您可以使用多个groupby来实现这一点。因为我们知道一周有7天，所以我们可以指定面板的数量。如果您使用groupby(df.Date.dt.dayofweek)，您可以使用组索引作为子图轴的索引：

示例数据

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

n = 10000
np.random.seed(123)
df = pd.DataFrame({'Date': pd.date_range('2010-01-01', freq='1.09min', periods=n),
                   'A': np.random.randint(1,10,n),
                   'B': np.random.normal(0,1,n)})

编码：

fig, ax = plt.subplots(ncols=7, figsize=(30,5))
plt.subplots_adjust(wspace=0.05)  #Remove some whitespace between subplots

for idx, gp in df.groupby(df.Date.dt.dayofweek):
    ax[idx].set_title(gp.Date.dt.day_name().iloc[0])  #Set title to the weekday

    (gp.groupby(gp.Date.dt.hour).size().rename_axis('Tweet Hour').to_frame('')
        .reindex(np.arange(0,24,1)).fillna(0)
        .plot(kind='bar', ax=ax[idx], rot=0, ec='k', legend=False))

    # Ticks and labels on leftmost only
    if idx == 0:
        _ = ax[idx].set_ylabel('Counts', fontsize=11)

    _ = ax[idx].tick_params(axis='both', which='major', labelsize=7,
                            labelleft=(idx == 0), left=(idx == 0))

# Consistent bounds between subplots. 
lb, ub = list(zip(*[axis.get_ylim() for axis in ax]))
for axis in ax:
    axis.set_ylim(min(lb), max(ub)) 

plt.show()

如果你想让长宽比不那么极端，那么考虑绘制一个4x2的网格。这是一个非常类似的图，如上所述，一旦我们flatten轴数组。有一些整数和余数除法来计算哪些axes需要标签。

fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(20,10))
fig.delaxes(ax[1,3])  #7 days in a week, remove 8th panel
ax = ax.flatten()  #Far easier to work with a flattened array

lsize=8
plt.subplots_adjust(wspace=0.05, hspace=0.15)  #Remove some whitespace between subplots

for idx, gp in df.groupby(df.Date.dt.dayofweek):
    ax[idx].set_title(gp.Date.dt.day_name().iloc[0])  #Set title to the weekday

    (gp.groupby(gp.Date.dt.hour).size().rename_axis([None]).to_frame()
        .reindex(np.arange(0,24,1)).fillna(0)
        .plot(kind='bar', ax=ax[idx], rot=0, ec='k', legend=False))

    # Titles on correct panels
    if idx%4 == 0:
        _ = ax[idx].set_ylabel('Counts', fontsize=11)
    if (idx//4 == 1) | (idx%4 == 3):
        _ = ax[idx].set_xlabel('Tweet Hour', fontsize=11) 

    # Ticks on correct panels
    _ = ax[idx].tick_params(axis='both', which='major', labelsize=lsize,
                            labelbottom=(idx//4 == 1) | (idx%4 == 3), 
                            bottom=(idx//4 == 1) | (idx%4 == 3),
                            labelleft=(idx%4 == 0), 
                            left=(idx%4 == 0))

# Consistent bounds between subplots. 
lb, ub = list(zip(*[axis.get_ylim() for axis in ax]))
for axis in ax:
    axis.set_ylim(min(lb), max(ub)) 

plt.show()

seaborn是什么？sns.FacetGrid是这样做的：

import pandas as pd
import seaborn as sns

# make some data
date = pd.date_range('today', periods=100, freq='2.5H')

# put in dataframe
df = pd.DataFrame({
    'date' : date
})

# create day_of_week and hour columns
df['dow'] = df.date.dt.day_name()
df['hour'] = df.date.dt.hour

# create facet grid
g = sns.FacetGrid(data=df.groupby([
    'dow',
    'hour'
]).hour.count().to_frame(name='day_hour_count').reset_index(), col='dow', col_order=[
    'Sunday',
    'Monday',
    'Tuesday',
    'Wednesday',
    'Thursday',
    'Friday',
    'Saturday'
], col_wrap=3)

# map barplot to each subplot
g.map(sns.barplot, 'hour', 'day_hour_count');