Reference previous row when iterating through dataframe

Question

Is there a simple way to reference the previous row when iterating through a dataframe? In the following dataframe I would like column B to change to 1 when A > 1

Answer 1

Similar question here: Reference values in the previous row with map or apply .
My impression is that pandas should handle iterations and we shouldn't have to do it on our own... Therefore, I chose to use the DataFrame 'apply' method.

Here is the same answer I posted on other question linked above...

You can use the dataframe 'apply' function and leverage the unused the 'kwargs' parameter to store the previous row.

import pandas as pd

df = pd.DataFrame({'a':[0,1,2], 'b':[0,10,20]})

new_col = 'c'

def apply_func_decorator(func):
    prev_row = {}
    def wrapper(curr_row, **kwargs):
        val = func(curr_row, prev_row)
        prev_row.update(curr_row)
        prev_row[new_col] = val
        return val
    return wrapper

@apply_func_decorator
def running_total(curr_row, prev_row):
    return curr_row['a'] + curr_row['b'] + prev_row.get('c', 0)

df[new_col] = df.apply(running_total, axis=1)

print(df)
# Output will be:
#    a   b   c
# 0  0   0   0
# 1  1  10  11
# 2  2  20  33

This example uses a decorator to store the previous row in a dictionary and then pass it to the function when Pandas calls it on the next row.

Disclaimer 1: The 'prev_row' variable starts off empty for the first row so when using it in the apply function I had to supply a default value to avoid a 'KeyError'.

Disclaimer 2: I am fairly certain this will be slower the apply operation but I did not do any tests to figure out how much.

Answer 2

Try this: If the first value is neither >= 1 or < -1 set to 0 or whatever you like.

df["B"] = None
df["B"] = np.where(df['A'] >= 1, 1,df['B'])
df["B"] = np.where(df['A'] < -1, -1,df['B'])
df = df.ffill().fillna(0)

This solves the problem stated, But the real solution to reference previous row is use .shift() or .index() -1

Answer 3

This is what you are trying to do?

In [38]: df = DataFrame(randn(10,2),columns=list('AB'))

In [39]: df['B'] = np.nan

In [40]: df.loc[df.A<-1,'B'] = -1

In [41]: df.loc[df.A>1,'B'] = 1

In [42]: df.ffill()
Out[42]: 
          A  B
0 -1.186808 -1
1 -0.095587 -1
2 -1.921372 -1
3 -0.772836 -1
4  0.016883 -1
5  0.350778 -1
6  0.165055 -1
7  1.101561  1
8 -0.346786  1
9 -0.186263  1