CODEWITHSUNDEEP
python-3.xpandasdataframenumpy
usert4jju7
usert4jju7

Reputation: 1813

Dataframe - Create new column based on a given column's previous & current row value

I am dealing with a dataframe which has several thousands of rows & has several columns. The column of interest is called customer_csate_score.

The data looks like below

customer_csate_score
    0.000
    -0.4
    0
    0.578
    0.418
    -0.765
    0.89

What I'm trying to do is create a new column in the dataframe called customer_csate_score_toggle_status which will have true if the value changed either from a positive value to a negative value or vice-versa. It will have value false if the polarity didn't reverse.

Expected Output for toggle status column

 customer_csate_score_toggle_status
        False
        True
        False
        True
        False
        True
        True

I've tried few different things but haven't been able to get this to work. Here's what I've tried -

Attempt - 1

   def update_cust_csate_polarity(curr_val, prev_val):
        return True if (prev_val <= 0 and curr_val > 0) or  (prev_val >= 0 and curr_val < 0) else False
    
    data['customer_csate_score_toggle_status'] = data.apply(lambda x: update_cust_csate_polarity(data['customer_csate_score'], data['customer_csate_score'].shift()))

Attempt - 2

//Testing just one condition
    data['customer_csate_score_toggle_status'] = data[(data['customer_csate_score'].shift() < 0) & (data['customer_csate_score']) > 0]

Could I please request help to get this right?

Upvotes: 0

Views: 109

Answers (1)

akuiper
akuiper

Reputation: 214927

Calculate the sign change using np.sign(df.customer_csate_score)[lambda x: x != 0].diff() != 0:

  1. Get the sign of values;
  2. Filter out 0s so sequence like 5 0 1 won't get marked incorrectly;
  3. Check if the sign has changed using diff.
import numpy as np
df['customer_csate_score_toggle_status'] = np.sign(df.customer_csate_score)[lambda x: x != 0].diff() != 0
df['customer_csate_score_toggle_status'] = df['customer_csate_score_toggle_status'].fillna(False)

df
   customer_csate_score  customer_csate_score_toggle_status
0                 0.000                               False
1                -0.400                                True
2                 0.000                               False
3                 0.578                                True
4                 0.418                               False
5                -0.765                                True
6                 0.890                                True

Upvotes: 1

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