Mathematical calculation using for loop in a dataframe

Question

I have a dataframe which looks like this

df= 

    Time                  x          y
0   2018-09-13 01:17:00  5.0        0.0
1   2018-09-13 02:17:00  9.0        0.0
2   2018-09-13 03:17:00  2.0        1.0
3   2018-09-13 04:17:00  1.0        0.0

....... I want to iterate through this whole dataframe and calculate a new variable z. The value of z would be z= z[prev]+ x-y

for example, the final output would be

Time                         z
0   2018-09-13 01:17:00      5    #[0+5-0]
1   2018-09-13 02:17:00      14   #[5+9-0]
2   2018-09-13 03:17:00      15   #[14+2-1]
3   2018-09-13 04:17:00      16   #[15+1-0]

.....

I am finding it difficult to iterate over the time series data.

I have tried the following but it is not working.

for i,row in df.iterrows():
    z=0
    row['z']=row['z']+row['x']-row['y']

print[z]

Abhyuday Vaish · Accepted Answer · 2022-02-27 23:49:35Z

2

In your case do cumsum

df['new'] = df.x.sub(df.y).cumsum()
Out[410]: 
0  2018-09-13     5.0
1  2018-09-13    14.0
2  2018-09-13    15.0
3  2018-09-13    16.0
dtype: float64

edited Feb 27, 2022 at 23:49

Abhyuday Vaish

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answered Feb 27, 2022 at 17:21

BENY

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Comments

Vishal Balaji · Accepted Answer · 2022-02-27 17:24:35Z

0

You can use indices

z = []
for i in range(len(df)):
    if i == 0:
        z.append(df.loc[i]['x'] - df.loc[i]['y'])
    else:
        z.append(z[i-1] + df.loc[i]['x'] - df.loc[i]['y'])

df['z'] = z

answered Feb 27, 2022 at 17:24

Vishal Balaji

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1 Comment

Abdullah Al Mamun Over a year ago

Awesome! This will help me to understand the concept of for loop!

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