Create new column based on existing columns-CodePudding

I have a Pandas dataframe which look like this.

Deviated_price  standard_price   
744,600          789,276
693,600          789,276
693,600          735,216
                 735,216
744,600          735,216
                 735,216

I want to create a new column called net_standard_price. values for the net standard price will be based on Deviated_price and standard_price columns.

If Deviated price is not blank then net_standard_price should be blank. If Deviated price is blank then net_standard_price should contain standard_price value.

Net_standard_price should look like this.

Deviated_price  standard_price  Net_standard_price   
                 789,276           789,276
693,600          789,276
693,600          735,216
                 735,216           735,216
744,600          735,216
                 735,216           735,216

I tried below code using np.where but Net_standard_price is empty for all the records.

df['Net_standard_price'] = np.where(df['Deviated_price'] != '',
                                        '', df['standard_price'])

What's the most efficient way to do this?

CodePudding user response：

Moving to numpy domain gave some performance boost

import pandas as pd
import numpy as np
from timeit import Timer


def make_df():
    random_state = np.random.RandomState()
    df = pd.DataFrame(random_state.random((10000, 2)), columns=['Deviated_price', 'standard_price'], dtype=str)
    df['Deviated_price'][random_state.randint(0, 2, len(df)).astype(np.bool)] = None
    return df


def test1(df):
    df['Net_standard_price'] = np.where(df['Deviated_price'] != '',
                                        '', df['standard_price'])

def test2(df):
    df['Net_standard_price'] = np.where(df['Deviated_price'].isna(), df['standard_price'], None)

def test3(df):
    temp = df['standard_price'].values
    temp2 = df['Deviated_price'].values
    net_standard_price = temp.copy()
    net_standard_price[temp2 == ''] = ''
    df['Net_standard_price'] = net_standard_price

timing = Timer(setup='df = make_df()', stmt='test1(df)', globals=globals()).timeit(500)
print('test1: ', timing)

timing = Timer(setup='df = make_df()', stmt='test2(df)', globals=globals()).timeit(500)
print('test2: ', timing)

timing = Timer(setup='df = make_df()', stmt='test3(df)', globals=globals()).timeit(500)
print('test3: ', timing)

test1:  0.42146812000000006
test2:  0.417552648
test3:  0.2913768969999999