In [1]:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
# とりあえず生データを全部出力
data = pd.read_csv('d:/Temporary/civ4log.csv')
data
Out[1]:
| ターン数 | 指導者 | 都市名 | 金銭 | 研究 | 文化 | スパイ | 生産 | 維持費 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 1 | 0 | TXT_KEY_LEADER_HUAYNA_CAPAC | TXT_KEY_CITY_NAME_CUZCO | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 2 | 0 | TXT_KEY_LEADER_CYRUS | TXT_KEY_CITY_NAME_PERSEPOLIS | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 3 | 0 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_WASHINGTON | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 4 | 0 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_AKSUM | 0.0 | 9.0 | 4.0 | 4.0 | 1.0 | 0.0 |
| 5 | 0 | TXT_KEY_LEADER_HAMMURABI | TXT_KEY_CITY_NAME_BABYLON | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 6 | 0 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_BEIJING | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 7 | 1 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 8 | 1 | TXT_KEY_LEADER_HUAYNA_CAPAC | TXT_KEY_CITY_NAME_CUZCO | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 9 | 1 | TXT_KEY_LEADER_CYRUS | TXT_KEY_CITY_NAME_PERSEPOLIS | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 10 | 1 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_WASHINGTON | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 11 | 1 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_AKSUM | 0.0 | 9.0 | 4.0 | 4.0 | 1.0 | 0.0 |
| 12 | 1 | TXT_KEY_LEADER_HAMMURABI | TXT_KEY_CITY_NAME_BABYLON | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 13 | 1 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_BEIJING | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 14 | 2 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 15 | 2 | TXT_KEY_LEADER_HUAYNA_CAPAC | TXT_KEY_CITY_NAME_CUZCO | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 16 | 2 | TXT_KEY_LEADER_CYRUS | TXT_KEY_CITY_NAME_PERSEPOLIS | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 17 | 2 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_WASHINGTON | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 18 | 2 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_AKSUM | 0.0 | 9.0 | 4.0 | 4.0 | 1.0 | 0.0 |
| 19 | 2 | TXT_KEY_LEADER_HAMMURABI | TXT_KEY_CITY_NAME_BABYLON | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 20 | 2 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_BEIJING | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 21 | 3 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 22 | 3 | TXT_KEY_LEADER_HUAYNA_CAPAC | TXT_KEY_CITY_NAME_CUZCO | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 23 | 3 | TXT_KEY_LEADER_CYRUS | TXT_KEY_CITY_NAME_PERSEPOLIS | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 24 | 3 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_WASHINGTON | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 25 | 3 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_AKSUM | 0.0 | 9.0 | 4.0 | 4.0 | 1.0 | 0.0 |
| 26 | 3 | TXT_KEY_LEADER_HAMMURABI | TXT_KEY_CITY_NAME_BABYLON | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 27 | 3 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_BEIJING | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| 28 | 4 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 29 | 4 | TXT_KEY_LEADER_HUAYNA_CAPAC | TXT_KEY_CITY_NAME_CUZCO | 0.0 | 9.0 | 2.0 | 4.0 | 2.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 12571 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_PHILADELPHIA | 10.0 | 78.0 | 42.0 | 66.0 | 33.0 | 2.0 |
| 12572 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_ATLANTA | 4.0 | 31.0 | 22.0 | 102.0 | 36.0 | 0.0 |
| 12573 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_CHICAGO | 2.0 | 26.0 | 6.0 | 2.0 | 2.0 | 4.0 |
| 12574 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_SEATTLE | 3.0 | 77.0 | 20.0 | 40.0 | 33.0 | 6.0 |
| 12575 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_CUZCO | 4.0 | 33.0 | 2.0 | 44.0 | 48.0 | 4.0 |
| 12576 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_DEBRE_BERHAN | 0.0 | 11.0 | 0.0 | 32.0 | 6.0 | 6.0 |
| 12577 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_MACHU | 16.0 | 59.0 | 32.0 | 44.0 | 54.0 | 5.0 |
| 12578 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_BORSIPPA | 2.0 | 18.0 | 12.0 | 44.0 | 38.0 | 4.0 |
| 12579 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_MARI | 2.0 | 18.0 | 16.0 | 44.0 | 78.0 | 5.0 |
| 12580 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_HUAMANGA | 3.0 | 43.0 | 4.0 | 0.0 | 18.0 | 11.0 |
| 12581 | 361 | TXT_KEY_LEADER_ROOSEVELT | TXT_KEY_CITY_NAME_TIWANAKU | 3.0 | 31.0 | 2.0 | 0.0 | 12.0 | 8.0 |
| 12582 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_AKSUM | 44.0 | 150.0 | 63.0 | 52.0 | 83.0 | 1.0 |
| 12583 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_GONDAR | 10.0 | 137.0 | 32.0 | 44.0 | 65.0 | 2.0 |
| 12584 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_LALIBELA | 8.0 | 64.0 | 13.0 | 44.0 | 23.0 | 2.0 |
| 12585 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_ADDIS_ABABA | 4.0 | 43.0 | 12.0 | 44.0 | 73.0 | 1.0 |
| 12586 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_YEHA | 3.0 | 35.0 | 8.0 | 2.0 | 1.0 | 2.0 |
| 12587 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_NAVAJO | 1.0 | 16.0 | 2.0 | 0.0 | 6.0 | 2.0 |
| 12588 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_ADULIS | 0.0 | 6.0 | 3.0 | 44.0 | 28.0 | 1.0 |
| 12589 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_QOHAITO | 2.0 | 23.0 | 6.0 | 0.0 | 1.0 | 4.0 |
| 12590 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_MATARA | 2.0 | 26.0 | 3.0 | 6.0 | 1.0 | 1.0 |
| 12591 | 361 | TXT_KEY_LEADER_ZARA_YAQOB | TXT_KEY_CITY_NAME_HAWULTI | 2.0 | 21.0 | 3.0 | 0.0 | 2.0 | 3.0 |
| 12592 | 361 | TXT_KEY_LEADER_HAMMURABI | TXT_KEY_CITY_NAME_AKKAD | 10.0 | 61.0 | 25.0 | 111.0 | 35.0 | 0.0 |
| 12593 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_BEIJING | 54.0 | 162.0 | 40.0 | 52.0 | 44.0 | 1.0 |
| 12594 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_SHANGHAI | 31.0 | 78.0 | 16.0 | 24.0 | 30.0 | 5.0 |
| 12595 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_SIPPAR | 10.0 | 26.0 | 8.0 | 0.0 | 15.0 | 7.0 |
| 12596 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_NANJING | 12.0 | 31.0 | 21.0 | 0.0 | 21.0 | 4.0 |
| 12597 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_XIAN | 11.0 | 28.0 | 8.0 | 0.0 | 3.0 | 7.0 |
| 12598 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_CHENGDU | 9.0 | 25.0 | 7.0 | 2.0 | 10.0 | 3.0 |
| 12599 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_OPIS | 7.0 | 19.0 | 18.0 | 0.0 | 3.0 | 5.0 |
| 12600 | 361 | TXT_KEY_LEADER_QIN_SHI_HUANG | TXT_KEY_CITY_NAME_GUANGZHOU | 5.0 | 9.0 | 7.0 | 40.0 | 27.0 | 5.0 |
12601 rows × 9 columns
In [2]:
# AIのデータは邪魔なので除外
data = data.query("指導者 == 'kojim'")
data
Out[2]:
| ターン数 | 指導者 | 都市名 | 金銭 | 研究 | 文化 | スパイ | 生産 | 維持費 | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 7 | 1 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 14 | 2 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 21 | 3 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 28 | 4 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 35 | 5 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 42 | 6 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 49 | 7 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 56 | 8 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 63 | 9 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 70 | 10 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 77 | 11 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 84 | 12 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 91 | 13 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 98 | 14 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 105 | 15 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 112 | 16 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 119 | 17 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 126 | 18 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 133 | 19 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 140 | 20 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 147 | 21 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 11.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 154 | 22 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 11.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 161 | 23 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 11.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 168 | 24 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 175 | 25 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 11.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 182 | 26 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 11.0 | 2.0 | 4.0 | 1.0 | 0.0 |
| 189 | 27 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 10.0 | 2.0 | 4.0 | 5.0 | 0.0 |
| 196 | 28 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 17.0 | 2.0 | 4.0 | 6.0 | 0.0 |
| 203 | 29 | kojim | TXT_KEY_CITY_NAME_DELHI | 0.0 | 17.0 | 2.0 | 4.0 | 6.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11190 | 332 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1354.0 | 2304.0 | 78.0 | 444.0 | 0.0 |
| 11237 | 333 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1354.0 | 2304.0 | 78.0 | 444.0 | 0.0 |
| 11284 | 334 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1354.0 | 2304.0 | 78.0 | 444.0 | 0.0 |
| 11331 | 335 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11378 | 336 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11425 | 337 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11472 | 338 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11519 | 339 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11566 | 340 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 324.0 | 0.0 |
| 11613 | 341 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11660 | 342 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 378.0 | 0.0 |
| 11707 | 343 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 324.0 | 0.0 |
| 11754 | 344 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 388.0 | 0.0 |
| 11801 | 345 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 388.0 | 0.0 |
| 11848 | 346 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 388.0 | 0.0 |
| 11895 | 347 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 333.0 | 0.0 |
| 11942 | 348 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1253.0 | 2304.0 | 78.0 | 333.0 | 0.0 |
| 11989 | 349 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1242.0 | 2304.0 | 78.0 | 333.0 | 0.0 |
| 12036 | 350 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1368.0 | 2304.0 | 78.0 | 390.0 | 0.0 |
| 12083 | 351 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1368.0 | 2304.0 | 78.0 | 520.0 | 0.0 |
| 12130 | 352 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1368.0 | 2304.0 | 78.0 | 520.0 | 0.0 |
| 12177 | 353 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1368.0 | 2304.0 | 78.0 | 390.0 | 0.0 |
| 12224 | 354 | kojim | TXT_KEY_CITY_NAME_DELHI | 108.0 | 1368.0 | 2304.0 | 78.0 | 390.0 | 0.0 |
| 12272 | 355 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12319 | 356 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12366 | 357 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12413 | 358 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12460 | 359 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12507 | 360 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 850.0 | 2304.0 | 522.0 | 390.0 | 0.0 |
| 12554 | 361 | kojim | TXT_KEY_CITY_NAME_DELHI | 112.0 | 871.0 | 2315.0 | 438.0 | 333.0 | 0.0 |
362 rows × 9 columns
In [3]:
# 雑にグラフにしてみる
data.plot.bar(x='ターン数', y=['金銭', '研究', '文化', 'スパイ'], alpha=0.6, figsize=(24,8))
plt.title('OCC都市出力', size=16)
Out[3]:
<matplotlib.text.Text at 0x8947d30>
In [4]:
# 重要な研究力と生産力に絞ってグラフ化
data = data.query("ターン数%5 == 0")
data.plot.bar(x='ターン数', y=['研究', '生産'], alpha=0.6, figsize=(24,8))
plt.title('OCC都市出力', size=16)
Out[4]:
<matplotlib.text.Text at 0x8fc9dd8>
In [ ]: