How to construct int for datetime on x-axis using nautical tree?
I am trying to use seaborn to plot a graph
sns.lmplot(x="when_start", y="how_long",hue= 'state',
data=apps_pd.loc[(apps_pd['user'] == 'xavi')],lowess=True);
Where apps_pd is a dataframe . And the files in apps_pd ['when_start'] are int like 1499963856220 and also for how_long .
Script result
However, I got a really messy graph if I don't change the data format.
Is there anyway I can change the unit of the x-axis to "yyyy-mm-dd"? I want to group all my data at a daily level.
And also can I change the unit of the y-axis to hour-min-second?
Here are the first 5 lines of my frame.
source to share
First of all, when you send data, place it in text format, not in an image.
You can convert col when_start
format to date format like below:
apps_pd['when_start'] = pd.to_datetime(apps_pd['when_start'], unit='ms')
However, the scatter plot, which is one of the lmplot calls , does not support the datetime format . You have to replace your xtick signs after plotting like in this example:
import pandas as pd
import seaborn as sns
import matplotlib.pylab as plt
cols = ['how_long', 'state', 'user', 'when_start']
data = [[62297, 'FINISHED', 'xavi', 1499963793923],
[25761, 'FINISHED', 'xavi', 1499963446385],
[20082, 'FINISHED', 'xavi', 1499963221203],
[20508, 'FINISHED', 'xavi', 1499963156760],
[580975, 'FINISHED', 'xavi', 1499962435293]]
apps_pd = pd.DataFrame(data, columns = cols)
# convert ms timestamps of dataframe to date time format by pandas
#apps_pd['when_start'] = pd.to_datetime(apps_pd['when_start'], unit='ms')
print (apps_pd)
sns.lmplot(x='when_start', y='how_long', hue= 'state',
data=apps_pd[(apps_pd['user'] == 'xavi')],lowess=True)
# get current axis
ax = plt.gca()
# get current xtick labels
xticks = ax.get_xticks()
# convert all xtick labels to selected format from ms timestamp
ax.set_xticklabels([pd.to_datetime(tm, unit='ms').strftime('%Y-%m-%d\n %H:%M:%S') for tm in xticks],
rotation=50)
plt.show()
source to share