I am working with the following code:
url = 'https://raw.githubusercontent.com/dothemathonthatone/maps/master/fertility.csv'
df = pd.read_csv(url)
year regional_schlüssel Aus15 Deu15 Aus16 Deu16 Aus17 Deu17 Aus18 Deu18 ... aus36 aus37 aus38 aus39 aus40 aus41 aus42 aus43 aus44 aus45
0 2000 5111000 0 4 8 25 20 45 56 89 ... 935 862 746 732 792 660 687 663 623 722
1 2000 5113000 1 1 4 14 13 33 19 48 ... 614 602 498 461 521 470 393 411 397 400
2 2000 5114000 0 11 0 5 2 13 7 20 ... 317 278 265 235 259 228 204 173 213 192
3 2000 5116000 0 2 2 7 3 28 13 26 ... 264 217 206 207 197 177 171 146 181 169
4 2000 5117000 0 0 3 1 2 4 4 7 ... 135 129 118 116 128 148 89 110 124 83
I would like to create a new set of columns fertility_deu15, ..., fertility_deu45 and fertility_aus15, ..., fertility_aus45 such that aus15 / Aus15 = fertiltiy_aus15 and deu15/ Deu15 = fertility_deu15 for each ausi and Ausj where j == i \n [15-45] and deui:Deuj where j == i \n [15-45]
I'm not sure what is up with that data but we need to fix it to make it numeric. I'll end up doing that while filtering
numerator = df.filter(regex='^[a-z]+\d+$') # Lower case ones
numerator = numerator.apply(pd.to_numeric, errors='coerce') # Fix numbers
denominator = df.filter(regex='^[A-Z][a-z]+\d+$').rename(columns=str.lower)
denominator = denominator.apply(pd.to_numeric, errors='coerce')
numerator.div(denominator).add_prefix('fertility_')
Related
I have a data table derived via unstacking an existing dataframe:
Day 0 1 2 3 4 5 6
Hrs
0 223 231 135 122 099 211 217
1 156 564 132 414 156 454 157
2 950 178 121 840 143 648 192
3 025 975 151 185 341 145 888
4 111 264 469 330 671 201 345
-- -- -- -- -- -- -- --
I want to simply change the column titles so I have the days of the week displayed instead of numbered. Something like this:
Day Mon Tue Wed Thu Fri Sat Sun
Hrs
0 223 231 135 122 099 211 217
1 156 564 132 414 156 454 157
2 950 178 121 840 143 648 192
3 025 975 151 185 341 145 888
4 111 264 469 330 671 201 345
-- -- -- -- -- -- -- --
I've tried .rename(columns = {'original':'new', etc}, inplace = True) and other similar functions, none of which have worked.
I also tried going to the original dataframe and creating a dt.day_name column from the parsed dates, but it come out with the days of the week mixed up.
I'm sure it's a simple fix, but I'm living off nothing but caffeine, so help would be appreciated.
You can try:
import pandas as pd
df = pd.DataFrame(columns=[0,1,2,3,4,5,6])
df.columns = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
I have df1 which has three columns (loadgroup, cartons, blocks) like this
loadgroup
cartons
blocks
cartonsPercent
blocksPercent
1
2269
14
26%
21%
2
1168
13
13%
19%
3
937
8
11%
12%
4
2753
24
31%
35%
5
1686
9
19%
13%
total(sum of column)
8813
68
100%
100%
The interpretation is like this: out of df1 26% cartons which is also 21% of blocks are assigned to loadgroup 1, etc. we can assume blocks are 1 to 68, cartons are 1 to 8813.
I also have df2 which also has cartons and blocks columns. but does not have loadgroup.
My goal is to assign loadgroup (1-5 as well) to df2 (100 blocks 29608 cartons in total), but keep the proportions, for example, for df2, 26% cartons 21% blocks assign loadgroup 1, 13% cartons 19% blocks assign loadgroup 2, etc.
df2 is like this:
block
cartons
0
533
1
257
2
96
3
104
4
130
5
71
6
68
7
87
8
99
9
51
10
291
11
119
12
274
13
316
14
87
15
149
16
120
17
222
18
100
19
148
20
192
21
188
22
293
23
120
24
224
25
449
26
385
27
395
28
418
29
423
30
244
31
327
32
337
33
249
34
528
35
528
36
494
37
540
38
368
39
533
40
614
41
462
42
350
43
618
44
463
45
552
46
397
47
401
48
397
49
365
50
475
51
379
52
541
53
488
54
383
55
354
56
760
57
327
58
211
59
356
60
552
61
401
62
320
63
368
64
311
65
421
66
458
67
278
68
504
69
385
70
242
71
413
72
246
73
465
74
386
75
231
76
154
77
294
78
275
79
169
80
398
81
227
82
273
83
319
84
177
85
272
86
204
87
139
88
187
89
263
90
90
91
134
92
67
93
115
94
45
95
65
96
40
97
108
98
60
99
102
total 100 blocks
29608 cartons
I want to add loadgroup column to df2, try to keep those proportions as close as possible. How to do it please? Thank you very much for the help.
I don't know how to find loadgroup column based on both cartons percent and blocks percent. But generate random loadgroup based on either cartons percent or blocks percent is easy.
Here is what I did. I generate 100,000 seeds first, then for each seed, I add column loadgroup1 based on cartons percent, loadgroup2 based on blocks percent, then calculate both percentages, then compare with df1 percentages, get absolute difference, record it. For these 100,000 seeds, I take the minimum difference one as my solution, which is sufficient for my job.
But this is not the optimal solution, and I am looking for quick and easy way to do this. Hope somebody can help.
Here is my code.
df = pd.DataFrame()
np.random.seed(10000)
seeds = np.random.randint(1, 1000000, size = 100000)
for i in range(46530, 46537):
print(seeds[i])
np.random.seed(seeds[i])
df2['loadGroup1'] = np.random.choice(df1.loadgroup, len(df2), p = df1.CartonsPercent)
df2['loadGroup2'] = np.random.choice(df1.loadgroup, len(df2), p = df1.blocksPercent)
df2.reset_index(inplace = True)
three = pd.DataFrame(df2.groupby('loadGroup1').agg(Cartons = ('cartons', 'sum'), blocks = ('block', 'count')))
three['CartonsPercent'] = three.Cartons/three.Cartons.sum()
three['blocksPercent'] = three.blocks/three.blocks.sum()
four = df1[['CartonsPercent','blocksPercent']] - three[['CartonsPercent','blocksPercent']]
four = four.abs()
subdf = pd.DataFrame({'i':[i],'Seed':[seeds[i]], 'Percent':['CartonsPercent'], 'AbsDiff':[four.sum().sum()]})
df = pd.concat([df,subdf])
three = pd.DataFrame(df2.groupby('loadGroup2').agg(Cartons = ('cartons', 'sum'), blocks = ('block', 'count')))
three['CartonsPercent'] = three.Cartons/three.Cartons.sum()
three['blocksPercent'] = three.blocks/three.blocks.sum()
four = df1[['CartonsPercent','blocksPercent']] - three[['CartonsPercent','blocksPercent']]
four = four.abs()
subdf = pd.DataFrame({'i':[i],'Seed':[seeds[i]], 'Percent':['blocksPercent'], 'AbsDiff':[four.sum().sum()]})
df = pd.concat([df,subdf])
df.sort_values(by = 'AbsDiff', ascending = True, inplace = True)
df = df.head(10)
Actually the first row of df will tell me the seed I am looking for, I kept 10 rows just for curiosity.
Here is my solution.
block
cartons
loadgroup
0
533
4
1
257
1
2
96
4
3
104
4
4
130
4
5
71
2
6
68
1
7
87
4
8
99
4
9
51
4
10
291
4
11
119
2
12
274
2
13
316
4
14
87
4
15
149
5
16
120
3
17
222
2
18
100
2
19
148
2
20
192
3
21
188
4
22
293
1
23
120
2
24
224
4
25
449
1
26
385
5
27
395
3
28
418
1
29
423
4
30
244
5
31
327
1
32
337
5
33
249
4
34
528
1
35
528
1
36
494
5
37
540
3
38
368
2
39
533
4
40
614
5
41
462
4
42
350
5
43
618
4
44
463
2
45
552
1
46
397
3
47
401
3
48
397
1
49
365
1
50
475
4
51
379
1
52
541
1
53
488
2
54
383
2
55
354
1
56
760
5
57
327
4
58
211
2
59
356
5
60
552
4
61
401
1
62
320
1
63
368
3
64
311
3
65
421
2
66
458
5
67
278
4
68
504
5
69
385
4
70
242
4
71
413
1
72
246
2
73
465
5
74
386
4
75
231
1
76
154
4
77
294
4
78
275
1
79
169
4
80
398
4
81
227
4
82
273
1
83
319
3
84
177
4
85
272
5
86
204
3
87
139
1
88
187
4
89
263
4
90
90
4
91
134
4
92
67
3
93
115
3
94
45
2
95
65
2
96
40
4
97
108
2
98
60
2
99
102
1
Here are the summaries.
loadgroup
cartons
blocks
cartonsPercent
blocksPercent
1
7610
22
26%
22%
2
3912
18
13%
18%
3
3429
12
12%
12%
4
9269
35
31%
35%
5
5388
13
18%
13%
It's very close to my target though.
I have just started with python and need some help. I have a dataframe which looks like "Input Data", What I want is stack by every nth column. In other words, I want a dataframe where every nth Column is appended below to first m rows
id
city
Col 1
Col 2
Col 3
Col 4
Col 5
Col 6
Col 7
Col 8
Col 9
Col 10
1
1
51
155
255
355
455
666
777
955
55
553
2
0
52
155
255
355
455
666
777
595
55
553
3
NAN
53
155
255
355
455
666
777
559
55
535
4
1
54
155
255
355
545
666
777
559
55
535
5
7
55
155
255
355
455
666
777
955
55
535
Required Output
id
city
Col 1
Col 2
Col 3
Col 4
Col 5
1
1
51
155
255
355
455
2
0
52
155
255
355
455
3
NAN
53
155
255
355
455
4
1
54
155
255
355
545
5
7
55
155
255
355
455
1
1
666
777
955
55
553
2
0
666
777
595
55
553
3
NAN
666
777
559
55
535
4
1
666
777
559
55
535
5
7
666
777
955
55
535
I am trying to do something opposite of this
Input & required Output
In [74]: column_list = [df.columns[k:k+5] for k in range(2, len(df.columns), 5)]
In [75]: column_list
Out[75]:
[Index(['Col 1', 'Col 2', 'Col 3', 'Col 4', 'Col 5'], dtype='object'),
Index(['Col 6', 'Col 7', 'Col 8', 'Col 9', 'Col 10'], dtype='object')]
In [76]: dfs = [df[['id', 'city'] + columns.tolist()].rename(columns=dict(zip(columns, range(5)))) for columns in column_list]
In [77]: dfs
Out[77]:
[ id city 0 1 2 3 4
0 1 1.0 51 155 255 355 455
1 2 0.0 52 155 255 355 455
2 3 NaN 53 155 255 355 455
3 4 1.0 54 155 255 355 545
4 5 7.0 55 155 255 355 455,
id city 0 1 2 3 4
0 1 1.0 666 777 955 55 553
1 2 0.0 666 777 595 55 553
2 3 NaN 666 777 559 55 535
3 4 1.0 666 777 559 55 535
4 5 7.0 666 777 955 55 535]
In [78]: pd.concat(dfs, ignore_index=True)
Out[78]:
id city 0 1 2 3 4
0 1 1.0 51 155 255 355 455
1 2 0.0 52 155 255 355 455
2 3 NaN 53 155 255 355 455
3 4 1.0 54 155 255 355 545
4 5 7.0 55 155 255 355 455
5 1 1.0 666 777 955 55 553
6 2 0.0 666 777 595 55 553
7 3 NaN 666 777 559 55 535
8 4 1.0 666 777 559 55 535
9 5 7.0 666 777 955 55 535
To explain :
First generate the required columns for each slice
pd.concat requires the column names of all the dataframes in the list to be the same, hence the renames in rename(columns=dict(zip(columns, range(5)))). We are just renaming the sliced columns to 0,1,2,3,4
Last step is to concat everything.
EDIT
Based on the comments by OP:
Sorry #Asish M. but how to add a column for dataset number in each dataset of dfs, eg- here we split our dataset into 2, so I need one column which says for first 1 to 5 ids - 'first' (or 1), then again for another 1 to 5 ids - 'second' (or 2) in the output. I hope it's making scenes
dfs = [df[['id', 'city'] + columns.tolist()].assign(split_group=idx).rename(columns=dict(zip(columns, range(5)))) for idx, columns in enumerate(column_list)]
df.assign(split_group=idx) creates a column 'split_group' with value = idx. You get the idx from enumerating the column_list
I have this df:
ID Date XXX 123_Var 456_Var 789_Var 123_P 456_P 789_P
A 07/16/2019 1 987 551 313 22 12 94
A 07/16/2019 9 135 748 403 92 40 41
A 07/18/2019 8 376 938 825 14 69 96
A 07/18/2019 5 259 176 674 52 75 72
B 07/16/2019 9 690 304 948 56 14 78
B 07/16/2019 8 819 185 699 33 81 83
B 07/18/2019 1 580 210 847 51 64 87
I want to group the df by ID and Date, aggregate the XXX column by the maximum value, and aggregate 123_Var, 456_Var, 789_Var columns by the minimum value.
* Note: The df contains many of these columns. The shape is: {some int}_Var.
This is the current code I've started to write:
df = (df.groupby(['ID','Date'], as_index=False)
.agg({'XXX':'max', list(df.filter(regex='_Var')): 'min'}))
Expected result:
ID Date XXX 123_Var 456_Var 789_Var
A 07/16/2019 9 135 551 313
A 07/18/2019 8 259 176 674
B 07/16/2019 9 690 185 699
B 07/18/2019 1 580 210 847
Create dictionary dynamic with dict.fromkeys and then merge it with {'XXX':'max'} dict and pass to GroupBy.agg:
d = dict.fromkeys(df.filter(regex='_Var').columns, 'min')
df = df.groupby(['ID','Date'], as_index=False).agg({**{'XXX':'max'}, **d})
print (df)
ID Date XXX 123_Var 456_Var 789_Var
0 A 07/16/2019 9 135 551 313
1 A 07/18/2019 8 259 176 674
2 B 07/16/2019 9 690 185 699
3 B 07/18/2019 1 580 210 847
have a schema with 9 fields and i want to take only two fields(6,7 i.e $5,$6) and i want to calculate the average of $5 and i want to sort the $6 in ascending order so how to do this task can some one help me.
Input Data:
N368SW 188 170 175 17 -1 MCO MHT 1142
N360SW 100 115 87 -10 5 MCO MSY 550
N626SW 114 115 90 13 14 MCO MSY 550
N252WN 107 115 84 -10 -2 MCO MSY 550
N355SW 104 115 85 -1 10 MCO MSY 550
N405WN 113 110 96 14 11 MCO ORF 655
N456WN 110 110 92 24 24 MCO ORF 655
N743SW 144 155 124 7 18 MCO PHL 861
N276WN 142 150 129 -2 6 MCO PHL 861
N369SW 153 145 134 30 22 MCO PHL 861
N363SW 151 145 137 5 -1 MCO PHL 861
N346SW 141 150 128 51 60 MCO PHL 861
N785SW 131 145 118 -15 -1 MCO PHL 861
N635SW 144 155 127 -6 5 MCO PHL 861
N242WN 298 300 276 68 70 MCO PHX 1848
N439WN 130 140 111 -4 6 MCO PIT 834
N348SW 140 135 124 7 2 MCO PIT 834
N672SW 136 135 122 9 8 MCO PIT 834
N493WN 151 160 136 -9 0 MCO PVD 1073
N380SW 170 155 155 13 -2 MCO PVD 1073
N705SW 164 160 147 6 2 MCO PVD 1073
N233LV 157 160 143 1 4 MCO PVD 1073
N786SW 156 160 139 6 10 MCO PVD 1073
N280WN 160 160 146 1 1 MCO PVD 1073
N282WN 104 95 81 10 1 MCO RDU 534
N694SW 89 100 77 3 14 MCO RDU 534
N266WN 94 95 82 9 10 MCO RDU 534
N218WN 98 100 77 12 14 MCO RDU 534
N355SW 47 50 35 15 18 MCO RSW 133
N388SW 44 45 30 37 38 MCO RSW 133
N786SW 46 50 31 4 8 MCO RSW 133
N707SA 52 50 33 10 8 MCO RSW 133
N795SW 176 185 153 -9 0 MCO SAT 1040
N402WN 176 185 161 4 13 MCO SAT 1040
N690SW 123 130 107 -1 6 MCO SDF 718
N457WN 135 130 105 20 15 MCO SDF 718
N720WN 144 155 131 13 24 MCO STL 880
N775SW 147 160 135 -6 7 MCO STL 880
N291WN 136 155 122 96 115 MCO STL 880
N247WN 144 155 127 43 54 MCO STL 880
N748SW 179 185 159 -4 2 MDW ABQ 1121
N709SW 176 190 158 21 35 MDW ABQ 1121
N325SW 110 105 97 36 31 MDW ALB 717
N305SW 116 110 90 107 101 MDW ALB 717
N403WN 145 165 128 -6 14 MDW AUS 972
N767SW 136 165 125 59 88 MDW AUS 972
N730SW 118 120 100 28 30 MDW BDL 777
i have written the code like this but it is not working properly:
a = load '/path/to/file' using PigStorage('\t');
b = foreach a generate (int)$5 as field_a:int,(chararray)$6 as field_b:chararray;
c = group b all;
d = foreach c generate b.field_b,AVG(b.field_a);
e = order d by field_b ASC;
dump e;
I am facing error at order by:
grunt> a = load '/user/horton/sample_pig_data.txt' using PigStorage('\t');
grunt> b = foreach a generate (int)$5 as fielda:int,(chararray)$6 as fieldb:chararray;
grunt> describe #;
b: {fielda: int,fieldb: chararray}
grunt> c = group b all;
grunt> describe #;
c: {group: chararray,b: {(fielda: int,fieldb: chararray)}}
grunt> d = foreach c generate b.fieldb,AVG(b.fielda);
grunt> e = order d by fieldb ;
2017-01-05 15:51:29,623 [main] ERROR org.apache.pig.tools.grunt.Grunt - ERROR 1025:
<line 6, column 15> Invalid field projection. Projected field [fieldb] does not exist in schema: :bag{:tuple(fieldb:chararray)},:double.
Details at logfile: /root/pig_1483631021021.log
I want output like(not related to input data):
(({(Bharathi),(Komal),(Archana),(Trupthi),(Preethi),(Rajesh),(siddarth),(Rajiv) },
{ (72) , (83) , (87) , (75) , (93) , (90) , (78) , (89) }),83.375)
If you have found the answer, best practice is to post it so that others referring to this can have a better understanding.