如何在Matplotlib中控制x轴上的日期？

Question

好吧，我一直在研究这个温度时间序列数据，我想知道是否有办法控制 x 轴上的日期。 Matplotlib 随机选择日期作为 x 轴。我的意思是我想显示异常下方的确切日期，然后将其命名为 EQ，同时将其他值设置为 EQ 之前和之后的天数。

例如，x 轴可能变为 [-25, -20, -15, -10, -5, "EQ", 5, 10]

这是我当前正在使用的数据：

数据

这就是我想要实现的目标。

这是我迄今为止编写的代码！

fig, ax = plt.subplots(figsize=(8,4))

sns.scatterplot(data =at_day, x=at_day.index, y="AT_Day_Time_Values", hue=at_day["Legend"], palette="bright", s = 80);
sns.lineplot(at_day["AT_Day_Time_Values"], linewidth=1.5, linestyle = "--", color = "black", ax = ax, label="AT (Day)")
sns.rugplot(data =at_day, x=at_day.index, y="AT_Day_Time_Values", hue=at_day["Legend"],  ax= ax)

ax.set_xticks(at_day.index.date)

ax.xaxis.set_major_locator(mdates.DayLocator(interval=5))
ax.set(ylim=[295, 307], ylabel="K");
ax.grid(axis="y")

请暂时忽略其他诸如色调之类的东西，我只对学习控制 xaxis 上的日期感兴趣

Answer 1

您可以使用这种方法自动生成报价。我假设您总是想要 5 天的差异，所以如果您愿意，您也可以稍后调整：

import pandas as pd
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
df = pd.read_csv("air_temp.csv")
df["Dates"] = pd.to_datetime(df["Dates"])
# get limits for prompt
minDate = df["Dates"].min()
maxDate = df["Dates"].max()
EQ = "" # init
while EQ not in df["Dates"].values: # check value, do until...
    # prompt user to input dates between two values, liek t his yyyy-mm-dd
    EQ = input(f"Choose a date between {minDate.date()} and {maxDate.date()}: ")
    EQ = pd.to_datetime(EQ) # convert to date
df['DatesDiff'] = df['Dates'] - EQ # get time difference
plt.figure() # generate a figure
plt.plot(df['DatesDiff'].dt.days, df['AT_Day_Time_Values']) # plot
minVal = np.floor(min(df["DatesDiff"].dt.days) / 5) * 5 # get suitable limit in negative
maxVal = np.ceil(max(df["DatesDiff"].dt.days) / 5) * 5 # get suitable limit in positive
ticks = np.arange(minVal, maxVal + 1, 5) # define the ticks you want to set, they will include 0 and have 5 spacing
labels = [str(int(tick)) if tick != 0 else 'EQ' for tick in ticks] # generate labels as string, with "EQ" at 0
plt.xticks(ticks, labels) # set the ticks
plt.title("EQ = "+str(EQ.date())) # set title for OP

这是用EQ：2022-08-05

我猜你想自动识别峰值，所以这里是做到这一点的方法：

df = pd.read_csv("air_temp.csv")
df["Dates"] = pd.to_datetime(df["Dates"])
# get limits for prompt
minDate = df["Dates"].min()
maxDate = df["Dates"].max()
EQ = df["Dates"].loc[df["AT_Day_Time_Values"].idxmax()] # get maximum temp
df["DatesDiff"] = df["Dates"] - EQ # get time difference
plt.figure() # generate a figure
plt.plot(df["DatesDiff"].dt.days, df["AT_Day_Time_Values"]) # plot
minVal = np.floor(min(df["DatesDiff"].dt.days) / 5) * 5 # get suitable limit in negative
maxVal = np.ceil(max(df["DatesDiff"].dt.days) / 5) * 5 # get suitable limit in positive
ticks = np.arange(minVal, maxVal + 1, 5) # define the ticks you want to set, they will include 0
labels = [str(int(tick)) if tick != 0 else 'EQ' for tick in ticks] # generate labels as string, with "EQ" at 0
plt.xticks(ticks, labels) # set the ticks
plt.title("EQ = "+str(EQ.date())) # set title for OP

我想因为这次是最大的，所以这很容易。当数据变得更加嘈杂时，检测异常会变得更加复杂。结果如下：