I have multiple .csv files for months. Below is a sample for one month.
Output - I need to merge all the .csv files and put a month value for each row in the file and then merge the data of multiple sheets.
Post that I would like to find the correlation in the data by Team and Month for any two selected variable in the data.
Input -
| Team_Name |
IDWwMember |
|
Resources |
Workout |
Volume |
Diverted |
Downtime |
Time_Worked |
Core_Time |
Staff_Complement |
Annual_Leave |
Temporary_Staff |
Overtime |
Borrowed |
Loaned |
Flexitime |
OneOnOne |
System |
numberOfCreatedMonths |
numberOfTeamTenureMonths |
| A |
30406 |
|
12750 |
5850 |
122 |
1355 |
|
12750 |
11395 |
14790 |
0 |
0 |
510 |
0 |
2550 |
0 |
30 |
220 |
57.3 |
3.81 |
| A |
33185 |
|
14340 |
11910 |
257 |
1325 |
1275 |
13065 |
11740 |
14790 |
1020 |
0 |
570 |
0 |
0 |
0 |
30 |
710 |
41.43 |
22.51 |
| A |
33188 |
|
10200 |
10380 |
208 |
400 |
|
10200 |
9800 |
14790 |
5100 |
0 |
510 |
0 |
0 |
0 |
30 |
|
41.43 |
26.97 |
| A |
21052 |
|
14025 |
7800 |
161 |
700 |
3315 |
10710 |
10010 |
14790 |
765 |
0 |
0 |
0 |
0 |
0 |
30 |
150 |
88.68 |
1.97 |
| A |
32506 |
|
16320 |
13560 |
291 |
2075 |
1020 |
15300 |
13225 |
14790 |
0 |
0 |
1530 |
0 |
0 |
0 |
30 |
670 |
44.75 |
33.74 |
| A |
25834 |
|
14535 |
8250 |
166 |
1255 |
1530 |
13005 |
11750 |
14790 |
765 |
0 |
510 |
0 |
0 |
0 |
30 |
|
72.74 |
1.74 |
| A |
24714 |
|
15240 |
9420 |
204 |
880 |
510 |
14730 |
13850 |
14790 |
0 |
0 |
570 |
0 |
120 |
0 |
30 |
240 |
77.08 |
1.74 |
| A |
25420 |
|
12960 |
5490 |
111 |
1850 |
510 |
12450 |
10600 |
14790 |
510 |
0 |
1590 |
0 |
2910 |
0 |
30 |
710 |
74.28 |
1.74 |
| A |
24185 |
|
15300 |
8550 |
176 |
1295 |
2550 |
12750 |
11455 |
14790 |
0 |
0 |
510 |
0 |
0 |
0 |
30 |
90 |
78.75 |
1.74 |
| A |
33181 |
|
15360 |
12720 |
290 |
2209 |
|
15360 |
13151 |
14790 |
0 |
0 |
1080 |
0 |
510 |
0 |
50 |
698 |
41.43 |
22.51 |
| A |
33759 |
|
15360 |
10440 |
219 |
3170 |
1800 |
13560 |
10390 |
14790 |
510 |
0 |
1080 |
0 |
0 |
0 |
30 |
1960 |
38.93 |
11.96 |
| A |
32174 |
|
15615 |
12600 |
279 |
3265 |
510 |
15105 |
11840 |
14790 |
0 |
0 |
825 |
0 |
0 |
0 |
30 |
255 |
47.28 |
47.25 |
| A |
33724 |
|
13320 |
6900 |
145 |
1605 |
|
13320 |
11715 |
14790 |
1530 |
0 |
1080 |
0 |
1020 |
0 |
30 |
300 |
39 |
1.74 |
| A |
12077 |
|
13590 |
6930 |
139 |
700 |
510 |
13080 |
12380 |
14790 |
1530 |
0 |
330 |
0 |
0 |
0 |
30 |
60 |
117.09 |
1.74 |
| A |
14411 |
|
13065 |
4800 |
103 |
1190 |
2295 |
10770 |
9580 |
14790 |
255 |
0 |
1080 |
0 |
2550 |
0 |
30 |
690 |
110.29 |
1.74 |
| A |
32502 |
|
14025 |
6030 |
138 |
515 |
7140 |
6885 |
6370 |
14790 |
765 |
0 |
0 |
0 |
0 |
0 |
30 |
180 |
44.75 |
33.84 |
| A |
35017 |
|
12289 |
8670 |
189 |
1100 |
2040 |
10249 |
9149 |
14790 |
3060 |
0 |
510 |
0 |
0 |
49 |
30 |
525 |
33.12 |
22.51 |
| A |
33187 |
|
15411 |
13140 |
301 |
1165 |
510 |
14901 |
13736 |
14790 |
0 |
0 |
570 |
0 |
0 |
51 |
30 |
540 |
41.43 |
26.97 |
| A |
24276 |
|
3825 |
1290 |
25 |
545 |
|
3825 |
3280 |
7650 |
765 |
0 |
0 |
0 |
3060 |
0 |
30 |
|
78.26 |
1.41 |
| B |
18900 |
|
4905 |
2250 |
62 |
751 |
|
4905 |
4154 |
7650 |
0 |
0 |
510 |
0 |
3255 |
0 |
45 |
121 |
97.09 |
31.54 |
| B |
22521 |
|
15600 |
5847 |
150 |
3669 |
1020 |
14580 |
10911 |
14790 |
1020 |
0 |
1830 |
0 |
0 |
0 |
75 |
542 |
83.98 |
48.76 |
| B |
27462 |
|
15300 |
4680 |
111 |
3696 |
1140 |
14160 |
10464 |
14790 |
0 |
0 |
510 |
0 |
0 |
0 |
98 |
1424 |
68.37 |
1.97 |
| B |
35443 |
|
15810 |
6948 |
154 |
2300 |
35 |
15775 |
13475 |
14790 |
510 |
0 |
1530 |
0 |
0 |
0 |
90 |
910 |
31.11 |
8.25 |
| B |
30322 |
|
14790 |
3300 |
77 |
1158 |
7680 |
7110 |
5952 |
14790 |
0 |
0 |
0 |
0 |
0 |
0 |
30 |
513 |
57.46 |
6.37 |
| B |
14572 |
|
12240 |
3828 |
92 |
2819 |
1020 |
11220 |
8401 |
14790 |
510 |
0 |
510 |
0 |
2550 |
0 |
20 |
1170 |
109.77 |
30 |
| B |
35447 |
|
14280 |
6450 |
147 |
2156 |
|
14280 |
12124 |
14790 |
1530 |
0 |
1020 |
0 |
0 |
0 |
54 |
847 |
31.11 |
8.25 |
| B |
30843 |
|
3375 |
1080 |
31 |
1147 |
|
3375 |
2228 |
7650 |
1020 |
0 |
510 |
0 |
3765 |
0 |
|
439 |
55.33 |
34.04 |
| C |
35021 |
|
16320 |
5931 |
157 |
1681 |
|
16320 |
14639 |
14790 |
0 |
0 |
1530 |
0 |
0 |
0 |
30 |
661 |
33.12 |
1.28 |
| C |
28128 |
|
16320 |
6024 |
173 |
1350 |
|
16320 |
14970 |
14790 |
0 |
0 |
1530 |
0 |
0 |
0 |
30 |
300 |
65.61 |
1.51 |
| C |
35020 |
|
15810 |
5464 |
138 |
1275 |
|
15810 |
14535 |
14790 |
510 |
0 |
1530 |
0 |
0 |
0 |
30 |
361 |
33.12 |
1.28 |
| C |
26318 |
|
15300 |
5382 |
148 |
1648 |
510 |
14790 |
13142 |
14790 |
510 |
0 |
1020 |
0 |
0 |
0 |
30 |
525 |
71.56 |
2.04 |
Output
| Team |
Month |
Cor workout: Coretime |
Cor WorkOut : Timeworked |
| A |
Jan |
-0.19 |
0.49 |
| A |
Feb |
0.69 |
-0.38 |
| A |
Mar |
0.22 |
0.96 |
| B |
Jan |
-0.38 |
0.23 |
| B |
Feb |
0.08 |
-0.72 |
| B |
Mar |
0.38 |
0.53 |
| C |
Jan |
-0.37 |
0.65 |
| C |
Feb |
-0.51 |
-0.37 |
| C |
Mar |
0.47 |
0.56 |