%matplotlib inline
import pandas as pd
df = pd.read_excel("omaezaki8.xls", sheet_name="Sheet2",header=1, encoding="SHIFT_JIS")
# df.plot.line(x="年月日",y=["最高気温","17最高気温"])
# df.plot.line(x="年月日",y=["最低気温","17最低気温"])
df.plot.line(x="年月日",y=["最高気温","H最高気温"])
df.plot.line(x="年月日",y=["最低気温","H最低気温"])
# df.plot.line(x="年月日",y="気温差")
df.plot.bar(x="年月日",y=["降水量","H降水量"])
df
| 年月日 | 最高気温 | 平均気温 | 最低気温 | 気温差 | 降水量 | H最高気温 | H平均気温 | H最低気温 | H気温差 | H降水量 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019/8/1 | 30.8 | 28.3 | 26.8 | 4.0 | 0.0 | 34.0 | 29.7 | 27.1 | 6.9 | 0.0 |
| 1 | 2019/8/2 | 30.8 | 28.2 | 27.0 | 3.8 | 0.0 | 32.5 | 28.7 | 26.8 | 5.7 | 0.0 |
| 2 | 2019/8/3 | 31.3 | 28.1 | 26.3 | 5.0 | 0.0 | 32.1 | 28.5 | 26.2 | 5.9 | 0.0 |
| 3 | 2019/8/4 | 30.9 | 28.1 | 25.5 | 5.4 | 0.0 | 32.8 | 29.0 | 26.5 | 6.3 | 0.0 |
| 4 | 2019/8/5 | 32.6 | 28.8 | 26.9 | 5.7 | 0.0 | 35.1 | 29.6 | 25.9 | 9.2 | 0.0 |
| 5 | 2019/8/6 | 31.4 | 28.5 | 26.8 | 4.6 | 0.0 | 33.3 | 29.3 | 26.8 | 6.5 | 0.0 |
| 6 | 2019/8/7 | 31.5 | 28.5 | 26.6 | 4.9 | 0.0 | 33.2 | 28.9 | 26.5 | 6.7 | 0.0 |
| 7 | 2019/8/8 | 31.5 | 28.2 | 26.1 | 5.4 | 0.0 | 32.7 | 28.7 | 26.2 | 6.5 | 0.0 |
| 8 | 2019/8/9 | 31.5 | 28.4 | 26.3 | 5.2 | 0.0 | 32.4 | 28.6 | 25.6 | 6.8 | 0.0 |
| 9 | 2019/8/10 | 31.0 | 28.3 | 26.5 | 4.5 | 0.0 | 33.1 | 29.3 | 26.5 | 6.6 | 0.0 |
| 10 | 2019/8/11 | 31.5 | 28.4 | 25.0 | 6.5 | 0.0 | 33.8 | 29.5 | 26.0 | 7.8 | 0.0 |
| 11 | 2019/8/12 | 32.3 | 29.0 | 26.5 | 5.8 | 0.0 | 35.4 | 30.0 | 26.1 | 9.3 | 0.0 |
| 12 | 2019/8/13 | 32.2 | 29.3 | 27.4 | 4.8 | 1.5 | 35.1 | 30.6 | 27.7 | 7.4 | 0.0 |
| 13 | 2019/8/14 | 31.3 | 28.4 | 26.5 | 4.8 | 9.5 | 32.3 | 28.1 | 26.2 | 6.1 | 26.5 |
| 14 | 2019/8/15 | 30.8 | 28.6 | 27.7 | 3.1 | 1.5 | 29.6 | 28.1 | 26.8 | 2.8 | 9.5 |
| 15 | 2019/8/16 | 30.2 | 28.2 | 27.2 | 3.0 | 0.0 | 29.6 | 27.9 | 26.8 | 2.8 | 15.5 |
| 16 | 2019/8/17 | 31.0 | 28.3 | 27.0 | 4.0 | 0.0 | 32.9 | 28.8 | 26.3 | 6.6 | 0.0 |
| 17 | 2019/8/18 | 31.1 | 28.2 | 26.4 | 4.7 | 0.0 | 32.3 | 28.6 | 26.2 | 6.1 | 0.0 |
| 18 | 2019/8/19 | 31.4 | 28.2 | 25.9 | 5.5 | 0.0 | 32.3 | 28.7 | 25.8 | 6.5 | 0.0 |
| 19 | 2019/8/20 | 31.0 | 28.2 | 26.1 | 4.9 | 4.0 | 32.5 | 28.2 | 26.1 | 6.4 | 9.0 |
| 20 | 2019/8/21 | 31.0 | 28.1 | 25.6 | 5.4 | 0.0 | 34.5 | 29.4 | 26.0 | 8.5 | 0.0 |
| 21 | 2019/8/22 | 32.0 | 29.0 | 27.0 | 5.0 | 0.0 | 34.9 | 29.6 | 26.5 | 8.4 | 0.0 |
| 22 | 2019/8/23 | 28.9 | 26.6 | 24.4 | 4.5 | 1.0 | 27.8 | 25.7 | 23.6 | 4.2 | 28.5 |
| 23 | 2019/8/24 | 28.2 | 25.7 | 23.6 | 4.6 | 1.5 | 29.4 | 26.2 | 22.9 | 6.5 | 0.5 |
| 24 | 2019/8/25 | 29.4 | 26.2 | 24.0 | 5.4 | 0.0 | 33.3 | 27.6 | 24.0 | 9.3 | 0.0 |
| 25 | 2019/8/26 | 29.3 | 26.0 | 23.1 | 6.2 | 0.0 | 30.5 | 26.5 | 22.8 | 7.7 | 0.0 |
| 26 | 2019/8/27 | 29.3 | 25.2 | 22.7 | 6.6 | 15.0 | 29.9 | 24.6 | 22.0 | 7.9 | 38.5 |
| 27 | 2019/8/28 | 28.6 | 26.7 | 23.7 | 4.9 | 4.5 | 28.0 | 25.7 | 22.7 | 5.3 | 7.5 |
| 28 | 2019/8/29 | 29.3 | 27.0 | 25.5 | 3.8 | 0.0 | 31.6 | 27.3 | 23.4 | 8.2 | 0.0 |
| 29 | 2019/8/30 | 28.2 | 25.3 | 22.9 | 5.3 | 120.0 | 27.8 | 25.2 | 23.6 | 4.2 | 28.5 |
| 30 | 2019/8/31 | 28.6 | 25.7 | 23.8 | 4.8 | 0.0 | 31.9 | 26.4 | 23.4 | 8.5 | 0.0 |
| 31 | 2019/9/1 | 29.1 | 26.1 | 23.6 | 5.5 | 0.0 | 32.5 | 27.2 | 23.8 | 8.7 | 0.0 |
| 32 | 2019/9/2 | 30.0 | 26.9 | 24.3 | 5.7 | 1.5 | 31.8 | 27.5 | 24.0 | 7.8 | 0.0 |
| 33 | 2019/9/3 | 30.3 | 27.4 | 24.8 | 5.5 | 0.0 | 31.8 | 27.9 | 24.9 | 6.9 | 0.0 |
| 34 | 2019/9/4 | 29.9 | 26.9 | 24.9 | 5.0 | 0.0 | 31.2 | 27.3 | 24.2 | 7.0 | 9.5 |