I will show you some steps that you can use to solve your problem.
import numpy as np
import pandas as pd
# Creating a 2 dimensional numpy array
data = np.array([[1, 2,3,4,5,1, 2,3,4,5],[10, 20,30,40,50,10, 20,30,40,50],['s.40','s.50','s.40','s.50','s.40','s.50','s.40','s.50','s.40','s.50']])
# Creating pandas dataframe from numpy array
df= pd.DataFrame({'Column1': data[0,:], 'Column2': data[1,:],'Column3': data[2,:]})
I create a dataframe from a numpy array.
In your dataframe in Timestamp Column you have data and time you need splint it
and create a new column. You can do it as follow in my example:
def split_function(Column3):
return Column3.split('.')[1]
df['split_column']=df.apply(lambda x: split_function(x.Column3), axis=1)
In your case u can use a blank space ' ' to do your split in return Column3.split(' ')[1]
Than u need garante that ur column have the right type so u can use:
df['Column1']=pd.to_numeric(df['Column1'])
df['Column2']=pd.to_numeric(df['Column2'])
(See also to_datetime() and to_timedelta()
Than u need know how many interval you have, so you can use max and min method to help you find it:
In my example :
df['Column1'].max()
df['Column1'].min()
df['Column2'].max()
df['Column2'].min()
It will return
5
1
and
50
10
Let supose i want a interval of 25 based on my Column 2,
So i will have 2 groups interval.
Finally i just need select the rows based on a column condition
you can see more info about it here :
How do I select rows from a DataFrame based on column values?
list_dataframe_interval=[]
for i in range(2):
print(i*25)
print(type(i))
df1=df.loc[(df['Column2'] >=i*25) & (df['Column1'] >= i*2.5)]
df2=df1.loc[(i*25+25 >=df1['Column2']) & (i*2.5+2.5>=df1['Column1'])]
list_dataframe_interval.append(df2)
So you can see the dataframe in this list
list_dataframe_interval[0]
list_dataframe_interval[1]
