如果另一列中存在任何值> 0，则需要为数据框分配值

Question

I'm working with a transaction database query set, and I wasn't able to pull specific dates for payments, so I'm trying to write sone code in python to assign the dates for me. My first thought was to do it in excel, but the dataset is 800,000+ rows X 100+ columns, so it's not practical to do this any other way. The dataset has values in some of the rows in the payments column, so I need to add a date column with dates only in the rows that contain a payment amount.

I have created all of the columns to store the dates, and my idea was to loop through the rows and assign a date if that row contains a value greater than zero (as there are 0s in the columns, as well as NULL values).

df['Payment Date] = ''

for value in df:
    if value > 0 :
        df['Payment Date'] = '06/01/2019'

I expect the output to have dates assigned to the rows from the payment date column that have actual values.

Answer 1

If I understand correctly, you are trying to (1) identify rows in your Dataframe with values that are greater than zero, and (2) assign a specific date to a new column for all of those rows.

First, for reproducibility and clarity, let's generate some random data that is representative of your dataset:

import pandas as pd

# Generate a random 5x4 Dataframe
df = pd.DataFrame(np.random.randn(5,4), columns=list('ABCD'))

# Set many of the values to zero 
df[df > 0] = 0

Now, we want to create a new column to store the desired dates:

df['Payment Date'] = ''

And finally, set that column to the date desired for all rows that contain any values greater than zero (note that this requires that the sum across all rows, skipping N/As, is greater than zero, which is the condition tested below):

row_inds = df.sum(axis=1, skipna=True)>0
df.loc[row_inds, 'Payment Date'] = '06/01/2019'

Which gives you the desired result.