代码之家 › 专栏 › 技术社区 › Martin Thoma

如何为小时/工作日绘图设置两级刻度?

matplotlib pandas python

Martin Thoma · 技术社区 · 7 年前

我有一个数据框架,其结构类似于以下内容:

from datetime import datetime
import pandas as pd
from mpu.datetime import generate  # pip install mpu

mind, maxd = datetime(2018, 1, 1), datetime(2018, 12, 30)
df = pd.DataFrame({'datetime': [generate(mind, maxd) for _ in range(10)]})

我想了解这些数据是如何在一天中的几个小时和一周中的几天中分布的。我可以通过以下途径获得:

df['weekday'] = df['datetime'].dt.weekday
df['hour'] = df['datetime'].dt.hour

最后我有了情节:

ax = df.groupby(['weekday', 'hour'])['datetime'].count().plot(kind='line', color='blue')
ax.set_ylabel("#")
ax.set_xlabel("time")
plt.show()

这给了我:

3 回复 | 直到 7 年前

Martin Thoma 7 年前

如果假设每个可能的工作日和小时都实际出现在数据中,那么轴单位将仅为小时,周一午夜为0,周日23小时为24*7-1=167。

import numpy as np; np.random.seed(42)
import datetime as dt
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FuncFormatter, NullFormatter

# Generate example data
N = 5030
delta = (dt.datetime(2019, 1, 1) - dt.datetime(2018, 1, 1)).total_seconds()
df = pd.DataFrame({'datetime': np.array("2018-01-01").astype(np.datetime64) + 
                               (delta*np.random.rand(N)).astype(np.timedelta64)})

# Group the data
df['weekday'] = df['datetime'].dt.weekday
df['hour'] = df['datetime'].dt.hour

counts = df.groupby(['weekday', 'hour'])['datetime'].count()

ax = counts.plot(kind='line', color='blue')
ax.set_ylabel("#")
ax.set_xlabel("time")
ax.grid()
# Now we assume that there is data for every hour and day present
assert len(counts) == 7*24
# Hence we can tick the axis with multiples of 24h
ax.xaxis.set_major_locator(MultipleLocator(24))
ax.xaxis.set_minor_locator(MultipleLocator(1))

days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
def tick(x,pos):
    if x % 24 == 12:
        return days[int(x)//24]
    else:
        return ""
ax.xaxis.set_major_formatter(NullFormatter())
ax.xaxis.set_minor_formatter(FuncFormatter(tick))
ax.tick_params(which="major", axis="x", length=10, width=1.5)
plt.show()

Pedro Borges 7 年前

这不完全是你提到的可视化,但一个想法是,解开你的熊猫时间序列,然后进行绘图。

df.groupby(['weekday', 'hour'])['datetime'].count().unstack(level=0).plot()

根据您在代码中提供的数据,结果如下:

Mr. T Andres Pinzon 7 年前

major and minor ticks 和 define their grid qualities 分别:

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

#create sample data and plot it
from io import StringIO
data = StringIO("""
X,A,B
2018-11-21T12:04:20,1,8
2018-11-21T18:14:17,6,7
2018-11-22T02:18:21,8,14
2018-11-22T12:31:54,7,8
2018-11-22T20:33:20,5,5
2018-11-23T12:23:12,13,2
2018-11-23T21:31:05,7,12
""")
df = pd.read_csv(data, parse_dates = True, index_col = "X")
ax=df.plot()

#format major locator
ax.xaxis.set_major_locator(mdates.DayLocator())
#format minor locator with specific hours
ax.xaxis.set_minor_locator(mdates.HourLocator(byhour = [8, 12, 18]))
#label major ticks
ax.xaxis.set_major_formatter(mdates.DateFormatter('%a %d %m'))
#label minor ticks
ax.xaxis.set_minor_formatter(mdates.DateFormatter("%H:00"))
#set grid for major ticks
ax.grid(which = "major", axis = "x", linestyle = "-", linewidth = 2)
#set grid for minor ticks with different properties
ax.grid(which = "minor", axis = "x", linestyle = "--", linewidth = 1)

plt.show()

样本输出: