Subtracting the rows of a column from the preceding rows in a python pandas dataframe

Question

I have a .dat file which takes thousands of rows in a column (say, the column is time, t), now I want to find the interval between the rows in the column, that means subtracting the value of second row from first row, and so on.. (to find dt). Then I wish to make a new column with those interval values and plot it against the original column. If any other language other than python is helpful in this case, I appreciate their suggestion too.
I have written a pseudo python code for that:

    import pandas as pd
import numpy as np
from sys import argv
from pylab import *


import csv



script, filename = argv


# read flash.dat to a list of lists
datContent = [i.strip().split() for i in open("./flash.dat").readlines()]

# write it as a new CSV file
with open("./flash.dat", "wb") as f:
    writer = csv.writer(f)
    writer.writerows(datContent)


columns_to_keep = ['#time']
dataframe = pd.read_csv("./flash.csv", usecols=columns_to_keep)


df = pd.DataFrame({"#time"})
df["#time"] = df["#time"]  + [pd.Timedelta(minutes=m) for m in np.random.choice(a=range(60), size=df.shape[0])]
df["value"] = np.random.normal(size=df.shape[0])

df["prev_time"] = [np.nan] + df.iloc[:-1]["#time"].tolist()
df["time_delta"] = df.time - df.prev_time
df

pd.set_option('display.height', 1000)
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

dataframe.plot(x='#time', y='time_delta', style='r')

print dataframe

show()

Updated my code, and i am also sharing the .dat file I am working on. https://www.dropbox.com/s/w4jbxmln9e83355/flash.dat?dl=0

Answer 1

One easy way to perform an operation involving values from different rows is simply to copy the required values one the same row and then apply a simple row-wise operation.

For instance, in your example, we'd have a dataframe with one time column and some other data, like so:

import pandas as pd
import numpy as np 

df = pd.DataFrame({"time":  pd.date_range("24 sept 2016",  periods=5*24, freq="1h")})
df["time"] = df["time"]  + [pd.Timedelta(minutes=m) for m in np.random.choice(a=range(60), size=df.shape[0])]
df["value"] = np.random.normal(size=df.shape[0])

If you want to compute the time delta from the previous (or next, or whatever) row, you can simply copy the value from it, and then perform the subtraction:

df["prev_time"] = [np.nan] + df.iloc[:-1]["time"].tolist()
df["time_delta"] = df.time - df.prev_time
df