如何从多个文件中绘制 min-max fill_between 图答案

【问题标题】：How to plot a min-max fill_between plot from multiple files如何从多个文件中绘制 min-max fill_between 图
【发布时间】：2021-01-15 14:36:15
【问题描述】：

提前感谢您的帮助！（代码如下）(Link to 1st piece of data) (Link to data I want to add)

我正在尝试从第二个 CSV（上图）导入数据，并根据该 CSV 数据在该图中添加第二行。这样做的最佳方法是什么？（下图）

图上的波浪线代表数据的范围。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

raw_data = pd.read_csv('all-deep-soil-temperatures.csv', index_col=1, parse_dates=True)
df_all_stations = raw_data.copy()

selected_soil_station = 'Minot'
df_selected_station = df_all_stations[df_all_stations['Station'] == selected_soil_station]
df_selected_station.fillna(method = 'ffill', inplace=True);
df_selected_station_D=df_selected_station.resample(rule='D').mean()
df_selected_station_D['Day'] = df_selected_station_D.index.dayofyear
mean=df_selected_station_D.groupby(by='Day').mean()
mean['Day']=mean.index

maxx=df_selected_station_D.groupby(by='Day').max()
minn=df_selected_station_D.groupby(by='Day').min()
mean['maxx20']=maxx['20 cm']
mean['minn20']=minn['20 cm']

plt.style.use('ggplot')
bx = mean.plot(x='Day', y='20 cm',color='black')
plt.fill_between(mean['Day'],mean['minn20'],mean['maxx20'],color='blue',alpha = 0.2);
bx.set_xlabel("Day of the year")
bx.set_ylabel("Temperature in Celsius")
bx.set_title("Soil Temp, Air Temp, and Snow Depth for " + str(selected_soil_station))

我有什么：

我想要的：

样本数据

all-deep-soil-temperatures.csv

Station,Time,5 cm,10 cm,20 cm,30 cm,40 cm,50 cm,60 cm,80 cm,100 cm,125 cm,150 cm,175 cm,200 cm,225 cm
Adams,2018-06-21 1700,32.8,27.74,23.06,20.28,18.16,16.64,15.33,13.07,11.19,9.35,7.919,6.842,6.637,5.686
Adams,2018-06-21 1800,31.78,27.66,23.41,20.52,18.31,16.77,15.46,13.23,11.34,9.51,8.06,6.894,6.681,5.781
Adams,2018-06-21 1900,30.5,27.24,23.61,20.73,18.54,17.02,15.73,13.51,11.63,9.8,8.36,7.262,6.681,5.893
Adams,2018-06-21 2000,29.12,26.74,23.72,20.9,18.66,17.14,15.85,13.62,11.8,10.03,8.69,7.65,6.684,5.904
Adams,2018-06-21 2100,27.5,26.08,23.74,21.07,18.86,17.36,16.12,13.96,12.19,10.43,9.11,8.1,6.823,6.069
Adams,2018-06-21 2200,26.05,25.41,23.66,21.2,18.98,17.43,16.15,13.96,12.15,10.41,9.09,8.11,6.909,6.164
Adams,2018-06-21 2300,24.89,24.75,23.48,21.21,19.01,17.42,16.1,13.9,12.07,10.33,9.01,7.997,6.886,6.132
Adams,2018-06-22 0000,24.09,24.19,23.31,21.22,19.06,17.43,16.1,13.88,12.04,10.31,8.97,7.964,6.887,6.125
Adams,2018-06-22 0100,23.49,23.74,23.11,21.2,19.1,17.49,16.13,13.87,12.01,10.23,8.88,7.89,6.89,6.128
Adams,2018-06-22 0200,22.92,23.3,22.91,21.19,19.15,17.53,16.16,13.88,12.02,10.25,8.91,7.911,6.902,6.14
Adams,2018-06-22 0300,22.32,22.86,22.68,21.11,19.14,17.52,16.14,13.84,11.98,10.21,8.87,7.858,6.892,6.121
Adams,2018-06-22 0400,21.81,22.46,22.44,21.05,19.15,17.55,16.16,13.85,11.99,10.21,8.86,7.84,6.899,6.111
Williston,2020-09-21 0500,14.69,15.29,15.61,15.68,15.48,15.22,14.99,14.7,14.51,14.27,14.06,13.85,,
Williston,2020-09-21 0600,14.39,15.09,15.49,15.61,15.43,15.19,14.99,14.68,14.46,14.2,13.97,13.73,,
Williston,2020-09-21 0700,14.16,14.93,15.39,15.56,15.4,15.18,14.99,14.69,14.47,14.22,13.99,13.74,,
Williston,2020-09-21 0800,13.72,14.54,15.05,15.22,15.05,14.84,14.68,14.37,14.09,13.92,13.64,13.35,,
Williston,2020-09-21 0900,13.64,14.35,14.87,15.08,14.95,14.78,14.63,14.32,14.04,13.88,13.61,13.33,,
Williston,2020-09-21 1000,13.9,14.33,14.79,15.06,14.99,14.85,14.72,14.41,14.14,13.99,13.74,13.51,,
Williston,2020-09-21 1100,14.46,14.43,14.78,15.07,15.04,14.93,14.78,14.49,14.24,14.07,13.84,13.62,,
Williston,2020-09-21 1200,15.34,14.77,14.89,15.15,15.17,15.09,14.97,14.7,14.47,14.28,14.06,13.87,,
Williston,2020-09-21 1300,16.26,15.19,15.03,15.22,15.28,15.24,15.16,14.89,14.69,14.49,14.28,14.06,,
Williston,2020-09-21 1400,17.2,15.74,15.24,15.29,15.35,15.31,15.24,15,14.82,14.62,14.41,14.22,,
Williston,2020-09-21 1500,18.04,16.35,15.54,15.35,15.37,15.32,15.23,14.97,14.77,14.55,14.35,14.15,,
Williston,2020-09-21 1600,18.59,16.89,15.83,15.42,15.36,15.28,15.16,14.89,14.69,14.47,14.28,14.09,,
Williston,2020-09-21 1700,18.68,17.21,16.1,15.52,15.4,15.3,15.23,14.95,14.78,14.54,14.35,14.14,,

allStationsDailyAirTemp1.csv

Station,Date,Temp
Adams,2018-06-21,22.723
Adams,2018-06-22,23.358
Adams,2018-06-23,20.986
Adams,2018-06-24,20.524
Adams,2018-06-25,19.699
Adams,2018-06-26,22.146
Adams,2018-06-27,21.239
Adams,2018-06-28,21.367
Adams,2018-06-29,20.701
Adams,2018-06-30,18.613
Adams,2018-07-01,19.376
Adams,2018-07-02,19.079
Adams,2018-07-03,20.747
Adams,2018-07-04,19.622
Adams,2018-07-05,18.029
Adams,2018-07-06,18.883
Adams,2018-07-07,25.655
Adams,2018-07-08,22.953
Adams,2018-07-09,20.281
Williston,2020-09-05,21.69
Williston,2020-09-06,16.595
Williston,2020-09-07,5.917
Williston,2020-09-08,3.863
Williston,2020-09-09,8.996
Williston,2020-09-10,14.488
Williston,2020-09-11,15.689
Williston,2020-09-12,16.002
Williston,2020-09-13,11.219
Williston,2020-09-14,16.695
Williston,2020-09-15,12.77
Williston,2020-09-16,9.523
Williston,2020-09-17,13.186
Williston,2020-09-18,16.992
Williston,2020-09-19,16.85
Williston,2020-09-20,17.235
Williston,2020-09-21,17.595
Williston,2020-09-22,19.115
Williston,2020-09-23,16.43
Williston,2020-09-24,21.035
Williston,2020-09-25,17.01
Williston,2020-09-26,14.109

【问题讨论】：

标签： python pandas matplotlib time-series

【解决方案1】：

使用新代码查看内联符号
删除了 plt.style.use('ggplot')，因为它很难看到 fill_between 颜色
另见How to create a min-max lineplot by month?
不要在 python 中使用;
将其他文件中的数据加载到单独的数据帧中
根据需要清理和聚合新数据
- 将日期列设置为日期时间格式
- 提取一年中的某一天
- groupby 一年中的某一天和聚合 mean、min 和 max 温度
将新数据绘制到与原始图相同的axes bx。

df_all_stations = pd.read_csv('data/so_data/2020-09-29 64128817/all-deep-soil-temperatures.csv', index_col=1, parse_dates=True)

# load air temp data
at = pd.read_csv('data/so_data/2020-09-29 64128817/allStationsDailyAirTemp1.csv')

# set Date to a datetime format
at.Date = pd.to_datetime(at.Date)

# extract day of year
at['doy'] = at.Date.dt.dayofyear

# selet data from Minot
at = at[at.Station == 'Minot']

# groupby the day of year (doy) and aggregate min max and mean
atg = at.groupby('doy')['Temp'].agg([min, max, 'mean'])

selected_soil_station = 'Minot'
df_selected_station = df_all_stations[df_all_stations['Station'] == selected_soil_station].copy()  # make a copy here, otherwise there will be warning
df_selected_station.fillna(method = 'ffill', inplace=True)
df_selected_station_D=df_selected_station.resample(rule='D').mean()
df_selected_station_D['Day'] = df_selected_station_D.index.dayofyear
mean=df_selected_station_D.groupby(by='Day').mean()
mean['Day']=mean.index

maxx=df_selected_station_D.groupby(by='Day').max()
minn=df_selected_station_D.groupby(by='Day').min()
mean['maxx20']=maxx['20 cm']
mean['minn20']=minn['20 cm']

bx = mean.plot(x='Day', y='20 cm', color='black', figsize=(9, 6), label='20 cm Soil Temp')
plt.fill_between(mean['Day'], mean['minn20'], mean['maxx20'], color='blue', alpha = 0.2, label='20 cm Soil Temp Range')

# add air temp plot to the bx plot with ax=bx
atg['mean'].plot(ax=bx, label='Mean Air Temp')

# add air temp fill between plot to the bx plot
bx.fill_between(atg.index, atg['min'], atg['max'], color='cyan', alpha = 0.2, label='Air Temp Range')

bx.set_xlabel("Day of the year")
bx.set_ylabel("Temperature in Celsius")
bx.set_title("Soil Temp, Air Temp, and Snow Depth for " + str(selected_soil_station))

# grid
bx.grid()

# set legend location
bx.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# remove margin spaces
plt.margins(0, 0)

plt.show()

【讨论】：

感谢您的所有帮助，特伦顿！