CODEWITHSUNDEEP
pythonpython-3.xpandas
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NothingToSeeHere

Reputation: 2373

Is not NaN conditional statement for python 3 and pandas

I am trying to create a new column in a pandas data frame by and calculating the value from existing columns.

I have 3 existing columns ("launched_date", "item_published_at", "item_created_at")

However, my "if row[column_name] is not None:" statement is allowing columns with NaN value and not skipping to the next statement.

In the code below, I would not expect the value of "nan" to be printed after the first conditional, I would expect something like "2018-08-17"

df['adjusted_date'] = df.apply(lambda row: adjusted_date(row), axis=1)



def adjusted_launch(row): 
    if row['launched_date']is not None:
        print(row['launched_date'])
        exit()
        adjusted_date = date_to_time_in_timezone(row['launched_date']) 

    elif row['item_published_at'] is not None:
        adjusted_date = row['item_published_at']#make datetime in PST
    else:
        adjusted_date = row['item_created_at'] #make datetime in PST
    return adjusted_date

How can I structure this conditional statement correctly?

Upvotes: 1

Views: 27542

Answers (2)

tawab_shakeel
tawab_shakeel

Reputation: 3749

First fill "nan" as string where the data is empty

df.fillna("nan",inplace=True)

Then in function you can apply if condition like:

def adjusted_launch(row): 
    if row['launched_date'] !='nan':
......

Second Sol

import numpy as np
df.fillna(np.nan,inplace=True)

#suggested by  @ShadowRanger
def funct(row):
    if row['col'].notnull():
        pass

Upvotes: 8

Yash Thenuan
Yash Thenuan

Reputation: 631

df = df.where((pd.notnull(df)), None)

This will replace all nans with None, No other modifications required.

Upvotes: 2

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