I create a heatmap with seaborn
df1.index = pd.to_datetime(df1.index)
df1 = df1.set_index(\'TIMESTAMP\')
df1 = df1.resample(\'30min\').mean()
ax = sns.heatmap(
add plt.figure(figsize=(16,5))
before the sns.heatmap and play around with the figsize numbers till you get the desired size
...
plt.figure(figsize = (16,5))
ax = sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5)
I do not know how to solve this using code, but I do manually adjust the control panel at the right bottom in the plot figure, and adjust the figure size like:
f, ax = plt.subplots(figsize=(16, 12))
at the meantime until you get a matched size colobar. This worked for me.
You could alter the figsize by passing a tuple
showing the width, height
parameters you would like to keep.
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,10)) # Sample figsize in inches
sns.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)
EDIT
I remember answering a similar question of yours where you had to set the index as TIMESTAMP
. So, you could then do something like below:
df = df.set_index('TIMESTAMP')
df.resample('30min').mean()
fig, ax = plt.subplots()
ax = sns.heatmap(df.iloc[:, 1:6:], annot=True, linewidths=.5)
ax.set_yticklabels([i.strftime("%Y-%m-%d %H:%M:%S") for i in df.index], rotation=0)
For the head
of the dataframe you posted, the plot would look like: