val time
5.6 2021-11-18 03:00:00
2.034 2021-11-18 05:00:00
1.171 2021-11-18 07:00:00
3.023 2021-11-18 09:00:00
4.202 2021-11-18 16:00:00
1.202 2021-11-18 17:00:00
5.202 2021-11-18 18:00:00
7.202 2021-11-18 19:00:00
2.202 2021-11-18 20:00:00
12.202 2021-11-18 21:00:00
1.202 2021-11-18 21:00:00
above is my dataframe and i want to plot it (x=time,y=value), and make value plot red where (val>5).
plt.plot(ab['time'], ab['value'], '-gD', markevery=marks, label='line with select markers')
where marks [7.202,12.202] is a list which i created manually. but this does not work.
error -: markevery is iterable but not a valid numpy fancy index
Line plot with different markers if condition is true python 3 i found one here, but if points are alot, this is time consuming
>Solution :
- The easiest solution is to use Boolean indexing to create a separate dataframe for values greater then 5, and then plot them as a scatter plot with
pandas.DataFrame.plot - The x-axis is formatted as
%M-%d %Hautomatically. The format will change when there’s more data, and there are other answers discussing how to format pandas datetime axis.
import pandas as pd
import matplotlib.pyplot as plt
# sample data
data = {'val': [5.6, 2.034, 1.171, 3.023, 4.202, 1.202, 5.202, 7.202, 2.202, 12.202, 1.202], 'time': ['2021-11-18 03:00:00', '2021-11-18 05:00:00', '2021-11-18 07:00:00', '2021-11-18 09:00:00', '2021-11-18 16:00:00', '2021-11-18 17:00:00', '2021-11-18 18:00:00', '2021-11-18 19:00:00', '2021-11-18 20:00:00', '2021-11-18 21:00:00', '2021-11-18 21:00:00']}
df = pd.DataFrame(data)
# convert the time column to a datetime dtype
df.time = pd.to_datetime(df.time)
# get the values greater than 5
masked = df[df.val.gt(5)]
# plot the line plot
ax = df.plot(x='time', marker='o', figsize=(15, 5), zorder=0)
# plot those greater than 5
masked.plot(kind='scatter', x='time', y='val', color='red', ax=ax, s=30, label='outliers')
