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 |