Copying and replacing Pandas cell values

Question

Is it possible to grab a given value in a Pandas column and change it to a previous row value?

For instance, I have this Dataframe:


Date        Price   Signal
2018-01-01  13380.00    1
2018-01-02  14675.11    0
2018-01-03  14919.51    0
2018-01-04  15059.54    0
2018-01-05  16960.39    0
2018-01-06  17069.79    -1
2018-01-07  16150.03    0
2018-01-08  14902.54    0
2018-01-09  14400.00    1
2018-01-10  14907.09    0
2018-01-11  13238.78    0
2018-01-12  13740.01    -1
2018-01-13  14210.00    0

I would like to replace the zeros in the Signal column for either 1 or -1. The final DF should be this:


Date        Price   Signal
2018-01-01  13380.00    1
2018-01-02  14675.11    1
2018-01-03  14919.51    1
2018-01-04  15059.54    1
2018-01-05  16960.39    1
2018-01-06  17069.79    -1
2018-01-07  16150.03    -1
2018-01-08  14902.54    -1
2018-01-09  14400.00    1
2018-01-10  14907.09    1
2018-01-11  13238.78    1
2018-01-12  13740.01    -1
2018-01-13  14210.00    -1

Does this answer your question? [How to replace NaNs by preceding values in pandas DataFrame?](https://stackoverflow.com/questions/27905295/how-to-replace-nans-by-preceding-values-in-pandas-dataframe) — MrNobody33, Aug 18 '20 at 17:39

score 0 · Answer 1 · answered Aug 18 '20 at 17:15

0

If what you want is propagate previous values to the next rows use the following:

df["Signal"] = df["Signal"].ffill()

answered Aug 18 '20 at 17:15

Let's try

1,044
9
20

score 0 · Accepted Answer · answered Aug 18 '20 at 17:16

0

Try this:

df['Signal'].replace(to_replace=0, method='ffill')

(assuming your DataFrame is callled df)

answered Aug 18 '20 at 17:16

Ricardo Rendich

666
5
6

df['Signal'] = df['Signal'].replace(to_replace=0, method='ffill') This did the trick! Thanks! – Ronnie Ron Aug 18 '20 at 18:15

zabop · Answer 3 · 2020-08-18T17:36:41.487

import pandas as pd

Prepare a dataframe

If you have a dataframe:

df = pd.DataFrame([1,0,1,0,1], columns=['col1'])

Use pure apply()

You can do:

def replace(num):
    if num==1: return 1
    if num==0: return -1

Then apply it to the column holding the values you want to replace:

df['new']=df['col1'].apply(replace)

apply() lambda functions

You can achieve the same with a lambda function:

df['col1'].apply(lambda row: 1 if row == 1 else -1)

Use Built-in methods

Using the dataframe we prepared, can do:

df['new'] = df['col1'].replace(to_replace=0, value=-1)

If you don't want to create a new column, just straight replace the the values in the existing one, can do it inplace:

df['col1'].replace(to_replace=0, value=-1,inplace=True)

Clean up

If created a new column & don't want to keep the old column, can drop it:

df.drop('col1',axis=1)