Python: Plotting time delta-CodePudding

I have a DataFrame with a column of the time and a column in which I have stored a time lag. The data looks like this:

 2020-04-18 14:00:00                         0 days 03:00:00
 2020-04-19 02:00:00                         1 days 13:00:00    
 2020-04-28 14:00:00                         1 days 17:00:00
 2020-04-29 20:00:00                         2 days 09:00:00
 2020-04-30 19:00:00                         2 days 11:00:00

Time, Length: 282, dtype: datetime64[ns]    Average time lag, Length: 116, dtype: object

I want to plot the Time on the x-axis vs the time lag on the y-axis. However, I keep having errors with plotting the second column. Any tips on how to handle this data for the plot?

CodePudding user response：

In order to plot the time lag on the y-axis, you will need to convert the time lag from a timedelta object to a numerical value that can be used in the plot. One way to do this is to convert the time lag to seconds using the total_seconds method, and then plot the resulting values on the y-axis.

Here is an example of how you can do this:

import pandas as pd
import matplotlib.pyplot as plt

# Create a dataframe with the time and time lag data
data = [    ['2020-04-18 14:00:00', '0 days 03:00:00'],
    ['2020-04-19 02:00:00', '1 days 13:00:00'],
    ['2020-04-28 14:00:00', '1 days 17:00:00'],
    ['2020-04-29 20:00:00', '2 days 09:00:00'],
    ['2020-04-30 19:00:00', '2 days 11:00:00'],
]
df = pd.DataFrame(data, columns=['time', 'time_lag'])

# Convert the time and time lag columns to datetime and timedelta objects
df['time'] = pd.to_datetime(df['time'])
df['time_lag'] = pd.to_timedelta(df['time_lag'])

# Convert the time lag to seconds
df['time_lag_seconds'] = df['time_lag'].dt.total_seconds()

# Create a scatter plot with the time on the x-axis and the time lag in seconds on the y-axis
plt.scatter(df['time'], df['time_lag_seconds'])
plt.show()