未能使用 Seaborn 对 pandas 中的 groupby 数据绘制每个 canvas 中的一个图形
failed to boxplot one figure in each canvas using Seaborn for groupby data in pandas
我有一个数据集，如下：
funding_round_type variable value
venture raised_amount_usd 7.0000000000
seed raised_amount_usd 5.8450980400
venture raised_amount_usd 6.5323565818
venture raised_amount_usd 6.3010299957
seed raised_amount_usd 4.6370892735
venture raised_amount_usd 5.8570253658
seed raised_amount_usd 6.4771212547
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 6.2304489214
venture raised_amount_usd 6.9493900066
seed raised_amount_usd 5.1760912591
seed raised_amount_usd 6.0000217142
seed raised_amount_usd 4.6020599913
private_equity raised_amount_usd 6.7781512504
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 5.6020599913
seed raised_amount_usd 4.6020599913
seed raised_amount_usd 5.6989700043
angel raised_amount_usd 7.6427005481
venture raised_amount_usd 6.4014005408
seed raised_amount_usd 4.1760912591
venture raised_amount_usd 5.4247836644
venture raised_amount_usd 6.4634199325
venture raised_amount_usd 6.1760912591
venture raised_amount_usd 5.8075350281
seed raised_amount_usd 5.5910646070
venture raised_amount_usd 6.8197211705
venture raised_amount_usd 6.2397773156
seed raised_amount_usd 5.1211296041
venture raised_amount_usd 6.2552725051
venture raised_amount_usd 6.9493900066
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.1760912591
seed raised_amount_usd 5.6720978579
venture raised_amount_usd 6.5477747054
seed raised_amount_usd 5.8750612634
venture raised_amount_usd 5.6989700043
venture raised_amount_usd 7.5440680444
seed raised_amount_usd 4.6989700043
seed raised_amount_usd 5.0000000000
seed raised_amount_usd 6.2041199827
angel raised_amount_usd 5.0000000000
venture raised_amount_usd 7.7403626895
venture raised_amount_usd 5.7403626895
seed raised_amount_usd 6.2920142616
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 6.2552725051
seed raised_amount_usd 5.9708116109
seed raised_amount_usd 5.3979400087
seed raised_amount_usd 5.2304489214
seed raised_amount_usd 4.1461280357
angel raised_amount_usd 5.6989700043
seed raised_amount_usd 4.7403626895
venture raised_amount_usd 5.1768636160
seed raised_amount_usd 5.6989700043
venture raised_amount_usd 6.0000000000
venture raised_amount_usd 7.1139433523
venture raised_amount_usd 6.7323937598
seed raised_amount_usd 5.5784237281
venture raised_amount_usd 7.0791576125
seed raised_amount_usd 5.1160628741
venture raised_amount_usd 6.2574520108
angel raised_amount_usd 5.7920371044
venture raised_amount_usd 6.3802112417
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 7.1760912591
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 6.7118072290
venture raised_amount_usd 7.0934216852
venture raised_amount_usd 6.7781512504
venture raised_amount_usd 7.3010299957
seed raised_amount_usd 5.8450980400
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.3979400087
seed raised_amount_usd 5.6989700043
seed raised_amount_usd 6.3424226808
angel raised_amount_usd 6.0000000000
angel raised_amount_usd 5.3010299957
seed raised_amount_usd 5.3010299957
venture raised_amount_usd 5.3009366123
angel raised_amount_usd 4.6989700043
venture raised_amount_usd 7.2041199827
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 7.1461280357
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 6.0969100130
venture raised_amount_usd 6.2011192509
venture raised_amount_usd 5.6803482066
venture raised_amount_usd 7.9157813085
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 8.0000000000
venture raised_amount_usd 6.8450980400
venture raised_amount_usd 7.6020599913
venture raised_amount_usd 6.7944880467
venture raised_amount_usd 6.6989700043
private_equity raised_amount_usd 8.3010299957
angel raised_amount_usd 4.6020599913
venture raised_amount_usd 6.3979400087
seed raised_amount_usd 4.6020599913
seed raised_amount_usd 4.6989700043
seed raised_amount_usd 6.2304489214
venture raised_amount_usd 6.1613680022
seed raised_amount_usd 5.9542425094
seed raised_amount_usd 5.3010299957
seed raised_amount_usd 6.0000000000
venture raised_amount_usd 5.2125391607
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 5.3222192947
seed raised_amount_usd 3.3010299957
venture raised_amount_usd 7.4771212547
seed raised_amount_usd 5.6989700043
angel raised_amount_usd 5.3455540773
seed raised_amount_usd 4.9665265584
angel raised_amount_usd 5.6503667852
venture raised_amount_usd 7.1760912591
seed raised_amount_usd 4.3010299957
seed raised_amount_usd 5.1335389084
venture raised_amount_usd 4.4771212547
venture raised_amount_usd 7.1760912591
venture raised_amount_usd 7.6232492904
angel raised_amount_usd 5.8750612634
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 5.6989700043
venture raised_amount_usd 6.0000000000
venture raised_amount_usd 6.2201080880
seed raised_amount_usd 4.7634279936
seed raised_amount_usd 5.3100323422
venture raised_amount_usd 6.3089940138
venture raised_amount_usd 5.1760912591
venture raised_amount_usd 6.3341345481
venture raised_amount_usd 7.4734869701
venture raised_amount_usd 6.9030899870
venture raised_amount_usd 7.4149733480
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 6.8573324964
seed raised_amount_usd 5.0599570200
seed raised_amount_usd 5.5073835557
seed raised_amount_usd 5.9030899870
seed raised_amount_usd 5.0791812460
seed raised_amount_usd 6.3222192947
venture raised_amount_usd 6.3010299957
venture raised_amount_usd 5.9030899870
seed raised_amount_usd 4.6989700043
angel raised_amount_usd 6.1655790462
venture raised_amount_usd 6.8041058192
private_equity raised_amount_usd 8.5118833610
private_equity raised_amount_usd 5.3025646833
private_equity raised_amount_usd 7.9294189257
private_equity raised_amount_usd 7.6989700043
private_equity raised_amount_usd 6.6232492904
private_equity raised_amount_usd 8.0000000000
private_equity raised_amount_usd 6.5287849786
private_equity raised_amount_usd 6.8450980400
private_equity raised_amount_usd 5.5865648018
private_equity raised_amount_usd 6.4771212547
private_equity raised_amount_usd 7.8750612634
private_equity raised_amount_usd 8.0000000000
private_equity raised_amount_usd 8.0413926852
private_equity raised_amount_usd 8.0638509231
private_equity raised_amount_usd 7.9030899870
private_equity raised_amount_usd 7.3010299957
private_equity raised_amount_usd 7.3010299957
private_equity raised_amount_usd 6.5326671293
private_equity raised_amount_usd 7.8864887680
private_equity raised_amount_usd 8.7403626895
private_equity raised_amount_usd 8.0413926852
private_equity raised_amount_usd 8.6253124510
private_equity raised_amount_usd 8.3521825181
private_equity raised_amount_usd 7.9364383376
private_equity raised_amount_usd 7.6020599913
private_equity raised_amount_usd 7.6532125138
我想在不同的 canvas（2 x 2 帧，即 2 行和 2 列）中为每个 funding_round_type 绘制箱线图。
我试图为此目的使用 seaborn 包，我的代码看起来像这样：
fig, axs = plt.subplots(ncols=4)
df.groupby('funding_round_type')['raised_amount_usd'].plot.box(subplots=True)
plt.show()
它没有产生我正在寻找的结果。
那我也试了：
sns.regplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[0])
sns.regplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[1])
sns.boxplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[2])
sns.boxplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[3])
这给了我一个错误：
....
.......
2902 if tolerance is not None:
KeyError: 'raised_amount_usd'
如何在 canvas 的每一帧中为每个 funding_round_type 获取单个箱线图？
你试过了吗pandas.DataFrame.boxplot？
这看起来像您要求的结果。
df.groupby('funding_round_type').boxplot(subplots=True, figsize=(6,6))
输出
有很多方法可以为每个子图创建箱线图。
下面的代码假定您希望每个方框有不同的 x 范围（否则您可以简单地将它们放在一个图中）。循环遍历每个轴，示例代码将 x 轴限制在 5% 和 95% 之间的间隔，因此异常值被切除。
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.DataFrame({"funding_round_type": np.random.choice(["seed", "venture", "angel", "private_equity"], 50),
"value": np.random.normal(50, 10, 50)})
g = sns.FacetGrid(df, col="funding_round_type", col_wrap=2, sharex=False)
g.map(sns.boxplot, "value")
for col, ax in zip(g.col_names, g.axes.flat):
ax.set_xlim(df[df["funding_round_type"] == col]["value"].quantile([0.05, 0.95]))
plt.tight_layout()
plt.show()
要获得垂直框，一个问题是这些函数需要一个x参数。一个技巧是添加一个空名称且充满空字符串的虚拟列。这是一个使用 catplot:
的例子
df[""] = "" # create a dummy column to serve as x
g = sns.catplot(data=df, x="", y="value", col="funding_round_type", kind="box", col_wrap=2, sharey=False)
for col, ax in zip(g.col_names, g.axes.flat):
ax.set_ylim(df[df["funding_round_type"] == col]["value"].quantile([0.05, 0.95]))
plt.tight_layout()
plt.show()
我有一个数据集，如下：
funding_round_type variable value
venture raised_amount_usd 7.0000000000
seed raised_amount_usd 5.8450980400
venture raised_amount_usd 6.5323565818
venture raised_amount_usd 6.3010299957
seed raised_amount_usd 4.6370892735
venture raised_amount_usd 5.8570253658
seed raised_amount_usd 6.4771212547
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 6.2304489214
venture raised_amount_usd 6.9493900066
seed raised_amount_usd 5.1760912591
seed raised_amount_usd 6.0000217142
seed raised_amount_usd 4.6020599913
private_equity raised_amount_usd 6.7781512504
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 5.6020599913
seed raised_amount_usd 4.6020599913
seed raised_amount_usd 5.6989700043
angel raised_amount_usd 7.6427005481
venture raised_amount_usd 6.4014005408
seed raised_amount_usd 4.1760912591
venture raised_amount_usd 5.4247836644
venture raised_amount_usd 6.4634199325
venture raised_amount_usd 6.1760912591
venture raised_amount_usd 5.8075350281
seed raised_amount_usd 5.5910646070
venture raised_amount_usd 6.8197211705
venture raised_amount_usd 6.2397773156
seed raised_amount_usd 5.1211296041
venture raised_amount_usd 6.2552725051
venture raised_amount_usd 6.9493900066
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.1760912591
seed raised_amount_usd 5.6720978579
venture raised_amount_usd 6.5477747054
seed raised_amount_usd 5.8750612634
venture raised_amount_usd 5.6989700043
venture raised_amount_usd 7.5440680444
seed raised_amount_usd 4.6989700043
seed raised_amount_usd 5.0000000000
seed raised_amount_usd 6.2041199827
angel raised_amount_usd 5.0000000000
venture raised_amount_usd 7.7403626895
venture raised_amount_usd 5.7403626895
seed raised_amount_usd 6.2920142616
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 6.2552725051
seed raised_amount_usd 5.9708116109
seed raised_amount_usd 5.3979400087
seed raised_amount_usd 5.2304489214
seed raised_amount_usd 4.1461280357
angel raised_amount_usd 5.6989700043
seed raised_amount_usd 4.7403626895
venture raised_amount_usd 5.1768636160
seed raised_amount_usd 5.6989700043
venture raised_amount_usd 6.0000000000
venture raised_amount_usd 7.1139433523
venture raised_amount_usd 6.7323937598
seed raised_amount_usd 5.5784237281
venture raised_amount_usd 7.0791576125
seed raised_amount_usd 5.1160628741
venture raised_amount_usd 6.2574520108
angel raised_amount_usd 5.7920371044
venture raised_amount_usd 6.3802112417
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 7.1760912591
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 6.7118072290
venture raised_amount_usd 7.0934216852
venture raised_amount_usd 6.7781512504
venture raised_amount_usd 7.3010299957
seed raised_amount_usd 5.8450980400
venture raised_amount_usd 6.4771212547
venture raised_amount_usd 6.3979400087
seed raised_amount_usd 5.6989700043
seed raised_amount_usd 6.3424226808
angel raised_amount_usd 6.0000000000
angel raised_amount_usd 5.3010299957
seed raised_amount_usd 5.3010299957
venture raised_amount_usd 5.3009366123
angel raised_amount_usd 4.6989700043
venture raised_amount_usd 7.2041199827
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 7.1461280357
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 6.0969100130
venture raised_amount_usd 6.2011192509
venture raised_amount_usd 5.6803482066
venture raised_amount_usd 7.9157813085
venture raised_amount_usd 6.6989700043
venture raised_amount_usd 7.0000000000
venture raised_amount_usd 7.3010299957
venture raised_amount_usd 8.0000000000
venture raised_amount_usd 6.8450980400
venture raised_amount_usd 7.6020599913
venture raised_amount_usd 6.7944880467
venture raised_amount_usd 6.6989700043
private_equity raised_amount_usd 8.3010299957
angel raised_amount_usd 4.6020599913
venture raised_amount_usd 6.3979400087
seed raised_amount_usd 4.6020599913
seed raised_amount_usd 4.6989700043
seed raised_amount_usd 6.2304489214
venture raised_amount_usd 6.1613680022
seed raised_amount_usd 5.9542425094
seed raised_amount_usd 5.3010299957
seed raised_amount_usd 6.0000000000
venture raised_amount_usd 5.2125391607
venture raised_amount_usd 6.6020599913
seed raised_amount_usd 5.3222192947
seed raised_amount_usd 3.3010299957
venture raised_amount_usd 7.4771212547
seed raised_amount_usd 5.6989700043
angel raised_amount_usd 5.3455540773
seed raised_amount_usd 4.9665265584
angel raised_amount_usd 5.6503667852
venture raised_amount_usd 7.1760912591
seed raised_amount_usd 4.3010299957
seed raised_amount_usd 5.1335389084
venture raised_amount_usd 4.4771212547
venture raised_amount_usd 7.1760912591
venture raised_amount_usd 7.6232492904
angel raised_amount_usd 5.8750612634
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 5.6989700043
venture raised_amount_usd 6.0000000000
venture raised_amount_usd 6.2201080880
seed raised_amount_usd 4.7634279936
seed raised_amount_usd 5.3100323422
venture raised_amount_usd 6.3089940138
venture raised_amount_usd 5.1760912591
venture raised_amount_usd 6.3341345481
venture raised_amount_usd 7.4734869701
venture raised_amount_usd 6.9030899870
venture raised_amount_usd 7.4149733480
venture raised_amount_usd 6.3979400087
venture raised_amount_usd 6.8573324964
seed raised_amount_usd 5.0599570200
seed raised_amount_usd 5.5073835557
seed raised_amount_usd 5.9030899870
seed raised_amount_usd 5.0791812460
seed raised_amount_usd 6.3222192947
venture raised_amount_usd 6.3010299957
venture raised_amount_usd 5.9030899870
seed raised_amount_usd 4.6989700043
angel raised_amount_usd 6.1655790462
venture raised_amount_usd 6.8041058192
private_equity raised_amount_usd 8.5118833610
private_equity raised_amount_usd 5.3025646833
private_equity raised_amount_usd 7.9294189257
private_equity raised_amount_usd 7.6989700043
private_equity raised_amount_usd 6.6232492904
private_equity raised_amount_usd 8.0000000000
private_equity raised_amount_usd 6.5287849786
private_equity raised_amount_usd 6.8450980400
private_equity raised_amount_usd 5.5865648018
private_equity raised_amount_usd 6.4771212547
private_equity raised_amount_usd 7.8750612634
private_equity raised_amount_usd 8.0000000000
private_equity raised_amount_usd 8.0413926852
private_equity raised_amount_usd 8.0638509231
private_equity raised_amount_usd 7.9030899870
private_equity raised_amount_usd 7.3010299957
private_equity raised_amount_usd 7.3010299957
private_equity raised_amount_usd 6.5326671293
private_equity raised_amount_usd 7.8864887680
private_equity raised_amount_usd 8.7403626895
private_equity raised_amount_usd 8.0413926852
private_equity raised_amount_usd 8.6253124510
private_equity raised_amount_usd 8.3521825181
private_equity raised_amount_usd 7.9364383376
private_equity raised_amount_usd 7.6020599913
private_equity raised_amount_usd 7.6532125138
我想在不同的 canvas（2 x 2 帧，即 2 行和 2 列）中为每个 funding_round_type 绘制箱线图。
我试图为此目的使用 seaborn 包，我的代码看起来像这样：
fig, axs = plt.subplots(ncols=4)
df.groupby('funding_round_type')['raised_amount_usd'].plot.box(subplots=True)
plt.show()
它没有产生我正在寻找的结果。
那我也试了：
sns.regplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[0])
sns.regplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[1])
sns.boxplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[2])
sns.boxplot(x='funding_round_type', y='raised_amount_usd', data=df, ax=axs[3])
这给了我一个错误：
....
.......
2902 if tolerance is not None:
KeyError: 'raised_amount_usd'
如何在 canvas 的每一帧中为每个 funding_round_type 获取单个箱线图？
你试过了吗pandas.DataFrame.boxplot？
这看起来像您要求的结果。
df.groupby('funding_round_type').boxplot(subplots=True, figsize=(6,6))
输出
有很多方法可以为每个子图创建箱线图。
下面的代码假定您希望每个方框有不同的 x 范围（否则您可以简单地将它们放在一个图中）。循环遍历每个轴，示例代码将 x 轴限制在 5% 和 95% 之间的间隔，因此异常值被切除。
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.DataFrame({"funding_round_type": np.random.choice(["seed", "venture", "angel", "private_equity"], 50),
"value": np.random.normal(50, 10, 50)})
g = sns.FacetGrid(df, col="funding_round_type", col_wrap=2, sharex=False)
g.map(sns.boxplot, "value")
for col, ax in zip(g.col_names, g.axes.flat):
ax.set_xlim(df[df["funding_round_type"] == col]["value"].quantile([0.05, 0.95]))
plt.tight_layout()
plt.show()
要获得垂直框，一个问题是这些函数需要一个x参数。一个技巧是添加一个空名称且充满空字符串的虚拟列。这是一个使用 catplot:
df[""] = "" # create a dummy column to serve as x
g = sns.catplot(data=df, x="", y="value", col="funding_round_type", kind="box", col_wrap=2, sharey=False)
for col, ax in zip(g.col_names, g.axes.flat):
ax.set_ylim(df[df["funding_round_type"] == col]["value"].quantile([0.05, 0.95]))
plt.tight_layout()
plt.show()