从Pandas列中的当前行值中减去前一行的值

Question

I have a pandas column with the name 'values' containing respective values 10 15 36 95 99 . I want to subtract the each value from the next value so that I get the following format: 10 5 21 59 4

I've tried to solve this using a for loop that loops over all the data-frame but this method was time consuming.

for i in range(1,length_colulmn):
    df['value'].iloc[i] = df['value'].iloc[i]-df['value'].iloc[i-1]

Is there a straightforward method the dataframe functions to solve this problem quickly? The output we desire is the following:

['input']                                       
11
15
22
27
36
69
77

['output']                                        
11
4
7
5
9
33
8

Answer 1

Use pandas.Series.diff with fillna :

import pandas as pd

s = pd.Series([11,15,22,27,36,69,77])
s.diff().fillna(s)

Output:

0    11.0
1     4.0
2     7.0
3     5.0
4     9.0
5    33.0
6     8.0
dtype: float64

Answer 2

You can use the pythonic shift function. see how I did it. Let me know if it works.

Code here:

import pandas as pd

df = pd.DataFrame({ 'input': [11, 15, 22, 27, 36, 69, 77]})

df['output']=df['input'] -df['input'].shift(1)

df
#df['output'].dropna()

Explanation:

1. create dataframe
2. create a column output such that the next row minus the current row
3. print dataframe

Result:

    input   output
0   11  NaN
1   15  4.0
2   22  7.0
3   27  5.0
4   36  9.0
5   69  33.0
6   77  8.0

you can remove NaN with dropna() .