What I have is a basic dataframe that I want to pull two values out of, based on index position. So for this:

I want to extract the values in row 1 and row 2 (1 2) out of first_column, then extract values in row 2 and row 3 (2 3) out of the first_column, so on and so forth until I've iterated over the entire column. I ran into an issue with the four loop and am stuck with getting the next index value.

I have code like below:

import pandas as pd
data = {'first_column':  [1, 2, 3, 4, 5],
        'second_column': [1, 2, 3, 4, 5],
        }

df = pd.DataFrame(data)

for index, row in df.iterrows():
    print(index, row['first_column']) # value1
    print(index   1, row['first_column'].values(index   1)) # value2 <-- error in logic here

Ignoring the prints, which will eventually become variables that are returned, how can I improve this to return (1 2), (2 3), (3 4), (4 5), etc.?

Also, is this easier done with iteritems() method instead of iterrows?

CodePudding user response：

If you Want to take these items in the same fashion you should consider using iloc instead of using iterrow.

for i in range(len(df)-1):
    print(i, df.iloc[i]["first_column"])
    print(i 1, df.iloc[i 1]["first_column"])

CodePudding user response：

Not sure if this is what you want to achieve:

(temp= df.assign(second_column = df.second_column.shift(-1))
         .dropna()
         .assign(second_column = lambda df: df.second_column.astype(int))
)

[*zip(temp.first_column.array, temp.second_column.array)]
[(1, 2), (2, 3), (3, 4), (4, 5)]

A simpler solution from @HenryEcker:

list(zip(df['first_column'], df['first_column'].iloc[1:]))

CodePudding user response：

I don't know if this answers your question, but maybe you can try this :

for i, val in enumerate(df['first_column']):
    if val 1>5:
        break
    else:
        print(val,", ",val 1)