使用 Pandas TimeSeries 创建热图
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Create heatmap using pandas TimeSeries
提问by szu
I need to create MatplotLib heatmap (pcolormesh) using Pandas DataFrame TimeSeries column (df_all.ts) as my X-axis.
我需要使用 Pandas DataFrame TimeSeries 列 (df_all.ts) 作为 X 轴创建 MatplotLib 热图 (pcolormesh)。
How to convert Pandas TimeSeries column to something which can be used as X-axis in np.meshgrid(x, y) function to create heatmap? The workaround is to create Matplotlib drange using same parameters as in pandas column, but is there a simple way?
如何将 Pandas TimeSeries 列转换为可在 np.meshgrid(x, y) 函数中用作 X 轴以创建热图的内容？解决方法是使用与 pandas 列中相同的参数创建 Matplotlib drange，但有没有简单的方法？
x = pd.date_range(df_all.ts.min(),df_all.ts.max(),freq='H')
xt = mdates.drange(df_all.ts.min(), df_all.ts.max(), dt.timedelta(hours=1))
y = arange(ylen)
X,Y = np.meshgrid(xt, y)
回答by behzad.nouri
I do not know what you mean by heat map for a time series, but for a dataframe you may do as below:
我不知道您所说的时间序列热图是什么意思，但对于数据帧，您可以执行以下操作：
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from itertools import product
from string import ascii_uppercase
from matplotlib import patheffects
m, n = 4, 7 # 4 rows, 7 columns
df = pd.DataFrame(np.random.randn(m, n),
columns=list(ascii_uppercase[:n]),
index=list(ascii_uppercase[-m:]))
ax = plt.imshow(df, interpolation='nearest', cmap='Oranges').axes
_ = ax.set_xticks(np.linspace(0, n-1, n))
_ = ax.set_xticklabels(df.columns)
_ = ax.set_yticks(np.linspace(0, m-1, m))
_ = ax.set_yticklabels(df.index)
ax.grid('off')
ax.xaxis.tick_top()
optionally, to print actual values in the middle of each square, with some shadows for readability, you may do:
可选地，要在每个方块的中间打印实际值，并带有一些阴影以提高可读性，您可以执行以下操作：
path_effects = [patheffects.withSimplePatchShadow(shadow_rgbFace=(1,1,1))]
for i, j in product(range(m), range(n)):
_ = ax.text(j, i, '{0:.2f}'.format(df.iloc[i, j]),
size='medium', ha='center', va='center',
path_effects=path_effects)
