做

except ValueError:
            pass

没有至少打印一个警告通常是一个坏主意，并已沉默的错误，在您的情况下。
您的数据缺少TMIN和TMAX值，因此在某些时候

data['TMAX'][i] = int(data['TMAX'][i])

将

data['TMAX'][i] = int('')

并抛出值错误。这个错误被捕获了，你永远不知道，因为你有那个except子句，你的for循环停止迭代和转换你的data['TMAX']，这会留下一些字符串值和一些整型值。您可以在打印前打印data['TMAX']来检查这一点。这就是为什么你的y轴最终没有被数字排序。我建议您将每个转换 Package 到一个单独的try...except块中，并在丢失条目的情况下使用特定值进行替换，并向屏幕发出警告。我不知道什么是合理的数据，所以我改为float，它允许使用nan作为缺失值：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.ticker import MultipleLocator
from datetime import datetime
import csv

# Open the csv file and create a dictionary with the information from the file
def get_weather_data(path, data, missingValue=float("nan")):
    """Get the high and low temperature from the csv file."""
    with open (path, 'r') as csv_file:
        csv_reader = csv.reader(csv_file)
        headers = next(csv_reader)

        for title in headers:
            data[title] = []

        for row in csv_reader:
            for i, title in enumerate(headers):
                data[title].append(row[i])

        for i in range (len(data["DATE"])):
            data['DATE'][i] = datetime.strptime(data["DATE"][i],"%Y-%m-%d")

        for i in range(len(data['TMAX'])):
            #CHANGED HERE
            try:
                data['TMAX'][i] = float(data['TMAX'][i])
            except ValueError:
                print("WARNING: Encountered value \"{}\" in row {} for TMAX, replacing with {}".format(data['TMAX'][i], i, missingValue))
                data['TMAX'][i] = missingValue

        for i in range(len(data["TMIN"])):
            #CHANGED HERE
            try:
                data["TMIN"][i] = float(data['TMIN'][i])
            except ValueError:
                print("WARNING: Encountered value \"{}\" in row {} for TMIN, replacing with {}".format(data['TMIN'][i], i, missingValue))
                data['TMIN'][i] = missingValue

# Tell the path of the file
path_valley = 'weather_data/death_valley_2021_full.csv'
path_sitka = 'weather_data/sitka_weather_2021_full.csv'

# Gets the data of Death Valley
data = {}
get_weather_data(path_valley,data)

# Plot the chart
plt.style.use('seaborn')
fig, ax = plt.subplots()

# Plot the data for Death Valey
ax.plot(data['DATE'], data['TMAX'], color='r', label='Death Valley - High Temp.')
ax.plot(data['DATE'], data['TMIN'], color='b', label='Death Valley - Low Temp.')

# Get the data of Sitka
data = {}
get_weather_data(path_sitka,data)

# Plot the data for Sitka
ax.plot(data['DATE'], data['TMAX'], color='m', label='Sitka - High Temp.')
ax.plot(data['DATE'], data['TMIN'], color='c', label='Sitka - Low Temp.')

# Format plot
title = 'Daily High and Low Temperature - 2021'
title += '\nSitka, AK and Death Valley, CA'
ax.set_title(title)

# Configure the axis
ax.set_xlabel('', fontsize=16)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%B'))
fig.autofmt_xdate()

ax.set_ylabel('Temperatures(F)', fontsize=16)
ax.tick_params(labelsize=14)

plt.legend()
plt.show()

备注
由于您遵循的是课程手册，因此我试图尽可能接近您的原始代码，因为它可能符合课程的进展程度。对于这种任务，pandas可能是一个不错的选择，因为它可以检测和替换缺失的值：

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.ticker import MultipleLocator
from datetime import datetime
import import pandas as pd

path_valley = r'C:\Users\1544Jochheim\Downloads/death_valley_2021_full.csv'
path_sitka = r'C:\Users\1544Jochheim\Downloads/sitka_weather_2021_full.csv'

# Get the data of Valley
data_valley = pd.read_csv(path_valley, parse_dates=["DATE"])

plt.style.use('seaborn')
fig, ax = plt.subplots()

# Plot the data for Death Valey
ax.plot(data_valley['DATE'], data_valley['TMAX'], color='r', label='Death Valley - High Temp.')
ax.plot(data_valley['DATE'], data_valley['TMIN'], color='b', label='Death Valley - Low Temp.')

# Get the data of Sitka
data_sitka = pd.read_csv(path_sitka, parse_dates=["DATE"])

# Plot the data for Sitka
ax.plot(data_sitka['DATE'], data_sitka['TMAX'], color='m', label='Sitka - High Temp.')
ax.plot(data_sitka['DATE'], data_sitka['TMIN'], color='c', label='Sitka - Low Temp.')

# Format plot
title = 'Daily High and Low Temperature - 2021'
title += '\nSitka, AK and Death Valley, CA'
ax.set_title(title)

# Configure the axis
ax.set_xlabel('', fontsize=16)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%B'))
fig.autofmt_xdate()

ax.set_ylabel('Temperatures(F)', fontsize=16)
ax.tick_params(labelsize=14)

plt.legend()
plt.show()

• 另一个答案充分解释了为什么'TMIN'和'TMAX'没有被转换为float类型。

1.这个答案使用pandas，它将正确地将列转换为float，并将缺失值保留为np.nan。
1.将所有数据加载到单个 Dataframe 中
1.清理数据
1.在单个plot调用中使用seaborn绘图
1.参见How to create a min-max plot by month with fill_between

导入和加载清理数据

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.dates as mdates

# csv file names
stations = ['death_valley_2021_full', 'sitka_weather_2021_full']

# create the path to each file
paths = [f'https://raw.githubusercontent.com/ehmatthes/pcc_3e/main/chapter_16/the_csv_file_format/weather_data/{station}.csv' for station in stations]

# read the needed columns of both data files from github and combine them into a single dataframe
df = pd.concat([pd.read_csv(path, usecols=['NAME', 'DATE', 'TMIN', 'TMAX']) for path in paths], ignore_index=True)

# split the values in the name column into separate columns
df[['NAME', 'STATE-COUNTRY']] = df.NAME.str.split(', ', expand=True)

# convert DATE to a datetime.date
df.DATE = pd.to_datetime(df.DATE, format='%Y-%m-%d').dt.date

# convert dataframe to long form (it's currently a wide form)
df = df.melt(id_vars=['NAME', 'DATE'], value_vars=['TMIN', 'TMAX'], value_name='Temperature (F)')

# shorten DEATH VALLEY NATIONAL PARK
df.NAME = df.NAME.str.replace('DEATH VALLEY NATIONAL PARK', 'DEATH VALLEY')

# change the format of capitalization
df.NAME = df.NAME.str.capitalize()

# change the names for TMIN and TMAX
df.variable = df.variable.map({'TMIN': 'Low Temp', 'TMAX': 'High Temp'})

# combine NAME with TMIN or TMAX
df.NAME = df.NAME + ' - ' + df.variable

使用seaborn绘图

fig, ax = plt.subplots(figsize=(10, 8))

hue_order = sorted(df.NAME.unique())
ax = sns.lineplot(data=df, x='DATE', y='Temperature (F)', hue='NAME', hue_order=hue_order, palette=['r', 'b', 'm', 'c'])

sns.move_legend(ax, bbox_to_anchor=(1, 0.5), loc='center left', frameon=False)

# Format plot
title = 'Daily High and Low Temperature - 2021'
title += '\nSitka, AK and Death Valley, CA'
ax.set_title(title)

# Configure the axis
ax.set_xlabel('', fontsize=16)
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%B'))
fig.autofmt_xdate()

ax.set_ylabel('Temperatures(F)', fontsize=16)
ax.tick_params(labelsize=14)

df

NAME        DATE   variable  Temperature (F)
0       Death valley - Low Temp  2021-01-01   Low Temp             51.0
1       Death valley - Low Temp  2021-01-02   Low Temp             42.0
2       Death valley - Low Temp  2021-01-03   Low Temp             41.0
3       Death valley - Low Temp  2021-01-04   Low Temp             41.0
4       Death valley - Low Temp  2021-01-05   Low Temp             40.0
1455  Sitka airport - High Temp  2021-12-27  High Temp             35.0
1456  Sitka airport - High Temp  2021-12-28  High Temp             35.0
1457  Sitka airport - High Temp  2021-12-29  High Temp             41.0
1458  Sitka airport - High Temp  2021-12-30  High Temp             38.0
1459  Sitka airport - High Temp  2021-12-31  High Temp             39.0