提问人：user308827 提问时间：11/13/2023 最后编辑：cottontailuser308827 更新时间：11/18/2023 访问量：296
排除没有任何数据的子图，并在 relplot 中左对齐其余部分
Exclude subplots without any data and left-align the rest in relplot
问：
与此问题相关：使用 relplot 绘制导致错误的 pandas 数据帧
可重现示例的数据如下：
import pandas as pd
data = {'Index': ['TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN', 'TN10p', 'CSU', 'PRCPTOT', 'SDII', 'CWD', 'R99p', 'R99pTOT', 'TX', 'MIN'],
'Stage': [10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, 28, 28, 28],
'Z-Score CEI': [-0.688363146221944, 0.5773502691896258, -0.1132178081286216, -0.4278470185781525, 1.0564189237269357, -0.2085144140570746, -0.2085144140570747, 0.2094308186874662, 0.7196177629619716, 0.0, 0.2085144140570762, -1.3803992008056865, -1.3414801279616884, -0.898669162696764, -0.3015113445777637, -0.2953788838542738, 1.1753566728623484, 0.887285779752818, -0.7071067811865475, 0.2847473987257496, 0.1877402877114761, -0.14246249364941, 0.9686648999069224, -0.3015113445777636, -0.2734952011457535, 0.5888914135578924, -0.4488478006064821, -0.7745966692414834, 0.3052145041378634, 0.8197566686157259, 0.3377616284580471, 1.1832159566199232, -0.3015113445777637, -0.2952684241380082, -0.7971688059921156, 0.4479595231454734, -0.5805577953661853, 0.3015113445777642, -0.610500944190139, -0.7734588159553295, -0.5434722467562666, -0.2085144140570747, -0.2085144140570747, 0.8838570486142397, -0.7976091842744983, 2.213211486674006, 0.3779644730092272, -0.6900911175081499, -0.4856558012299846, -0.6044504143545613, -0.2085144140570746, -0.2085144140570747, 1.6498242899497324, 0.463638205246897, -0.064684622735315, 0.5488212999484522, -0.665392754456709, -1.096398502672124, 0.9387247898517332, -0.2085144140570747, -0.2085144140570748, 1.5486212537866115, 0.6776076459912243, -0.7973761651368712, 0.4773960376293314, 0.2611306759187019, -0.2450438178293888, 0.1097642599896903, -0.2085144140570746, -0.2085144140570747, 1.2468175442040146, 0.4912008775378222, -0.8071397220005339, 0.3015113445777636, -0.4051430868010012, -0.9843673918740764, 0.4231429298696365, -0.2085144140570746, -0.2182178902359924, 1.0617336112420042, 0.4221998839727844, -0.2267786838055363, 0.2847473987257496, 1.2708306299144654, 2.4058495687034616, -0.1042572070285372, 4.79583152331272, 4.79583152331272, -0.1758750648062869, 0.9614146130140746, -0.6493094697110509, 0.2847473987257496, -0.0566333001085325, 0.0970016157961683, -0.3380617018914065, -0.2085144140570746, -0.2132007163556104, 1.6462867435913509, 0.8920062635166146, -0.649519052838329, 0.2847473987257496, -0.5727902328114448, -0.385256843427376, 0.123403510468459, -0.2085144140570747, -0.2085144140570747, 0.7206954054604126, -0.0169294393471337, -0.1547646465068273, 0.3900382256192578, -0.91200685504817, -0.7643838011372592, -0.8553913029328061, -0.2085144140570746, -0.2132007163556104, 1.999517273479448, 0.2135313581345105, 0.3577708763999664, 0.2085144140570741, -0.5245759407883583, -0.3972170332271401, 0.1363988678940945, -0.2085144140570746, -0.2085144140570747, 2.180043023382912, 0.6949201395674811, -0.0345238339879863, 0.3872983346207417, -1.054383845470446, -0.7524909974608698, -0.79555728417573, -0.2085144140570747, -0.2085144140570747, 2.597515932302782, -0.0173575308522844, -0.7839294959021852, 0.5496481403962044, 0.3346732026206391, -0.1729151200242987, 0.8108848540793832, -0.2085144140570747, -0.2085144140570747, -0.1975075078549267, -0.1333012766349092, -0.7300956427599692, 0.3495310368212778, -0.9383516638143292, 0.3757624051611033, -0.9198662110078, -0.2085144140570747, -0.2085144140570747, 0.1077379509580834, -0.0391099277150297, -0.8006407690254357, 0.5226257719601375, 0.2650955994479978, -0.3323178678594628, 1.348187695720845, -0.2085144140570746, -0.2085144140570748, 0.6009413558916348, 0.455353435995126, -0.5933908290969269, 0.0, 0.1226864783178058, -0.0252747129054563, 0.8212299340934688, -0.2085144140570746, -0.2132007163556105, -0.8954835101738379, -1.1134420487718968],
'Type': ['Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI', 'Cold', 'Heat', 'Rain', 'Rain', 'Rain', 'Rain', 'Rain', 'Temperature', 'VI']}
df = pd.DataFrame(data)
我想绘制数据;行应该基于列，cols 应该基于列，x 轴应该基于，y 轴应该基于列。目前，我正在这样做：TypeIndexZ-Score CEIStagerelplot
df = df.groupby('Index').filter(lambda x: not x['Z-Score CEI'].isna().all())
df["Type"] = df["Type"].astype("category")
df["Index"] = df["Index"].astype("category")
df["Type"] = df["Type"].cat.remove_unused_categories()
df["Index"] = df["Index"].cat.remove_unused_categories()
g = sns.relplot(
data=df,
x='Z-Score CEI',
y='Stage',
col='Index',
row='Type',
facet_kws={'sharey': True, 'sharex': True},
kind='line',
legend=False,
)
for (i,j,k), data in g.facet_data():
if data.empty:
ax = g.facet_axis(i, j)
ax.set_axis_off()
但是，这会导致一个图，其中空图会扭曲子图与数据的位置。我希望没有空旷的地方。
当前输出如下所示：
在上图中，我想删除所有没有数据的子图。这将导致不同的行具有不同数量的子图，例如，第 1 行可能有 5 个子图，而第 2 行只有 4 个子图，以此类推。
我希望每一行只有相同的，而不是混合多个。TypeType
答：
3赞
puchal
11/17/2023
#1
我不相信你是否可以通过使用来实现你想要的.relplot
我的建议是创建，然后根据需要通过删除最正确的图来调整每行的列数，然后为每个 .FacetGridlineplotType | Index
import seaborn as sns
import matplotlib.pyplot as plt
df = df.groupby('Index').filter(lambda x: not x['Z-Score CEI'].isna().all())
df["Type"] = df["Type"].astype("category")
df["Index"] = df["Index"].astype("category")
df["Type"] = df["Type"].cat.remove_unused_categories()
df["Index"] = df["Index"].cat.remove_unused_categories()
# Get unique types
types = df['Type'].unique()
# Get unique indexes
indices = df['Index'].unique()
indices_len = len(indices)
# Create FaceGrid
g = sns.FacetGrid(df, col="Index", row="Type")
row_number = 0
for t in types:
# Filter for Type
df_type = df[df['Type'] == t]
# Determine the number of columns for this type
num_cols = len(df_type['Index'].unique())
# Remove plots that are not needed
for i in range(indices_len - num_cols):
ax = g.facet_axis(row_number, indices_len - i - 1)
plt.delaxes(ax)
col_number = 0
for index in indices:
# Filter data for each index
df_index = df_type[df_type['Index'] == index]
if not df_index.empty:
# if dataframe not empty add lineplot
ax = g.facet_axis(row_number, col_number)
sns.lineplot(data=df_index, x='Z-Score CEI', y='Stage', ax=ax)
ax.set_title(f"Type: {t} | Index: {index}")
col_number += 1
row_number += 1
输出图：
3赞
cottontail
11/18/2023
#2
这是另一个基于@mwaskom在评论中的建议的解决方案。基本思想是创建一个辅助列，其中每个 Type 的现有索引值标记为 0,1,2,...它将充当 FacetGrid 中的列索引。然后，在绘制 之后，删除所有没有数据的轴，并通过将列索引替换为“真实”索引值来修复带有数据的轴的标题。relplot
# label existing Type-Index pairs
col_idx = df.value_counts(['Type', 'Index']).groupby(level=0, observed=False).cumcount().astype(str)
# map the labels back to the dataframe
df1 = df.merge(col_idx.reset_index(name='column_loc'), on=['Type', 'Index'], how='left')
# plot replot
g = sns.relplot(
data=df1, # <--- new dataframe
x='Z-Score CEI',
y='Stage',
col='column_loc', # <--- column is by the newly created column
row='Type',
facet_kws={'sharey': True, 'sharex': True},
kind='line',
legend=False,
)
for ax in g.axes.flat:
if not ax.lines:
g.fig.delaxes(ax) # remove empty subplots
else:
# fix the title
typ, loc = (x.split(' = ')[1] for x in ax.get_title().split(' | '))
idx, = col_idx[col_idx==loc].loc[typ].index
ax.set_title(f"Type = {typ} | Index = {idx}")
我认为对于这个特定的任务，matplotlib 非常容易使用 IMO。这是因为 和 列都是 dtype 分类的，所以通过传递给 pandas ，我们可以简单地删除每个类型不存在的索引值。基本上，我们可以使用嵌套的 groupby 来创建一个可以输入到 .但是，由于我们需要手动绘制每个线图，因此它可能会很慢（也许不会，因为无论如何都很慢）。TypeIndexobserved=Truegroupbylineplotrelplot
import matplotlib.pyplot as plt
gby_obj = df.groupby('Type', observed=True)
nrows = gby_obj.ngroups
ncols = gby_obj['Index'].nunique().max()
fig, axs = plt.subplots(nrows, ncols, figsize=(20,20), sharey=True, sharex=True)
for i, (typ, g1) in enumerate(gby_obj):
for j, (idx, g2) in enumerate(g1.groupby('Index', observed=True)):
sns.lineplot(data=g2, x='Z-Score CEI', y='Stage', ax=axs[i,j])
axs[i,j].set_title(f'Type = {typ} | Index = {idx}')
for a in axs[i,j+1:]:
fig.delaxes(a)
sns.despine(fig, top=True, right=True)
fig.tight_layout()
评论
0赞
cottontail
11/22/2023
这个解决方案@user308827适合您？如果您还有其他问题，请告诉我。干杯
评论
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