Posts_by_type.boxplot(column='Likes', by='Type', grid=True)
这将产生以下箱线图:
然而，我还想在箱线图上标记中位数和须的对应数值。
在matplotlib中是否可以实现？如果可以，有没有人能够指导我如何做到这一点？
一种解决方案，还可以为框中的值进行相加。
import random
import string
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
def get_x_tick_labels(df, grouped_by):
tmp = df.groupby([grouped_by]).size()
return ["{0}: {1}".format(k,v) for k, v in tmp.to_dict().items()]
def series_values_as_dict(series_object):
tmp = series_object.to_dict().values()
return [y for y in tmp][0]
def generate_dataframe():
# Create a pandas dataframe...
_likes = [random.randint(0,300) for _ in range(100)]
_type = [random.choice(string.ascii_uppercase[:5]) for _ in range(100)]
_shares = [random.randint(0,100) for _ in range(100)]
return pd.DataFrame(
{'Likes': _likes,
'Type': _type,
'shares': _shares
})
def add_values(bp, ax):
""" This actually adds the numbers to the various points of the boxplots"""
for element in ['whiskers', 'medians', 'caps']:
for line in bp[element]:
# Get the position of the element. y is the label you want
(x_l, y),(x_r, _) = line.get_xydata()
# Make sure datapoints exist
# (I've been working with intervals, should not be problem for this case)
if not np.isnan(y):
x_line_center = x_l + (x_r - x_l)/2
y_line_center = y # Since it's a line and it's horisontal
# overlay the value: on the line, from center to right
ax.text(x_line_center, y_line_center, # Position
'%.3f' % y, # Value (3f = 3 decimal float)
verticalalignment='center', # Centered vertically with line
fontsize=16, backgroundcolor="white")
posts_by_type = generate_dataframe()
fig, axes = plt.subplots(1, figsize=(20, 10))
bp_series = posts_by_type.boxplot(column='Likes', by='Type',
grid=True, figsize=(25, 10),
ax=axes, return_type='dict', labels=labels)
# This should return a dict, but gives me a Series object, soo...
bp_dict = series_values_as_dict(bp_series)
#Now add the values
add_values(bp_dict, axes)
# Set a label on X-axis for each boxplot
labels = get_x_tick_labels(posts_by_type, 'Type')
plt.xticks(range(1, len(labels) + 1), labels)
# Change some other texts on the graphs?
plt.title('Likes per type of post', fontsize=22)
plt.xlabel('Type', fontsize=18)
plt.ylabel('Likes', fontsize=18)
plt.suptitle('This is a pretty graph')
plt.show()
labels变量之前应该先定义它。 - Al Guymatplotlib绘制的须线不容易直接从数据中计算得出。matplotlib.pyplot.boxplot所示，须线应该在Q1-1.5IQR和Q3+1.5IQR处，然而只有当存在异常值时，才会将须线绘制到这些值上。否则，须线只会绘制到Q1下方的最小值，和/或Q3上方的最大值。days_total_bill.min()可以看到所有低须线只绘制到列中的最小值（{'Thur': 7.51, 'Fri': 5.75, 'Sat': 3.07, 'Sun': 7.25}）。
如何获取matplotlib箱线图的数据展示了如何使用matplotlib.cbook.boxplot_stats提取matplotlib使用的所有箱线图统计数据。
boxplot_stats适用于不包含NaN的值数组。在样本数据的情况下，每天（注释1.）的值数量不相同，因此不能使用boxplot_stats(days_total_bill.values)，而是使用列表推导式（注释2.）来获取每列的统计数据。
tips是一个整洁的数据框，因此相关数据（'day'和'total_bill'）被转换为宽数据框，使用pandas.DataFrame.pivot，因为boxplot_stats需要数据以这种形式提供。.iloc仅选择要进行注释的统计数据。此步骤是为了在进行注释时更容易迭代每天的相关统计数据。sns.boxplot绘制，但也可以使用pandas.DataFrame.plot。
box_plot = days_total_bill.plot(kind='box', figsize=(12, 8), positions=range(len(days_total_bill.columns)))，其中range指定从0开始索引，因为默认情况下箱线图从1开始索引。
在python 3.11.4、pandas 2.0.3、matplotlib 3.7.1、seaborn 0.12.2中测试通过。import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.cbook import boxplot_stats
# load sample data
tips = sns.load_dataset("tips")
# 1. pivot tips so there's a column for each day for total_bill
days_total_bill = tips.pivot(columns='day', values='total_bill')
# 2. extract the boxplot stats for each day
days_total_bill_stats = [boxplot_stats(days_total_bill[col].dropna().values)[0] for col in days_total_bill.columns]
# 3. create a dataframe for the stats for each day
stats = pd.DataFrame(days_total_bill_stats, index=days_total_bill.columns).iloc[:, [4, 5, 7, 8, 9]].round(2)
# plot
fig, ax = plt.subplots(figsize=(12, 8))
# directly plot the wide dataframe with only the total_bill data
box_plot = sns.boxplot(data=days_total_bill, ax=ax)
# same plot is created with the primary tips dataframe
# box_plot = sns.boxplot(x="day", y="total_bill", data=tips, ax=ax)
# annotate
for xtick in box_plot.get_xticks():
for col in stats.columns:
box_plot.text(xtick, stats[col][xtick], stats[col][xtick], horizontalalignment='left', size='medium', color='k', weight='semibold', bbox=dict(facecolor='lightgray'))
提示 total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
days_total_billday Thur Fri Sat Sun
0 NaN NaN NaN 16.99
1 NaN NaN NaN 10.34
2 NaN NaN NaN 21.01
3 NaN NaN NaN 23.68
4 NaN NaN NaN 24.59
...
239 NaN NaN 29.03 NaN
240 NaN NaN 27.18 NaN
241 NaN NaN 22.67 NaN
242 NaN NaN 17.82 NaN
243 18.78 NaN NaN NaN
days_total_bill_stats[{'mean': 17.682741935483868,
'iqr': 7.712500000000002,
'cilo': 14.662203087202318,
'cihi': 17.73779691279768,
'whishi': 29.8,
'whislo': 7.51,
'fliers': array([32.68, 34.83, 34.3 , 41.19, 43.11]),
'q1': 12.442499999999999,
'med': 16.2,
'q3': 20.155},
{'mean': 17.15157894736842,
'iqr': 9.655000000000001,
'cilo': 11.902436010483171,
'cihi': 18.85756398951683,
'whishi': 28.97,
'whislo': 5.75,
'fliers': array([40.17]),
'q1': 12.094999999999999,
'med': 15.38,
'q3': 21.75},
{'mean': 20.441379310344825,
'iqr': 10.835,
'cilo': 16.4162347275501,
'cihi': 20.063765272449896,
'whishi': 39.42,
'whislo': 3.07,
'fliers': array([48.27, 44.3 , 50.81, 48.33]),
'q1': 13.905000000000001,
'med': 18.24,
'q3': 24.740000000000002},
{'mean': 21.41,
'iqr': 10.610000000000001,
'cilo': 17.719230764952172,
'cihi': 21.540769235047826,
'whishi': 40.55,
'whislo': 7.25,
'fliers': array([48.17, 45.35]),
'q1': 14.987499999999999,
'med': 19.63,
'q3': 25.5975}]
统计 whishi whislo q1 med q3
day
Thur 29.80 7.51 12.44 16.20 20.16
Fri 28.97 5.75 12.10 15.38 21.75
Sat 39.42 3.07 13.90 18.24 24.74
Sun 40.55 7.25 14.99 19.63 25.60
stats = tips.groupby(['day'])['total_bill'].quantile([0.25, 0.75]).unstack(level=1).rename({0.25: 'q1', 0.75: 'q3'}, axis=1)
stats.insert(0, 'iqr', stats['q3'].sub(stats['q1']))
stats['w_low'] = stats['q1'].sub(stats['iqr'].mul(1.5))
stats['w_hi'] = stats['q3'].add(stats['iqr'].mul(1.5))
stats = stats.round(2)
iqr q1 q3 w_low w_hi
day
Thur 7.71 12.44 20.16 0.87 31.72
Fri 9.66 12.10 21.75 -2.39 36.23
Sat 10.84 13.90 24.74 -2.35 40.99
Sun 10.61 14.99 25.60 -0.93 41.51