如何使用颜色图在X轴上设置时间范围和在Y轴上设置日期范围

Question

I have created a code, which shows a heatmap of the data in the CSV file. The code is as follows:

import pandas as pd
import matplotlib.pyplot as plt
data= pd.read_csv("data.csv" , sep=';', header=0, 
index_col='Date')
fig=plt.imshow(data, cmap='YlOrBr', interpolation='nearest')
plt.colorbar()
plt.xlabel("Time (UTC)")
plt.ylabel("Date")
plt.show()

The dataset is as follows:

The time range varies from 00:00 till 23:50 with steps of 10 minutes. I want the x axis to show the time from 00:00 till 23:50 in steps per hour.

The index is set as date. The date range is from 29-Oct-2017 till 24-Mar-2018. I want the Y axis to show the date range in steps of months.

Answer 1

You can stack columns, then groupby month and hour and then unstack it back (I'm taking mean values here when aggregating, but you can change to sum or whatever aggregation should be done there):

df = pd.DataFrame(np.nan,
                  columns=pd.date_range('00:00', '23:50', freq='10min'),
                  index=pd.date_range('2017-10-29', '2018-03-24'))
df[df.columns] = np.random.randint(0, 100, df.shape)

fig, ax = plt.subplots(2, figsize=(10,6))
ax[0].imshow(df, cmap='YlOrBr')

ix = df.stack().index
l1 = ix.get_level_values(0).month
l2 = ix.get_level_values(1).hour

df2 = df.stack().groupby([l1,l2], sort=False).mean().unstack(1)

ax[1].imshow(df2, cmap='YlOrBr')

Output (original DataFrame above, processed below):

Update:

If the goal is just to put monthly and hourly labels on the same plot, please see below:

df = pd.DataFrame(np.nan,
                  columns=pd.date_range('00:00', '23:50', freq='10min').astype(str),
                  index=pd.date_range('2017-10-29', '2018-03-24').astype(str))

df[df.columns] = np.random.randn(*(df.shape))

fig, ax = plt.subplots(1, figsize=(10,6))

l1 = pd.to_datetime(df.index).month
l2 = pd.to_datetime(df.columns).hour
x = pd.Series(l2).drop_duplicates()
y = pd.Series(l1).drop_duplicates()

ax.imshow(df, cmap='YlOrBr')

ax.set_xticks(x.index)
ax.set_xticklabels(x)

ax.set_yticks(y.index)
ax.set_yticklabels(y)

Output: