如何在 seaborn 图形级箱线图上叠加数据点
How to overlay data points on seaborn figure-level boxplots
我有以下数据框和下面的图 - 我想为因子图中的每个箱线图添加数据点，但我无法将箱线图和带状图组合到同一个图中（即它们不重叠，它们出现在彼此下方）。有解决办法吗？
import pandas as pd
import datetime
idx = pd.date_range('01-01-2020', '01-25-2020')
d = pd.Series({'01-01-2020': 1,
'01-25-2020': 1})
d.index = pd.DatetimeIndex(d.index)
d = d.reindex(idx, fill_value=0)
df = pd.DataFrame(d).rename_axis("dt").reset_index()
df.drop(columns=df.columns[1], inplace=True)
# calculate week number
df["week"] = df["dt"].dt.week
# create column counts for 'nhsbt centres'
df["A"] = np.random.randint(0, 50, df.shape[0])
df["B"] = np.random.randint(0, 30, df.shape[0])
df["C"] = np.random.randint(0, 20, df.shape[0])
# melt dataframe
df1 = df[["A", "B", "C", "week"]]
df1 = df1.set_index("week")
df1 = df1.melt(ignore_index=False)
df1["week"] = df1.index
# make boxplot
sns.factorplot("week", "value", col="variable", data=df1, kind="box")
如果你有这样的数据框：
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
idx = pd.date_range('01-01-2020', '01-25-2020')
d = pd.Series({'01-01-2020': 1,
'01-25-2020': 1})
d.index = pd.DatetimeIndex(d.index)
d = d.reindex(idx, fill_value=0)
df = pd.DataFrame(d).rename_axis("dt").reset_index()
df.drop(columns=df.columns[1], inplace=True)
df["week"] = df["dt"].dt.week
df["groupA"] = np.random.randint(0, 50, df.shape[0])
df["groupB"] = np.random.randint(0, 30, df.shape[0])
df["groupC"] = np.random.randint(0, 20, df.shape[0])
df1 = df[["groupA", "groupB", "groupC", "week"]]
df1 = df1.set_index("week")
df1 = df1.melt(ignore_index=False)
df1["week"] = df1.index
variable value week
week
1 groupA 24 1
1 groupA 30 1
1 groupA 38 1
1 groupA 41 1
1 groupA 42 1
2 groupA 47 2
2 groupA 9 2
2 groupA 16 2
2 groupA 24 2
2 groupA 3 2
2 groupA 27 2
2 groupA 48 2
3 groupA 46 3
3 groupA 29 3
3 groupA 2 3
3 groupA 46 3
3 groupA 48 3
3 groupA 26 3
3 groupA 36 3
4 groupA 48 4
4 groupA 38 4
4 groupA 19 4
4 groupA 13 4
4 groupA 38 4
4 groupA 34 4
1 groupB 11 1
1 groupB 15 1
1 groupB 14 1
1 groupB 29 1
1 groupB 6 1
2 groupB 20 2
2 groupB 14 2
2 groupB 26 2
2 groupB 11 2
2 groupB 14 2
2 groupB 0 2
2 groupB 11 2
3 groupB 20 3
3 groupB 17 3
3 groupB 16 3
3 groupB 24 3
3 groupB 24 3
3 groupB 16 3
3 groupB 22 3
4 groupB 10 4
4 groupB 26 4
4 groupB 3 4
4 groupB 7 4
4 groupB 16 4
4 groupB 18 4
1 groupC 12 1
1 groupC 4 1
1 groupC 1 1
1 groupC 9 1
1 groupC 16 1
2 groupC 6 2
2 groupC 12 2
2 groupC 6 2
2 groupC 14 2
2 groupC 2 2
2 groupC 18 2
2 groupC 10 2
3 groupC 13 3
3 groupC 11 3
3 groupC 15 3
3 groupC 9 3
3 groupC 18 3
3 groupC 7 3
3 groupC 4 3
4 groupC 8 4
4 groupC 13 4
4 groupC 3 4
4 groupC 1 4
4 groupC 5 4
4 groupC 4 4
然后您可以将 seaborn.boxplot and seaborn.swarmplot or seaborn.stripplot 与 for 循环结合使用：
fig, ax = plt.subplots(1, len(df1['variable'].unique()), figsize = (15, 5))
for i, group in enumerate(df1['variable'].unique(), 0):
sns.boxplot(ax = ax[i], data = df1[df1['variable'] == group], x = 'week', y = 'value')
sns.swarmplot(ax = ax[i], data = df1[df1['variable'] == group], x = 'week', y = 'value', color = 'black')
ax[i].set_title(group)
plt.show()
seaborn functionplot
seaborn.swarmplot
seaborn.stripplot
注释
据我所知，你不能使用seaborn.factorplot（现在是seaborn.catplot），因为它是一个图形级函数，所以它确实不允许您在同一张图中组合多个图。
- 将轴级图添加到图形级图的最清晰方法，如
seaborn.catplot, is to use .map, as shown in seaborn: Building structured multi-plot grids
seaborn.stripplot can be used in place of seaborn.swarmplot
seaborn.factorplot 已重命名为 .catplot，但是，如果您使用的是旧版本的 seaborn，.map 应该仍然有效。.dt.week 已弃用，应替换为 .dt.isocalendar().week- 在
pandas 1.3.2、seaborn 0.11.2 和 matplotlib 3.4.3 中测试
import seaborn as sns
# make boxplot with data from the OP
g = sns.catplot(x="week", y="value", col="variable", data=df1, kind="box")
g.map(sns.swarmplot, 'week', 'value', color='k', order=sorted(df1.week.unique()))
我有以下数据框和下面的图 - 我想为因子图中的每个箱线图添加数据点，但我无法将箱线图和带状图组合到同一个图中（即它们不重叠，它们出现在彼此下方）。有解决办法吗？
import pandas as pd
import datetime
idx = pd.date_range('01-01-2020', '01-25-2020')
d = pd.Series({'01-01-2020': 1,
'01-25-2020': 1})
d.index = pd.DatetimeIndex(d.index)
d = d.reindex(idx, fill_value=0)
df = pd.DataFrame(d).rename_axis("dt").reset_index()
df.drop(columns=df.columns[1], inplace=True)
# calculate week number
df["week"] = df["dt"].dt.week
# create column counts for 'nhsbt centres'
df["A"] = np.random.randint(0, 50, df.shape[0])
df["B"] = np.random.randint(0, 30, df.shape[0])
df["C"] = np.random.randint(0, 20, df.shape[0])
# melt dataframe
df1 = df[["A", "B", "C", "week"]]
df1 = df1.set_index("week")
df1 = df1.melt(ignore_index=False)
df1["week"] = df1.index
# make boxplot
sns.factorplot("week", "value", col="variable", data=df1, kind="box")
如果你有这样的数据框：
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
idx = pd.date_range('01-01-2020', '01-25-2020')
d = pd.Series({'01-01-2020': 1,
'01-25-2020': 1})
d.index = pd.DatetimeIndex(d.index)
d = d.reindex(idx, fill_value=0)
df = pd.DataFrame(d).rename_axis("dt").reset_index()
df.drop(columns=df.columns[1], inplace=True)
df["week"] = df["dt"].dt.week
df["groupA"] = np.random.randint(0, 50, df.shape[0])
df["groupB"] = np.random.randint(0, 30, df.shape[0])
df["groupC"] = np.random.randint(0, 20, df.shape[0])
df1 = df[["groupA", "groupB", "groupC", "week"]]
df1 = df1.set_index("week")
df1 = df1.melt(ignore_index=False)
df1["week"] = df1.index
variable value week
week
1 groupA 24 1
1 groupA 30 1
1 groupA 38 1
1 groupA 41 1
1 groupA 42 1
2 groupA 47 2
2 groupA 9 2
2 groupA 16 2
2 groupA 24 2
2 groupA 3 2
2 groupA 27 2
2 groupA 48 2
3 groupA 46 3
3 groupA 29 3
3 groupA 2 3
3 groupA 46 3
3 groupA 48 3
3 groupA 26 3
3 groupA 36 3
4 groupA 48 4
4 groupA 38 4
4 groupA 19 4
4 groupA 13 4
4 groupA 38 4
4 groupA 34 4
1 groupB 11 1
1 groupB 15 1
1 groupB 14 1
1 groupB 29 1
1 groupB 6 1
2 groupB 20 2
2 groupB 14 2
2 groupB 26 2
2 groupB 11 2
2 groupB 14 2
2 groupB 0 2
2 groupB 11 2
3 groupB 20 3
3 groupB 17 3
3 groupB 16 3
3 groupB 24 3
3 groupB 24 3
3 groupB 16 3
3 groupB 22 3
4 groupB 10 4
4 groupB 26 4
4 groupB 3 4
4 groupB 7 4
4 groupB 16 4
4 groupB 18 4
1 groupC 12 1
1 groupC 4 1
1 groupC 1 1
1 groupC 9 1
1 groupC 16 1
2 groupC 6 2
2 groupC 12 2
2 groupC 6 2
2 groupC 14 2
2 groupC 2 2
2 groupC 18 2
2 groupC 10 2
3 groupC 13 3
3 groupC 11 3
3 groupC 15 3
3 groupC 9 3
3 groupC 18 3
3 groupC 7 3
3 groupC 4 3
4 groupC 8 4
4 groupC 13 4
4 groupC 3 4
4 groupC 1 4
4 groupC 5 4
4 groupC 4 4
然后您可以将 seaborn.boxplot and seaborn.swarmplot or seaborn.stripplot 与 for 循环结合使用：
fig, ax = plt.subplots(1, len(df1['variable'].unique()), figsize = (15, 5))
for i, group in enumerate(df1['variable'].unique(), 0):
sns.boxplot(ax = ax[i], data = df1[df1['variable'] == group], x = 'week', y = 'value')
sns.swarmplot(ax = ax[i], data = df1[df1['variable'] == group], x = 'week', y = 'value', color = 'black')
ax[i].set_title(group)
plt.show()
seaborn function |
plot |
|---|---|
seaborn.swarmplot |
|
seaborn.stripplot |
注释
据我所知，你不能使用seaborn.factorplot（现在是seaborn.catplot），因为它是一个图形级函数，所以它确实不允许您在同一张图中组合多个图。
- 将轴级图添加到图形级图的最清晰方法，如
seaborn.catplot, is to use.map, as shown in seaborn: Building structured multi-plot gridsseaborn.stripplotcan be used in place ofseaborn.swarmplot
seaborn.factorplot已重命名为.catplot，但是，如果您使用的是旧版本的seaborn，.map应该仍然有效。.dt.week已弃用，应替换为.dt.isocalendar().week- 在
pandas 1.3.2、seaborn 0.11.2和matplotlib 3.4.3 中测试
import seaborn as sns
# make boxplot with data from the OP
g = sns.catplot(x="week", y="value", col="variable", data=df1, kind="box")
g.map(sns.swarmplot, 'week', 'value', color='k', order=sorted(df1.week.unique()))