英文:
How to plot time line (start and end time pair) in pandas?
问题
You can use the following code to plot the start/end time pairs in pandas:
import pandas as pd
import matplotlib.pyplot as plt
data = [['set1', 0, 10], ['step2', 3, 12], ['step3', 8, 15]]
df = pd.DataFrame(data, columns=['name', 'start', 'end'])
plt.figure(figsize=(10, 2))
for index, row in df.iterrows():
plt.barh(row['name'], width=row['end'] - row['start'], left=row['start'])
plt.xlabel('Time')
plt.ylabel('Step')
plt.title('Start/End Time Pairs')
plt.show()
这段代码可以用来绘制 pandas 中的起始/结束时间对，以显示每个步骤的起始时间和持续时间。
英文:
How to plot start/end time pair in pandas like the following?I need a plot to easily show which step from when, and how long it is.
data = [['set1', 0,10], ['step2', 3,12], ['step3', 8,15]]
df = pd.DataFrame(data, columns=['name', 'start','end'])
答案1
得分: 1
水平堆叠条形图可以实现类似的效果：
ax = df.plot(kind='barh', stacked=True)
ax.set_xticks(range(0, df[['start','end']].sum(1).max() + 1, 1))
图片链接：
英文:
A quite similar effect can be achieved with horizontal bar (stacked) plot:
ax = df.plot(kind='barh', stacked=True)
ax.set_xticks(range(0, df[['start','end']].sum(1).max() + 1, 1))
答案2
得分: 1
以下是翻译好的内容：
下面的代码提供了类似的功能。不确定是否正好符合您的要求。条形图具有均匀的颜色，而不是分段。
from matplotlib import patches
import numpy as np
#
# 示例数据
#
# 原始数据
data = [['set1', 0, 10], ['step2', 3, 12], ['step3', 8, 15]]
df = pd.DataFrame(data, columns=['name', 'start', 'end'])
# 具有更多步骤的数据
df = pd.DataFrame(
{
step: [start, start + duration]
for step, start, duration in
zip([f'step_{i}' for i in range(30)],
0.5 * np.random.randn(30) + np.arange(0, 30),
np.random.uniform(5, 10, (30,))
)
},
index=['start', 'end']
).T.reset_index()
df.rename(columns={'index': 'name'}, inplace=True)
#
# 创建图表
#
height = 0.9
f, ax = plt.subplots(figsize=(10, 6))
for idx in range(len(df)):
y0 = (idx + 1) - height / 2
x0 = df.iloc[idx].start
width = df.iloc[idx].end - x0
ax.add_patch( patches.Rectangle((x0, y0), width, height) )
ax.hlines(y0 + height / 2,
xmin=df.start.min(),
xmax=x0,
color='k', linestyles=':', linewidth=0.5)
ax.axis((df.start.min(), df.end.max(), 1 - height, len(df) + height))
ax.set_xlabel('时间')
ax.set_ylabel('名称')
ax.set_yticks(range(1, len(df) + 1))
ax.set_yticklabels(df.name)
plt.show()
英文:
The code below provides similar functionality. Not sure if it's exactly what you were after. Bars have a uniform colour rather than being segmented.
from matplotlib import patches
import numpy as np
#
# Example data
#
#Original data
data = [['set1', 0, 10], ['step2', 3, 12], ['step3', 8, 15]]
df = pd.DataFrame(data, columns=['name', 'start', 'end'])
#Data with more steps
df = pd.DataFrame(
{
step: [start, start + duration]
for step, start, duration in
zip([f'step_{i}' for i in range(30)],
0.5 * np.random.randn(30) + np.arange(0, 30),
np.random.uniform(5, 10, (30,))
)
},
index=['start', 'end']
).T.reset_index()
df.rename(columns={'index': 'name'}, inplace=True)
#
# Create plot
#
height = 0.9
f, ax = plt.subplots(figsize=(10, 6))
for idx in range(len(df)):
y0 = (idx + 1) - height / 2
x0 = df.iloc[idx].start
width = df.iloc[idx].end - x0
ax.add_patch( patches.Rectangle((x0, y0), width, height) )
ax.hlines(y0 + height / 2,
xmin=df.start.min(),
xmax=x0,
color='k', linestyles=':', linewidth=0.5)
ax.axis((df.start.min(), df.end.max(), 1 - height, len(df) + height))
ax.set_xlabel('time')
ax.set_ylabel('name')
ax.set_yticks(range(1, len(df) + 1))
ax.set_yticklabels(df.name)
plt.show()
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