I am working in excel using SUMIF formula, my data is as follows:
Region Opr Qty Cost Combo(col B&A)
192 114 50 500 104192
192 104 453 548 104192
192 114 125 54654 114192
192 114 155 1545 114192
192 124 12 1553 124192
192 134 12222 1554545 134192
192 174 256 15478 174192
192 104 12 1555 104192
192 104 210 1156 104192
192 114 47 448953 114192
192 114 29 59479 114192
192 124 124 32451 124192
192 134 114 290240 134192
4192 10 210 115656 104192
4192 10 47 44896 104192
4192 11 29 12866 114192
4192 11 549 290240 114192
4192 12 124 59480 124192
4192 13 114 61343 134192
4192 17 310 45339 174192
4192 10 56 32451 104192
4192 10 103 82483 104192
4192 11 685 111380 114192
4192 11 646 201858 114192
4192 12 26 6489 124192
4192 13 87 44543 134192
If you see the last column it's giving same combination result but the operator and region are not always the same. I want to do SUMIF against Region which is throwing wrong values.
You can try SUMPRODUCT:
=SUMPRODUCT(((B2:B27&A2:A27)*1<>E2:E27)*1)
If the concatenation of column B to A is not equal to the Combo, count as 1, then add all the 1 together in SUMPRODUCT.
Change the range accordingly.
The *1 convert any text to number.
Related
So I want to fit a function with a dataset using gnuplot. In the file "cn20x2012", at the lines [1:300] I have this data:
1 -7.576723949519277e-06
2 4.738414366971162e-05
3 2.5908117324519247e-05
4 7.233786749999952e-06
5 4.94720225240387e-06
6 -1.857620375000113e-06
7 5.697280584855734e-06
8 -1.867760712716345e-05
9 6.64096591257211e-05
10 2.756199717307687e-05
11 4.7755705550480866e-05
12 6.590865376225963e-05
13 4.1522206877403805e-05
14 3.145294946394234e-05
15 5.9346948090625035e-05
16 5.405458204471163e-05
17 0.0001484469089218749
18 0.00011236895265264405
19 0.00010798644697620197
20 8.656723035552881e-05
21 0.00019917737876442313
22 0.00022625750686778835
23 0.00023183354141658626
24 0.0003373178915148073
25 0.00032313619574999994
26 0.0003451188893915866
27 0.0003303809005983172
28 0.0003534148565745192
29 0.00039690566743750015
30 0.0004182810016802884
31 0.00045198626877403865
32 0.00047311462195192373
33 0.0004962054400408655
34 0.0004969566757524037
35 0.0005561838221274039
36 0.0005353567324539659
37 0.00052834133201923
38 0.0005980226227637016
39 0.0005446277144831731
40 0.0005960780049278846
41 0.0006076488594567314
42 0.000710219997610289
43 0.0006714079307259616
44 0.0006990041531870184
45 0.000694646402266827
46 0.0006910307645889419
47 0.0007918124250492787
48 0.0007699669760728367
49 0.0007850042712259613
50 0.0007735240355776444
51 0.0008333605652980768
52 0.0007914544977620185
53 0.0008254284036610573
54 0.0008578590784536057
55 0.0008597165395913466
56 0.0009350752655120189
57 0.0009355867078822116
58 0.0009413161534519229
59 0.001003045837043269
60 0.0009530084342740383
61 0.000981287851927885
62 0.000986143934318509
63 0.00096895140692548
64 0.0010671633388319713
65 0.0010884129846995196
66 0.0010974424039567304
67 0.0011198829067163459
68 0.0010649422789374995
69 0.0010909547135769227
70 0.0010858300892451934
71 0.00114890178018774
72 0.0011503018930817308
73 0.0012209814370937495
74 0.001264080502711538
75 0.0012453762294132222
76 0.0012725116258625
77 0.0012649334953990384
78 0.0012195748153341352
79 0.0013151443892213466
80 0.0013003322635283651
81 0.0013099768888799042
82 0.0013227992394807694
83 0.0013325137669168274
84 0.001356943212587259
85 0.0014541924819278852
86 0.0014094004314177883
87 0.0014273633669975969
88 0.0014393176087403859
89 0.0014372794673365393
90 0.0015051545220959143
91 0.0015432813234807683
92 0.0015832276965293275
93 0.001540622433288461
94 0.0016007491118125
95 0.0016195978358533654
96 0.0016447077023067317
97 0.0016350138695504803
98 0.0017352804136629807
99 0.001731106189370192
100 0.0017407015898704323
101 0.0017367582300937506
102 0.0018164239404875008
103 0.0017829769448653838
104 0.0018303930988165871
105 0.0017893320000211548
106 0.0018727349292259614
107 0.0018745909637668267
108 0.0018425366172147846
109 0.0019053739892581727
110 0.0018849885474855762
111 0.0018689524590103368
112 0.0019431807910961535
113 0.001951890517350962
114 0.0019308973497776446
115 0.0019990349471177894
116 0.002009245176572116
117 0.0020004240575882213
118 0.002020795320423557
119 0.0020148423748725963
120 0.002070277553975961
121 0.002112121992170673
122 0.002081609846093749
123 0.0020899822853341346
124 0.002214996736841347
125 0.002210968677028846
126 0.002204230691923077
127 0.0022059340675168264
128 0.002244672249610577
129 0.002243725570633895
130 0.002198417606970913
131 0.002326686848007212
132 0.002298981945014423
133 0.002412905193465384
134 0.0023317473012668287
135 0.0023255737818221145
136 0.0024042900543605767
137 0.0023814333208341345
138 0.002414946342495192
139 0.002451134140336538
140 0.002435468088014424
141 0.002541540709086779
142 0.0024759180712812523
143 0.002562872725209133
144 0.002554363054353367
145 0.002525350243064904
146 0.0026228594448966342
147 0.002640361090600963
148 0.0026968734518557683
149 0.002687729582449518
150 0.0026799173813848555
151 0.002751626483175481
152 0.0026916526068317286
153 0.002682602742860577
154 0.0027658840884567304
155 0.0028385319315024035
156 0.002733288245524039
157 0.002805041072350961
158 0.002798724552451201
159 0.00284738398885577
160 0.002833892571264423
161 0.0028506943730673084
162 0.0028578405825413463
163 0.0028141271324870197
164 0.0029047532288887
165 0.002916689246838943
166 0.003006111659274039
167 0.0030388357088942325
168 0.0030117903270181707
169 0.003023639132084136
170 0.0030182642660336535
171 0.0029788478969250015
172 0.003086049268993511
173 0.0030530940010240377
174 0.00309287048297596
175 0.0030892688902187473
176 0.0032070964353437493
177 0.0031308958387163454
178 0.003262165689711538
179 0.0032348496648947093
180 0.003334092027257212
181 0.0032702121678230764
182 0.0032887867663149036
183 0.00333782536743269
184 0.0033132179587812513
185 0.003400563164048078
186 0.003322215536028365
187 0.0033691419445264436
188 0.00340692471343654
189 0.003370118822997599
190 0.003414042435545674
191 0.003460621729710913
192 0.003487680921019232
193 0.0034814484875360595
194 0.003528280852358173
195 0.0035260558732403864
196 0.0035947047098653846
197 0.003583761358336538
198 0.003589446784643749
199 0.0035488957604610572
200 0.0036106514596322115
201 0.003633161542855769
202 0.003596668943564904
203 0.003621647520017789
204 0.0037260161142259616
205 0.0036873544761057684
206 0.003693311409786057
207 0.0037485618958747594
208 0.0037277801700697126
209 0.003731768419286058
210 0.0037200943660144225
211 0.0037368698886754786
212 0.0038266932486634626
213 0.003786905602120193
214 0.0038484308669038464
215 0.003837662506102065
216 0.003877989966946875
217 0.0038711451977908673
218 0.0039796825709810125
219 0.003955763375971154
220 0.003983664920576924
221 0.004019112007471154
222 0.003996646585913461
223 0.004061509550884613
224 0.004015245551199519
225 0.004009779120920672
226 0.004148229009661058
227 0.0040645974335312505
228 0.0041522345293678545
229 0.004216267765944711
230 0.004191517977733654
231 0.004280319721466346
232 0.004210795761447114
233 0.004258393462563462
234 0.004267925011272355
235 0.00427713419340625
236 0.004323331966394231
237 0.004361159201735935
238 0.004351708975694715
239 0.004359997178644953
240 0.00437384325853894
241 0.004375188742463941
242 0.004424559629495192
243 0.004461955226487498
244 0.004489655863850963
245 0.0045503420149230756
246 0.0045185560829999975
247 0.004506067166336778
248 0.004585396025798076
249 0.004530840472406252
250 0.0045934151490120215
251 0.004602146584228363
252 0.004643262102497593
253 0.004707265035608172
254 0.004766505116052884
255 0.004744165929896635
256 0.0047756718030625015
257 0.004802170611427885
258 0.004896239463478368
259 0.0048845448341901425
260 0.004845213594302884
261 0.004915008781204327
262 0.004838528640802884
263 0.0048121374747617796
264 0.004895357859576925
265 0.0048793476575266816
266 0.004958465852682693
267 0.005007965180538941
268 0.0049839032653341345
269 0.005068383734646637
270 0.00498556504900495
271 0.005014623260019232
272 0.005066327855785335
273 0.0050290740743365375
274 0.005152934708140861
275 0.005174238921781968
276 0.005123581464772355
277 0.005155969777822114
278 0.005169396608004327
279 0.00516497090489663
280 0.005145110646115385
281 0.005209611399110575
282 0.005163211771749997
283 0.005181044847507209
284 0.005281641245183894
285 0.005323840847189907
286 0.005230924322329326
287 0.005256136984014422
288 0.005374876757439424
289 0.0053137727444009615
290 0.005468482116127402
291 0.005453857539401205
292 0.005417081656274039
293 0.005393994523838937
294 0.005506909240446873
295 0.005449365350307692
296 0.005551215606367787
297 0.005505932791992786
298 0.0055918512302572145
299 0.005663100163579326
300 0.0056382443690432705
When I do
f(x) = a/b*(1-exp(-b*x))
fit[1:300] f(x) "cn20x2012" using 1:2 via a,b
The curve fits perfectly. But when I try to fit the curve with
a/b*(1-exp(-b*x/(3e-26))
I get the error message. Note that I've only added a constant to the exponential part of the function.
What can I do to fit the function with the constant 3e-26?
I'm using gnuplot 5.2 patchlevel 8 on linux
Adding that constant makes the values of exp(-b*x/(3.e-26) so close to zero that the term (1-exp(-b*x/(3e-26)) differs from 1 by less than the precision available for IEEE double precision floating point numbers. So you are essentially fitting the function g(x) = a/b, which is a very poor fit to your data.
Since you already have a good fit using your original function f(x), perhaps you can explain what your goal is to change the function to something else? What question are you trying to answer?
I am using the following code in gnuplot to draw a tree from different inputs.
### tree diagram with gnuplot
reset session
#ID Parent Name Colors shape
# put datablock into strings
IDs = Parents = Names = Colors = Shape = ""
set table $Dummy
plot "tmp.dat" u (IDs = IDs.strcol(1)." "): \
(Parents = Parents.strcol(2)." "): \
(Names = Names.strcol(3)." "): \
(Colors = Colors.strcol(4)." "): \
(Shape = Shape.strcol(5)." ") w table
unset table
# Top node has no parent ID "NaN"
Start(n) = int(sum [i=1:words(Parents)] (word(Parents,i) eq "NaN" ? int(word(IDs,i)) : 0))
# get list index by ID
ItemIdx(s,n) = n == n ? (tmp=NaN, sum [i=1:words(s)] ((word(s,i)) == n ? (tmp=i,0) : 0), tmp) : NaN
# get parent of ID n
Parent(n) = word(Parents,ItemIdx(IDs,n))
# get level of ID n, recursive function
Level(n) = n == n ? Parent(n)>0 ? Level(Parent(n))-1 : 0 : NaN
# get number of children of ID n
ChildCount(n) = int(sum [i=1:words(Parents)] (word(Parents,i)==n))
# Create child list of ID n
ChildList(n) = (Ch = " ", sum [i=1:words(IDs)] (word(Parents,i)==n ? (Ch = Ch.word(IDs,i)." ",1) : (Ch,0) ), Ch )
# m-th child of ID n
Child(n,m) = word(ChildList(n),m)
# List of leaves, recursive function
LeafList(n) = (LL="", ChildCount(n)==0 ? LL=LL.n." " : sum [i=1:ChildCount(n)]
(LL=LL.LeafList(Child(n,i)), 0),LL)
# create list of all leaves
LeafAll = LeafList(Start(0))
# get x-position of ID n, recursive function
XPos(n) = ChildCount(n) == 0 ? ItemIdx(LeafAll,n) : (sum [i=1:ChildCount(n)](XPos(Child(n,i))))/(ChildCount(n))
# create the tree datablock for plotting
set print $Tree
do for [j=1:words(IDs)] {
n = int(word(IDs,j))
print sprintf("% 3d % 7.2f % 4d % 5s % 8s", n, XPos(n), Level(n), word(Names,j), word(Colors,j))
}
set print
print $Tree
# get x and y distance from ID n to its parent
dx(n) = XPos(Parent(int(n))) - XPos(int(n))
dy(n) = Level(Parent(int(n))) - Level(int(n))
unset border
unset tics
set offsets 0.25, 0.25, 0.25, 0.25
array shape[words(IDs)] # pointtype 6 = circle, pointtype 4 = square
array color[words(IDs)]
do for [i=1:words(IDs)] {
color[i] = int(word(Colors,i))
shape[i] = int(word(Shape,i))
print sprintf("color[%2d] = %d",i,color[i])
}
plot $Tree u 2:3:(dx($1)):(dy($1)) w vec nohead ls -1 not,\
"" u 2:3:(shape[$1]+1):(color[$1]) w p pt variable ps 6 lc rgb variable not, \
"" u 2:3:(shape[$1]) w p pt variable ps 6 lw 1.5 lc rgb "black" not, \
"" u 2:3:4 w labels offset 0,0.1 center not
### end of code
for a small dataset like this one, the output works perfect
1 2.00 0 y_{45} 0xFE1034
2 1.00 -1 - 0x118C4B
3 2.99 -1 y_{37} 0xFE1034
4 2.00 -2 - 0xC6C1C1
5 3.98 -2 y_{13} 0xFE1034
6 3.00 -3 - 0x118C4B
7 4.97 -3 y_{14} 0xFE1034
8 4.00 -4 - 0x118C4B
9 5.94 -4 y_{20} 0xFE1034
10 5.00 -5 - 0xC6C1C1
11 6.88 -5 y_{27} 0xFE1034
12 6.00 -6 - 0xC6C1C1
13 7.75 -6 y_{41} 0xFE1034
14 7.00 -7 - 0xC6C1C1
15 8.50 -7 y_{54} 0xFE1034
16 8.00 -8 - 0xC6C1C1
17 9.00 -8 - 0xC6C1C1
But, for larger datasets the tree becomes cramped, the nodes overlap, and looks ugly.
Moreover, when there are more than a few hundred nodes like below, I get a stack overflow error and the plot does not appear. The error comes from this line
LeafAll = LeafList(Start(0))
Any help with this will be appreciated.
1 NaN y_{295} 0xFE1034 6
2 1 x_{0} 0x33B2FF 6
3 1 y_{1285} 0xFE1034 6
4 2 - 0xC6C1C1 8
5 2 - 0xC6C1C1 8
6 3 x_{3} 0x33B2FF 6
7 3 y_{18} 0xFE1034 6
8 6 - 0xC6C1C1 8
9 6 - 0xC6C1C1 8
10 7 x_{13} 0x33B2FF 6
11 7 y_{21} 0xFE1034 6
12 10 - 0xC6C1C1 8
13 10 - 0xC6C1C1 8
14 11 x_{10} 0x33B2FF 6
15 11 y_{50} 0xFE1034 6
16 14 - 0xC6C1C1 8
17 14 - 0xC6C1C1 8
18 15 - 0x118C4B 4
19 15 y_{62} 0xFE1034 6
20 19 - 0xC6C1C1 8
21 19 y_{48} 0xFE1034 6
22 21 x_{41} 0x33B2FF 6
23 21 y_{1839} 0xFE1034 6
24 22 - 0xC6C1C1 8
25 22 - 0xC6C1C1 8
26 23 - 0xC6C1C1 8
27 23 y_{44} 0xFE1034 6
28 27 x_{12} 0x33B2FF 6
29 27 y_{15} 0xFE1034 6
30 28 - 0xC6C1C1 8
31 28 - 0xC6C1C1 8
32 29 x_{58} 0x33B2FF 6
33 29 y_{127} 0xFE1034 6
34 32 - 0xC6C1C1 8
35 32 - 0xC6C1C1 8
36 33 - 0xC6C1C1 8
37 33 y_{60} 0xFE1034 6
38 37 - 0xC6C1C1 8
39 37 y_{1825} 0xFE1034 6
40 39 - 0xC6C1C1 8
41 39 y_{1878} 0xFE1034 6
42 41 - 0xC6C1C1 8
43 41 y_{33} 0xFE1034 6
44 43 - 0xC6C1C1 8
45 43 y_{3} 0xFE1034 6
46 45 - 0xC6C1C1 8
47 45 y_{1435} 0xFE1034 6
48 47 - 0xC6C1C1 8
49 47 y_{218} 0xFE1034 6
50 49 - 0xC6C1C1 8
51 49 y_{20} 0xFE1034 6
52 51 - 0xC6C1C1 8
53 51 y_{13} 0xFE1034 6
54 53 - 0xC6C1C1 8
55 53 y_{47} 0xFE1034 6
56 55 - 0xC6C1C1 8
57 55 y_{2321} 0xFE1034 6
58 57 - 0xC6C1C1 8
59 57 y_{28} 0xFE1034 6
60 59 - 0xC6C1C1 8
61 59 y_{52} 0xFE1034 6
62 61 - 0xC6C1C1 8
63 61 y_{2410} 0xFE1034 6
64 63 - 0xC6C1C1 8
65 63 y_{1751} 0xFE1034 6
66 65 - 0xC6C1C1 8
67 65 y_{186} 0xFE1034 6
68 67 - 0xC6C1C1 8
69 67 y_{1850} 0xFE1034 6
70 69 - 0xC6C1C1 8
71 69 y_{491} 0xFE1034 6
72 71 - 0xC6C1C1 8
73 71 y_{23} 0xFE1034 6
74 73 - 0xC6C1C1 8
75 73 y_{0} 0xFE1034 6
76 75 x_{52} 0x33B2FF 6
77 75 y_{1110} 0xFE1034 6
78 76 - 0xC6C1C1 8
79 76 - 0xC6C1C1 8
80 77 - 0xC6C1C1 8
81 77 y_{57} 0xFE1034 6
82 81 - 0xC6C1C1 8
83 81 y_{12} 0xFE1034 6
84 83 - 0xC6C1C1 8
85 83 y_{1269} 0xFE1034 6
86 85 - 0xC6C1C1 8
87 85 y_{1278} 0xFE1034 6
88 87 - 0x118C4B 4
89 87 y_{63} 0xFE1034 6
90 89 - 0xC6C1C1 8
91 89 y_{1338} 0xFE1034 6
92 91 - 0xC6C1C1 8
93 91 y_{1271} 0xFE1034 6
94 93 - 0xC6C1C1 8
95 93 y_{41} 0xFE1034 6
96 95 - 0xC6C1C1 8
97 95 y_{65} 0xFE1034 6
98 97 - 0x118C4B 4
99 97 y_{1630} 0xFE1034 6
100 99 - 0xC6C1C1 8
101 99 y_{2068} 0xFE1034 6
102 101 - 0xC6C1C1 8
103 101 y_{2532} 0xFE1034 6
104 103 - 0xC6C1C1 8
105 103 y_{1760} 0xFE1034 6
106 105 - 0xC6C1C1 8
107 105 y_{188} 0xFE1034 6
108 107 - 0xC6C1C1 8
109 107 y_{2405} 0xFE1034 6
110 109 - 0xC6C1C1 8
111 109 y_{1867} 0xFE1034 6
112 111 - 0xC6C1C1 8
113 111 y_{1482} 0xFE1034 6
114 113 - 0xC6C1C1 8
115 113 y_{79} 0xFE1034 6
116 115 - 0xC6C1C1 8
117 115 y_{11} 0xFE1034 6
118 117 - 0xC6C1C1 8
119 117 y_{5226} 0xFE1034 6
120 119 - 0xC6C1C1 8
121 119 y_{354} 0xFE1034 6
122 121 - 0xC6C1C1 8
123 121 y_{2748} 0xFE1034 6
124 123 - 0xC6C1C1 8
125 123 y_{27} 0xFE1034 6
126 125 - 0xC6C1C1 8
127 125 y_{426} 0xFE1034 6
128 127 - 0xC6C1C1 8
129 127 y_{12571} 0xFE1034 6
130 129 - 0xC6C1C1 8
131 129 y_{5089} 0xFE1034 6
132 131 - 0xC6C1C1 8
133 131 y_{2490} 0xFE1034 6
134 133 - 0xC6C1C1 8
135 133 y_{1752} 0xFE1034 6
136 135 - 0xC6C1C1 8
137 135 y_{1874} 0xFE1034 6
138 137 - 0xC6C1C1 8
139 137 y_{370} 0xFE1034 6
140 139 - 0xC6C1C1 8
141 139 y_{1453} 0xFE1034 6
142 141 - 0xC6C1C1 8
143 141 y_{2756} 0xFE1034 6
144 143 - 0xC6C1C1 8
145 143 y_{545} 0xFE1034 6
146 145 - 0xC6C1C1 8
147 145 y_{36} 0xFE1034 6
148 147 - 0xC6C1C1 8
149 147 y_{2409} 0xFE1034 6
150 149 - 0xC6C1C1 8
151 149 y_{96} 0xFE1034 6
152 151 - 0xC6C1C1 8
153 151 y_{82} 0xFE1034 6
154 153 - 0xC6C1C1 8
155 153 y_{1788} 0xFE1034 6
156 155 - 0xC6C1C1 8
157 155 y_{2812} 0xFE1034 6
158 157 - 0xC6C1C1 8
159 157 y_{10357} 0xFE1034 6
160 159 - 0xC6C1C1 8
161 159 y_{1801} 0xFE1034 6
162 161 - 0xC6C1C1 8
163 161 y_{55} 0xFE1034 6
164 163 - 0xC6C1C1 8
165 163 y_{2868} 0xFE1034 6
166 165 - 0xC6C1C1 8
167 165 y_{453} 0xFE1034 6
168 167 - 0xC6C1C1 8
169 167 y_{31} 0xFE1034 6
170 169 - 0xC6C1C1 8
171 169 y_{1281} 0xFE1034 6
172 171 - 0xC6C1C1 8
173 171 y_{17} 0xFE1034 6
174 173 - 0xC6C1C1 8
175 173 y_{1748} 0xFE1034 6
176 175 - 0xC6C1C1 8
177 175 y_{58} 0xFE1034 6
178 177 - 0xC6C1C1 8
179 177 y_{2420} 0xFE1034 6
180 179 - 0xC6C1C1 8
181 179 y_{7128} 0xFE1034 6
182 181 - 0xC6C1C1 8
183 181 y_{11164} 0xFE1034 6
184 183 - 0xC6C1C1 8
185 183 y_{1820} 0xFE1034 6
186 185 - 0xC6C1C1 8
187 185 y_{1713} 0xFE1034 6
188 187 - 0xC6C1C1 8
189 187 y_{387} 0xFE1034 6
190 189 - 0xC6C1C1 8
191 189 y_{5253} 0xFE1034 6
192 191 - 0xC6C1C1 8
193 191 y_{1699} 0xFE1034 6
194 193 - 0xC6C1C1 8
195 193 - 0xC6C1C1 8
The depth of gnuplot's evaluation stack is capped at at 250 to prevent run-away recursion. In order to increase that you would have to edit the source and recompile the program. If you really want to do that, the relevant definition is here:
[gnuplot-5.2.8/src] grep -n -A 3 -B 3 STACK_DEPTH eval.h
44-
45-#include <stdio.h> /* for FILE* */
46-
47:#define STACK_DEPTH 250 /* maximum size of the execution stack */
48-#define MAX_AT_LEN 150 /* max number of entries in action table */
49-
50-/* These are used by add_action() to index the subroutine list ft[] in eval.c */
I have not looked at your recursion algorithm very closely, but I would think it possible to re-order the evaluation so that the subtree information is computed bottom-up rather than top-down. In that direction it may become purely an iteration rather than a recursive descent.
On the other hand you also say that larger trees don't fit into a single plot. So another approach may be to split the tree at a depth that both fits on the page and doesn't exceed the stack depth. Then you restart the process over again for each node that was truncated, and mark that node with an arrow or annotation or other indication like "subtree continued in figure 1b". Here I have hand-mangled your large figure to show the idea
I am using sns.lineplot to show the confidence intervals in a plot.
sns.lineplot(x = threshold, y = mrl_array, err_style = 'band', ci=95)
plt.show()
I'm getting the following plot, which doesn't show the confidence interval:
What's the problem?
There is probably only a single observation per x value.
If there is only one observation per x value, then there is no confidence interval to plot.
Bootstrapping is performed per x value, but there needs to be more than one obsevation for this to take effect.
ci: Size of the confidence interval to draw when aggregating with an estimator. 'sd' means to draw the standard deviation of the data. Setting to None will skip bootstrapping.
Note the following examples from seaborn.lineplot.
This is also the case for sns.relplot with kind='line'.
The question specifies sns.lineplot, but this answer applies to any seaborn plot that displays a confidence interval, such as seaborn.barplot.
Data
import seaborn as sns
# load data
flights = sns.load_dataset("flights")
year month passengers
0 1949 Jan 112
1 1949 Feb 118
2 1949 Mar 132
3 1949 Apr 129
4 1949 May 121
# only May flights
may_flights = flights.query("month == 'May'")
year month passengers
4 1949 May 121
16 1950 May 125
28 1951 May 172
40 1952 May 183
52 1953 May 229
64 1954 May 234
76 1955 May 270
88 1956 May 318
100 1957 May 355
112 1958 May 363
124 1959 May 420
136 1960 May 472
# standard deviation for each year of May data
may_flights.set_index('year')[['passengers']].std(axis=1)
year
1949 NaN
1950 NaN
1951 NaN
1952 NaN
1953 NaN
1954 NaN
1955 NaN
1956 NaN
1957 NaN
1958 NaN
1959 NaN
1960 NaN
dtype: float64
# flight in wide format
flights_wide = flights.pivot("year", "month", "passengers")
month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
year
1949 112 118 132 129 121 135 148 148 136 119 104 118
1950 115 126 141 135 125 149 170 170 158 133 114 140
1951 145 150 178 163 172 178 199 199 184 162 146 166
1952 171 180 193 181 183 218 230 242 209 191 172 194
1953 196 196 236 235 229 243 264 272 237 211 180 201
1954 204 188 235 227 234 264 302 293 259 229 203 229
1955 242 233 267 269 270 315 364 347 312 274 237 278
1956 284 277 317 313 318 374 413 405 355 306 271 306
1957 315 301 356 348 355 422 465 467 404 347 305 336
1958 340 318 362 348 363 435 491 505 404 359 310 337
1959 360 342 406 396 420 472 548 559 463 407 362 405
1960 417 391 419 461 472 535 622 606 508 461 390 432
# standard deviation for each year
flights_wide.std(axis=1)
year
1949 13.720147
1950 19.070841
1951 18.438267
1952 22.966379
1953 28.466887
1954 34.924486
1955 42.140458
1956 47.861780
1957 57.890898
1958 64.530472
1959 69.830097
1960 77.737125
dtype: float64
Plots
may_flights has one observation per year, so no CI is shown.
sns.lineplot(data=may_flights, x="year", y="passengers")
sns.barplot(data=may_flights, x='year', y='passengers')
flights_wide shows there are twelve observations for each year, so the CI shows when all of flights is plotted.
sns.lineplot(data=flights, x="year", y="passengers")
sns.barplot(data=flights, x='year', y='passengers')
I need to find the max of two columns (p_1_logreg, p_2_logreg) where the comparison should be limited only to 14 rows.
My csv file
I tried to slice my index into:
int1_str1_str2_int2_str3_int4
The max should be found between rows where int1, str1, str2 int2 and str3 are fixed, and only the int4 would change (from index 0 to index 13, and so on).
I tried to fix each element at a time and use groupby, but I couldn't iterate over int4 value only.
Here is the code to find the max for column p_1_label, but the result is not what I am looking for.
max_1_row=raw_prob.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[1])['p_1_'+label].idxmax()]
max_1_row=max_1_row.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[3])['p_1_'+label].idxmax()]
max_1_row=max_1_row.loc[raw_prob.groupby(raw_prob['id'].str.split('_').str[5])['p_1_'+label].idxmax()]
Any ideas?
I think you need DataFrameGroupBy.idxmax by replaced last _ with empty string and then select by loc:
df = pd.read_csv('myProb.csv', index_col=[0])
idx = df.drop('id', 1).groupby(df['id'].str.replace('_\d+$', '')).idxmax()
print (idx.head(15))
p_0_logreg p_1_logreg p_2_logreg
id
6_PanaCleanerJune_sub_12_ICA 2 9 6
6_PanaCleanerJune_sub_13_ICA 17 19 23
6_PanaCleanerJune_sub_14_ICA 34 37 33
6_PanaCleanerJune_sub_15_ICA 52 51 43
6_PanaCleanerJune_sub_17_ICA 66 67 69
6_PanaCleanerJune_sub_18_ICA 82 79 76
6_PanaCleanerJune_sub_19_ICA 89 87 90
6_PanaCleanerJune_sub_20_ICA 98 103 104
6_PanaCleanerJune_sub_21_ICA 114 117 112
6_PanaCleanerJune_sub_22_ICA 129 133 127
6_PanaCleanerJune_sub_23_ICA 145 146 143
6_PanaCleanerJune_sub_24_ICA 155 166 161
6_PanaCleanerJune_sub_25_ICA 176 173 174
6_PanaCleanerJune_sub_26_ICA 186 191 189
6_PanaCleanerJune_sub_27_ICA 202 203 209
df1 = df.loc[idx['p_1_logreg']]
print (df1.head(15))
id p_0_logreg p_1_logreg p_2_logreg
9 6_PanaCleanerJune_sub_12_ICA_10 0.013452 0.985195 0.001353
19 6_PanaCleanerJune_sub_13_ICA_6 0.051184 0.948816 0.000000
37 6_PanaCleanerJune_sub_14_ICA_10 0.013758 0.979351 0.006890
51 6_PanaCleanerJune_sub_15_ICA_10 0.076056 0.923944 0.000000
67 6_PanaCleanerJune_sub_17_ICA_12 0.051060 0.947660 0.001280
79 6_PanaCleanerJune_sub_18_ICA_10 0.051184 0.948816 0.000000
87 6_PanaCleanerJune_sub_19_ICA_4 0.078162 0.917751 0.004087
103 6_PanaCleanerJune_sub_20_ICA_6 0.076400 0.921263 0.002337
117 6_PanaCleanerJune_sub_21_ICA_6 0.155002 0.791753 0.053245
133 6_PanaCleanerJune_sub_22_ICA_8 0.000000 0.998623 0.001377
146 6_PanaCleanerJune_sub_23_ICA_7 0.017549 0.973995 0.008457
166 6_PanaCleanerJune_sub_24_ICA_13 0.025215 0.974785 0.000000
173 6_PanaCleanerJune_sub_25_ICA_6 0.025656 0.960220 0.014124
191 6_PanaCleanerJune_sub_26_ICA_10 0.098872 0.895526 0.005602
203 6_PanaCleanerJune_sub_27_ICA_8 0.066493 0.932470 0.001037
df2 = df.loc[idx['p_2_logreg']]
print (df2.head(15))
id p_0_logreg p_1_logreg p_2_logreg
6 6_PanaCleanerJune_sub_12_ICA_7 0.000000 0.000351 0.999649
23 6_PanaCleanerJune_sub_13_ICA_10 0.000000 0.000351 0.999649
33 6_PanaCleanerJune_sub_14_ICA_6 0.080748 0.000352 0.918900
43 6_PanaCleanerJune_sub_15_ICA_2 0.017643 0.000360 0.981996
69 6_PanaCleanerJune_sub_17_ICA_14 0.882449 0.000290 0.117261
76 6_PanaCleanerJune_sub_18_ICA_7 0.010929 0.000360 0.988711
90 6_PanaCleanerJune_sub_19_ICA_7 0.010929 0.000351 0.988720
104 6_PanaCleanerJune_sub_20_ICA_7 0.006714 0.000360 0.992925
112 6_PanaCleanerJune_sub_21_ICA_1 0.869393 0.000339 0.130269
127 6_PanaCleanerJune_sub_22_ICA_2 0.000000 0.000351 0.999649
143 6_PanaCleanerJune_sub_23_ICA_4 0.017218 0.000360 0.982421
161 6_PanaCleanerJune_sub_24_ICA_8 0.369685 0.000712 0.629603
174 6_PanaCleanerJune_sub_25_ICA_7 0.307056 0.000496 0.692448
189 6_PanaCleanerJune_sub_26_ICA_8 0.850195 0.000368 0.149437
209 6_PanaCleanerJune_sub_27_ICA_14 0.000000 0.000351 0.999649
Detail:
print (df['id'].str.replace('_\d+$', '').head(15))
0 6_PanaCleanerJune_sub_12_ICA
1 6_PanaCleanerJune_sub_12_ICA
2 6_PanaCleanerJune_sub_12_ICA
3 6_PanaCleanerJune_sub_12_ICA
4 6_PanaCleanerJune_sub_12_ICA
5 6_PanaCleanerJune_sub_12_ICA
6 6_PanaCleanerJune_sub_12_ICA
7 6_PanaCleanerJune_sub_12_ICA
8 6_PanaCleanerJune_sub_12_ICA
9 6_PanaCleanerJune_sub_12_ICA
10 6_PanaCleanerJune_sub_12_ICA
11 6_PanaCleanerJune_sub_12_ICA
12 6_PanaCleanerJune_sub_12_ICA
13 6_PanaCleanerJune_sub_12_ICA
14 6_PanaCleanerJune_sub_13_ICA
Name: id, dtype: object
Data Block
119 122 140 141 155 163 170 179 203 226 232 233 238 243 244 245 247 248 253 254 255 256 257 261 262 263 264 265 266 270 272 273 275 278 279 281 287 288 289 801 802 808 863 865 1103 1115 1117 1118 1120 1747 1770 1772 1773 1854 1855 6301 6304 6305 6311 6319 6321 6323 6324 6327 6328 6331 6332 6334 6335 6340 6346 6349 6350 6351 6357 6361 6363 6364 6365 6367 6368 6369 6371 6374 6375 6377 6380 6851 6853 6864 6865 6869 6890 6921 6932 6935 6936 6951 6959 6974 8446 8447 8472 8528 8531 8926 8929 8954
Output separated rows
119
------
122
------
140
------
141
-------
155
------
163
Firs select cell and use data -> text to columns, and split data as columns, than copy the columns and paste special and select transpose check.