import numpy as np
import pandas as pd
from collections import OrderedDict #sorting participant df dict before pd.concat()
import matplotlib.pylab as plt
%matplotlib inline
#pd.options.display.mpl_style = 'default'
import cPickle as pickle
def col_matches(df, regex):
import re
cols = list(enumerate(df.columns))
matches = [c for (i, c) in cols
if re.findall(regex, c)]
return matches
def concat_matches(df, *args):
assert all([len(r) for r in args])
import re
col_match_lists = [col_matches(df, regex) for regex in args]
col_set = [df[matches] for matches in col_match_lists]
if len(col_set) == 0:
return None
elif len(col_set) == 1:
return col_set[0]
else:
return pd.concat(col_set, axis=1)
def hist_all(df, *args, **kwargs):
numcols = len(df.columns)
if numcols > 30:
yn = raw_input(str(numcols) + " columns. Proceed?")
if 'n' in yn: return None
for c in df:
print(c)
try:
plt.hist(df[c])
plt.show()
except:
print("\t(can't histogram this)\n")
def scatter_all(df, print_max=None, *args, **kwargs):
from itertools import combinations
numcols = len(df.columns)
if numcols > 6:
yn = raw_input(str(numcols) + " columns. Proceed?")
if 'n' in yn: return None
combos = combinations(df.columns, 2)
for c in combos:
print(c)
x = df[c[0]]
y = df[c[1]]
dfc = pd.concat([x, y], axis=1)
xsort = dfc.sort(columns=dfc.columns[0], inplace=False)
ysort = dfc.sort(columns=dfc.columns[1], inplace=False)
try:
dfc.plot(kind='scatter', x=0, y=1)
plt.show()
except:
print("can't plot")
if print_max:
print(xsort.head(print_max))
print(ysort.head(print_max))
df = pd.DataFrame.from_csv('RS2_from_spss_1102a.csv')
df.replace('77777', np.nan, inplace=True)
df.replace(77777, np.nan, inplace=True)
df
SCAL_order_500ms_first | SCAL_sex_femalezero | SCAL_calc_wasivocab_tscore | SCAL_calc_wasimatrix_tscore | SCAL_calc_wasi_tscore_total | SCAL_calc_fsiq2 | SCAL_calc_bfi_extraversion | SCAL_calc_bfi_agreeableness | SCAL_calc_bfi_conscientiousness | SCAL_calc_bfi_neuroticism | ... | lin_T_DPsd_trunc_log_z_mean1 | lin_J_DPsd_trunc_log_z_mean1 | mahal_6_means | p_mahal_6_means | mahal_6_means_outlier | margmean_stimtype_single | margmean_stimtype_grouped | margmean_timingtype_iso | margmean_timingtype_phase | margmean_timingtype_linear | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | |||||||||||||||||||||
15 | 0 | 0 | 49 | 38 | 87 | 89 | 2.125 | 3.666667 | 3.777778 | 3.125000 | ... | 2.41479863957074 | .488496545260892 | 33.6392207999273 | 7.89710605930694E-006 | 1 | 2.45062501248684 | .103663690638599 | .236212572256706 | 2.143573 | 1.45164759241582 |
16 | 1 | 1 | 78 | 53 | 131 | 127 | 4.000 | 4.111111 | 2.777778 | 3.250000 | ... | -.956139882350332 | -.902618789114951 | 2.24339446220351 | .896008294431634 | 0 | -.956508180951858 | -.78500194799631 | -.645448902472901 | -1.037437 | -.929379335732641 |
17 | 1 | 1 | 55 | 54 | 109 | 108 | 2.750 | 2.888889 | 3.555556 | 3.000000 | ... | .252346740556394 | .711638214236406 | 1.71921146741239 | .943628491922479 | 0 | .0662575348196777 | .59473994167723 | .291147928532393 | 0.218356 | .4819924773964 |
18 | 1 | 1 | 50 | 53 | 103 | 102 | 3.000 | 4.111111 | 4.555556 | 3.000000 | ... | -.232970866066621 | .143215667951748 | 1.83930695429923 | .93386824738905 | 0 | -.116083622371095 | .125817273375142 | .217271341157432 | -0.157793 | -.0448775990574368 |
19 | 1 | 1 | 55 | 55 | 110 | 109 | 4.500 | 4.444444 | 4.000000 | 1.750000 | ... | -.344137535143529 | .682892642352066 | 15.5781339636336 | .0162066318562994 | 0 | -.775698465456511 | .835927016285923 | -.453276887738083 | 0.374242 | .169377553604268 |
20 | 0 | 1 | 57 | 49 | 106 | 105 | 1.750 | 4.222222 | 3.111111 | 2.625000 | ... | .826072863176034 | .795536455929486 | 4.37106425113759 | .626595714767085 | 0 | .931883236384881 | .588333683348445 | .840826734745444 | 0.628694 | .81080465955276 |
21 | 0 | 0 | 53 | 42 | 95 | 95 | 2.125 | 3.888889 | 3.888889 | 3.875000 | ... | .157111396760972 | -.291339665377024 | 2.04172391165692 | .915821513140055 | 0 | -.00987775723761557 | -.407069533136371 | .0544826277013487 | -0.612789 | -.0671141343080258 |
22 | 0 | 0 | 57 | 46 | 103 | 102 | 3.250 | 3.555556 | 4.444444 | 3.250000 | ... | -1.34696863469955 | .290112218811589 | 6.18177543392616 | .403138791617383 | 0 | -1.03251044316227 | -.189685027020792 | -.786980773987168 | -0.517884 | -.528428207943981 |
24 | 1 | 0 | 44 | 55 | 99 | 99 | 2.500 | 5.000000 | 3.444444 | 2.125000 | ... | -.131413124738908 | -.0883182250488547 | 1.7005888003371 | .945075802760578 | 0 | -.306429048145833 | .00487689684286133 | -.0934068858130599 | -0.249056 | -.109865674893881 |
25 | 0 | 1 | 47 | 48 | 95 | 95 | 2.625 | 2.222222 | 2.555556 | 3.500000 | ... | .432647543673544 | -.250730624719894 | 1.55397455024741 | .955828934165556 | 0 | .423959347426022 | -.0466827965305185 | .249326296244289 | 0.225630 | .0909584594768247 |
27 | 1 | 1 | 53 | 53 | 106 | 105 | 3.500 | 3.222222 | 4.555556 | 3.625000 | ... | -1.29196809983856 | 2.11683674375929 | 19.9032863709987 | .00288131552522208 | 0 | -1.10371642547995 | .567023195685901 | -1.03330599738464 | -0.184168 | .412434321960363 |
28 | 1 | 1 | 63 | 55 | 118 | 116 | 2.250 | 2.888889 | 3.333333 | 3.125000 | ... | -.720778590757647 | -.786723939234748 | 1.05607337909654 | .98339703577509 | 0 | -.622006606382825 | -.621218918524863 | -.568390872087353 | -0.542696 | -.753751264996197 |
29 | 0 | 0 | 52 | 57 | 109 | 108 | 3.750 | 4.333333 | 4.111111 | 2.625000 | ... | 2.00213673295585 | .703742954108305 | 8.96259767003283 | .175692504383232 | 0 | 1.27110318797952 | .24193172142882 | .814236644784534 | 0.102376 | 1.35293984353208 |
30 | 1 | 0 | 61 | 56 | 117 | 115 | 2.750 | 4.222222 | 3.888889 | 3.375000 | ... | -.445056516023456 | -.848169743607257 | 3.13888867968774 | .791223867007373 | 0 | -.390565168012986 | -.462409180693642 | -.442769406375548 | -0.190079 | -.646613129815357 |
32 | 0 | 0 | 57 | 55 | 112 | 110 | 2.750 | 4.000000 | 2.555556 | 3.000000 | ... | -2.01932573775872 | -.274464137953002 | 6.42770037124039 | .377020684436971 | 0 | -1.6924176850492 | -.871916815347872 | -1.50231004590069 | -1.197297 | -1.14689493785586 |
33 | 1 | 1 | 61 | 58 | 119 | 117 | 3.750 | 4.222222 | 2.444444 | 2.375000 | ... | -.999704474134522 | -1.06769652100468 | 3.30742947779901 | .769388678347646 | 0 | -.711399589891362 | -.843494475113428 | -.837301345718662 | -0.461339 | -1.0337004975696 |
34 | 0 | 1 | 53 | 51 | 104 | 103 | 3.375 | 3.888889 | 3.555556 | 2.750000 | ... | -1.38645499059511 | -1.12424124965556 | 5.57585086646438 | .472336916342026 | 0 | -1.48201510976881 | -1.21590273984035 | -1.52560429712915 | -1.265924 | -1.25534812012534 |
35 | 1 | 0 | 57 | 52 | 109 | 108 | 2.500 | 4.333333 | 4.666667 | 3.000000 | ... | .00815031711864539 | -.130874950286611 | 3.98760280355613 | .678354188894757 | 0 | -.0136005988131506 | .0932535536910965 | .657795658767525 | -0.476954 | -.061362316583983 |
36 | 0 | 0 | 43 | 46 | 89 | 90 | 3.000 | 4.111111 | 3.222222 | 3.250000 | ... | 1.48022677653023 | 1.23571676121342 | 12.1654013884458 | .0583790008671147 | 0 | 1.56384749692658 | 1.44030453030157 | 1.74551488781656 | 1.402741 | 1.35797176887183 |
38 | 1 | 1 | 57 | 53 | 110 | 109 | 2.750 | 4.555556 | 3.333333 | 1.750000 | ... | .352521785405728 | .515165807556559 | 7.95382059066571 | .241506093850357 | 0 | -.318398601536322 | .508883798695831 | -.214516788531001 | 0.066401 | .433843796481144 |
39 | 0 | 1 | 35 | 60 | 95 | 95 | 3.375 | 4.111111 | 3.888889 | 2.875000 | ... | -.23441682240522 | .710511191502002 | 2.93955100347914 | .816394707152966 | 0 | -.156135970303389 | .0952160339803603 | -.476302458924801 | 0.146875 | .238047184548391 |
40 | 1 | 1 | 52 | 66 | 118 | 116 | 4.625 | 4.666667 | 4.000000 | 2.500000 | ... | -.601738322887631 | .885423450240969 | 6.19998316498271 | .401164969383206 | 0 | -.422669456091252 | -.0341489915222563 | -.427046766623458 | -0.400023 | .141842563676669 |
41 | 1 | 0 | 53 | 54 | 107 | 106 | 5.000 | 4.777778 | 4.000000 | 1.375000 | ... | -.713178670614387 | -.24118240206015 | 13.7252626666567 | .0328603158202417 | 0 | -.697242886852501 | .203863643968205 | -.634133254577276 | 0.371245 | -.477180536337269 |
43 | 1 | 0 | 38 | 38 | 76 | 79 | 3.250 | 3.444444 | 3.666667 | 3.125000 | ... | 1.31109496380364 | .486373028887212 | 4.86598859752482 | .561112597514703 | 0 | .66687171899048 | .42772160649977 | .723053229141439 | 0.020103 | .898733996345427 |
44 | 0 | 0 | 55 | 53 | 108 | 107 | 4.750 | 4.666667 | 3.444444 | 1.875000 | ... | .401096431561236 | 1.05898567973287 | 7.62470712302772 | .266907201432871 | 0 | -.209015538844806 | .196473619342274 | -.31710716173804 | -0.431747 | .730041055647055 |
46 | 0 | 1 | 51 | 42 | 93 | 94 | 2.375 | 3.666667 | 4.333333 | 2.625000 | ... | .320801016794381 | .0249997603893649 | .62158898869802 | .996029578566886 | 0 | .0928883169709507 | -.00609493234790095 | .0627801373515034 | -0.105490 | .172900388591873 |
47 | 0 | 1 | 57 | 68 | 125 | 122 | 3.750 | 4.000000 | 3.444444 | 3.125000 | ... | .123073984892548 | .0450592730138396 | 1.26212130572192 | .973705245858189 | 0 | .197462960612565 | -.0768469088947682 | .285938788824381 | -0.189081 | .0840666289531936 |
48 | 1 | 1 | 53 | 55 | 108 | 107 | 4.000 | 3.333333 | 3.666667 | 2.375000 | ... | .896507238457345 | -.773259473205702 | 8.57231536472688 | .199097527567765 | 0 | .623440508088971 | -.805722510861195 | -.16342455759894 | -0.171622 | .0616238826258213 |
49 | 1 | 0 | 47 | 57 | 104 | 103 | 3.250 | 4.111111 | 3.666667 | 3.750000 | ... | 2.41479863957074 | 2.46937357569529 | 2.79820024545946 | 2.826540 | 2.44208610763302 | |||||
51 | 0 | 0 | 53 | 51 | 104 | 103 | 3.250 | 4.444444 | 3.333333 | 3.125000 | ... | -.771950855700743 | -.839674855402413 | 1.94002516443163 | .925130956416922 | 0 | -.47685550496923 | -.652200653280166 | -.569122835967217 | -0.318649 | -.805812855551578 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
91 | 0 | 0 | 53 | 54 | 107 | 106 | 4.250 | 4.888889 | 3.888889 | 2.625000 | ... | -.710397533033634 | -1.13953324382673 | 3.37990008708332 | .759873789400962 | 0 | -.742319681908469 | -1.06820657411179 | -.973470255465226 | -0.817354 | -.924965388430184 |
92 | 0 | 0 | 47 | 50 | 97 | 97 | 4.000 | 4.444444 | 3.555556 | 2.250000 | ... | .612254919446989 | -.583591281013204 | 5.79753451582022 | .446248475898716 | 0 | .0213309262735052 | -.262421122887194 | -.0463930825763609 | -0.329574 | .0143318192168925 |
93 | 1 | 0 | 46 | 49 | 95 | 95 | 2.875 | 4.666667 | 4.111111 | 3.000000 | ... | .280831509224923 | .493283030267013 | .96341110961161 | .986961359704622 | 0 | .0848947730680617 | .386096071453332 | .322615336800209 | -0.003186 | .387057269745968 |
94 | 1 | 0 | 46 | 48 | 94 | 94 | 4.750 | 4.444444 | 4.555556 | 1.750000 | ... | .507222424677446 | .725764877979718 | 2.82003739806162 | .831071404921125 | 0 | .548907433510664 | .6208359378336 | .849584536964327 | 0.288537 | .616493651328582 |
95 | 0 | 1 | 61 | 51 | 112 | 110 | 2.625 | 3.777778 | 4.666667 | 1.625000 | ... | -.328191670945402 | .218982975967186 | 2.81928958080531 | .831162142651566 | 0 | -.367039884243291 | -.345580960631314 | -.342938930313872 | -0.671388 | -.0546043474891076 |
96 | 1 | 0 | 53 | 66 | 119 | 117 | 4.875 | 3.666667 | 3.333333 | 3.625000 | ... | -.221951836978301 | -.156105965090771 | 1.0630618286923 | .983108342848881 | 0 | -.393627568335165 | -.202551579986789 | -.103618434687316 | -0.601621 | -.189028901034536 |
97 | 0 | 1 | 53 | 51 | 104 | 103 | 4.000 | 4.222222 | 4.555556 | 2.125000 | ... | -.423553857016506 | .451032274705081 | 5.85539055581968 | .439582921242035 | 0 | -.188632068784232 | .307959893188 | .00558031420286767 | 0.159672 | .0137392088442872 |
98 | 1 | 1 | 50 | 66 | 116 | 114 | 4.125 | 2.555556 | 3.111111 | 2.875000 | ... | -.766030309958686 | -1.03788514368292 | 3.96814608815607 | .680987282872491 | 0 | -.929575911092688 | -.962506064099149 | -1.107811189837 | -0.828354 | -.901957726820804 |
99 | 0 | 1 | 48 | 44 | 92 | 93 | 4.625 | 3.444444 | 3.666667 | 2.375000 | ... | .250268128184729 | .183743396072473 | 6.38997022577941 | .380951344362772 | 0 | .28586512732849 | .194460771791742 | .325043530766778 | 0.178440 | .217005762128601 |
100 | 0 | 0 | 53 | 62 | 115 | 113 | 3.750 | 4.333333 | 4.111111 | 3.125000 | ... | -.308442142600742 | -.426260151905564 | 2.63214784625651 | .853394958660431 | 0 | -.624999155016179 | -.43137287629888 | -.767514240035447 | -0.449693 | -.367351147253153 |
101 | 1 | 1 | 55 | 46 | 101 | 101 | 2.375 | 4.444444 | 4.777778 | 2.125000 | ... | .543233631800473 | .124126104025427 | .716691512931554 | .994123735169601 | 0 | .388507781789705 | .217134917611209 | .462212144118556 | 0.112572 | .33367986791295 |
102 | 1 | 0 | 57 | 51 | 108 | 107 | 5.000 | 4.000000 | 3.333333 | 2.500000 | ... | .0945008478326534 | -.547560499427889 | 3.02911429053983 | .805183909655978 | 0 | -.31861204205403 | -.462504343356295 | -.322466952896479 | -0.622678 | -.226529825797618 |
103 | 0 | 1 | 55 | 48 | 103 | 102 | 3.250 | 4.444444 | 3.000000 | 1.875000 | ... | .0827393736129623 | -.668387548405243 | 3.22014257678594 | .780752546518135 | 0 | -.210056195083158 | -.158840283510097 | .104542393097411 | -0.365063 | -.29282408739614 |
104 | 1 | 0 | 37 | 50 | 87 | 89 | 4.125 | 3.666667 | 4.444444 | 2.375000 | ... | 1.28167225561274 | 1.26526959627687 | 22.002925560401 | .00120939612094861 | 0 | 1.26838728897433 | .677252995424165 | -.3185140544757 | 1.963504 | 1.2734709259448 |
105 | 1 | 0 | 50 | 53 | 103 | 102 | 3.500 | 3.000000 | 4.000000 | 1.750000 | ... | 1.94902617189729 | 1.92491287206375 | 11.9392907346174 | .063336938164602 | 0 | 1.38973660745287 | 1.44704590227239 | 1.37075760722074 | 0.947447 | 1.93696952198052 |
107 | 1 | 1 | 59 | 50 | 109 | 108 | 2.625 | 4.555556 | 3.111111 | 4.250000 | ... | -.672814771954987 | -.298339954679749 | 3.32823062436426 | .766664618888166 | 0 | -.847364043764748 | -.410644867709186 | -.634421207770746 | -0.767015 | -.485577363317368 |
108 | 0 | 0 | 50 | 46 | 96 | 96 | 4.125 | 3.888889 | 2.888889 | 2.375000 | ... | -.0753326044063024 | -.0358481349927302 | 1.08975392697057 | .98197990150424 | 0 | -.20214859386975 | -.306102348493442 | -.47422898472252 | -0.232557 | -.0555903696995163 |
109 | 1 | 0 | 45 | 52 | 97 | 97 | 3.000 | 2.444444 | 4.000000 | 3.375000 | ... | .372829073893633 | -1.13540676216059 | 6.59843200387173 | .35958394006545 | 0 | .348825180180162 | -.708586919419644 | -.15108063679623 | -0.007273 | -.381288844133479 |
110 | 1 | 1 | 74 | 51 | 125 | 122 | 2.500 | 2.777778 | 3.666667 | 3.375000 | ... | -1.10860549159537 | -.612411424812795 | 2.69700386094669 | .845801065276974 | 0 | -1.0273872060055 | -.910494817303793 | -1.07034598744963 | -0.975969 | -.860508458204084 |
111 | 0 | 0 | 55 | 66 | 121 | 118 | 4.125 | 4.000000 | 1.666667 | 4.375000 | ... | -.654179876305881 | -.650767968520565 | 3.36996187999363 | .761182551174769 | 0 | -.272381343609664 | -.561882400662002 | -.0629574634087936 | -0.535964 | -.652473922413223 |
112 | 0 | 0 | 39 | 46 | 85 | 87 | 3.875 | 5.000000 | 4.555556 | 2.250000 | ... | 1.84979954568774 | .58973382021773 | 6.85013205718724 | .334929667569969 | 0 | 1.04545150524094 | .506242781622853 | .816539071212095 | 0.291236 | 1.21976668295273 |
113 | 0 | 1 | 44 | 49 | 93 | 94 | 4.500 | 4.444444 | 4.000000 | 1.375000 | ... | -1.57330847081768 | -1.55435771450041 | 8.28025772134579 | .218281526374924 | 0 | -1.30607258457897 | -.940801897633747 | -1.28186598285215 | -0.524613 | -1.56383309265905 |
114 | 0 | 0 | 63 | 62 | 125 | 122 | 3.000 | 4.444444 | 2.888889 | 4.375000 | ... | 1.04652264247915 | 1.92254387846736 | 8.65397031881549 | .193993993109205 | 0 | .764001144662793 | 1.12308193467927 | 1.12442344429342 | 0.221668 | 1.48453326047326 |
115 | 0 | 0 | 74 | 53 | 127 | 123 | 3.125 | 2.888889 | 2.888889 | 2.500000 | ... | -.629134501444899 | -.5360241568474 | 7.84501406279816 | .249679340247795 | 0 | -.0700104203173417 | -.425548367197358 | -.159102511508208 | -0.001656 | -.58257932914615 |
116 | 1 | 1 | 52 | 71 | 123 | 120 | 4.875 | 3.666667 | 3.444444 | 2.125000 | ... | -1.46877782285761 | -1.25244595420381 | 4.50935463696025 | .608091722840933 | 0 | -1.38278279912808 | -.816288812945154 | -1.02904969310479 | -0.908946 | -1.36061188853071 |
117 | 1 | 0 | 46 | 49 | 95 | 95 | 4.375 | 4.555556 | 3.555556 | 3.875000 | ... | .955670305129287 | .579284339237895 | 4.21066475068459 | .648191708836232 | 0 | .569475554543558 | .0998554378006387 | -.196375067990528 | 0.432894 | .767477322183591 |
118 | 1 | 1 | 57 | 52 | 109 | 108 | 4.375 | 4.111111 | 2.888889 | 2.625000 | ... | -.0296848005262536 | -.270535874444911 | 13.4656641759038 | .0362091334305606 | 0 | -.144956963478635 | .193911571558929 | .300768668233922 | -0.077226 | -.150110337485582 |
119 | 1 | 0 | 51 | 55 | 106 | 105 | 4.250 | 3.666667 | 3.444444 | 2.875000 | ... | -.941882813884517 | -.492149490710709 | 2.99720672302836 | .809197335032507 | 0 | -.961191054944201 | -.89625084420077 | -1.09391490192188 | -0.975232 | -.717016152297613 |
120 | 0 | 1 | 43 | 57 | 100 | 100 | 3.000 | 3.111111 | 3.222222 | 3.250000 | ... | .287311020816552 | -1.04192420434122 | 6.46409173421341 | .373255934798917 | 0 | .106072553281915 | -.773589755276396 | -.441605756254155 | -0.182363 | -.377306591762334 |
121 | 1 | 0 | 48 | 49 | 97 | 97 | 3.250 | 3.333333 | 3.555556 | 3.142857 | ... | .679339934808758 | 1.5036938074405 | 6.96311947458522 | .324271371964703 | 0 | .725574150340369 | .603968317596509 | .157219843475762 | 0.745577 | 1.09151687112463 |
97 rows × 238 columns
df_mult = df[df.mahal_6_means_outlier == "0"]
#df.s_phase8t_DPm.hist()
covs = concat_matches(df, 'instrumentlevel|fsiq')
dpm = concat_matches(df, 't2_DPm|t_DPm|j_DPm')
logmeans = dpm.applymap(np.abs).applymap(np.log)
pd.concat([covs, logmeans], axis=1).corr()
SCAL_calc_fsiq2 | SCAL_qmusic_instrumentlevel | s_iso5t2_DPm | s_iso8t2_DPm | s_lin5t_DPm | s_lin8t_DPm | s_phase5t_DPm | s_phase8t_DPm | s_iso5j_DPm | s_iso8j_DPm | s_lin5j_DPm | s_lin8j_DPm | s_phase5j_DPm | s_phase8j_DPm | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SCAL_calc_fsiq2 | 1.000000 | 0.352960 | -0.225126 | -0.121247 | -0.059219 | -0.186593 | -0.182706 | 0.005738 | 0.103423 | -0.028989 | 0.097531 | 0.025279 | 0.031406 | -0.009130 |
SCAL_qmusic_instrumentlevel | 0.352960 | 1.000000 | -0.299548 | -0.247407 | -0.310401 | -0.395247 | -0.406680 | -0.209116 | -0.213889 | -0.135480 | -0.169150 | -0.112972 | -0.236322 | -0.178372 |
s_iso5t2_DPm | -0.225126 | -0.299548 | 1.000000 | 0.240606 | 0.372063 | 0.208959 | 0.554660 | 0.287308 | 0.391591 | 0.291631 | 0.358497 | 0.240362 | 0.431095 | 0.335425 |
s_iso8t2_DPm | -0.121247 | -0.247407 | 0.240606 | 1.000000 | 0.574186 | 0.290153 | 0.259606 | 0.380414 | 0.324889 | 0.431162 | 0.435744 | 0.289016 | 0.295702 | 0.406059 |
s_lin5t_DPm | -0.059219 | -0.310401 | 0.372063 | 0.574186 | 1.000000 | 0.252266 | 0.368437 | 0.425255 | 0.330675 | 0.445345 | 0.620027 | 0.359684 | 0.375063 | 0.388789 |
s_lin8t_DPm | -0.186593 | -0.395247 | 0.208959 | 0.290153 | 0.252266 | 1.000000 | 0.506525 | 0.386204 | 0.231868 | 0.221870 | 0.285723 | 0.205307 | 0.171472 | 0.124128 |
s_phase5t_DPm | -0.182706 | -0.406680 | 0.554660 | 0.259606 | 0.368437 | 0.506525 | 1.000000 | 0.333206 | 0.401023 | 0.141852 | 0.330363 | 0.163587 | 0.443219 | 0.155299 |
s_phase8t_DPm | 0.005738 | -0.209116 | 0.287308 | 0.380414 | 0.425255 | 0.386204 | 0.333206 | 1.000000 | 0.218833 | 0.346467 | 0.433153 | 0.246560 | 0.178326 | 0.377349 |
s_iso5j_DPm | 0.103423 | -0.213889 | 0.391591 | 0.324889 | 0.330675 | 0.231868 | 0.401023 | 0.218833 | 1.000000 | 0.388406 | 0.385640 | 0.435732 | 0.584698 | 0.229653 |
s_iso8j_DPm | -0.028989 | -0.135480 | 0.291631 | 0.431162 | 0.445345 | 0.221870 | 0.141852 | 0.346467 | 0.388406 | 1.000000 | 0.366890 | 0.367077 | 0.217356 | 0.465686 |
s_lin5j_DPm | 0.097531 | -0.169150 | 0.358497 | 0.435744 | 0.620027 | 0.285723 | 0.330363 | 0.433153 | 0.385640 | 0.366890 | 1.000000 | 0.462520 | 0.465879 | 0.348954 |
s_lin8j_DPm | 0.025279 | -0.112972 | 0.240362 | 0.289016 | 0.359684 | 0.205307 | 0.163587 | 0.246560 | 0.435732 | 0.367077 | 0.462520 | 1.000000 | 0.196470 | 0.242626 |
s_phase5j_DPm | 0.031406 | -0.236322 | 0.431095 | 0.295702 | 0.375063 | 0.171472 | 0.443219 | 0.178326 | 0.584698 | 0.217356 | 0.465879 | 0.196470 | 1.000000 | 0.136622 |
s_phase8j_DPm | -0.009130 | -0.178372 | 0.335425 | 0.406059 | 0.388789 | 0.124128 | 0.155299 | 0.377349 | 0.229653 | 0.465686 | 0.348954 | 0.242626 | 0.136622 | 1.000000 |
dfcols = lambda r: concat_matches(df, r)
marg_regs = concat_matches(df_mult, 'marg|fsiq2|calc_bfi|instrumentlevel')
list(marg_regs.columns)
['SCAL_calc_fsiq2', 'SCAL_calc_bfi_extraversion', 'SCAL_calc_bfi_agreeableness', 'SCAL_calc_bfi_conscientiousness', 'SCAL_calc_bfi_neuroticism', 'SCAL_calc_bfi_openness', 'SCAL_qmusic_instrumentlevel', 'margmean_stimtype_single', 'margmean_stimtype_grouped', 'margmean_timingtype_iso', 'margmean_timingtype_phase', 'margmean_timingtype_linear']
flts = marg_regs.applymap(np.float)
regs_all = concat_matches(df, 'fsiq2|calc_bfi|instrumentlevel')
fltsall = regs_all.applymap(np.float)
#flts.SCAL_calc_bfi_extraversion.hist()
for c in fltsall:
print(c)
flts[c].hist()
plt.show()
SCAL_calc_fsiq2
SCAL_calc_bfi_extraversion
SCAL_calc_bfi_agreeableness
SCAL_calc_bfi_conscientiousness
SCAL_calc_bfi_neuroticism
SCAL_calc_bfi_openness
SCAL_qmusic_instrumentlevel
list(fltsall)
['SCAL_calc_fsiq2', 'SCAL_calc_bfi_extraversion', 'SCAL_calc_bfi_agreeableness', 'SCAL_calc_bfi_conscientiousness', 'SCAL_calc_bfi_neuroticism', 'SCAL_calc_bfi_openness', 'SCAL_qmusic_instrumentlevel']
#levels = {x: flts[flts.SCAL_qmusic_instrumentlevel==x] for x in [0,1,2,3,4]}
#levels[0].SCAL_calc_fsiq2.hist()
#whis sets whisker length: upper whisker placement = Q3 + whis*IQR, where IQR = interquartile range (Q3-Q1)
fltsall.groupby('SCAL_qmusic_instrumentlevel').count().T
SCAL_qmusic_instrumentlevel | 0.0 | 1.0 | 2.0 | 3.0 | 4.0 |
---|---|---|---|---|---|
SCAL_calc_fsiq2 | 21 | 11 | 17 | 26 | 21 |
SCAL_calc_bfi_extraversion | 21 | 12 | 17 | 26 | 21 |
SCAL_calc_bfi_agreeableness | 21 | 12 | 17 | 26 | 21 |
SCAL_calc_bfi_conscientiousness | 21 | 12 | 17 | 26 | 21 |
SCAL_calc_bfi_neuroticism | 21 | 12 | 17 | 26 | 21 |
SCAL_calc_bfi_openness | 21 | 12 | 17 | 26 | 21 |
for v in ['SCAL_calc_fsiq2',
'margmean_timingtype_iso',
'margmean_timingtype_phase',
'margmean_timingtype_linear']:
print(v)
flts.boxplot(column=v, by='SCAL_qmusic_instrumentlevel',
whis=1,
figsize=(9,5), )
plt.show()
SCAL_calc_fsiq2
margmean_timingtype_iso
margmean_timingtype_phase
margmean_timingtype_linear
col_matches(df, 'marg')
['margmean_stimtype_single', 'margmean_stimtype_grouped', 'margmean_timingtype_iso', 'margmean_timingtype_phase', 'margmean_timingtype_linear']
df_comp = concat_matches(df, 'fsiq2|calc_bfi|instrumentlevel|marg')
#from itertools import combinations
#for (x, y) in combinations(df_comp.columns, 2):
# print (x,y)
df_comp.corr()
SCAL_calc_fsiq2 | SCAL_calc_bfi_extraversion | SCAL_calc_bfi_agreeableness | SCAL_calc_bfi_conscientiousness | SCAL_calc_bfi_neuroticism | SCAL_calc_bfi_openness | SCAL_qmusic_instrumentlevel | margmean_timingtype_phase | |
---|---|---|---|---|---|---|---|---|
SCAL_calc_fsiq2 | 1.000000 | -0.032720 | -0.153074 | -0.368615 | 0.084738 | 0.388978 | 0.352960 | -0.253542 |
SCAL_calc_bfi_extraversion | -0.032720 | 1.000000 | 0.202427 | 0.196809 | -0.423025 | -0.009048 | -0.036679 | 0.044203 |
SCAL_calc_bfi_agreeableness | -0.153074 | 0.202427 | 1.000000 | 0.228410 | -0.305756 | -0.017481 | 0.056820 | 0.096148 |
SCAL_calc_bfi_conscientiousness | -0.368615 | 0.196809 | 0.228410 | 1.000000 | -0.404519 | -0.073907 | -0.246126 | 0.280994 |
SCAL_calc_bfi_neuroticism | 0.084738 | -0.423025 | -0.305756 | -0.404519 | 1.000000 | -0.007542 | 0.022224 | -0.171622 |
SCAL_calc_bfi_openness | 0.388978 | -0.009048 | -0.017481 | -0.073907 | -0.007542 | 1.000000 | 0.308756 | -0.279819 |
SCAL_qmusic_instrumentlevel | 0.352960 | -0.036679 | 0.056820 | -0.246126 | 0.022224 | 0.308756 | 1.000000 | -0.374242 |
margmean_timingtype_phase | -0.253542 | 0.044203 | 0.096148 | 0.280994 | -0.171622 | -0.279819 | -0.374242 | 1.000000 |
tasks = concat_matches(df, '^s.*t_DP.*log$|^s.*t2_DP.*log$|^s.*j_DP.*log$|DPm')
tasks.corr()
s_iso5t2_DPsd_trunc_log | s_iso8t2_DPsd_trunc_log | s_lin5t_DPsd_trunc_log | s_lin8t_DPsd_trunc_log | s_phase5t_DPsd_trunc_log | s_phase8t_DPsd_trunc_log | s_iso5j_DPsd_trunc_log | s_iso8j_DPsd_trunc_log | s_lin5j_DPsd_trunc_log | s_lin8j_DPsd_trunc_log | ... | s_phase5j_DPm | s_phase8j_DPm | s_phase8j_psr_DPm | s_phase8t_psr_DPm | s_phase5j_psr_DPm | s_phase5t_psr_DPm | s_phase5t_nrm_DPm | s_phase8t_nrm_DPm | s_phase5j_nrm_DPm | s_phase8j_nrm_DPm | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
s_iso5t2_DPsd_trunc_log | 1.000000 | 0.738873 | 0.672036 | 0.756945 | 0.662287 | 0.662609 | 0.518349 | 0.595389 | 0.455247 | 0.201396 | ... | -0.427788 | -0.416483 | -0.493220 | -0.276938 | -0.354897 | -0.322481 | -0.329696 | -0.455115 | -0.488410 | -0.556382 |
s_iso8t2_DPsd_trunc_log | 0.738873 | 1.000000 | 0.802354 | 0.761823 | 0.604334 | 0.668939 | 0.568699 | 0.624056 | 0.492997 | 0.300413 | ... | -0.257789 | -0.391826 | -0.315092 | -0.225934 | -0.144133 | -0.212462 | -0.278046 | -0.370163 | -0.212503 | -0.436267 |
s_lin5t_DPsd_trunc_log | 0.672036 | 0.802354 | 1.000000 | 0.819063 | 0.621531 | 0.683805 | 0.471432 | 0.605766 | 0.578403 | 0.417489 | ... | -0.366896 | -0.352244 | -0.260047 | -0.232386 | -0.257753 | -0.233379 | -0.284758 | -0.456305 | -0.281695 | -0.397382 |
s_lin8t_DPsd_trunc_log | 0.756945 | 0.761823 | 0.819063 | 1.000000 | 0.730477 | 0.686592 | 0.592323 | 0.674277 | 0.517969 | 0.412354 | ... | -0.302963 | -0.388918 | -0.343525 | -0.298387 | -0.278940 | -0.196580 | -0.293536 | -0.461500 | -0.237192 | -0.429664 |
s_phase5t_DPsd_trunc_log | 0.662287 | 0.604334 | 0.621531 | 0.730477 | 1.000000 | 0.780417 | 0.541502 | 0.497258 | 0.389583 | 0.437062 | ... | -0.255543 | -0.165363 | -0.322945 | -0.119608 | -0.225098 | -0.122879 | -0.269279 | -0.311378 | -0.267075 | -0.346497 |
s_phase8t_DPsd_trunc_log | 0.662609 | 0.668939 | 0.683805 | 0.686592 | 0.780417 | 1.000000 | 0.435621 | 0.576680 | 0.398518 | 0.491541 | ... | -0.220838 | -0.118280 | -0.364022 | -0.171018 | -0.239501 | -0.297798 | -0.296518 | -0.284477 | -0.217098 | -0.378152 |
s_iso5j_DPsd_trunc_log | 0.518349 | 0.568699 | 0.471432 | 0.592323 | 0.541502 | 0.435621 | 1.000000 | 0.525210 | 0.440945 | 0.394228 | ... | -0.284170 | -0.248068 | -0.271728 | -0.162725 | -0.250636 | -0.145251 | -0.231012 | -0.291565 | -0.225744 | -0.322471 |
s_iso8j_DPsd_trunc_log | 0.595389 | 0.624056 | 0.605766 | 0.674277 | 0.497258 | 0.576680 | 0.525210 | 1.000000 | 0.431951 | 0.401644 | ... | -0.185610 | -0.311303 | -0.316039 | -0.273725 | -0.088432 | -0.181808 | -0.202298 | -0.411771 | -0.205818 | -0.457044 |
s_lin5j_DPsd_trunc_log | 0.455247 | 0.492997 | 0.578403 | 0.517969 | 0.389583 | 0.398518 | 0.440945 | 0.431951 | 1.000000 | 0.474816 | ... | -0.360133 | -0.218373 | -0.129994 | -0.281519 | -0.268563 | -0.365833 | -0.449330 | -0.267910 | -0.385703 | -0.316370 |
s_lin8j_DPsd_trunc_log | 0.201396 | 0.300413 | 0.417489 | 0.412354 | 0.437062 | 0.491541 | 0.394228 | 0.401644 | 0.474816 | 1.000000 | ... | -0.080230 | -0.035010 | -0.115116 | -0.288476 | -0.095215 | -0.287127 | -0.233761 | -0.244360 | -0.154673 | -0.272354 |
s_phase5j_DPsd_trunc_log | 0.358758 | 0.367336 | 0.346659 | 0.462156 | 0.565364 | 0.572382 | 0.490041 | 0.368592 | 0.434056 | 0.631864 | ... | 0.044288 | -0.017448 | -0.208556 | -0.136634 | 0.034726 | -0.158798 | -0.122321 | -0.098728 | -0.088317 | -0.218353 |
s_phase8j_DPsd_trunc_log | 0.549521 | 0.568442 | 0.602914 | 0.624568 | 0.597602 | 0.781318 | 0.499794 | 0.621293 | 0.420840 | 0.619189 | ... | -0.213630 | -0.076271 | -0.335715 | -0.249980 | -0.272238 | -0.334532 | -0.202728 | -0.279476 | -0.156063 | -0.510074 |
s_iso5t1_DPm | -0.263667 | -0.277807 | -0.314324 | -0.310733 | -0.227269 | -0.268219 | -0.291445 | -0.248331 | -0.376861 | -0.333059 | ... | 0.502807 | 0.165895 | 0.133390 | 0.278893 | 0.342427 | 0.523557 | 0.610060 | 0.267509 | 0.355182 | 0.242807 |
s_iso8t1_DPm | -0.453128 | -0.260685 | -0.247612 | -0.450193 | -0.399964 | -0.430069 | -0.257545 | -0.277189 | -0.160232 | -0.364730 | ... | 0.312741 | 0.195619 | 0.406009 | 0.402601 | 0.221782 | 0.503358 | 0.443866 | 0.665716 | 0.326517 | 0.459964 |
s_iso5t2_DPm | -0.521010 | -0.314634 | -0.332016 | -0.373079 | -0.295972 | -0.296939 | -0.270343 | -0.262067 | -0.300409 | -0.172267 | ... | 0.631358 | 0.301877 | 0.355788 | 0.411741 | 0.484682 | 0.564634 | 0.539484 | 0.470003 | 0.614620 | 0.322191 |
s_iso8t2_DPm | -0.462808 | -0.446748 | -0.403936 | -0.320582 | -0.328873 | -0.386191 | -0.430272 | -0.359249 | -0.281286 | -0.197488 | ... | 0.333593 | 0.429137 | 0.258739 | 0.413972 | 0.252941 | 0.386533 | 0.372264 | 0.635200 | 0.307257 | 0.500844 |
s_lin5t_DPm | -0.595526 | -0.615917 | -0.776115 | -0.629996 | -0.341041 | -0.500825 | -0.386844 | -0.538903 | -0.555868 | -0.367789 | ... | 0.496511 | 0.484499 | 0.418684 | 0.473363 | 0.430117 | 0.458389 | 0.442500 | 0.632934 | 0.459493 | 0.533097 |
s_lin8t_DPm | -0.272403 | -0.321586 | -0.350700 | -0.335300 | -0.247403 | -0.259107 | -0.335247 | -0.211514 | -0.426267 | -0.304842 | ... | 0.399704 | 0.230263 | 0.232268 | 0.332544 | 0.330797 | 0.555778 | 0.578327 | 0.417416 | 0.336991 | 0.338454 |
s_phase5t_DPm | -0.480683 | -0.353100 | -0.293628 | -0.366466 | -0.325489 | -0.434964 | -0.312651 | -0.270949 | -0.441105 | -0.351832 | ... | 0.605015 | 0.089656 | 0.307663 | 0.429999 | 0.487985 | 0.818007 | 0.903819 | 0.461933 | 0.611269 | 0.253733 |
s_phase8t_DPm | -0.449457 | -0.370976 | -0.364130 | -0.421083 | -0.290045 | -0.304259 | -0.284377 | -0.327212 | -0.311432 | -0.396540 | ... | 0.424297 | 0.473706 | 0.477662 | 0.710164 | 0.341019 | 0.545138 | 0.373220 | 0.916000 | 0.378292 | 0.611237 |
s_iso5j_DPm | -0.490276 | -0.342203 | -0.353038 | -0.413420 | -0.301621 | -0.361307 | -0.235643 | -0.220516 | -0.396096 | -0.192518 | ... | 0.696054 | 0.353772 | 0.410588 | 0.440696 | 0.657603 | 0.504652 | 0.457909 | 0.445976 | 0.675280 | 0.523832 |
s_iso8j_DPm | -0.476555 | -0.486733 | -0.456139 | -0.432630 | -0.266495 | -0.291446 | -0.357583 | -0.379294 | -0.316231 | -0.174024 | ... | 0.402355 | 0.822674 | 0.638924 | 0.500670 | 0.265768 | 0.316749 | 0.329880 | 0.594118 | 0.372656 | 0.757752 |
s_lin5j_DPm | -0.494297 | -0.472125 | -0.465136 | -0.499568 | -0.394134 | -0.438891 | -0.408935 | -0.314741 | -0.598732 | -0.353829 | ... | 0.616451 | 0.376938 | 0.443354 | 0.467820 | 0.558327 | 0.554347 | 0.436663 | 0.536155 | 0.577723 | 0.578612 |
s_lin8j_DPm | -0.538869 | -0.498960 | -0.540441 | -0.538409 | -0.423684 | -0.415677 | -0.496455 | -0.395155 | -0.515985 | -0.268514 | ... | 0.616334 | 0.429617 | 0.400641 | 0.377711 | 0.489232 | 0.423098 | 0.408327 | 0.480689 | 0.527056 | 0.543069 |
s_phase5j_DPm | -0.427788 | -0.257789 | -0.366896 | -0.302963 | -0.255543 | -0.220838 | -0.284170 | -0.185610 | -0.360133 | -0.080230 | ... | 1.000000 | 0.338758 | 0.243867 | 0.281805 | 0.854671 | 0.504060 | 0.466909 | 0.435806 | 0.924451 | 0.356306 |
s_phase8j_DPm | -0.416483 | -0.391826 | -0.352244 | -0.388918 | -0.165363 | -0.118280 | -0.248068 | -0.311303 | -0.218373 | -0.035010 | ... | 0.338758 | 1.000000 | 0.633289 | 0.451552 | 0.269735 | 0.089169 | 0.189124 | 0.638950 | 0.355894 | 0.925841 |
s_phase8j_psr_DPm | -0.493220 | -0.315092 | -0.260047 | -0.343525 | -0.322945 | -0.364022 | -0.271728 | -0.316039 | -0.129994 | -0.115116 | ... | 0.243867 | 0.633289 | 1.000000 | 0.495696 | 0.224187 | 0.286522 | 0.139130 | 0.563178 | 0.334145 | 0.832919 |
s_phase8t_psr_DPm | -0.276938 | -0.225934 | -0.232386 | -0.298387 | -0.119608 | -0.171018 | -0.162725 | -0.273725 | -0.281519 | -0.288476 | ... | 0.281805 | 0.451552 | 0.495696 | 1.000000 | 0.226594 | 0.468127 | 0.217090 | 0.730399 | 0.189505 | 0.582495 |
s_phase5j_psr_DPm | -0.354897 | -0.144133 | -0.257753 | -0.278940 | -0.225098 | -0.239501 | -0.250636 | -0.088432 | -0.268563 | -0.095215 | ... | 0.854671 | 0.269735 | 0.224187 | 0.226594 | 1.000000 | 0.399445 | 0.405024 | 0.386368 | 0.858581 | 0.328998 |
s_phase5t_psr_DPm | -0.322481 | -0.212462 | -0.233379 | -0.196580 | -0.122879 | -0.297798 | -0.145251 | -0.181808 | -0.365833 | -0.287127 | ... | 0.504060 | 0.089169 | 0.286522 | 0.468127 | 0.399445 | 1.000000 | 0.748800 | 0.449795 | 0.447031 | 0.264756 |
s_phase5t_nrm_DPm | -0.329696 | -0.278046 | -0.284758 | -0.293536 | -0.269279 | -0.296518 | -0.231012 | -0.202298 | -0.449330 | -0.233761 | ... | 0.466909 | 0.189124 | 0.139130 | 0.217090 | 0.405024 | 0.748800 | 1.000000 | 0.360550 | 0.446527 | 0.171051 |
s_phase8t_nrm_DPm | -0.455115 | -0.370163 | -0.456305 | -0.461500 | -0.311378 | -0.284477 | -0.291565 | -0.411771 | -0.267910 | -0.244360 | ... | 0.435806 | 0.638950 | 0.563178 | 0.730399 | 0.386368 | 0.449795 | 0.360550 | 1.000000 | 0.370171 | 0.643162 |
s_phase5j_nrm_DPm | -0.488410 | -0.212503 | -0.281695 | -0.237192 | -0.267075 | -0.217098 | -0.225744 | -0.205818 | -0.385703 | -0.154673 | ... | 0.924451 | 0.355894 | 0.334145 | 0.189505 | 0.858581 | 0.447031 | 0.446527 | 0.370171 | 1.000000 | 0.382653 |
s_phase8j_nrm_DPm | -0.556382 | -0.436267 | -0.397382 | -0.429664 | -0.346497 | -0.378152 | -0.322471 | -0.457044 | -0.316370 | -0.272354 | ... | 0.356306 | 0.925841 | 0.832919 | 0.582495 | 0.328998 | 0.264756 | 0.171051 | 0.643162 | 0.382653 | 1.000000 |
34 rows × 34 columns
tasks = concat_matches(df, 'P4_local')
tasks.corr()
IP4_local_trunc_mz58 | I5P4_local_trunc | I8P4_local_trunc | I8P4_localperc_trunc | I5P4_localperc_trunc | I5P4_local_trunc_log | I8P4_local_trunc_log | |
---|---|---|---|---|---|---|---|
IP4_local_trunc_mz58 | 1.000000 | -0.901436 | -0.890297 | -0.890297 | -0.901436 | -0.874469 | -0.873872 |
I5P4_local_trunc | -0.901436 | 1.000000 | 0.605406 | 0.605406 | 1.000000 | 0.982775 | 0.590171 |
I8P4_local_trunc | -0.890297 | 0.605406 | 1.000000 | 1.000000 | 0.605406 | 0.574182 | 0.984760 |
I8P4_localperc_trunc | -0.890297 | 0.605406 | 1.000000 | 1.000000 | 0.605406 | 0.574182 | 0.984760 |
I5P4_localperc_trunc | -0.901436 | 1.000000 | 0.605406 | 0.605406 | 1.000000 | 0.982775 | 0.590171 |
I5P4_local_trunc_log | -0.874469 | 0.982775 | 0.574182 | 0.574182 | 0.982775 | 1.000000 | 0.572082 |
I8P4_local_trunc_log | -0.873872 | 0.590171 | 0.984760 | 0.984760 | 0.590171 | 0.572082 | 1.000000 |
trunc = concat_matches(df, '^s.*t_DP.*trunc$|^s.*t2_DP.*trunc$|^s.*j_DP.*trunc$')
trunc.max()
s_iso5j_DPsd_trunc 9.760057 s_iso5t2_DPsd_trunc 10.355881 s_iso8j_DPsd_trunc 9.832596 s_iso8t2_DPsd_trunc 9.598785 s_lin5j_DPsd_trunc 11.244313 s_lin5t_DPsd_trunc 10.807008 s_lin8j_DPsd_trunc 12.841810 s_lin8t_DPsd_trunc 10.028023 s_phase5j_DPsd_trunc 19.459099 s_phase5t_DPsd_trunc 16.753541 s_phase8j_DPsd_trunc 25.852135 s_phase8t_DPsd_trunc 25.064911 dtype: float64
trunc.apply(lambda x: x[x==x.max()])
# Total of 9 participants had truncated scores
# Total of
s_iso5j_DPsd_trunc | s_iso5t2_DPsd_trunc | s_iso8j_DPsd_trunc | s_iso8t2_DPsd_trunc | s_lin5j_DPsd_trunc | s_lin5t_DPsd_trunc | s_lin8j_DPsd_trunc | s_lin8t_DPsd_trunc | s_phase5j_DPsd_trunc | s_phase5t_DPsd_trunc | s_phase8j_DPsd_trunc | s_phase8t_DPsd_trunc | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | ||||||||||||
27 | NaN | NaN | NaN | NaN | NaN | NaN | 12.055475 | NaN | NaN | NaN | NaN | NaN |
36 | NaN | NaN | NaN | 9.542804 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
64 | NaN | NaN | 9.832596 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
71 | 9.760057 | 8.176486 | NaN | NaN | NaN | NaN | NaN | 8.927513 | NaN | NaN | NaN | NaN |
86 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19.459099 | NaN | NaN | NaN |
104 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13.764273 | NaN | 18.893455 |
105 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 17.629538 | NaN |
112 | NaN | NaN | NaN | NaN | NaN | 10.807008 | NaN | NaN | NaN | NaN | NaN | NaN |
114 | NaN | NaN | NaN | NaN | 11.244313 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
trunc.apply(lambda x: x[x==x.max()])
# Total of 9 participants had truncated scores
# Total of
s_iso5j_DPsd_trunc | s_iso5t2_DPsd_trunc | s_iso8j_DPsd_trunc | s_iso8t2_DPsd_trunc | s_lin5j_DPsd_trunc | s_lin5t_DPsd_trunc | s_lin8j_DPsd_trunc | s_lin8t_DPsd_trunc | s_phase5j_DPsd_trunc | s_phase5t_DPsd_trunc | s_phase8j_DPsd_trunc | s_phase8t_DPsd_trunc | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
pid | ||||||||||||
15 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.028023 | NaN | 16.753541 | NaN | 25.064911 |
49 | 9.760057 | NaN | NaN | NaN | NaN | NaN | 12.84181 | 10.028023 | 19.459099 | NaN | 25.852135 | 25.064911 |
55 | NaN | 10.355881 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 16.753541 | 25.852135 | 25.064911 |
64 | NaN | NaN | 9.832596 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
71 | 9.760057 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
73 | 9.760057 | 10.355881 | NaN | 9.598785 | NaN | NaN | NaN | NaN | 19.459099 | 16.753541 | 25.852135 | NaN |
86 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 19.459099 | NaN | NaN | NaN |
89 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25.852135 | 25.064911 |
112 | NaN | NaN | NaN | NaN | NaN | 10.807008 | NaN | NaN | NaN | NaN | NaN | NaN |
114 | NaN | NaN | NaN | NaN | 11.244313 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
set(df.index).difference(df_mult.index)
{15, 49, 55, 68, 73, 89}