我有3张 table
<表类=“s-表”>
<标题>
早上
网球得分
棒球得分
<正文>
jack
10
2
艾玛
5
6
西奥
5
8
<表类=“s-表”>
<标题>
中午
网球得分
棒球得分
<正文>
jack
5
4
艾玛
8
2
西奥
12
9
<表类=“s-表”>
<标题>
晚上
网球得分
棒球得分
<正文>
jack
3
3
艾玛
6
7
西奥
8
3
我需要将表中的不同列组合起来并得到这样的图表。
dataf = pd.read_csv('score.txt', names=["Tennis","Baseball"])
dataf.plot(kind='bar')
我得到的情节是:
这里的值位于不同的 x 轴索引中。我如何将它们组合在一起？我还必须将不同表中的值组合起来并绘制在同一个图中。最终的情节应该是这样的:
如何将列分组在一起并将其绘制在同一个图表中？
最佳答案
给定这些数据帧:
m_df = pd.DataFrame({
'Morning': ['Jack', 'Emma', 'Theo'],
'Tennis Score': [10, 5, 5],
'Baseball Score': [2, 6, 8]
})
n_df = pd.DataFrame({
'Noon': ['Jack', 'Emma', 'Theo'],
'Tennis Score': [5, 8, 12],
'Baseball Score': [4, 2, 9]
})
e_df = pd.DataFrame({
'Evening': ['Jack', 'Emma', 'Theo'],
'Tennis Score': [3, 6, 8],
'Baseball Score': [3, 7, 3]
})
它们应该是concat在一起，每个 DataFrame 应该接收一个指示符列。此外，Morning、Noon 和 Evening 列应为 renamed以便它们在单列中对齐:
keys = ['Morning', 'Noon', 'Evening']
plot_df = pd.concat(
[df_.assign(id=label)
.rename(columns={label: 'Player'})
.set_index('Player')
for df_, label in zip([m_df, n_df, e_df], keys)]
).reset_index()
plot_df:
Player Tennis Score Baseball Score id
0 Jack 10 2 Morning
1 Emma 5 6 Morning
2 Theo 5 8 Morning
3 Jack 5 4 Noon
4 Emma 8 2 Noon
5 Theo 12 9 Noon
6 Jack 3 3 Evening
7 Emma 6 7 Evening
8 Theo 8 3 Evening
然后pivot从长到宽，reindex按 Morning、Noon 和 Evening 顺序(而不是按字母顺序)获取索引，swaplevel和 sort_index这样列就按玩家而不是分数类型进行分组:
plot_df = (
plot_df.pivot(index='id', columns='Player')
.reindex(keys)
.swaplevel(0, 1, 1)
.sort_index(level=0, axis=1)
.rename_axis(columns=["Player", 'Score Type'])
)
plot_df:
Player Emma Jack Theo
Score Type Baseball Score Tennis Score Baseball Score Tennis Score Baseball Score Tennis Score
id
Morning 6 5 2 10 8 5
Noon 2 8 4 5 9 12
Evening 7 6 3 3 3 8
这可以简单地绘制:
进口:
import pandas as pd
from matplotlib import pyplot as plt
绘图代码:
fig, ax = plt.subplots()
plot_df.plot(kind='bar', rot=0, ax=ax, xlabel='', ylabel='Score')
plt.tight_layout()
plt.show()
进口:
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
绘图代码:
fig, ax = plt.subplots()
plot_df.plot(kind='bar', rot=0, ax=ax, legend=False, xlabel='', ylabel='Score')
players = plot_df.columns.get_level_values(0).unique()
score_types = plot_df.columns.get_level_values(1).unique()
# Create hatches (should be same length as types of scores)
hatches = np.tile(np.repeat(['/', '.'], plot_df.shape[0]), len(players))
# Create Colors (should be same number of colours as number of players)
colours = np.repeat(['green', 'pink', 'purple'],
len(score_types) * plot_df.shape[0])
# Iterate over patches, colours, and hatches to set the facecolor and hatch
for patch, colour, hatch in zip(ax.patches, colours, hatches):
patch.set_facecolor(colour)
patch.set_hatch(hatch)
# Add legend:
ax.legend(loc=1)
plt.tight_layout()
plt.show()
或者更进一步使用自定义图例:
进口:
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
绘图代码:
fig, ax = plt.subplots()
plot_df.plot(kind='bar', rot=0, ax=ax, legend=False, xlabel='', ylabel='Score')
players = plot_df.columns.get_level_values(0).unique()
score_types = plot_df.columns.get_level_values(1).unique()
colours = ['green', 'pink', 'purple']
hatches = ['/', '.']
iter_colours = np.repeat(colours, len(score_types) * plot_df.shape[0])
iter_hatches = np.tile(np.repeat(hatches, plot_df.shape[0]), len(players))
for patch, colour, hatch in zip(ax.patches, iter_colours, iter_hatches):
patch.set_facecolor(colour)
patch.set_hatch(hatch)
# Add legends:
player_legend = ax.legend(
[Line2D([0], [0], color=colour, lw=4) for colour in colours],
players, title='Players', loc=1)
score_legend = ax.legend(
[Patch(hatch=hatch, facecolor='white') for hatch in hatches],
score_types, loc=2, title='Score Type', labelspacing=.65)
for patch in score_legend.get_patches():
patch.set_height(14)
patch.set_y(-3)
ax.add_artist(player_legend)
plt.tight_layout()
plt.show()
从 import 到 plt.show() 的 5 个玩家的完整工作示例:
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
m_df = pd.DataFrame({
'Morning': ['Jack', 'Emma', 'Theo', 'Matt', 'Thomas'],
'Tennis Score': [10, 5, 5, 7, 9],
'Baseball Score': [2, 6, 8, 2, 4]
})
n_df = pd.DataFrame({
'Noon': ['Jack', 'Emma', 'Theo', 'Matt', 'Thomas'],
'Tennis Score': [5, 8, 12, 3, 10],
'Baseball Score': [4, 2, 9, 5, 6]
})
e_df = pd.DataFrame({
'Evening': ['Jack', 'Emma', 'Theo', 'Matt', 'Thomas'],
'Tennis Score': [3, 6, 8, 4, 7],
'Baseball Score': [3, 7, 3, 9, 5]
})
# concat
keys = ['Morning', 'Noon', 'Evening']
plot_df = pd.concat(
[df_.assign(id=label)
.rename(columns={label: 'Player'})
.set_index('Player')
for df_, label in zip([m_df, n_df, e_df], keys)]
).reset_index()
# pivot to wide
plot_df = (
plot_df.pivot(index='id', columns='Player')
.reindex(keys)
.swaplevel(0, 1, 1)
.sort_index(level=0, axis=1)
.rename_axis(columns=["Player", 'Score Type'])
)
fig, ax = plt.subplots()
plot_df.plot(kind='bar', rot=0, ax=ax, legend=False, xlabel='', ylabel='Score')
players = plot_df.columns.get_level_values(0).unique()
score_types = plot_df.columns.get_level_values(1).unique()
hatches = np.tile(np.repeat(['/', '.'], plot_df.shape[0]), len(players))
colours = np.repeat(['red', 'green', 'blue', 'orange', 'pink'],
len(score_types) * plot_df.shape[0])
for patch, colour, hatch in zip(ax.patches, colours, hatches):
patch.set_facecolor(colour)
patch.set_hatch(hatch)
ax.legend(loc=1)
plt.tight_layout()
plt.show()
关于pandas - 如何将多组列组合为 pandas 中的条形图，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68304175/