150556	7.871587	8.278413	12.536270	13.626825
22	9.673333	8.264683	10.382302	5.195397	8.696825	5.802063	7.275873	7.533016	7.120873	7.247222	11.238810	11.770317
23	10.092143	8.980556	10.030476	5.304048	8.882143	5.816825	7.420952	7.387857	7.031587	7.173810	11.605635	12.395476
24	10.131905	8.741984	10.518254	5.498492	8.997381	6.189762	7.320317	7.597143	7.172857	7.259524	11.835873	12.041270
25	11.680238	9.898016	10.730397	6.294444	11.030397	7.410952	9.410238	8.822698	8.728016	8.677063	13.494762	14.329841
26	10.036000	8.770635	10.113333	5.798492	9.680159	6.474603	8.226032	7.920556	7.906270	7.937778	12.462222	12.993840
27	9.991984	7.916984	8.741032	5.276825	8.998175	5.852302	7.817680	7.182143	7.662143	7.451349	11.582857	13.096984
28	9.962143	8.272619	9.651349	5.461270	9.344206	5.951111	7.937857	7.341746	7.671905	7.267063	11.450159	12.932460
29	10.281746	8.422063	9.755079	5.672778	9.766667	6.300238	8.277619	7.568175	7.725952	7.616349	12.021667	12.880476
30	9.760397	8.211984	9.181587	5.195476	8.920873	5.657222	7.566905	6.904603	7.078571	7.010317	11.410794	12.279683
31	9.721984	8.218571	9.585556	5.078651	8.940397	5.692857	7.470397	6.646746	7.039206	6.615556	11.020317	12.348492
32	10.202640	8.669683	9.357619	5.524206	9.143492	6.091984	7.518413	6.896190	7.409365	7.057840	11.154365	12.746587
33	10.213254	8.000476	10.086667	5.169127	8.638413	5.652619	7.635635	6.591746	7.033413	6.502460	10.647222	11.315680
34	10.675556	9.065317	10.466190	5.380635	9.343651	6.275317	8.164603	7.503889	7.753730	7.779206	11.881667	13.764762
35	10.378571	8.525476	10.496746	5.398571	8.739444	5.938968	8.088889	6.907857	7.063016	7.213175	11.606825	13.390873
36	10.764365	9.296190	9.931270	5.387381	9.245873	6.353810	8.314603	7.012857	7.449762	7.221905	11.597619	13.754921
37	11.371190	9.722857	10.963889	5.638810	9.603968	6.646587	8.698492	7.704841	7.532143	7.645952	12.658968	14.827540
38	9.929524	8.863095	10.245280	4.462080	8.346190	5.408175	7.285079	6.724640	6.387381	6.423095	11.394127	13.094921
39	13.960635	12.361920	11.787302	6.898095	11.971032	7.882540	10.012619	9.489206	8.933413	9.402540	14.980000	16.928095
40	12.199841	10.619127	11.392698	5.854048	10.308175	6.841508	9.301905	8.581111	8.561667	8.519524	13.326429	15.758095
41	12.260800	10.581508	11.316190	5.468889	9.998254	6.667460	8.667778	8.279444	7.782540	8.176825	13.550952	15.663571
42	12.777460	10.966587	11.381667	6.349048	10.716032	7.526032	9.941349	8.802222	8.460873	9.039286	14.088175	17.116111
43	13.358651	11.915238	11.888095	6.554286	11.363571	7.769524	10.170873	9.458889	8.925840	9.800873	16.055873	18.580952
44	14.077179	12.215077	12.850000	6.805128	11.049692	7.597077	10.420667	9.005949	8.835436	9.559487	14.284103	17.692462
45	14.400698	12.762605	13.244791	6.862791	11.556355	7.894000	11.490186	9.447116	9.364977	9.642093	14.449116	18.232791
46	14.096846	12.565000	13.101462	6.983154	11.603462	7.844615	11.637769	9.014615	9.155154	9.582923	14.284692	19.428923
47	11.731508	10.361667	11.107698	5.413968	9.200952	6.172540	10.071920	7.322222	7.690397	7.933095	12.399762	16.473254
48	13.817239	12.136074	12.050061	6.297791	11.072699	7.641472	11.273804	8.655706	8.907791	9.249939	14.161288	17.849571
49	14.776094	13.048205	12.752650	7.306880	11.743291	8.209444	12.114231	9.496624	9.844060	10.012222	14.860000	18.847436
50	14.520350	12.493846	12.796224	7.096643	11.650769	8.277483	11.867343	9.456434	9.483706	9.968462	14.752168	18.312517
51	13.639921	11.697619	12.501667	5.991270	10.541667	7.275159	10.301667	8.462619	8.438730	8.687937	13.298889	17.182063
52	14.469134	11.692283	14.210000	7.018189	10.856905	7.379213	12.011969	9.121811	9.665197	9.677953	13.549055	19.183228
53	12.694286	9.451905	13.526190	5.343810	8.040476	5.884286	10.366190	5.556190	6.954286	7.820952	11.028095	17.103810

Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks.

data.groupby(data.index.isocalendar().week).describe()

	RPT								VAL		...	BEL		MAL
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
week
1	126.0	13.920000	6.341312	2.62	9.2600	13.020	17.3850	33.34	125.0	11.710880	...	18.8700	38.20	126.0	18.249841	7.047385	4.17	13.3225	17.435	23.2900	37.63
2	53.0	15.444717	5.462035	5.29	11.3400	15.090	19.7500	28.75	53.0	13.504528	...	18.8400	31.08	53.0	18.242830	6.881317	3.63	12.5000	19.040	23.3300	31.75
3	13.0	13.676154	4.875431	7.54	9.7100	11.460	18.0800	22.37	13.0	11.921538	...	15.5000	23.04	13.0	19.094615	6.579779	6.79	15.7500	19.550	20.8300	32.83
4	12.0	16.208333	6.112314	2.08	11.7600	17.870	19.5925	24.25	12.0	14.823333	...	20.8150	29.88	12.0	20.674167	8.356038	12.29	14.1975	17.795	25.5275	40.12
5	102.0	14.640098	5.648340	2.71	10.8425	14.310	18.5375	33.84	102.0	12.991569	...	19.1350	27.71	102.0	17.584608	6.482263	4.83	12.9350	17.625	22.1700	34.25
6	126.0	13.203730	5.620867	2.79	9.1800	12.105	17.3600	30.13	126.0	11.508492	...	17.8200	28.12	126.0	17.921587	6.092634	5.29	12.9600	17.330	21.6925	33.04
7	126.0	14.517460	5.916627	2.92	10.2725	14.190	18.2925	29.17	126.0	12.681349	...	18.2700	32.08	126.0	17.130159	6.819665	5.09	11.8225	16.315	21.7900	37.04
8	126.0	13.422857	5.923096	1.54	9.0625	12.310	17.4900	32.38	126.0	11.822937	...	16.7275	35.08	126.0	16.882540	7.251216	3.04	11.3025	16.130	22.2200	38.20
9	125.0	12.761360	5.240324	0.67	9.0800	12.580	16.3800	28.91	126.0	11.837460	...	18.7950	31.13	125.0	16.602080	6.191920	3.17	12.5000	15.750	20.5400	31.66
10	125.0	13.526720	6.165086	2.29	8.7100	12.620	17.0400	35.80	126.0	11.758095	...	17.2275	32.63	126.0	16.047381	6.572057	2.46	11.3900	15.670	20.4700	37.59
11	126.0	13.597143	6.412912	1.46	8.8725	12.665	18.1450	33.04	126.0	11.495079	...	19.0175	34.92	125.0	17.331280	7.428510	4.54	11.5400	16.880	21.4600	40.37
12	126.0	13.006825	5.509073	2.92	9.2400	12.645	16.8475	32.13	126.0	11.621032	...	18.6875	31.71	126.0	17.859365	6.902719	3.21	13.2650	18.185	22.3075	37.99
13	126.0	12.772540	5.278774	2.79	8.6125	12.895	16.0500	27.12	126.0	11.076349	...	17.5800	32.21	126.0	16.486508	6.644671	2.04	12.6525	15.955	20.3800	31.20
14	126.0	13.903968	5.698092	3.88	9.8225	13.790	17.4900	32.58	126.0	11.025238	...	17.4100	25.50	126.0	16.691349	6.979889	3.71	11.8225	16.125	20.9775	34.08
15	126.0	12.599444	5.335705	4.42	8.4300	11.650	15.6900	29.79	126.0	9.966667	...	15.9975	27.00	126.0	15.900476	6.795427	4.33	11.3225	14.335	20.7775	33.95
16	126.0	12.753968	5.248461	3.67	8.6325	12.145	16.2375	27.84	126.0	11.111587	...	15.5675	23.96	126.0	14.229524	5.328738	4.17	10.4800	13.750	17.6975	28.67
17	126.0	10.966349	5.378706	3.46	6.3225	9.960	14.0900	26.83	126.0	9.647698	...	15.7300	26.12	126.0	13.097460	6.018248	2.21	8.3800	12.580	16.8250	32.05
18	126.0	12.897222	4.367054	1.63	9.8825	12.830	16.0650	27.25	126.0	11.095079	...	16.3800	28.33	126.0	14.523810	6.216011	2.58	10.2000	13.415	18.6475	28.75
19	126.0	13.061429	5.517038	3.54	8.5925	13.025	16.4900	30.91	126.0	11.275159	...	17.2875	32.91	126.0	14.288016	5.699596	3.33	10.0625	14.500	18.3400	26.83
20	126.0	11.338571	5.404169	2.42	6.8400	10.125	15.0775	25.17	125.0	9.769200	...	16.1475	26.42	126.0	13.859286	5.865042	3.58	9.5225	13.480	17.3850	32.17
21	126.0	11.282698	5.200316	2.62	7.5600	10.815	13.9725	28.79	126.0	9.673889	...	15.6975	28.12	126.0	13.626825	5.258812	4.04	9.4550	13.480	16.6875	29.50
22	126.0	9.673333	4.053188	2.04	6.7300	9.250	11.9875	21.84	126.0	8.264683	...	13.6575	26.38	126.0	11.770317	5.001233	1.75	7.8525	11.650	15.1000	28.04
23	126.0	10.092143	4.516349	2.17	6.7600	9.665	12.5700	23.21	126.0	8.980556	...	14.5400	21.34	126.0	12.395476	5.383357	2.21	8.3825	11.895	16.0700	32.79
24	126.0	10.131905	4.410570	1.00	6.5400	9.960	12.8300	21.59	126.0	8.741984	...	14.6475	25.25	126.0	12.041270	5.419266	2.79	7.8525	11.685	15.3400	27.16
25	126.0	11.680238	4.415008	2.92	7.9700	11.145	14.5300	22.71	126.0	9.898016	...	16.2400	29.79	126.0	14.329841	5.873487	3.08	10.1400	14.020	17.4475	28.38
26	125.0	10.036000	3.868826	3.46	6.9200	9.670	12.7900	20.50	126.0	8.770635	...	15.6050	24.71	125.0	12.993840	4.516285	4.54	9.3300	12.710	15.9200	25.17
27	126.0	9.991984	4.742847	2.00	6.7900	8.960	12.4100	25.84	126.0	7.916984	...	14.5000	23.00	126.0	13.096984	5.292558	4.50	9.0800	12.560	16.2800	29.63
28	126.0	9.962143	4.661313	2.75	6.2500	9.105	13.3500	22.50	126.0	8.272619	...	14.4175	23.83	126.0	12.932460	5.565273	2.13	8.7400	12.420	16.3200	28.46
29	126.0	10.281746	4.578130	1.71	6.8325	10.330	13.4575	22.34	126.0	8.422063	...	15.2175	24.41	126.0	12.880476	5.472898	2.29	8.5000	12.810	17.3800	25.37
30	126.0	9.760397	4.056483	1.25	6.4800	9.335	12.5500	20.33	126.0	8.211984	...	14.2700	22.37	126.0	12.279683	5.055287	2.88	8.2100	12.310	15.6900	25.37
31	126.0	9.721984	4.143365	2.79	6.5825	9.420	12.1075	22.42	126.0	8.218571	...	13.7375	21.67	126.0	12.348492	5.053851	2.25	9.0925	12.625	16.3875	24.83
32	125.0	10.202640	3.982815	2.67	6.8700	10.250	13.1300	20.08	126.0	8.669683	...	13.6925	24.08	126.0	12.746587	5.353812	3.04	8.9650	12.500	16.3125	29.95
33	126.0	10.213254	4.914926	3.17	6.6400	9.040	13.4075	26.38	126.0	8.000476	...	13.5650	23.25	125.0	11.315680	5.587084	2.17	7.1700	10.750	14.6200	34.33
34	126.0	10.675556	4.874965	2.42	7.1700	10.460	14.2800	24.67	126.0	9.065317	...	16.0700	24.83	126.0	13.764762	5.642352	3.37	9.1250	13.290	18.0000	30.46
35	126.0	10.378571	4.976603	0.96	6.0800	10.040	13.5900	23.38	126.0	8.525476	...	14.7900	26.46	126.0	13.390873	5.962103	3.83	8.7500	12.395	17.3900	28.84
36	126.0	10.764365	5.086312	1.50	6.6500	10.400	14.3675	29.54	126.0	9.296190	...	14.9150	24.17	126.0	13.754921	5.692819	3.37	9.4700	13.940	17.0300	30.34
37	126.0	11.371190	5.662910	1.79	7.2600	10.775	14.9800	31.42	126.0	9.722857	...	16.2500	25.75	126.0	14.827540	6.350553	2.83	10.3800	14.830	18.0500	35.13
38	126.0	9.929524	4.585719	0.79	6.5325	9.165	12.7800	24.79	126.0	8.863095	...	14.7400	23.04	126.0	13.094921	5.420672	0.67	9.0825	12.670	17.2975	29.63
39	126.0	13.960635	5.313510	3.37	10.0100	13.545	17.1300	27.00	125.0	12.361920	...	19.4950	28.79	126.0	16.928095	6.943457	4.04	11.9125	15.895	20.8100	33.63
40	126.0	12.199841	4.458238	2.37	8.9200	12.380	15.0900	25.29	126.0	10.619127	...	17.4275	26.71	126.0	15.758095	6.364950	2.04	11.2900	15.565	20.4100	32.96
41	125.0	12.260800	5.400736	3.04	8.2900	11.790	15.1600	25.92	126.0	10.581508	...	17.4375	34.83	126.0	15.663571	6.537501	3.33	10.7200	15.120	19.9875	36.51
42	126.0	12.777460	5.718162	2.04	8.6600	12.145	16.2700	28.62	126.0	10.966587	...	17.0825	32.63	126.0	17.116111	6.401907	5.04	12.1125	16.395	21.5900	33.45
43	126.0	13.358651	6.116732	1.50	9.0200	12.670	17.0100	29.08	126.0	11.915238	...	20.7000	38.96	126.0	18.580952	6.799425	6.17	12.9900	17.965	23.4100	36.63
44	195.0	14.077179	5.833335	2.00	9.9600	13.620	17.5400	32.17	195.0	12.215077	...	18.1450	39.04	195.0	17.692462	7.021542	4.54	12.3550	17.250	21.8400	37.59
45	215.0	14.400698	6.254240	1.63	9.7700	13.790	18.1200	33.12	215.0	12.762605	...	18.7300	33.34	215.0	18.232791	7.140148	2.96	13.2900	17.960	23.1300	38.04
46	130.0	14.096846	6.405892	2.92	9.0800	13.000	18.7800	30.21	130.0	12.565000	...	18.8800	31.96	130.0	19.428923	8.585392	2.00	13.1800	18.935	24.7150	41.25
47	126.0	11.731508	5.234852	2.67	8.0950	10.750	14.8300	26.50	126.0	10.361667	...	16.4100	30.00	126.0	16.473254	6.677397	4.25	10.8625	15.710	20.8900	34.83
48	163.0	13.817239	6.125372	3.08	9.0800	13.170	17.8100	34.37	163.0	12.136074	...	18.7900	42.38	163.0	17.849571	6.642826	4.79	12.7300	17.290	22.5850	42.54
49	233.0	14.776094	6.192947	0.67	10.3400	13.920	19.0800	35.38	234.0	13.048205	...	18.8700	34.42	234.0	18.847436	7.020126	3.25	14.2300	19.165	23.0975	38.79
50	143.0	14.520350	6.803921	1.96	9.7300	14.420	18.2900	30.91	143.0	12.493846	...	18.7900	29.25	143.0	18.312517	6.500093	6.50	13.6700	18.710	21.8950	37.12
51	126.0	13.639921	5.313674	1.79	9.5225	13.270	16.9950	28.71	126.0	11.697619	...	17.2675	30.84	126.0	17.182063	6.303056	2.62	13.0100	16.685	20.7175	38.25
52	127.0	14.469134	5.701056	3.00	10.2450	13.620	18.1250	32.50	127.0	11.692283	...	17.2300	31.25	127.0	19.183228	6.834899	4.58	14.6450	18.710	22.7700	41.46
53	21.0	12.694286	5.804934	3.71	8.6700	11.750	16.7500	23.83	21.0	9.451905	...	13.3700	30.46	21.0	17.103810	5.909188	7.12	13.7000	16.580	23.1300	26.83

53 rows × 96 columns