CODEWITHSUNDEEP
pandasdataframeconcatenation
Jim421616
Jim421616

Reputation: 1536

Performing calculations on multiple columns in dataframe and create new columns

I'm trying to perform calculations based on the entries in a pandas dataframe. The dataframe looks something like this: enter image description here and it contains 1466 rows. I'll have to run similar calculations on other dfs with more rows later.

What I'm trying to do, is calculate something like mag='(U-V)/('R-I)' (but ignoring any values that are -999), put that in a new column, and then z_pred=10**((mag-c)m) in a new column (mag, c and m are just hard-coded variables). I have other columns I need to add too, but I figure that'll just be an extension of the same method.

I started out by trying

for i in range(1):
    current = qso[:]
    mag = (U-V)/(R-I)
    name = current['NED']
    z_pred = 10**((mag - c)/m)
    z_meas = current['z']

but I got either a Series for z, which I couldn't operate on, or various type errors when I tried to print the values or write them to a file.

I found this question which gave me a start, but I can't see how to apply it to multiple calculations, as in my situation.

How can I achieve this?

Upvotes: 0

Views: 166

Answers (1)

Zanshin
Zanshin

Reputation: 1272

Conditionally adding calculated columns row wise are usually performed with numpy's np.where;

df['mag'] = np.where(~df[['U', 'V', 'R', 'I']].eq(-999).any(1), (df.U - df.V) / (df.R - df.I), -999)

Note; assuming here that when any of the columns contain '-999' it will not be calculated and a '-999' is returned.

Upvotes: 1

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