- As of seaborn v0.12.0 https://seaborn.pydata.org/whatsnew/v0.12.0.html#more-flexible-errorbars, the
ci参数已更改为errorbar.
- See 统计估计和误差线 https://seaborn.pydata.org/tutorial/error_bars.html
errorbar : string, （字符串，数字）元组，可调用或None错误栏方法的名称（或者'ci', 'pi', 'se', or 'sd')，或具有方法名称和级别参数的元组，或从向量映射到 (min, max) 区间的函数，或None隐藏错误栏。
默认为errorbar=('ci', 95)
-
There is probably only a single observation per x value.
- 如果每个 x 值只有一个观测值，则没有可绘制的置信区间。
- Bootstrapping is performed per x value, but there needs to be more than one obsevation for this to take effect.
ci：使用估计器聚合时要绘制的置信区间的大小。'sd'表示绘制数据的标准差。设置为None将跳过引导。
- 请注意以下示例seaborn.lineplot https://seaborn.pydata.org/generated/seaborn.lineplot.html.
- 这也适用于sns.relplot https://seaborn.pydata.org/generated/seaborn.relplot.html with
kind='line'.
- 问题指定
sns.lineplot, but 这个答案适用于任何西伯恩情节 https://seaborn.pydata.org/api.html显示置信区间， 例如seaborn.barplot.
Data
import seaborn as sns
# load data
flights = sns.load_dataset("flights")
year month passengers
0 1949 Jan 112
1 1949 Feb 118
2 1949 Mar 132
3 1949 Apr 129
4 1949 May 121
# only May flights
may_flights = flights.query("month == 'May'")
year month passengers
4 1949 May 121
16 1950 May 125
28 1951 May 172
40 1952 May 183
52 1953 May 229
64 1954 May 234
76 1955 May 270
88 1956 May 318
100 1957 May 355
112 1958 May 363
124 1959 May 420
136 1960 May 472
# standard deviation for each year of May data
may_flights.set_index('year')[['passengers']].std(axis=1)
year
1949 NaN
1950 NaN
1951 NaN
1952 NaN
1953 NaN
1954 NaN
1955 NaN
1956 NaN
1957 NaN
1958 NaN
1959 NaN
1960 NaN
dtype: float64
# flight in wide format
flights_wide = flights.pivot("year", "month", "passengers")
month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
year
1949 112 118 132 129 121 135 148 148 136 119 104 118
1950 115 126 141 135 125 149 170 170 158 133 114 140
1951 145 150 178 163 172 178 199 199 184 162 146 166
1952 171 180 193 181 183 218 230 242 209 191 172 194
1953 196 196 236 235 229 243 264 272 237 211 180 201
1954 204 188 235 227 234 264 302 293 259 229 203 229
1955 242 233 267 269 270 315 364 347 312 274 237 278
1956 284 277 317 313 318 374 413 405 355 306 271 306
1957 315 301 356 348 355 422 465 467 404 347 305 336
1958 340 318 362 348 363 435 491 505 404 359 310 337
1959 360 342 406 396 420 472 548 559 463 407 362 405
1960 417 391 419 461 472 535 622 606 508 461 390 432
# standard deviation for each year
flights_wide.std(axis=1)
year
1949 13.720147
1950 19.070841
1951 18.438267
1952 22.966379
1953 28.466887
1954 34.924486
1955 42.140458
1956 47.861780
1957 57.890898
1958 64.530472
1959 69.830097
1960 77.737125
dtype: float64
Plots
-
may_flights每年有一次观察，因此没有显示 CI。
sns.lineplot(data=may_flights, x="year", y="passengers")
sns.barplot(data=may_flights, x='year', y='passengers')
-
flights_wide显示每年有 12 个观测值，因此 CI 显示当所有flights已绘制。
sns.lineplot(data=flights, x="year", y="passengers")
sns.barplot(data=flights, x='year', y='passengers')
