How to sum previous rows in a dataframe?

I'm not sure how to correctly word what I want to do, or if this is even possible, so sorry if this sounds confusing and doesn't make sense. What I want to do basically is some way to create a separate column in a dataframe that sums consecutive values. For instance, I have this column, shown below, and I would like a separate column, which contains the sum of 0.00871 and 0.001, and then 0.000603 and 0.0123, and 0.000871 and 4.37E-05, and etc. Does this make sense? If so, is there a way I can code for this, instead of doing it manually? All help is appreciated! I would prefer to do this on Python, but any language would suffice.

Not allowed to embed the image, so hopefully this works

Solution

given your data is presented as an image, I have simulated it
what you state
1. for every second row d.index % 2
2. sum current row and previous row d["sourceValue"] + d["sourceValue"].shift()

import numpy as np
import pandas as pd

pd.DataFrame({"sourceValue": np.random.uniform(0, 1, 30)}).assign(
    required=lambda d: np.where(
        d.index % 2, d["sourceValue"] + d["sourceValue"].shift(), np.nan
    )
)

	sourceValue	required
0	0.804529	nan
1	0.24858	1.05311
2	0.710556	nan
3	0.415231	1.12579
4	0.879645	nan
5	0.599331	1.47898
6	0.635365	nan
7	0.587647	1.22301
8	0.902475	nan
9	0.0398323	0.942307
10	0.357436	nan
11	0.469155	0.826591
12	0.732759	nan
13	0.721996	1.45476
14	0.030494	nan
15	0.0047623	0.0352563
16	0.01081	nan
17	0.130903	0.141713
18	0.643588	nan
19	0.837585	1.48117
20	0.63328	nan
21	0.506582	1.13986
22	0.855085	nan
23	0.100174	0.95526
24	0.146992	nan
25	0.64605	0.793041
26	0.460859	nan
27	0.818967	1.27983
28	0.809294	nan
29	0.664239	1.47353

supplementary - multiple columns

import numpy as np
import pandas as pd

df = pd.DataFrame({f"sourceValue{i+1}": np.random.uniform(0, 1, 30) for i in range(7)})

# static version
df["targetValue1"] = np.where(df.index%2, df["sourceValue1"]+df["sourceValue1"].shift(), np.nan)
df["targetValue2"] = np.where(df.index%2, df["sourceValue2"]+df["sourceValue2"].shift(), np.nan)
df["targetValue3"] = np.where(df.index%2, df["sourceValue3"]+df["sourceValue3"].shift(), np.nan)
df["targetValue4"] = np.where(df.index%2, df["sourceValue4"]+df["sourceValue4"].shift(), np.nan)
df["targetValue5"] = np.where(df.index%2, df["sourceValue5"]+df["sourceValue5"].shift(), np.nan)
df["targetValue6"] = np.where(df.index%2, df["sourceValue6"]+df["sourceValue6"].shift(), np.nan)
df["targetValue7"] = np.where(df.index%2, df["sourceValue7"]+df["sourceValue7"].shift(), np.nan)

# dynamic version
df = df.assign(**{c.replace("source", "target"): 
                  np.where(df.index % 2, df[c] + df[c].shift(), np.nan)
                  for c in df.columns if "sourceValue" in c})

df