pandas Matplotlib 时间线
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Matplotlib timelines
提问by Agrajag9
I'm looking to take a python DataFrame with a bunch of timelines in it and plot these in a single figure. The DataFrame indices are Timestamps and there's a specific column, we'll call "sequence", that contains strings like "A" and "B". So the DataFrame looks something like this:
我正在寻找一个带有一堆时间线的 python DataFrame 并将它们绘制在一个图中。DataFrame 索引是时间戳，有一个特定的列，我们称之为“序列”，其中包含像“A”和“B”这样的字符串。所以 DataFrame 看起来像这样：
+--------------------------+---+
| 2014-07-01 00:01:00.0000 | A |
+--------------------------+---+
| 2014-07-01 00:02:00.0000 | B |
+--------------------------+---+
| 2014-07-01 00:04:00.0000 | A |
+--------------------------+---+
| 2014-07-01 00:08:00.0000 | A |
+--------------------------+---+
| 2014-07-01 00:08:00.0000 | B |
+--------------------------+---+
| 2014-07-01 00:10:00.0000 | B |
+--------------------------+---+
| 2014-07-01 00:11:00.0000 | B |
+--------------------------+---+
I'm looking for a plot something like this:
我正在寻找这样的情节：
B | * * **
A | * * *
+------------
Timestamp
回答by Paul H
I would just map each category to a y-value using a dictionary.
我只会使用字典将每个类别映射到 y 值。
import random
import numpy as np
import matplotlib.pyplot as plt
import pandas
categories = list('ABCD')
# map categories to y-values
cat_dict = dict(zip(categories, range(1, len(categories)+1)))
# map y-values to categories
val_dict = dict(zip(range(1, len(categories)+1), categories))
# setup the dataframe
dates = pandas.DatetimeIndex(freq='20T', start='2012-05-05 13:00', end='2012-05-05 18:59')
values = [random.choice(categories) for _ in range(len(dates))]
df = pandas.DataFrame(data=values, index=dates, columns=['category'])
# determing the y-values from categories
df['plotval'] = df['category'].apply(cat_dict.get)
# make the plot
fig, ax = plt.subplots()
df['plotval'].plot(ax=ax, style='ks')
ax.margins(0.2)
# format y-ticks look up the categories
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, pos: val_dict.get(x)))
And I get:
我得到：
Note that since you probably already have a dataframe, you can build cat_dictand val_dictlike this:
注意，因为你可能已经有一个数据帧，可以构建cat_dict和val_dict这样的：
# map categories to y-values
cat_dict = dict(zip(pandas.unique(df['category']), range(1, len(categories)+1)))
# map y-values to categories
val_dict = dict(zip(range(1, len(categories)+1), pandas.unique(df['category'])))
