使用 pyplot 在 x 轴上打印月份

Question

我正在使用的数据：https://drive.google.com/file/d/1xb7icmocz-SD2Rkq4ykTZowxW0uFFhBl/view?usp=sharing

大家好

编辑情节时我有点卡住了。 基本上，我希望我的 x 值显示一年中的月份，但由于数据类型（？），它似乎不起作用。你知道我怎样才能让我的图在 x 轴上有几个月吗？

如果您需要有关数据的更多背景信息，请告诉我！！！

谢谢！

这是我的绘图代码和初始数据修改：

import matplotlib.pyplot as plt
import mplleaflet
import pandas as pd
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
import numpy as np

df = pd.read_csv("data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv")

df['degrees']=df['Data_Value']/10

df['Date'] =  pd.to_datetime(df['Date'])

df2 = df[df['Date']<'2015-01-01']

df3 = df[df['Date']>='2015-01-01']

max_temp = df2.groupby([(df2.Date.dt.month),(df2.Date.dt.day)])['degrees'].max()
min_temp = df2.groupby([(df2.Date.dt.month),(df2.Date.dt.day)])['degrees'].min()

max_temp2 = df3.groupby([(df3.Date.dt.month),(df3.Date.dt.day)])['degrees'].max()
min_temp2 = df3.groupby([(df3.Date.dt.month),(df3.Date.dt.day)])['degrees'].min()

max_temp.plot(x ='Date', y='degrees', kind = 'line')
min_temp.plot(x ='Date',y='degrees', kind= 'line')

plt.fill_between(range(len(min_temp)),min_temp, max_temp, color='C0', alpha=0.2)

ax = plt.gca()

ax.set(xlabel="Date",
       ylabel="Temperature",
       title="Extreme Weather in 2015")

plt.legend()
plt.tight_layout()

x = plt.gca().xaxis

for item in x.get_ticklabels():
    item.set_rotation(45)

plt.show()

我得到的情节：

Answer 1

选项 1（最相似的方法）

使用

Index.map

和

calendar

根据月份缩写更改索引

这只是为了

df2

：

import calendar

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("...")
df['degrees'] = df['Data_Value'] / 10
df['Date'] = pd.to_datetime(df['Date'])
df2 = df[df['Date'] < '2015-01-01']

max_temp = df2.groupby([df2.Date.dt.month, df2.Date.dt.day])['degrees'].max()
min_temp = df2.groupby([df2.Date.dt.month, df2.Date.dt.day])['degrees'].min()

# Update the index to be the desired display format for x-axis
max_temp.index = max_temp.index.map(lambda x: f'{calendar.month_abbr[x[0]]}')
min_temp.index = min_temp.index.map(lambda x: f'{calendar.month_abbr[x[0]]}')

max_temp.plot(x='Date', y='degrees', kind='line')
min_temp.plot(x='Date', y='degrees', kind='line')

plt.fill_between(range(len(min_temp)), min_temp, max_temp,
                 color='C0', alpha=0.2)

ax = plt.gca()
ax.set(xlabel="Date", ylabel="Temperature", title="Extreme Weather 2005-2014")

x = plt.gca().xaxis
for item in x.get_ticklabels():
    item.set_rotation(45)
    
plt.margins(x=0)
plt.legend()
plt.tight_layout()
plt.show()

---

顺便说一句：标题“2015年极端天气”不正确，因为该数据包括2015年之前的所有年份。这是

"Extreme Weather 2005-2014"

也可以使用

min

和

max

检查年份范围：

print(df2.Date.dt.year.min(), '-', df2.Date.dt.year.max())
# 2005 - 2014

标题可以通过编程方式生成：

title=f"Extreme Weather {df2.Date.dt.year.min()}-{df2.Date.dt.year.max()}"

---

选项2（逐级简化）

使用

groupby aggregate

简化代码以创建单个 DataFrame，然后以与上述相同的方式转换索引：

import calendar

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("...")
df['degrees'] = df['Data_Value'] / 10
df['Date'] = pd.to_datetime(df['Date'])
df2 = df[df['Date'] < '2015-01-01']
# Get Max and Min Degrees in Single Groupby
df2_temp = (
    df2.groupby([df2.Date.dt.month, df2.Date.dt.day])['degrees']
        .agg(['max', 'min'])
)
# Convert Index to whatever display format is desired:
df2_temp.index = df2_temp.index.map(lambda x: f'{calendar.month_abbr[x[0]]}')

# Plot
ax = df2_temp.plot(
    kind='line', rot=45,
    xlabel="Date", ylabel="Temperature",
    title=f"Extreme Weather {df2.Date.dt.year.min()}-{df2.Date.dt.year.max()}"
)
# Fill between
plt.fill_between(range(len(df2_temp)), df2_temp['min'], df2_temp['max'],
                 color='C0', alpha=0.2)
plt.margins(x=0)
plt.tight_layout()
plt.show()

---

选项 3（最佳整体功能）

使用

pd.to_datetime

将索引转换为日期时间。选择任何闰年来统一数据（它必须是闰年，这样 Feb-29 就不会引发错误）。然后使用格式字符串

set_major_formatter

设置

%b

以使用月份缩写：

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

df = pd.read_csv("...")
df['degrees'] = df['Data_Value'] / 10
df['Date'] = pd.to_datetime(df['Date'])
df2 = df[df['Date'] < '2015-01-01']
# Get Max and Min Degrees in Single Groupby
df2_temp = (
    df2.groupby([df2.Date.dt.month, df2.Date.dt.day])['degrees']
        .agg(['max', 'min'])
)

# Convert to DateTime of Same Year
# (Must be a leap year so Feb-29 doesn't raise an error)
df2_temp.index = pd.to_datetime(
    '2000-' + df2_temp.index.map(lambda s: '-'.join(map(str, s)))
)
# Plot
ax = df2_temp.plot(
    kind='line', rot=45,
    xlabel="Date", ylabel="Temperature",
    title=f"Extreme Weather {df2.Date.dt.year.min()}-{df2.Date.dt.year.max()}"
)
# Fill between
plt.fill_between(df2_temp.index, df2_temp['min'], df2_temp['max'],
                 color='C0', alpha=0.2)

# Set xaxis formatter to month abbr with the %b format string
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
plt.tight_layout()
plt.show()

这种方法的好处是索引是日期时间，因此格式比选项 1 和 2 的字符串表示形式更好。

Answer 2

我们也可以使用图书馆

seaborn

。然而，用月份名称标记x轴有点棘手，我们需要从要标记的数据中获取相应的x索引。这是代码：

import seaborn as sns
from calendar import month_abbr

fig, ax = plt.subplots(figsize=(15,7))

m = sns.lineplot(x='Date', y='Temp', data=min_temp, label = 'min degrees', ax=ax)
M = sns.lineplot(x='Date', y='Temp', data=max_temp, label = 'max degrees', ax=ax)

line = M.get_lines()

xdata, ydata_m, ydata_M = line[0].get_xdata(), line[0].get_ydata(), line[1].get_ydata()
ax.fill_between(xdata, ydata_m, ydata_M, color='gray', alpha=.25)

mdata = np.array([month_abbr[i] for i in df_high_05_14_agg.Date.dt.month.values]) # months
indices = np.array([0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334]) # get the indices corresponding to start of months

ax.set_xticks(xdata[indices], mdata[indices], rotation=90)  
ax.set_title('Extreme Wather 2005-2014 vs. 2015', size=25)    
plt.xlabel('Date')
plt.ylabel('Temperature')
plt.show()

这是输出：