[英]change x-axis of a plot
I am trying to create a visualization of vehicles passing by in the first 25 weeks of the years 2015-2020 all in one graph (one curve for every year).我正在尝试将 2015-2020 年的前 25 周内经过的车辆可视化,所有这些都在一张图中(每年一条曲线)。
df_data_groups = df_data[(df_data['week']<=25)].groupby(['year','week'])
df_data_weekly = df_data_groups[['NO','nr_of_vehicles']].mean()
fig, ax = plt.subplots()
bp = df_data_weekly['nr_of_vehicles'].groupby('year').plot(ax=ax)
The following is what i get以下是我得到的
The x-axis is not right. x 轴不对。 It should not contain the year, only the weeks, but I don't know how to solve this correctly.
它不应该包含年份,只有几周,但我不知道如何正确解决这个问题。 It also is not allowing me to create a legend to show which lines belongs to the color of the line, by using:
它也不允许我创建一个图例来显示哪些线条属于线条的颜色,使用:
bp.set_legend()
The index shown, is the index of the last dataframe in the group.显示的索引是组中最后一个 dataframe 的索引。 This dataframe has a 2-level index: the year and the week.
这个 dataframe 有 2 级索引:年和周。 Dropping the first index (the year) will only show the week:
删除第一个索引(年份)将只显示周:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df_data = pd.DataFrame({'year': np.repeat(np.arange(2015, 2021), 52),
'week': np.tile(np.arange(1, 53), 6),
'nr_of_vehicles': 200_000 + np.random.randint(-9_000, 10_000, 52 * 6).cumsum()})
df_data_groups = df_data[(df_data['week'] <= 25)].groupby(['year', 'week'])
df_data_weekly = df_data_groups[['nr_of_vehicles']].mean()
fig, ax = plt.subplots()
for year, df in df_data_weekly['nr_of_vehicles'].groupby('year'):
df.reset_index(level=0, drop=True).plot(ax=ax, label=year)
ax.legend()
ax.margins(x=0.02)
plt.show()
PS: Note that in the question's code, bp
is a list of axes, one ax
per year. PS:请注意,在问题的代码中,
bp
是一个轴列表,每年一个ax
。 In this case, all of them point to the same ax
.在这种情况下,它们都指向同一个
ax
。 bp
is organized as a pandas Series, to obtain the legend, get one of the axes: bp[2015].legend()
(or bp.iloc[0].legend()
). bp
组织为 pandas 系列,要获得图例,请获取轴之一: bp[2015].legend()
(或bp.iloc[0].legend()
)。
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